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• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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Course Information

Course objectives, student learning outcomes, assessment, course objectives.

This course is designed to enable students to:

• identify and discuss the role and importance of research in the social sciences.
• identify and discuss the issues and concepts salient to the research process.
• identify and discuss the complex issues inherent in selecting a research problem, selecting an appropriate research design, and implementing a research project.
• identify and discuss the concepts and procedures of sampling, data collection, analysis and reporting.

Student Learning Outcomes:

Students who successfully complete this course will be able to:

• explain key research concepts and issues
• read, comprehend, and explain research articles in their academic discipline.

Learning Assessment:

• SLO #1 is assessed via student-led discussions of the textbook in Modules 1 through 5.
• SLO #2 is assessed via student led discussions of the 5 research designs presented in the Research Portfolio.
• Course Objectives, Student Learning Outcomes, Assessment. Authored by : WIlliam Pelz. Provided by : Herkimer College. Project : Research Methods in Social Science - Achieving the Dream course. License : CC BY-SA: Attribution-ShareAlike

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• Research Methodology

Research Methodology (CLRS90001)

Graduate coursework Points: 12.5 On Campus (Hawthorn)

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About this subject

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Contact information

School of Melbourne Custom Programs

Currently enrolled students:

• General information: http://www.commercial.unimelb.edu.au/courses
• Email: [email protected]

Future students:

• Further information: http://www.commercial.unimelb.edu.au/courses

The course focuses on social science research methods. Methods discussed include interview,content analysis, focus group discussions and surveys.

Intended learning outcomes

By the end of the subject students should be able to:

• Demonstrate the ability to choose methods appropriate to research aims and objectives
• Understand the limitations of particular research methods
• Develop skills in qualitative and quantitative data analysis and presentation
• Develop advanced critical thinking skills
• Demonstrate enhanced writing skills

Generic skills

• Demonstrate the ability to choose methods appropriate to research aims and objectives • Understand the limitations of particular research methods • Develop skills in qualitative and quantitative data analysis and presentation • Develop advanced critical thinking skills • Demonstrate enhanced writing skills

Last updated: 3 November 2022

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Module 2: Research Methods in Learning and Behavior

Module Overview

Module 2 will cover the critical issue of how research is conducted in the experimental analysis of behavior. To do this, we will discuss the scientific method, research designs, the apparatus we use, how we collect data, and dependent measures used to show that learning has occurred. We also will break down the structure of a research article and make a case for the use of both humans and animals in learning and behavior research.

Module Outline

2.1. The Scientific Method

2.2. research designs used in the experimental analysis of behavior, 2.3. dependent measures, 2.4. animal and human research.

Module Learning Outcomes

• Describe the steps in the scientific method and how this process is utilized in the experimental analysis of behavior.
• Describe specific research designs, data collection methods, and apparatus used in the experimental analysis of behavior.
• Understand the basic structure of a research article.
• List and describe dependent measures used in learning experiments.
• Explain why animals are used in learning research.
• Describe safeguards to protect human beings in scientific research.

Section Learning Objectives

• Define scientific method.
• Outline and describe the steps of the scientific method, defining all key terms.
• Define functional relationship and explain how it produces a contingency.
• Explain the concept of a behavioral definition.
• Distinguish between stimuli and responses and define related concepts.
• Distinguish types of contiguity, and the term from contingency.
• Describe the typical phases in learning research.

2.1.1. The Steps of The Scientific Method

In Module 1, we learned that psychology was the scientific study of behavior and mental processes. We will spend quite a lot of time on the behavior and mental processes part, but before we proceed, it is prudent to elaborate more on what makes psychology scientific. It is safe to say that most people not within our discipline or a sister science would be surprised to learn that psychology utilizes the scientific method at all.

So what is the scientific method? Simply, the scientific method is a systematic method for gathering knowledge about the world around us. The key word here is that it is systematic, meaning there is a set way to use it. What is that way? Well, depending on what source you look at it can include a varying number of steps. For our purposes, the following will be used:

Table 2.1: The Steps of the Scientific Method

2.1.2. Making Cause and Effect Statements in the Experimental Analysis of Behavior

As you have seen, scientists seek to make causal statements about what they are studying. In the study of learning and behavior, we call this a functional relationship. This occurs when we can say a target behavior has changed due to the use of a procedure/treatment/strategy and this relationship has been replicated at least one other time. A contingency is when one thing occurs due to another. Think of it as an if-then statement. If I do X then Y will happen. We can also say that when we experience Y that X preceded it. Concerning a functional relationship, if I introduce a treatment, then the animal responds as such or if that animal pushes the lever, then she receives a food pellet.

To help arrive at a functional relationship, we have to understand what we are studying. In science, we say we operationally define our variables. In the realm of learning, we call this a behavioral definition, or a precise, objective, unambiguous description of the behavior. The key is that we must state our behavioral definition with enough precision that anyone can read it and be able to accurately measure the behavior when it occurs.

2.1.3. Frequently Used Terms in the Experimental Analysis of Behavior

In the experimental analysis of behavior, we frequently talk about an animal or person experiencing a trial. Simply, a trial is one instance or attempt at learning. Each time a rat is placed in a maze this is considered one trial. We can then determine if learning is occurring using different dependent measures described in Section 2.3. If a child is asked to complete a math problem and then a second is introduced, and then a third, each practice problem represents a trial.

As you saw in Module 1, behaviorism is the science of stimuli and responses. What do these terms indicate? Stimuli are the environmental events that have the potential to trigger behavior, called a response . If your significant other does something nice for you and you say, ‘Thank you,’ the kind act is the stimulus which leads to your response of thanking him/her. Stimuli have to be sensed to bring about a response. This occurs through the five senses — vision, hearing, touch, smell, and taste. Stimuli can take on two forms. Appetitive stimuli are those that an organism desires and seeks out while aversive stimuli are readily avoided. An example of the former would be food or water and the latter is exemplified by extremes of temperature, shock, or a spanking by a parent.

As you will come to see in Module 6, we can make a stimulus more desirable or undesirable, called an establishing operation , or make it less desirable or undesirable, called an abolishing operation . Such techniques are called motivating operations . Food may be seen as more attractive, desirable, or pleasant if we are hungry but less desirable (or more undesirable) if we are full. A punishment such as taking away video games is more undesirable if the child likes to play games such as Call of Duty or Madden but is less undesirable (or maybe even has no impact) if they do not enjoy video games. Linked to the discussion above, food is an appetitive stimulus and could be an establishing operation if we are hungry. A valued video game also represents an establishing operation if we threaten its removal, and we will want to avoid such punishment, which makes the threat an aversive stimulus.

As noted earlier, the response is simply the behavior that is made and can take on many different forms. A dog may learn to salivate (response) to the sound of a bell (stimulus). A person may begin going to the gym if he or she seeks to gain tokens to purchase back up reinforcers (more on this in Module 7). A person may work harder in the future if they received a compliment from their boss today (either through email and visual or spoken or through hearing).

Another important concept is contiguity and occurs when two events are associated with one another because they occur together closely, whether in time called temporal contiguity or in space called spatial contiguity . In the case of time, we may come to associate thanking someone for saying ‘good job’ if we hear others doing this and the two verbal behaviors occur very close in time. Usually, the ‘Thank you’ (or other response) follows the praise within seconds. In the case of space, we may learn to use a spatula to flip our hamburgers on the grill if the spatula is placed next to the stove and not in another room. Do not confuse contiguity with contingency. Though the terms look the same they have very different meanings.

Finally, in learning research, we often distinguish two phases — baseline and treatment. Baseline Phase occurs before any strategy or strategies are put into effect. This phase will essentially be used to compare against the treatment phase. We are also trying to find out exactly how much of the target behavior the person or animal is engaging in. Treatment Phase occurs when the strategy or strategies are used, or you might say when the manipulation is implemented. Note that in behavior modification we also talk about what is called the maintenance phase. More on this in Module 7.

• List the five main research methods used in psychology.
• Describe observational research, listing its advantages and disadvantages.
• Describe the case study approach to research, listing its advantages and disadvantages.
• Describe survey research, listing its advantages and disadvantages.
• Describe correlational research, listing its advantages and disadvantages.
• Describe experimental research, listing its advantages and disadvantages.
• Define key terms related to experiments.
• Describe specific types of experimental designs used in learning research.
• Describe the ways we gather data in learning research (or applied behavior analysis).
• Outline the types of apparatus used in learning experiments.
• Outline the parts of a research article and describe their function.

Step 3 called on the scientist to test his or her hypothesis. Psychology as a discipline uses five main research designs to do just that. These include observational research, case studies, surveys, correlational designs, and experiments.

2.2.1. Observational Research

In terms of naturalistic observation , the scientist studies human or animal behavior in its natural environment which could include the home, school, or a forest. The researcher counts, measures, and rates behavior in a systematic way and at times uses multiple judges to ensure accuracy in how the behavior is being measured. This is called inter-rater reliability . The advantage of this method is that you witness behavior as it occurs and it is not tainted by the experimenter. The disadvantage is that it could take a long time for the behavior to occur and if the researcher is detected then this may influence the behavior of those being observed. In the case of the latter, the behavior of the observed becomes artificial .

Laboratory observation involves observing people or animals in a laboratory setting. The researcher might want to know more about parent-child interactions and so brings a mother and her child into the lab to engage in preplanned tasks such as playing with toys, eating a meal, or the mother leaving the room for a short period of time. The advantage of this method over the naturalistic method is that the experimenter can use sophisticated equipment and videotape the session to examine it later. The problem is that since the subjects know the experimenter is watching them, their behavior could become artificial.

2.2.2. Case Studies

Psychology can also utilize a detailed description of one person or a small group based on careful observation. The advantage of this method is that you arrive at a rich description of the behavior being investigated, but the disadvantage is that what you are learning may be unrepresentative of the larger population and so lacks generalizability . Again, bear in mind that you are studying one person or a very small group. Can you possibly make conclusions about all people from just one or even five or ten? The other issue is that the case study is subject to the bias of the researcher in terms of what is included in the final write up and what is left out. Despite these limitations, case studies can lead us to novel ideas about the cause of a behavior and help us to study unusual conditions that occur too infrequently to study with large sample sizes and in a systematic way.

