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Öğe Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images(MDPI, 2025) Ali, Abdallah Waleed; Kurnaz, SeferEarth observation (EO) satellites offer significant potential in wildfire detection and assessment due to their ability to provide fine spatial, temporal, and spectral resolutions. Over the past decade, satellite data have been systematically utilized to monitor wildfire dynamics and evaluate their impacts, leading to substantial advancements in wildfire management strategies. The present study contributes to this field by enhancing the frequency and accuracy of wildfire detection through advanced techniques for detecting, classifying, and segmenting wildfires using satellite imagery. Publicly available multi-sensor satellite data, such as Landsat, Sentinel-1, and Sentinel-2, from 2018 to 2020 were employed, providing temporal observation frequencies of up to five days, which represents a 25% increase compared to traditional monitoring approaches. Sophisticated algorithms were developed and implemented to improve the accuracy of fire detection while minimizing false alarms. The study evaluated the performance of three distinct models: an autoencoder, a U-Net, and a convolutional neural network (CNN), comparing their effectiveness in predicting wildfire occurrences. The results indicated that the CNN model demonstrated superior performance, achieving a fire detection accuracy of 82%, which is approximately 10% higher than the best-performing model in similar studies. This accuracy, coupled with the model's ability to balance various performance metrics and learnable weights, positions it as a promising tool for real-time wildfire detection. The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. Achieving 82% detection accuracy in real-world applications could drastically reduce response times, minimize the damage caused by wildfires, and enhance resource allocation for firefighting efforts, emphasizing the importance of continued research in this domain.Öğe Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design(MDPI, 2025) Daylak, Funda; Özoğuz, SerdarThis study presents an automated circuit design approach using neural networks to optimize the dynamic range (DR) of active filters, illustrated through the design of a 7th-order Chebyshev low-pass filter. Traditional design methods rely heavily on designer expertise, often resulting in time-intensive and energy-consuming processes. Two techniques are proposed: inverse modeling and forward modeling. In inverse modeling, artificial neural networks (ANNs) predict circuit parameters to meet specific performance goals. A randomly selected subset, comprising 0.05% of the 1,953,125 possible circuit configurations, was used to train and validate the model, providing an accurate representation of the entire dataset without requiring full-scale data analysis. In forward modeling, the same subset was used to train the network, which was then used to predict DR values for the remaining dataset. This approach enabled the identification of circuit parameters that resulted in optimal DR values. The results confirm the effectiveness of these techniques, with both inverse modeling and forward modeling outperforming the standard circuit design. At 160 kHz, a critical frequency for the operation of the designed filter, inverse modeling achieved a DR of 140.267 dB and forward modeling reached 136.965 dB, compared to 132.748 dB for the standard circuit designed using the traditional approach. These findings demonstrate that ANN-based methods can significantly enhance design accuracy, reduce time requirements, and improve energy efficiency in analog circuit optimization.Öğe Reinforcement Neural Network-Based Grid-Integrated PV Control and Battery Management System(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Thajeel, Salah Mahdi; Atilla, Doğu ÇağdaşA reinforcement neural network-based grid-integrated photovoltaic (PV) system with a battery management system (BMS) was developed to enhance the efficiency and reliability of renewable energy systems. In such a setup, the PV system generates electricity, which can be used immediately, stored in batteries, or fed into the grid. The challenge lies in dynamically optimizing the power flow between these components to minimize energy costs, maximize the use of renewable energy, and maintain grid stability. Reinforcement learning (RL) combined with NNs offers a powerful solution by enabling the system to learn and adapt its energy management strategy in real time. By using the proposed techniques, the convergence time was decreased with lower complexity compared with existing approaches. The RL agent interacts with the environment (i.e., the grid, PV system, and battery), continuously improving its decisions regarding when to store energy, draw from the battery, and supply power to the grid. This intelligent control approach ensures optimal performance, contributing to a more sustainable and resilient energy system.Öğe HawkFish Optimization Algorithm: A Gender-Bending Approach for Solving Complex Optimization Problems(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Alkharsan, Ali; Ata, OğuzInspired by the gender transition behavior seen in hawkfish, this paper introduces the HawkFish optimization algorithm, a nature-inspired optimization technique modeled on the unique gender transition behavior of hawkfish. By leveraging this biological phenomenon, the proposed method addresses optimization problems through dual fitness functions, combining an original and inverse fitness function to drive search space exploration while avoiding local minima. The algorithm’s performance is rigorously evaluated against benchmark problems, including the CEC/GECCO 2019 suite, and applied to real-world engineering challenges like welded beam and tension/compression spring design. The proposed method consistently outperforms existing algorithms in terms of convergence rate, accuracy, and solution quality. The results underscore the algorithm’s efficiency in exploring unknown search spaces and solving complex optimization tasks, making it a promising tool for various domains requiring high precision and optimization efficiency.