Automatic energy expenditure measurement for health science

Background and objective: It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. Methods: In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Results: Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. Conclusions: This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

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 Naïve 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.Funding: This research was funded by Molde University College-Specialized Univ. in Logistics, Norway for the support of Open Access fund.Scopus2-s2.0-8512699796

Techniques for calculating software product metrics threshold values: A systematic mapping study

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Funding: Authors thanks to the Molde University College-Specialized Univ. in Logistics, Norway for the support of Open access fund.Scopus2-s2.0-8512089773

Deep Learning-Based Defect Prediction for Mobile Applications

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. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Molde University College-Specialized Univ. in Logistics, Norway for the support of Open Access fund.Scopus2-s2.0-8513237612

Identification of phantom movements with an ensemble learning approach

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. 2022 The Author(s)This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no. EEEAG-117E579. The data that support the findings of this study are available on request from the principle investigator of the project EEEAG-117E579, Akhan Akbulut, PhD. The data are not publicly available due to the confidential information that could compromise the privacy of research participants. Open Access funding provided by the Qatar National Library.Scopus2-s2.0-8513934593

VinJect: Sızma Testi ve Güvenlik Açığı Taraması Aracı

Güvenilir yazılım ürünleri ve elektronik sistemlerin geliştirilmesinde sızma testi önemli rol oynamaktadır. Zaafiyet taramalarının düzenli olarak yapılması sayesinde, ticari sistemlerin sürdürülebilirliği sağlanmaktadır. Kalite güvence ve test firmalarının günümüzde yaygınlıklarını arttırdıkları bu dönemde,  kullanılan araç ve yöntemlerin etkinlikleri çok kritiktir. Bu makalede etkin bir sızma testi ve güvenlik açığı taraması için geliştirilmiş VinJect ismindeki yazılımın mimarisi anlatılmaktadır. Amaç, çok işparçacıklı yapıda çalışan bu uygulama ile zaafiyet barındıran yerlerin tespitinin daha kısa sürede yapılmasıdır. Önerdiğimiz uygulama, arka planında Wapiti ve SQLmap uygulamalarına ait servisleri kullanmaktadır. Kullanıcı dostu arayüzler ile çoğunlukla komut satırında çalışşan uygulamaların verdiği olumsuz kullanıcı tecrübesinin ortadan kaldırılması hedeflenmiştir. Yaptığımız testlerde, WinJect'in daha etkin bir kullanım sunduğu ve zaafiyet taramaları çok daha kısa sürede tamamladığı görüldü

AN ARCHITECTURAL MODEL FOR CONTENT MANAGEMENT IN E-COMMERCE APPLICATIONS USING INTELLIGENT AGENTS

In e-Commerce applications, the size of the architecture is huge and there are too many member shops and too many customers that bring along management difficulties. So, different technologies need to be used and usually autonomous structures work with unmanned decision-making processes for the managerial processes of an e-Mall where hundreds of e-shop catalogues are displayed. In this study, our recent works are developed and their applications are actualized. Additionally, an architectural solution that will ease the content management for an e-Mall structure is offered

Control in networked systems with fuzzy logic

Recently, the development of control systems based on network-based architecture is getting very high attention. Because of its fast data communication, network-based control is in high demand. However, there are some disadvantages, such as delays, data packet dropouts, and communication constraints. Network-based control systems are getting attention in the development of this architecture because of these disadvantages. Optimization of the system is supplied to improve the existing structure. The optimization is categorized into 2 main structures: software optimization and hardware optimization. The structure of the overall system is designed with these optimization strategies. A hierarchical and communicational structure has emerged as a result of constructing the overall structure

CAVE Sanal Gerçeklik Teknolojisinin Üniversite-Sanayi İşbirliği Açısından Değerlendirilmesi ve Örnek bir Durum Çalışması

The CAVE Automatic Virtual Environment (CAVE) virtual reality technology infrastructure has recently started to be built bydifferent universities and research institutes using different funds, but according to our analysis, we did not encounter such an infrastructure in any university in Turkey due to the high investment costs. In this study, we present the benefits of universities and industry in Turkey and also provide synergistic artefacts. By opening this centre to the use of other educational institutions in certain time periods, itwill present a different perspective for the education and make it easier to understand difficult conceptsin a visual and 3-dimensional virtual reality environment. In addition to the contributions to the education, ergonomics analysis and user experience tests will beperformed by moving 3D models to the CAVE environment before the first prototypes of products at the industrial scale are manufactured. It is possible to develop new R&D projects to reach relevant institutions and organizations through this infrastructure. In the Case Study, a CAVE infrastructure to be installed in 130 square-meter area is introduced, necessary materials and equipment are introduced, and in-depth evaluation of this technology is presented. We introduce a Logical Frame Matrix for the universities and companies

Wallet-Based Transaction Fraud Prevention Through LightGBM With the Focus on Minimizing False Alarms

E-wallets’ rising popularity can be attributed to the fact that they facilitate a wide variety of financial activities such as payments, transfers, investments, etc., and eliminate the need for actual cash or cards. The confidentiality, availability, and integrity of a user’s financial information stored in an electronic wallet can be compromised by threats such as phishing, malware, and social engineering; therefore, fintech platforms employ intelligent fraud detection mechanisms to mitigate the problem. The purpose of this study is to detect fraudulent activity using cutting-edge machine learning techniques on data obtained from the leading e-wallet platform in Turkey. After a comprehensive analysis of the dataset’s features via feature engineering procedures, we found that the LightGBM approach had the highest detection accuracy of fraudulent activity with 97% in the experiments conducted. An additional key objective of reducing false alerts was accomplished, as the number of false alarms went from 13,024 to 6,249. This approach resulted in the establishment of a machine-learning model suitable for use by relatively small fraud detection teams