2.2.3. Surveys/Self-Report Data

A survey is a questionnaire consisting of at least one scale with a number of questions that assess a psychological construct of interest such as parenting style, depression, locus of control, attitudes, or sensation-seeking behavior. It may be administered by paper and pencil or computer. Surveys allow for the collection of large amounts of data quickly, but the actual survey could be tedious for the participant, and social desirability , or when a participant answers questions dishonestly so that he/she is seen in a more favorable light, could be an issue. For instance, if you are asking high school students about their sexual activity, they may not give genuine answers for fear that their parents will find out. Or if you wanted to know about prejudiced attitudes of a group of people, you could use the survey method. You could alternatively gather this information via an interview in a structured, semi-structured, or unstructured fashion. Important to survey research is that you have random sampling, or when everyone in the population has an equal chance of being included in the sample. This helps the survey to be representative of the population, and in terms of key demographic variables such as gender, age, ethnicity, race, education level, and religious orientation. Surveys are not frequently used in the experimental analysis of behavior.

2.2.4. Correlational Research

This research method examines the relationship between two variables or two groups of variables. A numerical measure of the strength of this relationship is derived, called the correlation coefficient , and can range from -1.00, which indicates a perfect inverse relationship meaning that as one variable goes up the other goes down, to 0 or no relationship at all, to +1.00 or a perfect relationship in which as one variable goes up or down so does the other. In terms of a negative correlation we might say that as a parent becomes more rigid, controlling, and cold, the attachment of the child to parent goes down. In contrast, as a parent becomes warmer, more loving, and provides structure, the child becomes more attached. The advantage of correlational research is that you can correlate anything. The disadvantage is also that you can correlate anything. Variables that do not have any relationship to one another could be viewed as related. Yes. This is both an advantage and a disadvantage. For instance, we might correlate instances of making peanut butter and jelly sandwiches with someone we are attracted to sitting near us at lunch. Are the two related? Not likely, unless you make a really good PB&J, but then the person is probably only interested in you for food and not companionship. The main issue here is that correlation does not allow you to make a causal statement.

2.2.5. Experiments

An experiment is a controlled test of a hypothesis in which a researcher manipulates one variable and measures its effect on another. A variable is anything that varies over time or from one situation to the next. Patience could be an example of a variable. Though we may be patient in one situation, we may have less if a second situation occurs close in time. The first could have lowered our ability to cope making an emotional reaction quicker to occur even if the two situations are about the same in terms of impact. Another variable is weight. Anyone who has tried to shed some pounds and weighs in daily knows just how much weight can vary from day to day, or even on the same day. In terms of experiments, the variable that is manipulated is called the independent variable (IV) and the one that is measured is called the dependent variable (DV) .

A common feature of experiments is to have a control group that does not receive the treatment, or is not manipulated, and an experimental group that does receive the treatment or manipulation. If the experiment includes random assignment, participants have an equal chance of being placed in the control or experimental group. The control group allows the researcher to make a comparison to the experimental group, making a causal statement possible, and stronger.

Within the experimental analysis of behavior (and applied behavior analysis), experimental procedures take on several different forms. In discussing each, understand that we will use the following notations:

A will represent the baseline phase and B will represent the treatment phase.

• A-B design — This is by far the most basic of all designs used in behavior modification and includes just one rotation from baseline to treatment phase and from that we see if the behavior changed in the predicted manner. The issue with this design is that no functional relationship can be established since there is no replication. It is possible that the change occurred not due to the treatment that was used, but due to an extraneous variable , or an unseen and unaccounted for factor on the results and specifically our DV.
• A-B-A-B Reversal Design — In this design, the baseline and treatment phases are implemented twice. After the first treatment phase occurs, the individual(s) is/are taken back to baseline and then the treatment phase is implemented again. Replication is built into this design, allowing for a causal statement, but it may not be possible or ethical to take the person back to baseline after a treatment has been introduced, and one that likely is working well. What if you developed a successful treatment to reduce self-injurious behavior in children or to increase feelings of self-worth? You would want to know if the decrease in this behavior or increase in the positive thoughts was due to your treatment and not extraneous behaviors, but can you take the person back to baseline? Is it ethical to remove a treatment for something potentially harmful to the person? Now let’s say a teacher developed a new way to teach fractions to a fourth-grade class. Was it the educational paradigm or maybe additional help a child received from his/her parents or a tutor that accounts for improvement in performance? Well, we need to take the child back to baseline and see if the strategy works again, but can we? How can the child forget what has been learned already? ABAB Reversal Designs work well at establishing functional relationships if you can take the person back to baseline but are problematic if you cannot. An example of them working well includes establishing a system, such as a token economy (more on this later), to ensure your son does his chores, having success with it, and then taking it away. If the child stops doing chores and only restarts when the token economy is put back into place, then your system works. Note that with time the behavior of doing chores would occur on its own and the token economy would be fazed out.
• Multiple-baseline designs — This design can take on three different forms. In an across-subjects design, there is a baseline and treatment phase for two or more subjects for the same target behavior. For example, an applied behavior analyst is testing a new intervention to reduce disruptions in the classroom. The intervention involves a combination of antecedent manipulations, prompts, social support, differential reinforcement, and time-outs. He uses the intervention on six problematic students in a 6th period math class. Secondly, the across-settings design has a baseline and treatment phase for two or more settings in the same person for which the same behavior is measured. What if this same specialist now tests the intervention with one student but across her other five classes which include social studies, gym, science, English, and shop. Finally, in an across-behaviors design , there is a baseline and treatment phase for two or more different behaviors the same participant makes. The intervention continues to show promise and now the ABA specialist wants to see if it can help the same student but with his problem with procrastination and inability to organize.
• Changing-Criterion Design — In this design, the performance criteria changes as the subject achieves specific goals. The individual may go from having to workout at the gym 2 days a week to 3 days, then 4 days, and then finally 5 days. Once the goal of 2 days a week is met, the criterion changes to 3 days a week. In a learning study, a rat may have to press the lever 5 times to receive a food pellet and then once this is occurring regularly, the schedule changes to 10 times to receive the same food pellet. We are asking the rat to make more behaviors for the same consequence. The changing-criterion design has an A-B design but rules out extraneous variables since the person or animal continues meeting the changing criterion/new goals using the same treatment plan or experimental manipulation. Hence successfully moving from one goal to the next must be due to the strategies that were selected.

2.2.6. Ways We Gather Data

When we record, we need to decide what method we will use. Several strategies are possible to include continuous, product or outcome, and interval. First, in continuous recording, we watch a person or animal continuously throughout an observation period , or time when observations will be made, and all occurrences of the behavior are recorded. This technique allows you to record both frequency and duration. The frequency is reported as a rate, or the number of responses that occur per minute. Duration is the total time the behavior takes from start to finish. You can also record the intensity using a rating scale in which 1 is low intensity and 5 is high intensity. Finally, latency can be recorded by noting how long it took the person to engage in the desirable behavior, or to discontinue a problem behavior, from when the demand was uttered. You can also use real-time recording in which you write down the time when the behavior starts and when it ends, and then do this each time the behavior occurs. You can look at the number of start-stops to get the frequency and then average out the time each start-stop lasted to get the duration. For instance:

Next is product or outcome recording . This technique can be used when there is a tangible outcome you are interested in, such as looking at how well a student has improved his long division skills by examining his homework assignment or a test. Or you might see if your friend’s plan to keep a cleaner house is working by inspecting his or her house randomly once a week. This will allow you to know if an experimental teaching technique works. It is an indirect assessment method meaning that the observer does not need to be present. You can also examine many types of behaviors. But because the observer is not present, you are not sure if the person did the work himself or herself. It may be that answers were looked up online, cheating occurred as in the case of a test, or someone else did the homework for the student such as a sibling, parent, or friend. Also, you have to make sure you are examining the result/outcome of the behavior and not the behavior itself.

Finally, interval recording occurs when you take the observation period and divide it up into shorter periods of time. The person or animal is observed, and the target behavior recorded based on whether it occurs during the entire interval, called whole interval recording, or some part of the interval, called partial interval recording. With the latter, you are not interested in the dimensions of duration and frequency. We also say the interval recording is continuous if each subsequent interval follows immediately after the current one. Let’s say you are studying students in a classroom. Your observation period is the 50 minutes the student is in his home economics class and you divide it up into ten, 5-minute intervals. If using whole, then the behavior must occur during the entire 5-minute interval. If using partial, it only must occur sometime during the 5-minute interval. You can also use what is called time sample recording in which you divide the observation period into intervals of time but then observe and record during part of each interval (the sample). There are periods of time in between the observation periods in which no observation and recording occur. As such, the recording is discontinuous. This is a useful method since the observer does not have to observe the entire interval and the level of behavior is reported as the percentage of intervals in which the behavior occurred. Also, more than one behavior can be observed.

2.2.7. The Apparatus We Use

What we need to understand next in relation to learning research is what types of apparatus’ are used. As you might expect, the maze is the primary tool and has been so for over 100 years. Through the use of mazes, we can determine general principles about learning that apply to not only animals such as rats, but to human beings too. The standard or classic maze is built on a large platform with vertical walls and a transparent ceiling. The rat begins at a start point or box and moves through the maze until it reaches the end or goal box. There may be a reward at the end such as food or water to encourage the rat to learn the maze. Through the use of such a maze, we can determine how many trials it takes for the rat to reach the goal box without making a mistake. As you will see, in Section 2.3, we can also determine how long it took the rat to run the maze.

