Mühendislik Fakültesi / Faculty of Engineering

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    PublicationOpen Access
    Deep Learning-Based Defect Prediction for Mobile Applications
    (MPDI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, Alok
    Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.
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    PublicationOpen Access
    Identification of Phantom Movements With an Ensemble Learning Approach
    (Pergamon-Elsevier Science Ltd., 2022) AKBULUT, AKHAN; Güngör, Feray; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdul Halim; Çatal, Çağatay
    Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.
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    PublicationOpen Access
    Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review
    (MDPI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, Alok
    Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naive Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.
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    PublicationOpen Access
    Prediction of University Students' Subjective Well-Being with Sleep and Physical Activity Data using Classification Algorithms
    (Elsevier B.V., 2022) KILIÇ, AKİF CAN; Karakuş, Ahmet; Alptekin, Emre
    Daily activities affect mental health. One of the most used scales is "subjective well-being (SWB)", which is a self-reported questionnaire. This study aimed to predict SWBs using step count, heart rate and sleep duration data from sensors instead of questionnaires. NetHealth data from the University of Notre Dame1 has been used. Attributes included average daily steps, average heart rate, heartbeat standard deviation, average sleep duration, and sleep duration deviation. Preprocessing, processing, classification, and evaluation followed. Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Ensemble classifiers were used. Performance metrics include accuracy, precision, recall, F1-Score, and ROC (Receiver Operating Characteristic) curves. Model accuracy was 62%. This indicates that machine learning could be beneficial in detecting SWB levels using sensor data. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022).
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    PublicationOpen Access
    Wearable Sensor Device for Posture Monitoring and Analysis During Daily Activities: A Preliminary Study
    (Dr. Ceyhun YILMAZ, 2022) AKBULUT, FATMA PATLAR; ÖZGÜL, GİZEM
    The increase in technological advancements in recent years has led to the emergence of a new lifestyle. Although being assisted by machines for small-scale tasks in daily housework makes daily life easier, this has caused people to reduce their daily active movements and negatively affects human health. Especially during the COVID-19 pandemic, with the conversion of the working style to the home environment, working hours spent at the desk are more than ever. Due to the prolongation of the working time, the employees stay in the same position more inactive, thus their muscles weaken and they start to have muscle disease. Weaknesses in the muscles have occurred to the formation of postural problems in people. In our study, a smart vest system was developed to detect and control posture disorders. The proposed system is designed to recommend the most suitable exercises to avoid any physical discomforts. It is also aimed to detect hunched posture by collecting data on the person wearing the vest through sensors. Besides, it is encouraged to correct the posture disorder by warning the person audibly during the hunched posture. The experiments conducted with eight participants showed that the proposed system warns the users with necessary posture corrections, proving its potential use.