Browsing by Author "Ozcanhan, Mehmet Hilal"

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Citation - WoS: 1
Citation - Scopus: 2
An Ultra-light PRNG Passing Strict Randomness Tests and Suitable for Low Cost Tags
(UNIV SUCEAVA FAC ELECTRICAL ENG, 2016) Mehmet Hilal Ozcanhan; Mehmet Suleyman Unluturk; Gokhan Dalkilic; Unluturk, Mehmet Suleyman; Dalkilic, Gokhan; Ozcanhan, Mehmet Hilal
A pseudo-random number generator for low-cost RFID tags is presented. The scheme is simple sequential and secure yet has a high performance. Despite its lowest hardware complexity our proposal represents a better alternative than previous proposals for low-cost tags. The scheme is based on the well-founded pseudo random number generator Mersenne Twister. The proposed generator takes low-entropy seeds extracted from a physical characteristic of the tag and produces outputs that pass popular randomness tests. Contrarily previous proposal tests are based on random number inputs from a popular online source which are simply unavailable to tags. The high performance and satisfactory randomness of present work are supported by extensive test results and compared with similar previous works. Comparison using proven estimation formulae indicates that our proposal has the best hardware complexity power consumption and the least cost.
MTWIN: A Mental Health Monitoring Framework toward a Digital Twin
(Elsevier, 2026) Unluturk, Mehmet Suleyman; Yucel, Koray; Ozcanhan, Mehmet Hilal
Digital Twins (DTs) are emerging as a transformative technology across industries, offering a virtual representation of physical objects, including machines, cars, industrial robots, processes, and even individuals. The present work proposes a DT-inspired mental health monitoring framework, MTWIN, that aggregates personal and medical information, excluding sensitive visual data such as facial photographs or medical imagery. Our work provides healthcare professionals with a tool to monitor and evaluate the patient's psychological progress throughout treatment. Data sources for MTWIN include manually entered information (e.g., diary entries, medication intake, eating habits, social interactions, and self-reported general health status) and sensor data from wearable devices (e.g., physical activity, sleep duration), combined with weather data based on the patient's location. Additionally, MTWIN integrates predictions from a Machine Learning (ML) model trained on the TREMO dataset (a validated emotion analysis dataset in Turkish, with 4709 participants) and the Turkish Tweets dataset. Based on diary entries, the model analyzes emotions such as joy, sadness, fear, anger, surprise, and disgust. MTWIN is visualized through mobile, tablet, and web applications, providing patients with feedback to improve their daily physical activity, sleep duration, and eating habits, while offering healthcare professionals a historical view of their mental health progression. Preliminary prototype simulations indicate that the framework can successfully aggregate and visualize the data necessary to track a patient's response to medication and treatment. The ML model, DistilBERTurk fine-tuned with 23,462 entries, achieves 93.5% accuracy on the validation dataset.