An alternative to this design is what is called the T-maze which obtains its name from its characteristic T-structure. The rat begins in a start box and proceeds up the corridor until it reaches a decision point – go left or right. We might discover if rats have a side preference or how fast they can learn if food-deprived the night before. One arm would have a food pellet while the other would not. It is also a great way to distinguish place and response learning (Blodgett & McCutchan, 1947). Some forms of the T-maze have multiple T-junctions in which the rat can make the correct decision and continues in the maze or makes a wrong decision. The rat can use cues in the environment to learn how to correctly navigate the maze and once learned, the rat will make few errors and run through it very quickly (Gentry, Brown, & Lee, 1948; Stone & Nyswander, 1927).

Similar to the T-maze is what is called the Y-maze . Starting in one arm, the rat moves forward and then has to choose one of two arms. The turns are not as sharp as in a T-maze making learning a bit easier.  There is also a radial arm maze (Olton, 1987; Olton, Collison, & Werz, 1977) in which a rat starts in the center and can choose to enter any of 8, 12, or 16 spokes radiating out from this central location. It is a great test of short-term memory as the rat has to recall which arms have been visited and which have not. The rat successfully completes the maze when all arms have been visited.

One final maze is worth mentioning. The Morris water maze (Morris, 1984) is an apparatus that includes a large round tub of opaque water. There are two hidden platforms 1-2 cm under the water’s surface. The rat begins on a start platform and swims around until the other platform is located and it stands on it. It utilizes external cues placed outside the maze to find the end platform and run time is the typical dependent measure that is used.

To learn more about rat mazes, please visit: http://ratbehavior.org/RatsAndMazes.htm

Check this Out

Do you want to increase how fast rats learn their way through a multiple T-maze? Research has shown that you can do this by playing Mozart. Rats were exposed in utero plus 60 days to either a complex piece of music in the form of a sonata from Mozart, minimalist music, white noise, or silence. They were then tested over 5 days with 3 trials per day in a multiple T-maze. Results showed that rats exposed to Mozart completed the maze quicker and made fewer errors than the rats in the other conditions. The authors state that exposure to complex music facilitates spatial-temporal learning in rats and this matches results found in humans (Rauscher, Robinson, & Jens, 1998). Another line of research found that when rats were stressed they performed worse in water maze learning tasks than their non-stressed counterparts (Holscher, 1999).

So when you are studying for your quizzes or exams in this class (or other classes), play Mozart and minimize stress. These actions could result in a higher grade.

Outside of mazes, learning researchers may also utilize a Skinner Box . This is a small chamber used to conduct operant conditioning experiments with animals such as rats or pigeons. Inside the chamber, there is a lever for rats to push or a key for pigeons to peck which results in the delivery of food or water. The behavior of pushing or pecking is recorded through electronic equipment which allows for the behavior to be counted or quantified. This device is also called an operant conditioning chamber .

Finally, Edward Thorndike (1898) used a puzzle box to arrive at his law of effect or the idea that an organism will be more likely to repeat a behavior if it produced a satisfying effect in the past than if the effect was negative. This later became the foundation upon which operant conditioning was built. In his experiments, a hungry cat was placed in a box with a plate of fish outside the box. It was close enough that the cat could see and smell it but could not touch it. To get to the food, the cat had to figure out how to escape the box or which mechanism would help it to escape. Once free, the cat would take a bite, be placed back into the box, and then had to work to get out again. Thorndike discovered that the cat was able to get out quicker each time which demonstrated learning.

2.2.8. The Scientific Research Article

In scientific research, it is common practice to communicate the findings of our investigation. By reporting what we found in our study, other researchers can critique our methodology and address our limitations. Publishing allows psychology to grow its knowledge base about human behavior. We can also see where gaps still exist. We move it into the public domain so others can read and comment on it. Scientists can also replicate what we did and possibly extend our work if it is published.

As noted earlier, there are several ways to communicate our findings. We can do so at conferences in the form of posters or oral presentations, through newsletters from APA itself or one of its many divisions or other organizations, or through research journals and specifically scientific research articles. Published journal articles represent a form of communication between scientists and in them, the researchers describe how their work relates to previous research, how it replicates and/or extends this work, and what their work might mean theoretically.

Research articles begin with an abstract or a 150-250-word summary of the entire article. The purpose is to describe the experiment and allows the reader to decide whether he or she wants to read it further. The abstract provides a statement of purpose, overview of the methods, main results, and a brief statement of what these results mean. Keywords are also given that allow for students and other researchers alike to find the article when doing a search.

The abstract is followed by four major sections – Introduction, Method, Results, and Discussion. First, the introduction is designed to provide a summary of the current literature as it relates to the topic. It helps the reader to see how the researcher arrived at their hypothesis and the design of the study. Essentially, it gives the logic behind the decisions that were made.

Next, is the method section. Since replication is a required element of science, we must have a way to share information on our design and sample with readers. This is the essence of the method section and covers three major aspects of a study — the participants, materials or apparatus, and procedure. The reader needs to know who was in the study so that limitations related to the generalizability of the findings can be identified and investigated in the future. The researcher will also state the operational/behavioral definition, describe any groups that were used, identify random sampling or assignment procedures, and provide information about how a scale was scored or if a specific piece of apparatus was used, etc. Think of the method section as a cookbook. The participants are the ingredients, the materials or apparatus are whatever tools are needed, and the procedure is the instructions for how to bake the cake.

Third, is the results section. In this section, the researcher states the outcome of the experiment and whether it was statistically significant or not. The researchers can also present tables and figures. It is here we will find both descriptive and inferential statistics.

Finally, the discussion section starts by restating the main findings and hypothesis of the study. Next, is an interpretation of the findings and what their significance might be. Finally, the strengths and limitations of the study are stated which will allow the researcher to propose future directions or for other researchers to identify potential areas of exploration for their work. Whether you are writing a research paper for a class, preparing an article for publication, or reading a research article, the structure and function of a research article is the same. Understanding this will help you when reading articles in learning and behavior but also note, this same structure is used across disciplines.

• List typical dependent measures used in learning experiments.
• Describe the use of errors as a dependent measure.
• Describe the use of frequency as a dependent measure.
• Describe the use of intensity as a dependent measure.
• Describe the use of duration/run time/speed as a dependent measure.
• Describe the use of latency as a dependent measure.
• Describe the use of topography as a dependent measure.
• Describe the use of rate as a dependent measure.
• Describe the use of fluency as a dependent measure.

As we have learned, experiments include dependent and independent variables. The independent variable is the manipulation we are making while the dependent variable is what is being measured to see the effect of the manipulation. So, what types of DVs might we use in the experimental analysis of behavior or applied behavior analysis? We will cover the following: errors, frequency, intensity, duration, latency, topography, rate, and fluency.

2.3.1. Errors

A very simple measure of learning is to assess the number of errors made. If an animal running a maze has learned the maze, he/she should make fewer errors or mistakes with each trial, compared to say the first trial when many errors were made. The same goes for a child learning how to do multiplication. There will be numerous errors at start and then fewer to none later.

2.3.2. Frequency

Frequency is a measure of how often a behavior occurs. If we want to run more often, we may increase the number of days we run each week from 3 to 5. In terms of behavior modification, I once had a student who wished to decrease the number of times he used expletives throughout the day.

2.3.3. Intensity

Intensity is a measure of how strong the response is. For instance, a person on a treadmill may increase the intensity from 5 mph to 6 mph meaning the belt moves quicker and so the runner will have to move faster to keep up. We might tell children in a classroom to use their inside voices or to speak softer as opposed to their playground voices when they can yell.

2.3.4. Duration/Run Time/Speed

Duration is a measure of how long the behavior lasts. A runner may run more often (frequency), faster (intensity), or may run longer (duration). In the case of the latter, the runner may wish to build endurance and run for increasingly longer periods of time. A parent may wish to decrease the amount of time a child plays video games or is on his/her phone before bed. For rats in a maze, the first few attempts will likely take longer to reach the goal box than later attempts once the path needed to follow is learned. In other words, duration, or run time, will go down which demonstrates learning.

2.3.5. Latency

Latency represents the time it takes for a behavior to follow from the presentation of a stimulus. For instance, if a parent tells a child to take out the trash and he does so 5 minutes later, then the latency for the behavior of walking the trash outside is 5 minutes.

2.3.6. Topography

Topography represents the physical form a behavior takes. For instance, if a child is being disruptive, in what way is this occurring? Could it be the child is talking out of turn, being aggressive with other students, fidgeting in his/her seat, etc? In the case of rats and pushing levers, the mere act of pushing may not be of interest, but which paw is used or how much pressure is applied to the lever?

2.3.7. Rate

Rate is a measure of the change in response over time, or how often a behavior occurs. We may wish the rat to push the lever more times per minute to earn food reinforcement. Initially, the rat was required to push the lever 20 times per minute and now the experimenter requires 35 times per minute to receive a food pellet. In humans, a measure of rate would be words typed per minute. I may start at 20 words per minute but with practice (representing learning) I could type 60 words per minute or more.

2.3.8. Fluency

Though I may type fast, do I type accurately? This is where fluency comes in. Think about a foreign language. If you are fluent you speak it well. So, fluency is a measure of the number of correct responses made per minute. I may make 20 errors per minute of typing but with practice, I not only get quicker (up to 60 words per minute) but more accurate and reduce mistakes measure to 5 errors per minute. A student taking a semester of Spanish may measure learning by how many verbs he can correctly conjugate in a minute. Initially, he could only conjugate 8 verbs per minute but by the end of the semester can conjugate 24.

• Defend the use of animals in research.
• Describe safeguards to protect human research subjects.

2.4.1. Animal Models of Behavior

Learning research frequently uses animal models. According to AnimalResearch.info , animals are used “…when there is a need to find out what happens in the whole, living body, which is far more complex than the sum of its parts. It is difficult, and in most cases simply not yet possible, to replace the use of living animals in research with alternative methods.” They cite four main reasons to use animals. First, to advance scientific understanding such as how living things work to apply that knowledge for the benefit of both humans and animals. They state, “Many basic cell processes are the same in all animals, and the bodies of animals are like humans in the way that they perform many vital functions such as breathing, digestion, movement, sight, hearing, and reproduction.”