Öğe Diagnosis of Epileptic seizures and Hypoxic-ischemic encephalopathy using Artificial Intelligence based on EEG signal: A review(Institute of Electrical and Electronics Engineers Inc., 2024) Kadhim, Ezzaddin; Al-Jumaili, Saif; Uçan, Osman NuriThe brain is the nucleus for cognition and controls voluntary and involuntary activities inside the human body. Any neurological illness, regardless of its cause, will impair the brain's functionality. Certain neurological illnesses manifest symptoms as seizures. Epilepsy and Hypoxic-ischemic Encephalopathy (HIE) are the most similar disorders in symptoms, but at the neurological level, they are two completely different disorders. This difference is measured at the level of neural activity, as Electroencephalography (EEG) is one of the most distinctive tools used to measure neural activity in the brain. Experts use EEG to diagnose disorders through recorded brain activity, including seizures, but the diagnosis process consumes much time and effort. Adopting Artificial Intelligence (AI) techniques to extract the patterns of brain illnesses is a more efficient process for diagnosing disorders because it depends on computing and, thus, has high accuracy in diagnosing brain illnesses. In this research, we reviewed the most effective stages and methods adopted by researchers to diagnose brain disorders based on EEG and artificial intelligence techniques.Öğe Acoustical imaging of the nearshore seafloor depositions and deformations, a key study for Western Istanbul, Türkiye(Springer Verlag, 2024) Özgan, Sinan; Alp, Hakan; Bayat, Oğuz; Vardar, DenizhanTo protect the sustainability of the benefits from seas and near coastal areas, which have under the effect of the very complex hydrodynamic conditions and intensive human activities, without disrupting the balance of nature, it is necessary to image the status of the seafloor features. Therefore, this study presents the deformations, depositional conditions, underwater constructions, and the other non-natural impacts on the seafloor of the nearshore area at western Istanbul (between Küçükçekmece and Büyükçekmece lagoons) where it intensely used by the citizens. The results of the study may provide some guidance for understanding the impacts and risk factors of uses that are or will be conducted in coastal and/or near-coastal areas. Construction planning for civil coastal structures and areas should be done in great harmony with nature, minimizing negative environmental impacts. Although sediment distribution in the area is generally quite complex, the current state of the region, wave action, hydrodynamic conditions, the amount of material transported from the land, and bathymetry are important influencing factors. The seafloor has been damaged primarily by anchor deformation and associated bottom scanning, as well as disturbing trawl tracks. The seafloor was observed as partially shallowing near the constructions (such as natural gas pipelines, fishermen’s shelter, and port piles) of coastal areas and associated with sand deposits. Therefore, scanning the seafloor using side-scan sonar may provide valuable frequency data to prevent future disruptions.Öğe Enhanced energy efficiency through path planning for off-road missions of unmanned tracked electric vehicle(Multidisciplinary Digital Publishing Institute (MDPI), 2024) İnal, Taha Taner; Cansever, Galip; Yalçın, Barış; Çetin, Gürkan; Hartavi, Ahu EceThe primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.Öğe Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems(2024) Jasim, Abdulrahman Ahmed; Ata, Oğuz; Salman, Omar HusseinBackground and objective: The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). Methods: The study's methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. Results: The RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. Conclusions: The efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.Öğe Detection of Animals and humans in forest fires using Yolov8(2024) Alsamurai, Mustafa Qays Fadhil; Çevik, Mesut- The study uses the YOLOv8 deep learning algorithm to detect fire, smoke, humans, and animals in outdoor images. The importance of forests in protecting the biosphere is emphasized, and forest fires are identified as a major risk to the environment and living beings. The researchers created a custom dataset of outdoor images and manually annotated them. The YOLOv8 model was trained on this dataset, and its overall performance was evaluated, with varying results for different object classes. The study identified areas for improvement in the model's ability to detect small instances of fire and smoke and differentiate between animals and humans. The impact of image quality on the model's performance was also highlighted. Overall, the study provides a comprehensive evaluation of YOLOv8's performance in detecting outdoor objects and identifies areas for improvement.