https://hdl.handle.net/20.500.12619/1220 Sat, 11 Apr 2026 19:19:31 GMT 2026-04-11T19:19:31Z https://hdl.handle.net/20.500.12619/103351 Anomaly Detection in IoT Device Traffic Data Using Deep Learning Buken, Ayse Betul; Akgun, Devrim The Internet of Medical Things (IoMT) is important in modern healthcare systems by enabling continuous monitoring and data­driven medical services. However, adopting IoMT is associated with cybersecurity challenges due to the different protocols and vulnerabilities to network­based threats. This study examines anomaly detection in IoMT traffic utilizing the CICIoMT2024 dataset, a comprehensive benchmark that includes 18 cyberattacks across 40 devices in Wi­Fi, Bluetooth, and MQTT environments. We evaluate machine learning models such as Logistic Regression, Random Forest, AdaBoost, and Deep Neural Networks (DNN). Additionally, we explore deep learning architectures that combine convolutional layers with Long Short­Term Memory (LSTM) units. Our experiments cover a range of model assessments for binary, six­class, and nineteen­class classification tasks. Model performance is measured using standard metrics such as accuracy, precision, recall, and F1­score. This research demonstrates the potential of deep learning to enhance the security of IoMT networks and supports further development of better detection models in the healthcare environment. Sun, 01 Jun 2025 00:00:00 GMT https://hdl.handle.net/20.500.12619/103351 2025-06-01T00:00:00Z https://hdl.handle.net/20.500.12619/103020 ANOMALY DETECTION IN INDUSTRIAL MACHINES USING EXPLAINABLE AI AND ACOUSTIC SIGNALS ÇAĞLAR, Betül Sena; Akgün, Devrim In recent years, anomaly detection in industrial machinery has become a critical topic aimed at preventing failures, improving maintenance processes, and enhancing production efficiency. As industrial machines operate continuously under varying conditions, early detection of anomalies can reduce unplanned delays and maintenance costs. Acoustic signals are important data sources for evaluating the health of industrial equipment. This study proposes a lightweight machine learning (ML) approach for anomaly detection in industrial machines using acoustic signals. For this purpose, audio features are extracted, and their statistical metrics are calculated to define a new dataset for anomaly detection. Explainable Artificial Intelligence (XAI) techniques are employed to determine the most effective features that contribute to anomaly detection of machines. The proposed method tested using the publicly available MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection. The results demonstrate that the proposed approach achieves highly promising outcomes, with an accuracy of 99.7% and an AUC score of 99.9% in classifying anomalies in industrial machines. Wed, 25 Dec 2024 00:00:00 GMT https://hdl.handle.net/20.500.12619/103020 2024-12-25T00:00:00Z https://hdl.handle.net/20.500.12619/101336 Speech Recognition Using Deep Learning Model With Volterra Series-Based Layers in Tensorflow Alyafawi, Z. , Akgun, D. The Volterra series is a mathematical tool widely used to analyze and model nonlinear systems. The Volterra model expands a nonlinear system's response in terms of a series of integral equations. Like linear convolution, nonlinear convolution operators can be integrated into deep learning layers. This research proposes a new layer based on a second-order 1D Volterra series expansion using the TensorFlow environment. To develop the Volt1D, we first analyzed a linear convolutional layer's performance on a human speech dataset. The Volterra series has been particularly successful in speech recognition, as it allows for modeling the nonlinear dynamics of the human vocal tract. Volt1D allowed us to capture higher-order nonlinearities in the system, significantly improving the model's accuracy. To validate the effectiveness of the Volt1D, we conducted extensive experiments on a dataset of the human speech command. Overall, our research demonstrates the potential of the Volt1D as a powerful tool for training speech recognition models. Thu, 29 Dec 2022 00:00:00 GMT https://hdl.handle.net/20.500.12619/101336 2022-12-29T00:00:00Z https://hdl.handle.net/20.500.12619/101335 A Literature Review on Smart Attendance Systems Abdalkarim, Bawar Ali Abdalkarim; Akgün, Devrim Every institution that depends on people must account for its employees as a first step in the modern-day. As a result, creating and maintaining a suitable management system costs the different organizations a substantial sum of money. In many countries, government organizations and educational institutions keep track of attendance using paper-based methods. For example, to maintain track of each student's attendance, it takes time to call out their name at the beginning of the course. False signs, names missing from spreadsheets, manually inputting data into systems, and the possibility of proxy attendance are further problems. Such techniques have a few problems that have grown over time. To track attendance, it is crucial to swap out these outdated practices for modern ones. As a result, a lot of work and research has been done in this area using current technologies. Especially, automatic recognition of a particular individual based on distinguishing characteristics such as QR code, ID and password, face recognition, fingerprint recognition is of interest to researcher. This paper presents a literature overview of the recent works on automated and smart attendance tracking systems. Concerning technology, application domain,and key findings, our critical assessment has emphasized research in the body of literature. Wed, 20 Jul 2022 00:00:00 GMT https://hdl.handle.net/20.500.12619/101335 2022-07-20T00:00:00Z