Second, animals can serve as models to study disease. For example, “Dogs suffer from cancer, diabetes, cataracts, ulcers and bleeding disorders such as hemophilia, which make them natural candidates for research into these disorders. Cats suffer from some of the same visual impairments as humans.” Therefore, animal models help us to understand how diseases affect the body and how our immune system responds.

Third, animals can be used to develop and test potential treatments for these diseases. As the website says, “Data from animal studies is essential before new therapeutic techniques and surgical procedures can be tested on human patients.”

Finally, animals help protect the safety of people, other animals, and our environment. Before a new medicine can go to market, it must be tested to ensure that the benefits outweigh the harmful effects. Legally and ethically, we have to move away from in vitro testing of tissues and isolated organs to suitable animal models and then testing in humans.

In conducting research with animals, three principles are followed. First, when possible, animals should be replaced with alternative techniques such as cell cultures, tissue engineering, and computer modeling. Second, the number of animals used in research should be reduced to a minimum. We can do this by “re-examining the findings of studies already conducted (e.g. by systematic reviews), by improving animal models, and by use of good experimental design.” Finally, we should refine the way experiments are conducted to reduce any suffering the animals may experience as much as possible. This can include better housing and improving animal welfare. Outside of the obvious benefit to the animals, the quality of research findings can also increase due to reduced stress in the animals. This framework is called the 3Rs.

Please visit: http://www.animalresearch.info/en/

One way to guarantee these principles are followed is through what is called the Institutional Animal Care and Use Committee (IACUC). The IACUC is responsible for the oversight and review of the humane care and use of animals; upholds standards set forth in laws, policies, and guidance; inspects animal housing facilities; approves protocols for use of animals in research, teaching, or education; addresses animal welfare concerns of the public; and reports to the appropriate bodies within a university, accrediting organizations, or government agencies. At times, projects may have to be suspended if found to be noncompliant with the regulations and policies of that institution.

• For more on the IACUC within the National Institutes of Health, please visit: https://olaw.nih.gov/resources/tutorial/iacuc.htm
• For another article on the use of animals in research, please check out the following published in the National Academies Press – https://www.nap.edu/read/10089/chapter/3
• The following is an article published on the ethics of animal research and discusses the 3Rs in more detail – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2002542/
• And finally, here is a great article published by the Washington State University IACUC on the use of animals in research and teaching at WSU – https://research.wsu.edu/frequently-asked-questions-about-animal-care-and-use-at-washington-state-university/

2.4.2. Human Models of Behavior

Throughout this module, we have seen that it is important for researchers to understand the methods they are using. Equally important, they must understand and appreciate ethical standards in research. As we have seen already in Section 2.3.1, such standards exist for the use of animals in research. The American Psychological Association (APA) identifies high standards of ethics and conduct as one of its four main guiding principles or missions and as it relates to humans. To read about the other three, please visit https://www.apa.org/about/index.aspx . Studies such as Milgram’s obedience study, Zimbardo’s Stanford prison study, and others, have necessitated standards for the use of humans in research. The standards can be broken down in terms of when they should occur during the process of a person participating in the study.

2.4.2.1. Before participating. First, researchers must obtain informed consent or when the person agrees to participate because they are told what will happen to them. They are given information about any risks they face, or potential harm that could come to them, whether physical or psychological. They are also told about confidentiality or the person’s right not to be identified. Since most research is conducted with students taking introductory psychology courses, they have to be given the right to do something other than a research study to likely earn required credits for the class. This is called an alternative activity and could take the form of reading and summarizing a research article. The amount of time taken to do this should not exceed the amount of time the student would be expected to participate in a study.

2.4.2.2. While participating. Participants are afforded the ability to withdraw or the person’s right to exit the study if any discomfort is experienced.

2.4.2.3. After participating . Once their participation is over, participants should be debriefed or when the true purpose of the study is revealed and they are told where to go if they need assistance and how to reach the researcher if they have questions. So, can researchers deceive participants, or intentionally withhold the true purpose of the study from them? According to the APA, a minimal amount of deception is allowed.

Human research must be approved by an Institutional Review Board or IRB. It is the IRB that will determine whether the researcher is providing enough information for the participant to give consent that is truly informed, if debriefing is adequate, and if any deception is allowed or not. According to the Food and Drug Administration (FDA), “The purpose of IRB review is to assure, both in advance and by periodic review, that appropriate steps are taken to protect the rights and welfare of humans participating as subjects in the research. To accomplish this purpose, IRBs use a group process to review research protocols and related materials (e.g., informed consent documents and investigator brochures) to ensure the protection of the rights and welfare of human subjects of research.”

If you would like to learn more about how to use ethics in your research, please read: https://opentext.wsu.edu/carriecuttler/chapter/putting-ethics-into-practice/

To learn more about IRBs, please visit: https://www.fda.gov/RegulatoryInformation/Guidances/ucm126420.htm

 Module Recap

That’s it. In Module 2 we discussed the process of research used when studying learning and behavior. We learned about the scientific method and its steps which are universally used in all sciences and social sciences. Our breakdown consisted of six steps but be advised that other authors could combine steps or separate some of the ones in this module. Still, the overall spirit is the same. In the experimental analysis of behavior, we do talk about making a causal statement in the form of an If-Then statement, or respectfully we discuss functional relationships and contingencies. We also define our terms clearly, objectively, and precisely through a behavioral definition. In terms of research designs, psychology uses five main ones and our investigation of learning and behavior focuses on three of those designs, with experiment and observation being the main two. Methods by which we collect data, the apparatus we use, and later, who our participants/subjects are, were discussed. The structure of a research article was outlined which is consistent across disciplines and we covered some typical dependent variables or measures used in the study of learning and behavior. These include errors, frequency, intensity, duration, latency, topography, rate, and fluency.

Armed with this information we begin to explore the experimental analysis of behavior by investigating elicited behaviors and more in Module 3. From this, we will move to a discussion of respondent and then operant conditioning and finally observational learning. Before closing out with complementary cognitive processes we will engage in an exercise to see how the three models complement one another and are not competing with each other.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• Learning Objectives

What is a Learning Objective?

Bloom (1956) suggests a six-stage hierarchy of cognitive competencies :

• Knowledge - Students can collect and restate information.
• Comprehension - Students can interpret and understand information.
• Application - Students can apply information to solve problems.
• Analysis - Students can organize and analyze information.
• Synthesis - Students can create information from information.
• Evaluation - Students can compare and assess information and ideas.

In an update to Bloom, Anderson and Krathwohl (2001) argue that students should be able to:

Undergraduate research experiences that engage students in the scientific method require and develop skills that can be mapped to Bloom's taxonomy and require students to do the tasks that Anderson and Krathwohl suggest they should be able to. And rather than offering isolated experiences with each of the steps of the scientific method, their relationships to one another become transparent, furthering one's understanding of what it means to "do science." Before going headfirst into an undergraduate research experience, you want to consider how directly and deeply you want students engaged in each step of the research process. This will help you determine where you place an undergraduate research experience in your curriculum or course, or if you do undergraduate research outside of the classroom instead (perhaps as part of a summer research experience).

As an example, if your key learning objectives are related to synthesis and evaluation, you may want the culminating project in your class to be a research paper and, if time is limited, you may want to supply students with the background literature and data for the project rather ask them to collect it themselves. If you have an opportunity to supervise the project as an independent study, though, you may have time to work on each of the six competencies more intensely and can involve your student just as seriously in tasks like reviewing the literature and collecting data as in evaluating evidence.

Krathwohl et al (1964) suggests a hierarchy of affective competencies , and you may consider forming some affective learning objectives as well. These competencies are:

• Receiving - Students can notice and tolerate ideas.
• Responding - Students can respond to ideas by investing in them in some way.
• Valuing - Students can demonstrate to others that they value some ideas.
• Organizing - Students can connect that value to existing ones.
• Characterizing - Students' actions are consistent with the internalized values.

Developing Learning and Content Objectives

Here are some tips for writing cognitive learning objectives from the Higher Education Academy.

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Crafting an Effective Research Proposal: Learning from Noteworthy PDF Examples

Research proposals are essential documents that outline the objectives, methodology, and significance of a research project. They serve as blueprints for researchers, guiding them through the process of conducting their investigations. While there are various formats and templates available, PDF examples of research proposals can be particularly beneficial in understanding the structure and content required for a successful proposal. In this article, we will explore some noteworthy PDF examples of research proposals and discuss what makes them effective.

Introduction

The introduction section of a research proposal sets the stage for the study by providing background information on the topic and stating the research problem or question. A well-crafted introduction should capture the reader’s interest and clearly articulate the significance of the proposed research.

One example of an effective introduction in a research proposal is a study on climate change’s impact on coastal communities. The introduction outlines key statistics related to rising sea levels and emphasizes the vulnerability of coastal areas to environmental changes. It also highlights gaps in existing literature and explains how the proposed study aims to address these gaps.

Literature Review

The literature review section demonstrates that you have thoroughly researched existing studies related to your topic and have identified a gap that your research will fill. It showcases your ability to critically analyze previous work while highlighting its relevance to your own study.

An exemplary PDF example of a literature review within a research proposal is one that explores mental health interventions among college students. This section summarizes various studies on mental health issues faced by college students, including stress, anxiety, and depression. It then highlights gaps in current intervention strategies and proposes new approaches based on emerging evidence.

Methodology

The methodology section describes how you will conduct your research, including details about data collection methods, sample selection criteria, and data analysis techniques. This section should demonstrate your ability to design a rigorous study that will yield reliable results.

A notable PDF example showcases a research proposal investigating the effects of a new teaching method on student performance in mathematics. The methodology section outlines the study’s design, including the selection of schools and participants, data collection through pre- and post-tests, and statistical analysis methods. It also discusses potential limitations and ethical considerations.

Significance and Expected Outcomes

The significance and expected outcomes section explains the potential impact of your research and how it contributes to existing knowledge in the field. It should highlight the practical implications of your findings and explain how they can be applied to real-world situations.

An informative PDF example of this section could be a research proposal on renewable energy sources. It discusses the significance of transitioning from fossil fuels to renewable energy for environmental sustainability. The proposal outlines expected outcomes such as reduced greenhouse gas emissions, increased energy efficiency, and long-term cost savings.