Öğe Examining the potential of deep learning in the early diagnosis of Alzheimer's disease using brain MRI images(2024) Mahmood, Anmar; Çevik, Mesut- Alzheimer's disease is a severe public health problem affecting millions worldwide. Deep Learning (DL) models can aid in detecting the disease using MRI data, and we evaluated three DL models for this purpose. We used detailed MRI images of Alzheimer's patients and healthy controls to train these models. A convolutional neural network (CNN) with two convolutional and two fully connected layers was employed in the initial model, which had a 95% accuracy rate. The second model, which included a leaky ReLU activation function, more fully connected layers, and a bigger kernel size, was an enhanced version of the previous one and had a 96% accuracy rate. The third model was a transfer learning model with two dense layers built on top of the VGG16 architecture, achieving an accuracy of 80%. Our findings imply how neural network models may assist with MRI data-based the disease assessment via evaluations of reliability, precision, recollection, and the F1 ranking. For enhancing the precision and usability of these gadgets for therapeutic usage, more study must be conducted.Öğe Study and analysis of PV system behavior during disturbances(Institute of Electrical and Electronics Engineers Inc., 2023) Mohammed, Ramzi Qasim; Mardikyan, Kevork; Çevik, MesutThe utilization of renewable energy sources has drawn attention because of the population's fast increase and growing worry over global warming. Renewable energy sources greatly improve the environment by lowering carbon emissions and generating cost-effective electricity. Solar energy is the most widely used renewable energy source. PV panels are becoming more affordable, have a bright commercial future, and are good for the environment. Recently, the PV solar system has drawn a lot of attention, especially in comparison to other renewable energy sources. The renewable energy source systems are intended to function normally within a set of operating parameters, but they may experience failures that alter their operational performance and behavior. The performance and behavior of a PV hybrid grid-connected system under disturbances, such as a quick change in load or a rapid change in applied irradiations, temperature and also fault conditions will be studied, analyzed, and discussed in this papers. The MATLAB Simulink application will be used to display the modeling of PV system results. In this papers the P&O MPPT technique-based power management of PV batteries is vulnerable to a number of eventualities, including dust impairing PV efficiency and raising temperature and failures. The results show that, the PV system's output power decreases as the temperature rises, but the output efficiency also suffers from dust buildup. The second goal is the direct proportion between PV current and irradiance. Different cases are proposed in this work to verify the energy management system. The results were within IEEE harmonic standards when the LC filter is used in this work.Öğe Enhancing smart grid efficiency: a modified ANN-LSTM approach for energy storage and distribution optimization(Institute of Electrical and Electronics Engineers Inc., 2023) Mohammed, Ramzi Qasim; Abdulrazzaq, Mohammed Majid; Mohammed, Ayoob Jasim; Mardikyan, Kevork; Çevik, MesutThe smart grid represents a paradigm shift in energy management, aiming to optimize energy storage and distribution while accommodating the growing demand for renewable energy sources. In this paper, we investigate the application of a modified Artificial Neural Network with Long Short-Term Memory (ANN-LSTM) in addressing the multifaceted challenges of the smart grid. Through rigorous experimentation and simulation, the ANN-LSTM is evaluated in four diverse scenarios, including normal operation, fluctuating renewable energy, peak demand, and grid instability. The results showcase the model's exceptional predictive accuracy, low Mean Squared Error (MSE), and rapid response times, outperforming other models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), Decision Tree (DT), and Fuzzy Logic. Our findings underscore the ANN-LSTM's potential to revolutionize energy storage and distribution in the smart grid, ushering in a new era of efficiency, sustainability, and resilience in energy management.Öğe Sub-diffraction focusing of light by aperiodic masks(Taylor and Francis Ltd., 2022) Mostafavi, Seyeddyako; Nutku, Ferhat; Ekşioğlu, YasaDiffraction of a spherical wave through various types of 2D aperiodic hollow masks is investigated computationally. Unlike a periodic transmissive grating, an aperiodic hollow mask can focus light into a hotspot with sub-wavelength diameter. In this work, several types of 2D aperiodic hollow masks are investigated in the framework of sub-diffraction focusing of light and generating superoscillations at the hotspot region.Öğe Improving IoT data security and integrity using lightweight blockchain dynamic table(MDPI, 2022) Hameedi, Saleem S.; Bayat, OğuzOver the past few years, the Internet of Things (IoT) is one of the most significant technologies ever used, as everything is connected to the Internet. Integrating IoT technologies with the cloud improves the performance, activity, and innovation of such a system. However, one of the major problems which cannot be ignored in such integration is the security of the data that are transferred between the client (IoT) and the server (cloud). Solving that problem leads to the use the of IoT technologies in more critical applications and fields. This paper proposes a new security framework by combining blockchain technology with the AES algorithm. Blockchain technology is used and modified to protect data integrity and generate unique device identification within minimal power consumption and best performance. The AES algorithm is used to improve the data confidentiality when being transmitted to the server. The outcomes demonstrated that the proposed solution improves the security system of the IoT healthcare data and proved its efficiency and power consumption compared to other methods.