| 1. |
- Nyholm, Joel, et al.
(author)
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Prediction of dementia based on older adults’ sleep disturbances using machine learning
- 2024
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In: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 171
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Journal article (peer-reviewed)abstract
- Background: The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia.Methods: This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care — Blekinge (). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors.Results: Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms.Conclusion: There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
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| 2. |
- Bakhtyar, Shoaib, et al.
(author)
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On Improving Research Methodology Course at Blekinge Institute of Technology
- 2016
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Conference paper (peer-reviewed)abstract
- The Research Methodology in Software Engineering and Computer Science (RM) is a compulsory course that must be studied by graduate students at Blekinge Institute of Technology (BTH) prior to undertaking their theses work. The course is focused on teaching research methods and techniques for data collection and analysis in the fields of Computer Science and Software Engineering. It is intended that the course should help students in practically applying appropriate research methods in different courses (in addition to the RM course) including their Master’s theses. However, it is believed that there exist deficiencies in the course due to which the course implementation (learning and assessment activities) as well as the performance of different participants (students, teachers, and evaluators) are affected negatively. In this article our aim is to investigate potential deficiencies in the RM course at BTH in order to provide a concrete evidence on the deficiencies faced by students, evaluators, and teachers in the course. Additionally, we suggest recommendations for resolving the identified deficiencies. Our findings gathered through semi-structured interviews with students, teachers, and evaluators in the course are presented in this article. By identifying a total of twenty-one deficiencies from different perspectives, we found that there exist critical deficiencies at different levels within the course. Furthermore, in order to overcome the identified deficiencies, we suggest seven recommendations that may be implemented at different levels within the course and the study program. Our suggested recommendations, if implemented, will help in resolving deficiencies in the course, which may lead to achieving an improved teaching and learning in the RM course at BTH.
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| 3. |
- Ghazi, Ahmad Nauman, 1983-, et al.
(author)
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Checklists to Support Test Charter Design in Exploratory Testing
- 2017
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In: Agile Processes in Software Engineering and Extreme Programming. - Cham : Springer. - 9783319576329 ; , s. 251-258
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Conference paper (peer-reviewed)abstract
- During exploratory testing sessions the tester simultaneously learns, designs and executes tests. The activity is iterative and utilizes the skills of the tester and provides flexibility and creativity. Test charters are used as a vehicle to support the testers during the testing. The aim of this study is to support practitioners in the design of test charters through checklists. We aimed to identify factors allowing practitioners to critically reflect on their designs and contents of test charters to support practitioners in making informed decisions of what to include in test charters. The factors and contents have been elicited through interviews. Overall, 30 factors and 35 content elements have been elicited.
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| 4. |
- Ghazi, Ahmad Nauman, 1983-
(author)
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Enhancing student engagement through active learning
- 2023
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Reports (pop. science, debate, etc.)abstract
- Teaching research methodologies to graduate students is important to help them succeed in their degree programs. At Blekinge Institute of Technology (BTH), research methodologies in software engineering and computer science is a critical course, as it provides the students with the skills and knowledge necessary to conduct high-quality research for their master's thesis. More importantly, this course is mandatory in most degree programs at BTH, and the students are required to successfully complete this course as a pre-requisite for their master's thesis. Over the years, this course has been delivered using traditional teaching methods, such as lectures, assignments, readings, and feedback sessions. During the past years, as the course responsible, I observed that there had been a decline in the success rate for students in this course and a lack of student engagement. We identified that traditional teaching methods may not be the most effective way to engage students in this course. To this end, I introduced the concept of flipped classrooms and active learning classrooms (ALCs). During the ALCs, the students were asked to complete different tasks related to the concepts acquired through pre-recorded lectures and mandatory reading assignments. These tasks are done in student groups, and teachers promote open discussions between the students by problematizing the concepts.
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| 5. |
- Ghazi, Ahmad Nauman, 1983-, et al.
(author)
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Levels of Exploration in Exploratory Testing : From Freestyle to Fully Scripted
- 2018
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In: IEEE Access. - : IEEE. - 2169-3536. ; 6, s. 26416-26423
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Journal article (peer-reviewed)abstract
- Exploratory testing (ET) is a powerful and efficient way of testing software by integrating design, execution, and analysis of tests during a testing session. ET is often contrasted with scripted testing, and seen as a choice of either exploratory testing or not. In contrast, we pose that exploratory testing can be of varying degrees of exploration from fully exploratory to fully scripted. In line with this, we propose a scale for the degree of exploration and define five levels. In our classification, these levels of exploration correspond to the way test charters are defined. We have evaluated this classification through focus groups at four companies and identified factors that influence the choice of exploration level. The results show that the proposed levels of exploration are influenced by different factors such as ease to reproduce defects, better learning, verification of requirements, etc., and that the levels can be used as a guide to structure test charters. Our study also indicates that applying a combination of exploration levels can be beneficial in achieving effective testing.
