Browsing by Author "Unluturk, Mehmet Suleyman"

Now showing 1 - 7 of 7

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.
Citation - WoS: 2
Citation - Scopus: 3
Application of Data Mining in Failure Estimation of Cold Forging Machines: An Industrial Research
(NATL INST R&D INFORMATICS-ICI, 2019) Buse Turkoglu; Murat Komesli; Mehmet Suleyman Unluturk; Turkoglu, Buse; Unluturk, Mehmet Suleyman; Komesli, Murat
The industrial companies are now reaching out for solutions that would enable them to reduce the number of manufacturing defects in production so that they may be able to compete and maintain their sustainability in the market. All production processes need to be uninterruptible. This study utilizes data mining algorithms to turn the data created by machines into information. These data mining algorithms are effective tools for reducing the cold forging machine downtime. Furthermore the selected data mining methodology the J48 model generates meaningful results for a large real-life data set and predicts the error according to a behavioral model. The J48 model successfully detected 28 failures from this data set which suggests that it can be a promising method for reducing the periods of downtime of the cold machine.
Citation - WoS: 8
Citation - Scopus: 10
Automated personnel-assets-consumables-drug tracking in ambulance services for more effective and efficient medical emergency interventions
(ELSEVIER IRELAND LTD, 2016) Semih Utku; Mehmet Hilal Ozcanhan; Mehmet Suleyman Unluturk; Unluturk, Mehmet Suleyman; Utku, Semih; Özcanhan, Mehmet Hilal
Patient delivery time is no longer considered as the only critical factor in ambulatory services. Presently five clinical performance indicators are used to decide patient satisfaction. Unfortunately the emergency ambulance services in rapidly growing metropolitan areas do not meet current satisfaction expectations, because of human errors in the management of the objects onboard the ambulances. But human involvement in the information management of emergency interventions can be reduced by electronic tracking of personnel assets consumables and drugs (PACD) carried in the ambulances. Electronic tracking needs the support of automation software which should be integrated to the overall hospital information system. Our work presents a complete solution based on a centralized database supported by radio frequency identification (RFID) and bluetooth low energy (BLE) identification and tracking technologies. Each object in an ambulance is identified and tracked by the best suited technology. The automated identification and tracking reduces manual paper documentation and frees the personnel to better focus on medical activities. The presence and amounts of the PACD are automatically monitored warning about their depletion non-presence or maintenance dates. The computerized two way hospital-ambulance communication link provides information sharing and instantaneous feedback for better and faster diagnosis decisions. A fully implemented system is presented with detailed hardware and software descriptions. The benefits and the clinical outcomes of the proposed system are discussed which lead to improved personnel efficiency and more effective interventions. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
Convolutional Neural Network for Cotton Yield Estimation
(National Institute for R and D in Informatics, 2024) Mehmet Suleyman Ünlütürk; Murat Komesli; Asli Keceli; Unluturk, Mehmet Suleyman; Komesli, Murat; Keceli, Asli
The objective of this paper was to estimate the cotton yield potential of different cotton varieties using high-resolution field images based on a convolutional neural network (CNN). The yield estimation for different cotton varieties in grams in breeding studies has a great importance for the determination of superior cultivars to be commercialized. Due to the cost and excessive time consumption typical of traditional methods alternative ways for cotton yield estimation have been investigated over the years. This paper proposes an automated system for cotton yield prediction based on color images obtained by an unmanned aerial vehicle (UAV). Two replicational field experiments including three different cotton genotypes were conducted at May Seed R&D station in Torbali Izmir Turkey. Three different planting patterns including three four and six rows respectively in ten-meter wide areas were used as experimental plots. The ground-truth yield values for a total of six hundred planted areas were obtained by weighing the harvested cotton bolls after field images were taken. Achieving an absolute difference of no more than 350 grams for 114 out of 120 planted areas which were randomly selected only for testing purposes indicates that the CNN can effectively capture important features related to cotton yield from the field images obtained by the UAV. The combination of drone technology with reliable CNN models holds great potential for optimizing agricultural practices improving agricultural productivity and reducing operational costs. © 2024 Elsevier B.V. All rights reserved.
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.
Citation - WoS: 5
Citation - Scopus: 6
Neural network-supported patient-adaptive fall prevention system
(Springer, 2020) Mehmet Hilal Özcanhan; Semih Utku; Mehmet Suleyman Ünlütürk; Unluturk, Mehmet Suleyman; Utku, Semih; Özcanhan, Mehmet Hilal
Patient falls due to unattended bed-exits are costly to patients healthcare personnel and hospitals. Numerous researches based on up to three predetermined factors have been conducted for preventing falls. The present comprehensive proposal is based on four sub-systems that synthesize six factors. A parameter is assigned to each factor with a coefficient specifically determined for each individual patient and per admittance. The parameters are aggregated in equations that lead to an early warning about a probable bed-exit or an alarm about an imminent bed-exit. The ultimate aim of our proposal is the generation of the earliest possible warning to grant the longest time for nurse intervention. Thus the probable fall of high-risk patients can be prevented by stopping the unattended bed-exits. The proposal is supported by a prototype multi-tier system design and the results of laboratory patient bed-exit scenarios carried out using the design. Comparison of the obtained results with previous work shows that our proposed solution is unmatched in providing the longest time for nurse intervention (up to 15.7 ± 1.1 s) because of the comprehensive six-factor synthesis specific to each individual patient and each admittance. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 8
QUANTIFYING PRODUCTIVITY OF INDIVIDUAL SOFTWARE PROGRAMMERS: PRACTICAL APPROACH
(SLOVAK ACAD SCIENCES INST INFORMATICS, 2015) Mehmet Suleyman Unluturk; Kaan Kurtel; Unluturk, Mehmet Suleyman; Kurtel, Kaan
Software measurement is a crucial part of a good software engineering. Software developers quantify the software to see if the use cases are complete if the analysis model is consistent with requirements and if the code is ready to be tested. Software project managers assess the software process and the software product to determine if it is going to be finished on time and within budget. Customers evaluate the final product if it meets their needs. Overall the main purpose of software engineering is to make software systems controllable and foreseeable activities with a solid method rather than intuitional complicated or unprincipled. Software measurement studies are about quantifying the software engineering entities and attributes both of which aim to support software development efforts and quality improvement. In this paper we quantify a set of relationships using the current size defect and object-oriented software metrics practically and pragmatically. Our paper proposes a method to measure the productivity of individual software programmers. Furthermore this method provides a common opinion for understanding controlling and improving the software engineering practices.