In conclusion, examining PDF examples of research proposals can provide valuable insights into crafting an effective proposal. By studying well-structured introductions, comprehensive literature reviews, detailed methodologies, and impactful significance sections, researchers can learn from successful proposals in their fields. These examples serve as guideposts for developing their own research proposals that are compelling, rigorous, and contribute meaningfully to their respective disciplines.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.

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Carnegie Melon University, Eberly Center - learning objective samples

This website provides a collection of sample learning objectives across various disciplines, serving as a practical resource for educators seeking inspiration and guidance in creating clear and effective learning outcomes. These examples cover a wide range of subjects and demonstrate how to formulate specific, measurable, and achievable objectives that align with instructional goals. This resource is a valuable tool for instructors looking to enhance their course design and ensure their teaching aligns with intended learning outcomes.

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Research Methodology: Best Practices for Rigorous, Credible, and Impactful Research

Student resources, learning objectives.

By the end of this chapter, you will be able to do the following:

2.1 Explain why you should care about ethical research.

2.2 Compare differences between two research philosophies: utilitarian and deontological.

2.3 Follow ethical standards in planning the purpose and study.

2.4 Execute ethical research that considers the rights of participants.

2.5 Consider special ethical requirements when conducting research in field settings.

2.6 Follow ethical standards in reporting your results.

2.7 Implement ethical standards when conducting research with online participants.

2.8 Enforce research ethics to prevent misconduct.

2.9 Apply your own ethical beliefs when considering ethical challenges and dilemmas.

TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices

• Open access
• Published: 26 June 2024
• Volume 21 , article number  121 , ( 2024 )

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You have full access to this open access article

• Abdussalam Elhanashi 1 ,
• Pierpaolo Dini 1 ,
• Sergio Saponara 1 &
• Qinghe Zheng 2  

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Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field.

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Avoid common mistakes on your manuscript.

1 Introduction

Stroke is a leading cause of death and disability worldwide, making early detection and diagnosis crucial for improving patient outcomes. Rapid and accurate identification of stroke symptoms, along with timely diagnostic testing, are essential for initiating appropriate treatment and minimizing long-term consequences. This introduction will provide an overview of stroke detection and diagnosis, including the signs and symptoms of stroke, diagnostic tools and techniques, and the importance of early intervention [ 1 , 2 , 3 , 4 ]. Stroke, also known as a cerebrovascular accident, occurs when the blood supply to the brain is interrupted, leading to damage or death of brain cells. There are two main types of strokes: ischemic stroke, caused by a blockage in a blood vessel supplying the brain, and hemorrhagic stroke, caused by bleeding into the brain. The common signs and symptoms of stroke can be remembered using the acronym FAST: face drooping, arm weakness, speech difficulty, time to call emergency services. Other symptoms include sudden numbness or weakness in the face, arm, or leg, especially on one side of the body; sudden confusion, trouble speaking or understanding speech; sudden trouble seeing in one or both eyes; sudden trouble walking, dizziness, loss of balance or coordination; and sudden severe headache with no known cause [ 5 ]. When a patient presents with symptoms suggestive of a stroke, healthcare providers must act quickly to confirm the diagnosis and determine the type of stroke in order to initiate appropriate treatment. Several diagnostic tools and techniques are used in the evaluation of stroke patients. These include imaging studies such as computed tomography (CT) scans, magnetic resonance imaging (MRI), and angiography to visualize the brain to assess electrical activity in the brain; and blood tests to evaluate for potential causes of stroke such as high cholesterol, clotting disorders, or infection [ 6 ]. Early intervention is critical in the management of stroke as it can help minimize brain damage and improve patient outcomes. The “time is brain” concept emphasizes the importance of rapid assessment and treatment to preserve brain function. For ischemic strokes, timely administration of thrombolytic therapy (such as tissue plasminogen activator) or endovascular clot retrieval can help restore blood flow to the affected area of the brain. In cases of hemorrhagic stroke, prompt neurosurgical intervention would be necessary to control bleeding and reduce pressure on the brain. Despite advancements in stroke diagnosis, current diagnostic tools, such as CT and MRI, often fail to detect minor strokes and differentiate between ischemic and hemorrhagic strokes in the acute phase. There is also a lack of portable, rapid, and cost-effective diagnostic devices for use in pre-hospital settings, where early detection is crucial. Existing biomarkers lack specificity and sensitivity, limiting their clinical utility. The role of telemedicine in stroke diagnosis is underexplored, especially in remote areas with limited access to advanced medical facilities. It is necessary to bridge these gaps and improve diagnostic accuracy and timeliness, ultimately enhancing patient outcomes and reducing the burden on healthcare systems. Therefore, accurate and timely diagnosis is essential for guiding appropriate interventions and improving patient prognosis [ 7 , 8 ]. In recent years, the rapid advancement of artificial intelligence (AI) and deep learning technologies has revolutionized various industries, including healthcare. These cutting-edge technologies have shown great promise in transforming the way medical diagnostics are conducted and in enhancing the delivery of e-healthcare services. By leveraging AI and deep learning, healthcare professionals can harness the power of data-driven insights to improve diagnostic accuracy, optimize treatment plans, and provide personalized care to patients. This introduction will explore the utilization of AI and deep learning in diagnostics and e-healthcare, highlighting their potential benefits and implications [ 9 ]. The integration of AI in healthcare has significantly impacted the way medical professionals approach diagnosis and treatment. AI algorithms have demonstrated the ability to analyze complex medical data, such as imaging scans, genetic information, and patient records, with remarkable speed and accuracy. This has led to the development of advanced diagnostic tools that can assist clinicians in detecting and predicting various medical conditions, ranging from cancer and cardiovascular diseases to neurological disorders [ 10 ].

The main contributions of this research are as the following:

The research introduces a novel approach to addressing the urgent need for timely stroke diagnosis based on facial paralysis acquired from the images by presenting a real-time stroke detection system. The proposed approach automates the process of stroke detection, potentially reducing the time required for diagnosis and treatment initiation.

This study emphasizes the importance of comprehensive datasets comprising both stroke and non-stroke faces of individuals for effective model training. By utilizing the YOLOv8 models, the study leverages their advanced architectural improvements to enhance real-time processing capabilities. Extensive training on diverse datasets enables the model to discern intricate patterns and features associated with strokes and non-stroke cases, thereby improving its accuracy and responsiveness in real-time applications. The improvements in YOLOv8, such as optimized network structures and enhanced computational efficiency, are crucial for meeting the real-time requirements of stroke detection systems.

This research adopts a federated learning technique, allowing the model to learn from data distributed across various clients without compromising patient privacy. By decentralizing the training process, sensitive patient information remains localized, addressing privacy concerns while maintaining model performance.

Implementation of the proposed methodology on NVIDIA platforms showcases the practical feasibility of real-time stroke detection. By harnessing advanced GPU capabilities, the system demonstrates remarkable speed and accuracy, revolutionizing stroke diagnosis and treatment with its efficient analysis.

The rest of the paper is organized as follows: Sect.  2 presents the related work; Sect.  3 presents the proposed algorithm and design methodology; Sect.  4 explores the experimental results and discussion. Section  5 describes real-time implementation on edge NVIDIA platforms. Finally, conclusions are drawn in Sect.  6 .

2 Related work

Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of stroke. CT scans are commonly used to rule out hemorrhagic stroke, while MRI is more sensitive in detecting ischemic stroke, aiding in the differentiation from non-stroke conditions. Deep learning methodologies have demonstrated substantial efficacy in the fields of image and video recognition, revolutionizing the way that computers interpret visual data [ 11 , 12 , 13 ]. Deep learning has revolutionized the fields of classification and modulation, offering robust performance gains over traditional methods [ 14 ]. Advanced imaging techniques such as perfusion imaging and diffusion-weighted imaging have shown promise in enhancing the accuracy of stroke diagnosis [ 15 ].

Utilizing deep learning for medical images has revolutionized diagnostic accuracy and patient care. Deep learning, particularly through convolutional neural networks (CNNs), has shown exceptional promise in various medical imaging applications, including ophthalmology, respiratory imaging, and breast disease. Research has documented instances of medical conditions such diabetic retinopathy, lung nodules, and breast cancer being diagnosed through the use of medical imaging techniques, including retinal fundus photography, CT scans, and mammograms.. Despite these advancements, challenges persist, such as the need for robust quantitative imaging biomarkers, concerns about data quality and bias, and the black-box nature of deep learning models. Efforts are underway to address these challenges and enhance the clinical impact of deep learning in medical imaging [ 16 , 17 , 18 ].

Deep learning models have the capacity to forecast patient outcomes, tailor treatment strategies, and streamline administrative operations within telemedicine frameworks. By incorporating deep learning into telemedicine services, healthcare professionals are enabled to deliver care that is both more efficient and economical, effectively overcoming the challenges posed by geographic limitations. This approach ensures that patients receive high-quality care tailored to their specific needs, regardless of where they are located, making healthcare more accessible and personalized [ 19 , 20 , 21 ]. Real-time deep learning has emerged as a powerful tool for enhancing diagnostic accuracy and efficiency. Deep learning models can analyze complex medical data such as imaging scans, genetic information, and patient records to aid in the identification of diseases and conditions. Real-time processing capabilities enable the analysis of incoming data streams, facilitating rapid decision-making and timely interventions [ 22 , 23 , 24 ].

