Araştırma Çıktıları / Research Outputs
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Browsing Araştırma Çıktıları / Research Outputs by Author "Abayomi-Alli, Olusola O."
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Article Citation Count: 28Deep learning based fall detection using smartwatches for healthcare applications(Elsevier Sci Ltd, 2022) Şengül, Gökhan; Karakaya, Murat; Karakaya, Kasım Murat; Mısra, Sanjay; Damasevicius, Robertas; Computer EngineeringWe implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.Article Citation Count: 3Enhancing Misuse Cases With Risk Assessment for Safety Requirements(Ieee-inst Electrical Electronics Engineers inc, 2020) Mısra, Sanjay; Misra, Sanjay; Abayomi-Alli, Olusola O.; Fernandez-Sanz, Luis; Computer EngineeringRisk-driven requirements elicitation represents an approach that allows assignment of appropriate countermeasure for the protection of the Information System (IS) depending on the risk level. Elicitation of safety requirements based on risk analysis is essential for those IS which will run on the open and dynamic Internet platform. Traditionally, misuse cases are used to find the weak points of an IS but cannot differentiate between the weak point that can lead to lenient hazard and/or serious hazard. In this paper, we present an enhanced misuse case approach to support IS safety risk assessment at the early stages of software process. We extensively examined and identified concepts which constitute a modelling technique for IS safety risk assessment and build a conceptual model for achieving IS safety risk assessment during the requirement analysis phase of software process. The risk assessment process follows an approach of consequential analysis based on misuse cases for safety hazard identification and qualitative risk measurement. The safety requirements are elicited according to the results of the risk assessment. A medical IS is used as a case study to validate the proposed model.Article Citation Count: 26Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic(Hindawi Ltd, 2020) Mısra, Sanjay; Ndaman, Israel O.; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, Robertas; Computer EngineeringThe use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients' access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.