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| 6. |
- Ghazi, Ahmad Nauman, 1983-
(author)
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Structuring Exploratory Testing through Test Charter Design and Decision Support
- 2017
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Doctoral thesis (other academic/artistic)abstract
- Context: Exploratory testing (ET) is an approach to test software with a strong focus on personal skills and freedom of the tester. ET emphasises the simultaneous design and execution of tests with minimal test documentation. Test practitioners often claim that their choice to use ET as an important alternative to scripted testing is based on several benefits ET exhibits over the scripted testing. However, these claims lack empirical evidence as there is little research done in this area. Moreover, ET is usually considered an ad-hoc way of doing testing as everyone does it differently. There have been some attempts in past to provide structure to ET. Session based test management (SBTM) is an approach that attempts to provide some structure to ET and gives some basic guidelines to structuring the test sessions. However, these guidelines are still very abstract and are very open to individuals' interpretation.Objective: The main objective of this doctoral thesis is to support practitioners in their decisions about choosing exploratory versus scripted testing. Furthermore, it is also aimed to investigate the empirical evidence in support of ET and find ways to structure ET and classify different levels of exploration that drive the choices made by exploratory testers. Another objective of this thesis is to provide a decision support system to select levels of exploration in overall test process.Method: The findings presented in this thesis are obtained through a controlled experiment with participants from industry and academia, exploratory surveys, interviews and focus groups conducted at different companies including Ericsson AB, Sony Mobile Communications, Axis Communications AB and Softhouse Consulting Baltic AB.Results: Using the exploratory survey, we found three test techniques to be most relevant in context of testing software systems and in particular heterogeneous systems. The most frequently used technique mentioned by the practitioners is ET which is not a much researched topic. We also found many interesting claims about ET in grey literature produced by practitioners in the form of informal presentations and blogs but these claims lacked any empirical evidence. Therefore, a controlled experiment was conducted with students and industry practitioners to compare ET with scripted testing. The experiment results show that ET detects significantly more critical defects compared to scripted testing and is more time efficient. However, ET has its own limitations and there is not a single way to use it for testing. In order to provide structure to ET, we conducted a study where we propose checklists to support test charter design in ET. Furthermore, two more industrial focus group studies at four companies were conducted that resulted in a taxonomy of exploration levels in ET and a decision support method for selecting exploration levels in ET. Lastly, we investigated different problems that researchers face when conducting surveys in software engineering and have presented mitigation strategies for these problems.Conclusion: The taxonomy for levels of exploration in ET, proposed in this thesis, provided test practitioners at the companies a better understanding of the underlying concepts of ET and a way to structure their test charters. A number of influence factors elicited as part of this thesis also help them prioritise which level of exploration suits more to their testing in the context of their products. Furthermore, the decision support method provided the practitioners to reconsider their current test focus to test their products in a more effective way.
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| 7. |
- Ghazi, Ahmad Nauman, 1983-, et al.
(author)
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Survey Research in Software Engineering : Problems and Mitigation Strategies
- 2019
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In: IEEE Access. - : IEEE. - 2169-3536. ; 7, s. 24703-24718
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Journal article (peer-reviewed)abstract
- Background: The need for empirical investigations in software engineering is growing. Many researchers nowadays, conduct and validate their solutions using empirical research. The Survey is an empirical method which enables researchers to collect data from a large population. The main aim of the survey is to generalize the findings.Aims: In this study, we aim to identify the problems researchers face during survey design and mitigation strategies.Method: A literature review, as well as semi-structured interviews with nine software engineering researchers, were conducted to elicit their views on problems and mitigation strategies. The researchers are all focused on empirical software engineering.Results: We identified 24 problems and 65 strategies, structured according to the survey research process. The most commonly discussed problem was sampling, in particular, the ability to obtain a sufficiently large sample. To improve survey instrument design, evaluation and execution recommendations for question formulation and survey pre-testing were given. The importance of involving multiple researchers in the analysis of survey results was stressed.Conclusions: The elicited problems and strategies may serve researchers during the design of their studies. However, it was observed that some strategies were conflicting. This shows that it is important to conduct a trade-off analysis between strategies.
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| 8. |
- Javeed, Ashir, 1989-, et al.
(author)
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Breaking barriers : a statistical and machine learning-based hybrid system for predicting dementia
- 2023
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In: Frontiers in Bioengineering and Biotechnology. - : Frontiers Media S.A.. - 2296-4185. ; 11
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Journal article (peer-reviewed)abstract
- Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia. Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency. Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535. Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.
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| 9. |
- Javeed, Ashir, 1989-, et al.
(author)
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Optimizing Depression Prediction in Older Adults : A Comparative Study of Feature Extraction and Machine Learning Models
- 2024
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Conference paper (peer-reviewed)abstract
- Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K- nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE.
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| 10. |
- Yasin, Affan, et al.
(author)
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Python Data Odyssey : Mining User feedback from Google Play store
- 2024
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In: Data in Brief. - : Elsevier. - 2352-3409. ; 54
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Journal article (peer-reviewed)abstract
- ContextThe Google Play Store is widely recognized as one of the largest platforms for downloading applications, both free and paid1. On a daily basis, millions of users avail themselves of this marketplace, sharing their thoughts through various means such as star ratings, user comments, suggestions, and feedback. These insights, in the form of comments and feedback, constitute a valuable resource for organizations, competitors, and emerging companies seeking to expand their market presence. These comments provide insights into app deficiencies, suggestions for new features, identified issues, and potential enhancements. Unlocking the potential of this repository of suggestions holds significant value.ObjectiveThis study sought to gather and analyze user reviews from the Google Play store for leading game apps. The primary aim was to construct a dataset for subsequent analysis utilizing requirements engineering, machine learning, and competitive assessment.MethodologyThe authors employed a Python-based web scraping method to extract a comprehensive set of over 429,000+ reviews from the Google Play pages of selected apps. The scraped data encompassed reviewer names (removed due to privacy), ratings, and the textual content of the reviews.ResultsThe outcome was a dataset comprising the extracted user reviews, ratings, and associated metadata. A total of 429,000+ reviews were acquired through the scraping process for popular apps like Subway Surfers, Candy Crush Saga, PUBG Mobile, among others. This dataset not only serves as a valuable educational resource for instructors, aiding in the training of students in data analysis, but also offers practitioners the opportunity for in-depth examination and insights (in the past data of top apps).
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