Biomarkers have garnered significant attention as potential tools for differentiating between stroke and non-stroke conditions. For instance, elevated levels of specific biomarkers such as brain natriuretic peptide (BNP) and D-dimer have been associated with an increased risk of stroke, serving as valuable indicators in the diagnostic process. Furthermore, ongoing research is exploring the utility of novel biomarkers in discriminating between different stroke subtypes and non-stroke etiologies [ 25 ]. Several clinical scales and scores have been developed to aid in the rapid assessment and differentiation of stroke from non-stroke conditions. The National Institutes of Health Stroke Scale (NIHSS) is widely used to quantify stroke severity and guide treatment decisions, while the Cincinnati Prehospital Stroke Scale (CPSS) enables prehospital providers to identify potential stroke cases with high specificity [ 26 ]. These tools contribute to streamlining the diagnostic process and facilitating intervention. The emergence of telemedicine has revolutionized the delivery of acute stroke care, allowing for remote assessment and diagnosis. TeleStroke networks leverage audiovisual communication to connect stroke specialists with healthcare facilities, enabling timely evaluation of patients presenting with stroke symptoms [ 27 ]. This approach has proven instrumental in extending expert guidance to underserved areas, ultimately improving access to accurate diagnosis and treatment.

Advancements in machine learning and deep learning have paved the way for innovative diagnostic tools in the realm of stroke care. AI algorithms trained on large datasets can analyze medical images and clinical data to differentiate between stroke and non-stroke conditions with high accuracy [ 28 ]. Moreover, AI-based decision support systems hold potential for enhancing the efficiency and precision of stroke diagnosis, heralding a new era of personalized medicine. Exploration of genomic and proteomic profiles has unveiled valuable insights into the pathophysiology of stroke and non-stroke conditions. Genetic variations have been linked to an increased susceptibility to certain types of strokes, underscoring the potential utility of genetic testing in risk stratification and differential diagnosis. Furthermore, proteomic profiling offers a window into the molecular signatures associated with different disease states, presenting opportunities for developing novel diagnostic biomarkers [ 29 ].

3 Proposed algorithm design methodology

In the experimental setup for real-time stroke detection, a combination of advanced technologies of deep learning is utilized to improve accuracy and maintain privacy. We examined several architectures of YOLOv8 models, which is a cutting-edge neural network architecture targeted for real-time object detection. The proposed models are utilized to identify stroke based on the facial paralysis and non-stroke condition from the faces of the individuals in the images. In this research, federated learning, a decentralized training method, is employed to train the model collaboratively across multiple clients while ensuring the confidentiality of sensitive proposed data. The setup involves a central server overseeing the training process and coordinating interactions with three different clients, each providing valuable data to enhance the model’s performance. To optimize the system’s capabilities, NVIDIA platforms are utilized to assess the deployed model’s inference. We expedite the assessment of proposed models, facilitating real-time stroke detection and diagnosis. This ensures that the system can efficiently process the recognition of the faces of individuals, promptly identifying signs of stroke and enabling timely medical intervention. Figure  1 illustrates the proposed framework for utilizing real-time deep learning models for stroke and non-stroke detection.

The proposed system for utilizing real-time deep learning model and federated learning for stroke and non-stroke detection

3.1 Dataset collection and labeling

The data used in this research consists of two different categories of images. One group includes individuals diagnosed with acute stroke, while the other group of individuals without such a diagnosis. In total, the dataset comprises 3745 images, offering a substantial pool for analysis. Table 1 provides the main description for the proposed dataset of stroke/non-stroke.

To enhance the efficacy of the model, various data augmentation methods were implemented. These techniques serve to expand the dataset by generating modified versions of the existing images. Specifically, augmentation involved operations such as flipping, rotating, and scaling the images. By applying these transformations, the dataset becomes more diverse and resilient, mimicking a broader range of real-world scenarios. By augmenting the dataset in this manner, the model’s accuracy is expected to improve. The varied representations provided by augmented data can help the model generalize better to unseen instances, thereby enhancing its performance when applied to classify images of individuals with or without acute stroke. In essence, data augmentation enriches the dataset, enabling the model to learn from a wider spectrum of image variations and complexities. Figure  2 illustrates the distribution for the two classes for the proposed dataset. These visual aids are invaluable for evaluating model performance, detecting class imbalances or dependencies, and guiding decisions related to model refinement and data handling strategies. Class frequency pertains to the distribution of data points among different categories or classes within a dataset. When considering normalized height and width space for stroke and non-stroke datasets, it indicates the number of data points falling within specific height and width ranges. Typically, labels denote the assigned categories or classes for each data point, distinguishing between stroke and non-stroke instances in relevant datasets. A height-width graph typically illustrates data point distributions based on their height and width values, often presented as a scatter plot with height on the x -axis and width on the y -axis. Alternatively, an x – y graph can visualize the data, particularly if additional features beyond height and width are involved, with each data point representing a point in a multi-dimensional space where each axis corresponds to a different feature.

Visualization of class distribution in the proposed dataset, with the upper bar graph showing a higher number of instances for ‘nostroke’ than ‘stroke’, and the lower scatter plots displaying the density and spread of feature coordinates ( x , y ) and bounding box dimensions (width, height) for each class

3.2 The proposed object detection architecture

The YOLOv8 architectures mark the forefront of the YOLO series, specializing in the real-time detection of individual faces. YOLOv8 architecture excels in real-time object detection due to its faster speed, increased accuracy, and anchor-free design. This model leverages computer vision, neural networks, deep learning, and image processing techniques to deliver outstanding object recognition capabilities. YOLOv8’s multi-scale prediction and enhanced backbone network further boost its object detection performance. Its ability to handle diverse datasets and ease of training make it a versatile tool for various applications. With faster and more precise results, YOLOv8 stands out as a powerful solution in the fields of artificial intelligence and computer vision. It represents the pinnacle of progress in the YOLO object detector lineage, offering unparalleled precision and speed. YOLOv8 models build upon the legacy of their forerunners by integrating groundbreaking features and enhancements, making it the go-to choose for a broad spectrum of object detection challenges across different settings. It leverages advanced backbone and neck architectures to improve feature extraction significantly, thus boosting the overall efficiency of object detection. With the introduction of an anchor-free split Ultralytics head, it achieves remarkable accuracy improvements and streamlines the detection workflow, outperforming conventional anchor-based methods. YOLOv8 strikes a perfect harmony between speed and accuracy, making it ideal for real-time detection needs in various application areas. It provides a wide range of pre-trained models, facilitating the selection of the most suitable model for specific requirements.

As the most compact variant in the YOLOv8 series, YOLOv8 is engineered for a wide array of detection tasks, from simple object identification to more complex challenges such as instance segmentation, detection of key points, orientation of objects, and categorization. The architecture is a refined version of the CSPDarknet53, incorporating 53 convolutional layers and cross-stage partial connections to bolster the flow of information between layers. The YOLOv8 head consists of several convolutional layers leading to fully connected layers, tasked with predicting bounding boxes, abjectness scores, and class probabilities for identified objects. A distinct feature of YOLOv8 is its self-attention mechanism within the head, enabling the model to focus on specific parts of an image and adjust feature significance according to the relevance of the task. Remarkably adept at detecting objects across multiple scales, it uses a feature pyramid network to identify objects of diverse sizes and scales within an image effectively. This multi-layered pyramid facilitates the detection of both large and small objects, underscoring YOLOv8’s adaptability and thoroughness in object detection. The architecture and capabilities of YOLOv8 are visually depicted in Fig.  3 .

The YOLOv8 architecture incorporates a modified CSPDarknet53 as its core framework. It introduces the C2f module as a replacement for the CSPLayer found in YOLOv5, enhancing its structure. To speed up the computation process, it employs a spatial pyramid pooling fast (SPPF) layer, which consolidates features into a uniform-size map [ 15 ]

In the architecture’s initial setup, the 6 × 6 convolution in the stem is replaced with a 3 × 3 convolution, altering the primary building block, and substituting C2f for C3 as shown in Fig.  4 . A summary of the module is provided in the accompanying image, indicating “f” as the feature count, “e” as the expansion rate, and CBS representing a structure that includes a Convolution, Batch Normalization, and a SiLU operation. In the C2f configuration, outputs from all Bottleneck stages (which consist of two 3 × 3 convolutions connected by residuals and are elaborately termed) are merged. Conversely, C3 utilizes only the output from the final Bottleneck stage. This Bottleneck component mirrors the one found in YOLOv5, with the exception that the initial convolution’s kernel size has been altered from 1 × 1 to 3 × 3. This change demonstrates that YOLOv8 is gradually returning to the use of the ResNet block.

The C2f layer for YOLOv8 architecture

Within the network’s neck section, feature concatenation occurs directly, bypassing the need for uniform channel dimensions. This approach serves to diminish both the parameter count and the overall tensor sizes, streamlining the network’s complexity.

Table 2 presents a comparison among different versions of YOLOv8: YOLOv8x, YOLOv8l, YOLOv8m, YOLOv8s, and YOLOv8n, which vary in complexity and performance.

All YOLOv8 versions are designed to process images with an input size of 640 pixels, maintaining consistency in image resolution across models. The complexity and capacity to learn from data are indicated by the number of parameters (Params), measured in millions (M), and the computational workload during inference is represented by floating-point operations per second (FLOPs), measured in billions (B). YOLOv8x, the most complex model, has 68.2 million parameters and requires 257.8 billion FLOPs, which has the highest capacity for learning and computational complexity. Conversely, YOLOv8n is the simplest with only 3.2 million parameters and 8.7 billion FLOPs, indicating it is the least complex and has the lowest computational demand. This gradient of complexity and performance from YOLOv8n to YOLOv8x allows for a range of applications, from lightweight deployments to scenarios requiring high accuracy and computational resources.

3.3 Federated learning

In this research, federated learning (FL) was employed to train the proposed model. FL is a decentralized machine learning method enabling multiple clients to jointly train a model without sharing raw data. Our objective was to emulate a FL setup with 3 clients operating on a single machine, where both the server and all 3 clients reside, sharing CPU, GPU, and memory resources. With 3 clients, we maintain 3 instances of FlowerClient in memory. Executing this on a single machine can strain available memory resources, even if only a subset of clients engages in a FL round. Leveraging the FLOWER platform, clients and the server with overlapping data contribute to model training. Notably, FLOWER boasts an efficient communication protocol, transmitting only model updates, not raw data, thereby significantly reducing communication overhead. This feature renders it ideal for scenarios with limited bandwidth or high-latency connections. Moreover, FLOWER ensures that raw data remains on local devices, with only model updates, in the form of weight differentials, shared with the central server, safeguarding sensitive information against data breaches and unauthorized access.

Our proposed approach implements an FL system for object detection utilizing the YOLOv8n model, comprising 3 clients and a server. Each client is equipped to train a YOLOvn8 on local data, employing the Ultralytics library, with the ( DetectionTrainer ) class managing training specifics. Each client possesses its dataset located in the “data/clients/{ idx } directory, periodically training its local model and computing metrics such as mAP, recall, precision, and loss. Subsequently, during the FL process, the client transmits its model parameters to the server. The server orchestrates the FL process within the Flower framework, aggregating model updates from the 3 clients utilizing the Federated Averaging ( FedAvg ) strategy provided by the flwr library. The FL process iterates for a specified number of epochs, with the server engaging in communication with the 3 clients. Following each round, the server aggregates model updates, evaluates the federated model, and logs the results in the “results” directory. Its primary responsibility entails overseeing and coordinating the training process, supervising the participation of the 3 clients in the FL process. These clients, structurally similar, differ mainly in the data they possess, each holding data pertaining to distinct classes of stroke and non-stroke cases. The following four phases outline the steps in a distributed deep learning workflow: from receiving and updating models to local training, evaluation, and secure transmission of improved data back to the server, ensuring a refined and robust predictive performance:

Receive updated model : Clients establish a secure connection with the server, ensuring data integrity and privacy, through which the server transmits the latest version of the global machine learning model, utilizing efficient communication protocols to minimize latency.

Local model training : Clients preprocess their local datasets, addressing outliers, missing values, and performing feature scaling as required to enhance data quality. Leveraging their computational resources, clients undertake model training locally, employing fine-tuning such as mini-batch gradient descent or federated learning to effectively handle the datasets

Model evaluation : After completing the model training phase, clients proceed to conduct thorough evaluations on the trained model, employing diverse metrics including accuracy, precision, recall, and F 1 score, customized to suit their particular application requirements. To ensure robustness and reliability, clients meticulously analyze the model’s predictions on their local datasets, conducting detailed error analysis to pinpoint instances of incorrect predictions and iteratively enhance the training process, thereby refining the model’s predictive capabilities and overall performance.

Send updated weights : Upon completion of local model training and evaluation, clients securely transmit the updated model weights back to the server for aggregation, ensuring data confidentiality. The server aggregates the received model updates from multiple clients using techniques such as federated averaging while synthesizing a new global model. Subsequently, the server conducts additional validation checks on the aggregated model to verify its integrity and stability before deploying it for further inference or subsequent rounds of training.

3.4 Training

The training procedure for YOLOv8 models entails optimizing several hyperparameters to achieve effective object detection. Table 3 illustrates the hyperparameters for tuning YOLOv8 models. The image size, set to 640 pixels, determines the dimensions of input images, crucial for feature extraction and localization. Batch size, specified as 32 defines the number of samples processed before updating the model’s parameters, affecting computational efficiency and convergence stability. With 30 epochs, the training duration is partitioned into iterations over the entire dataset, refining the model’s performance iteratively. Mosaic, at 0.8, incorporates a data augmentation technique blending multiple images to enhance generalization and robustness. Mixup, assigned a value of 0.2, further diversifies the dataset by linearly interpolating between pairs of images and their labels, augmenting training data diversity. The learning rate, set at 10 –5 , regulates the step size in updating model parameters, influencing convergence speed and optimization quality. Utilizing the AdamW optimizer, the training algorithm adjusts model weights to minimize the defined loss function effectively. Lastly, the cache parameter, set as False, controls whether to cache datasets in memory, affecting training speed and memory consumption. Overall, fine-tuning these hyperparameters orchestrates the training process to yield a YOLOv8 model optimized for object detection tasks. All the experiments have been conducted using AWS EC2 G4 instances equipped with 8 NVIDIA T4 GPUs, featuring 96 CPUs and a network bandwidth of 100 Gbps. Table 3 shows the hyperparameters for YOLOv8 training and tuning.

4 Experiment results and discussion

4.1 evaluation matrices.

To evaluate the proposed models, we meticulously performed assessments focusing on key metrics: recall, precision, F 1 score, and mean Average Precision (mAP). Recall, the measure of a model’s ability to identify all relevant instances, is crucial for understanding its sensitivity. Precision, on the other hand, evaluates how many of the identified instances are relevant, highlighting the model’s accuracy. The F 1 score harmonizes recall and precision, providing a single metric to assess a model’s balance between sensitivity and accuracy. Lastly, mAP offers a comprehensive evaluation of the model’s performance across different thresholds, encapsulating its ability to rank instances correctly. For each metric, equations play a pivotal role in quantification. Recall is calculated as the ratio of true positives to the sum of true positives and false negatives, precision as the ratio of true positives to the sum of true positives and false positives, F 1 score as the harmonic mean of precision and recall, and mAP as the average of the precision scores at different thresholds, offering a nuanced view of the model’s performance. mAP evaluates the balance between precision and recall, providing a comprehensive view of an algorithm’s effectiveness across various thresholds. Refer to Eqs. ( 1 – 5 ) for details, where \( k \) represents the number of queries and \(AP_ i \) denotes the average precision for a specific query \( i \). These metrics collectively offer a robust framework for evaluating the proposed models, ensuring a comprehensive analysis of their effectiveness:

4.2 Results of the proposed YOLOv8 models

Figure  5 illustrates the performance for the five YOLOv8 models across four metrics which include precision, recall, F 1 score, and mean average precision (mAP). All models demonstrate a rapid improvement in the first few epochs, indicating fast learning in the early stages. For YOLOv8l and YOLOv8m, precision, recall, and F 1 scores plateau near the maximum value of 1.0, which illustrates that these models are very accurate and maintain a strong balance between precision and recall. Their mAP, a measure of precision across different recall levels, also levels off high, indicating these models are consistently reliable across both classes. The YOLOv8n figures show a more dramatic learning curve with significant improvements in all metrics until stabilizing. However, the values for all metrics are lower than in the other models, especially the mAP, which illustrates this model has a lower overall object detection performance. For YOLOv8s, the figure indicates a similar trend to YOLOv8l and YOLOv8m, with all metrics reaching and maintaining high values, implying it is quite effective and stable. Lastly, YOLOv8x displays a high performance with all metrics, closely resembling the trends seen in YOLOv8l and YOLOv8m, signifying a robust model with high precision and recall.

The performance of YOLOv8 models verses other YOLO architectures, which illustrates recall, precision, mAP, and F 1: a YOLOv8n, b YOLOv8l, c YOLOv8m, d YOLOv8s, e YOLOv8x, f YOLOv5n, g YOLOv7, h YOLOv7x

Overall, except for YOLOv8n which appears slightly less effective, the models quickly achieve and maintain high precision, recall, and F 1 scores, with mAP also indicating strong predictive power across different thresholds. YOLOv8n is a simplified model prioritizing speed and efficiency, with fewer parameters and computational needs, leading to lower accuracy compared to more complex models such as YOLOv8x, YOLOv8l, YOLOv8m, and YOLOv8s. These larger models, with more parameters and higher computational demands, are more capable of complex pattern recognition, resulting in better performance on key metrics such as precision and recall. While YOLOv8n is beneficial for fast processing in resource-constrained environments, the larger models are preferable for tasks requiring high accuracy where resources are abundant.

In this research, we have compared the proposed architectures (YOLOv8 models) with YOLOv5n, YOLOv7, and YOLOv7x, all trained for the same number of epochs (30) to ensure a fair comparison. The experimental results indicated that YOLOv5n performed more effectively than both YOLOv7 and YOLOv7x. However, the YOLOv8 models demonstrated superior performance over the previously mentioned architectures, excelling in multiple key metrics including mean Average Precision (mAP), Recall, Precision, and F 1 score, see Fig.  5 . This enhanced performance underscores the advancements in the YOLOv8 models, making them a suitable choice for stroke and non-stroke application.

4.3 Results of federated learning

Federated learning is a machine learning approach where a model is trained across multiple decentralized edge devices (clients) without sharing data, improving privacy, and reducing data centralization. The server coordinates the process, aggregating the client models to form a global model. Here is a detailed analysis of each performance metric across the server and clients: Mean Average Precision (mAP): the first figure shows the mAP metric, which is a measure of precision across recall levels and is commonly used in ranking tasks, object detection, and information retrieval. It appears that the server and all clients quickly improve performance within the first five rounds, indicating a rapid learning rate in the early stages of training. After around five rounds, the server and client 0 show stable performance, while client 1 and client 2 have some variability but generally maintain a high mAP. This could imply that client 1 and client 2 are encountering more complex and variable data than client 0, leading to slight fluctuations in performance. Precision: the second figure measures precision, the ratio of true positive predictions to the total number of positive predictions. The precision for all entities climbs sharply within the initial rounds and then plateaus, indicating that the number of false positives did not significantly increase as more instances were classified as positives, which is good. The server maintains the highest precision, which shows that the aggregated model is accurate in its predictions compared to individual clients. Recall: the third figure represents recall, which is the ratio of true positive predictions to the total number of actual positives. Recall also increases rapidly at the beginning, which illustrates that the model’s has the ability to identify relevant instances with initial training rounds. The server shows the highest recall, indicating it is better at detecting true positives than the clients. Across all metrics, the performance of the server is consistently equal to and better than the clients, which is expected in federated learning because the server model benefits from the aggregated updates from all clients. Key observations: rapid learning: all metrics improve significantly in the initial rounds of training, indicating the clients’ updates are meaningful and improve the global model quickly. Stabilization: after the initial improvement, all metrics tend to stabilize, with the server typically showing the least variance, which indicates the aggregated model’s robustness. Client variability: there is some variability in the clients’ performance metrics, particularly in mAP and precision. This could be due to differences in local data distributions, that some clients have data that is not as well represented in the global model. Convergence: all clients seem to converge towards the server’s performance, especially in recall, which shows that over time, client-specific models are becoming more aligned with the aggregated server model, which is the desired outcome in federated learning for consistency and fairness. Figure  6 shows the performance which includes mean Average Precision (mAP), precision, and recall scores across multiple rounds of training for 3 clients and server in a federated learning system.

The performance evolution which include a mean Average Precision (mAP), b precision, and c recall scores across multiple rounds of training for 3 clients and server in a federated learning system

5 Real-time edge execution techniques

NVIDIA’s Jetson platform has a series of powerful and efficient AI computing devices designed to bring deep learning to the edge. The series encompasses a range of hardware tailored to various performance needs and power constraints, making AI accessible across a broad spectrum of applications, from robotics to embedded systems. At the entry-level, the Jetson Nano stands out as a compact yet capable device, providing a cost-effective solution for projects that require AI but are constrained by power and space. Scaling up, the Jetson Xavier AGX presents a significant leap in performance with its advanced GPU architecture and AI accelerators, aimed at more demanding tasks that require higher computational throughput. At the pinnacle is the Jetson Orin, the latest addition to the series, which represents the cutting-edge in edge AI performance. It offers breakthrough capabilities for autonomous machines, delivering the highest performance and energy efficiency for AI, robotics, and other compute-intensive tasks. Each of these platforms is engineered to provide flexibility, scalability, and ease-of-integration, all crucial for innovators looking to pioneer the next generation of intelligent machines. The graph compares the NVIDIA Jetson Nano, Jetson AGX Xavier, and Jetson Orin platforms, focusing on CUDA cores and AI performance (TOPS). The bar plot shows a significant increase in CUDA cores across the models, indicating enhanced parallel computing capability. The line plot illustrates AI performance, where Jetson Orin demonstrates a substantial leap, reaching up to 200 TOPS, compared to Jetson Nano’s 0.5 TOPS and Jetson AGX Xavier’s 32 TOPS. This progression underscores the evolution in processing power and AI capabilities, with Jetson Orin offering vastly superior performance for demanding AI applications and computational tasks. Figure  7 presents a comparative analysis of NVIDIA Jetson Platforms, displaying the count of CUDA cores and the AI performance capacity (quantified in TOPS—tera operations per second) among three variants: Jetson Nano, Jetson Xavier AGX, and Jetson Orin AGX.

A comparative analysis of NVIDIA Jetson Platforms, which illustrates number of CUDA cores and AI performance (measured in TOPS—tera operations per second) across three models: Jetson Nano, Jetson Xavier AGX, and Jetson Orin AGX

Figure  8 illustrates a real-time detection frames per second (FPS) comparison among different YOLOv8 models running on three NVIDIA Jetson platforms: Nano, Xavier AGX, and Orin. Across all models, the Jetson Xavier AGX and Jetson Orin outperform the Jetson Nano in terms of FPS, which illustrates that they have more processing power and are more efficient in handling these tasks. We compared the performance of the proposed approach on NVIDIA devices with and without the utilization of CUDA. This comparison underscores the critical importance of using CUDA and GPU acceleration for executing YOLOv8 models to meet real-time requirements. CUDA significantly enhances the speed, enabling the YOLOv8 models to process data at a much faster rate. By leveraging the parallel processing power of GPUs, CUDA reduces the execution time, making it possible to achieve real-time object detection and analysis. From Fig.  8 , it is evident that YOLOv8n achieves the highest FPS on all three platforms, indicating it is the fastest model for real-time detection. This aligns with its structure of being a streamlined model that prioritizes speed and efficiency. As a result, despite having lower accuracy metrics, YOLOv8n is highly suitable for applications where real-time processing is crucial. In contrast, YOLOv8x, which is the most computationally intensive model, exhibits the lowest FPS across all platforms. This illustrates that while YOLOv8x has the best accuracy metrics, its complexity makes it less suitable for scenarios requiring high-speed object detection. The other models, YOLOv8l, YOLOv8m, and YOLOv8s, show a gradation of FPS performance, with the more complex models (e.g., YOLOv8l) being slower than the simpler ones (e.g., YOLOv8s). The performance on the Jetson Xavier AGX and Jetson Orin is significantly better than on the Jetson Nano, which is the least powerful of the three hardware platforms.

The frames per second (FPS) performance for different YOLO models (YOLOv8n, YOLOv8l, YOLOv8m, YOLOv8s, YOLOv8x) across three NVIDIA Jetson devices (Nano, Xavier, Orin) with and without CUDA acceleration

The graph highlights the trade-off between accuracy and speed among the YOLOv8 models and underscores the importance of choosing the right model and hardware platform based on the specific requirements of the application. Real-time detection of strokes using the proposed deep learning object detection models is crucial for timely medical intervention and improved patient outcomes. The proposed models, in particular YOLOv8n architecture, are designed to quickly and accurately identify stroke indicators from facial features. By processing data in real time, the models can provide immediate feedback to healthcare professionals, enabling rapid decision-making. This rapid analysis is essential in emergency situations where every second counts. The ability to differentiate between stroke and non-stroke cases on the spot helps in prioritizing treatment, reducing the risk of long-term damage, and potentially saving lives.

Power consumption measurement has been carried across the three NVIDIA platforms. The Jetson Nano shows remarkable energy efficiency with consistently low power usage across all YOLO models, making it an ideal candidate for power-sensitive applications. In contrast, the Jetson Orin, while being the most power-hungry, presumably offers superior computational performance, a trade-off that might be justified for demanding tasks where processing speed is crucial. The Jetson Xavier AGX stands in between a moderate balance between power and performance. Notably, the progression from the YOLOv8l to YOLOv8m and further models exhibits a trend of increasing power requirements, which could indicate more computationally intensive processes and greater exploitation of the hardware’s capabilities. This increasing trend is most pronounced with the Jetson Orin, which shows a substantial leap in power consumption for the latest models, signifying its capability to unleash the full potential of advanced YOLO models, albeit at a greater energy cost. This data is vital for selecting the appropriate hardware for proposed models where both the performance and the power budget are to be optimized. Figure  9 illustrates the power consumption across various YOLO models when deployed on NVIDIA’s Jetson hardware platforms: Nano, Xavier AGX, and Orin.

Comparison of power consumption across NVIDIA Jetson platforms (Nano, Xavier AGX, Orin) running different YOLOv8 models, demonstrating the varying energy requirements of each model on the respective hardware

The temperature data provided for different YOLOv8 models across NVIDIA Jetson platforms Nano, Xavier AGX, and Orin show some interesting trends: Starting with the Jetson Nano, the temperatures for the YOLO models are higher compared to the other two platforms, with YOLOv8x reaching the highest temperature at 54 °C. This demonstrates that while the Nano is power-efficient, as seen in the previous analysis, it does not dissipate heat as effectively. This is due to a less robust cooling system and lower thermal capacity.

However, there is a clear downward trend in temperature from YOLOv8x to YOLOv8n, with the latter running at a cooler 40 °C. This indicates that the newer models are more efficient. The Jetson Xavier AGX shows a consistently cooler operational temperature across all YOLO models when compared to the Jetson Nano, which shows better thermal management. The temperatures range from 43 °C for YOLOv8x to 37 °C for YOLOv8n. The lower temperatures could also imply that the Xavier AGX is more capable of handling the computational demands of the YOLO models efficiently, thereby generating less heat. The Jetson Orin records temperatures similar to the Xavier AGX but is marginally cooler across all models except for YOLOv8s, where it equals the AGX at 39 °C. This could be due to the efficient power usage that translates into less heat output. YOLOv8n runs coolest on the Orin at 36 °C. Tables 4 , 5 and 6 provide operation temperature for the executed YOLOv8 model on NVIDIA platforms.

6 Conclusion

This research presented an innovative, real-time stroke detection system by utilization deep learning and federated learning to offer a solution that is both efficient and privacy conscious. By combining the advanced analytical capabilities of deep learning, particularly through the use of YOLOv8 models, with the privacy-preserving features of federated learning, we have developed a system that stands to significantly improve the timeliness and accuracy of stroke detection. This approach mitigates the limitations of traditional stroke detection methods, such as the reliance on manual interpretation which is slow and error-prone, and addresses the challenges of requiring extensive, diverse datasets and navigating privacy concerns. YOLOv8 models have demonstrated promising results in terms of mean Average Precision (mAP), recall, and precision. However, the YOLOv8n model appears to be slightly less effective. Designed to prioritize speed and efficiency, YOLOv8n is a simplified version with fewer parameters and reduced computational requirements. As a consequence, it achieves lower accuracy compared to the other models. The utilization of NVIDIA platforms for their superior GPU capabilities has enabled real-time processing and analysis, ensuring that the proposed system can function effectively in a clinical setting. The YOLOv8n model has been recognized for its superior real-time detection performance compared to earlier versions. This improvement is mainly because of its lightweight architecture. YOLOv8 models, while excelling in real-time detection on NVIDIA devices, demand high power consumption. This requirement limits their practicality for low-cost embedded devices, highlighting a significant challenge in balancing computational efficiency and performance in resource-constrained environments. Further optimization is needed to make these models viable for broader applications. The implications of this research are profound, offering a pathway to enhancing patient outcomes by enabling healthcare professionals to make quicker, more informed decisions. Further to our exploration, we will convert the proposed approach to TensorFlow Lite to optimize the architecture. This will enhance our results for real-time detection and reduce the demand for power consumption. In addition to that, we will continue further research and experimentation with the most recent object detection models, such as YOLOv9, which has been released recently.

Data availability

No datasets were generated or analyzed during the current study.

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Acknowledgements

This work has been partially supported by the Forelab Dipartimento di Eccellenza project and by the spoke 6 of CN1 on HPC, Big Data and quantum of the PNRR, both by MUR

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Abdussalam Elhanashi, Pierpaolo Dini & Sergio Saponara

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Elhanashi, A., Dini, P., Saponara, S. et al. TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices. J Real-Time Image Proc 21 , 121 (2024). https://doi.org/10.1007/s11554-024-01500-1

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DOI : https://doi.org/10.1007/s11554-024-01500-1

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