Öğe TETRA baz istasyonları arasında frekans planlaması(2022) Yılmaz, Şakir; Aydın, Çağatay; Atilla, Doğu ÇağdaşGünümüzde kısıtlı frekans kaynaklarının tekrarlanarak kullanılması frekans planlama açısından ciddi önem arz etmektedir. Özellikle kritik ses haberleşmesinde daha az baz istasyonu ile daha fazla alan kapsanmak istendiğinde frekans tekrarı sorunu daha çok göze batmaktadır. Bu makalede kritik ses haberleşmesi teknolojilerinden olan Karasal Trunk Telsiz sistemi (Terrestrial Trunk Radio, TETRA) üzerinde bir çalışma yapılmıştır. Baz istasyonları konumları, yayın yaptığı frekans bilgisi, baz istasyonu çıkış gücü, sisteme ait bant genişliği göz önüne alınarak komşu kanal ve ortak kanal girişimleri incelenmiştir. Sayısal telsiz sisteminde mevcut hesapların yetersiz kaldığı ve bu hesaplara ek olarak komşuluk ilişkilerinin de frekans planlamada önemli olduğu tespit edilmiştir. Ayrıca konuşma trafiği yoğunluğu (Erlang) incelenerek sistemde işlevsiz kullanılan frekanslar tespit edilmeye çalışılmıştır.Öğe CSK based on priority call algorithm for detection and securing platoon from inside attacks(2020) Al-Sheikhly, Mohammed; Kurnaz, SeferThe platooning is an emerging concept in VANETS that involves a group of vehicles behaving as a single unit via the coordination of movement. The emergence of autonomous vehicles has bolstered the evolution of platooning as a trend in mobility and transportation. The autonomous vehicles and the elimination of individual and manual capabilities introduces new risks. The safety of the cargos, passenger and the advanced technology had increased the complication of the security concerns in platooning as it may attract malicious actors. In improving the security of the platoon, the threat and their potential impacts on the vehicular systems should be identified to ensure the development of security features that will secure against the identified risks. In this paper, two critical types of security breaches were identified those are Sybil attack and Delay attacks. Those security attacks can be somewhat disruptive and dangerous to the regular operation of the platoon leading to severe injuries, increased fuel consumption and delay the performance of the network. The research in this paper focuses on design, detection and the mitigation of attacks in a vehicle platoon. priority call algorithm in combination with color-shift keying modulation is used to protect the platoon alleviating the undesirable impacts such as collisions, oscillations and disintegration in the platoon caused by the attacks.Öğe Voice to face recognition using spectral ERB-DMLP algorithms(Tech Science Press, 2022) Bala, Fauzi A.; Uçan, Osman Nuri; Bayat, OğuzDesigning an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment. This authentication is also vital to prevent any security threats or risks like compromises of business server, release of confidential data etc. Though conventional works attempted to accomplish better authentication, they lacked with respect to accuracy. Hence, the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning (DL) due to its ability to perform effective learning. The study proposes Equivalent Rectangular Bandwidth and Deep Multi-Layer Perceptron (ERB-DMLP) as it has the ability to perform efficient and relevant feature extraction and faster classification. This algorithm also has the ability to establish effective correlation between voices and images and achieve the semantic relationship between them. Voice preprocessing is initially performed to make it suitable for further processing by removing the noise and enhancing the quality of signal. This process is also vital to minimize the extra computations so that the overall efficacy of the system can be made flexible by considering the audio files as features and the images as labels to identify a person’s voice by classifying the extracted features from the ERB Feature Extraction. This is then passed as the input into DMLP model to classify the persons, and trained the model to make an accurate classification of audio with corresponding image labels, and perform the performance test based on the trained model. Flexibility, relevant feature extraction and faster classification ability of the proposed work has made it explore better outcomes that is confirmed through results.Öğe Efficient DC biased-PAM based OFDM for visible light communication system(Springer, 2022) Zaidan, Zahraa Mustafa; Bayat, Oğuz; Abdulkafi, Ayad AtiyahThe need of high spectral efficiency to increase bandwidth utilization have been the main drivers for next generation optical wireless communication including visible light communication (VLC) systems. However, conventional optical techniques adopted Hermitian symmetry to satisfy the real and positive constraints of intensity modulation with direct detection based VLC systems on the cost of spectral efficiency loss. In this paper, a new direct current biased- pulse amplitude modulated based orthogonal frequency division multiplexing (DC-PAM-OFDM) is proposed to utilize all subcarriers without using Hermitian symmetry and hence provide more spectral efficiency for VLC systems. The proposed scheme ensures the real-valued optical signals by inserting PAM signal in frequency domain side of OFDM system, clipping, splitting, and rearranging the time domain signal instead of using Hermitian symmetry. This results in improving spectral efficiency by 100% compared to PAM based discrete multitoned modulation (DMT). In addition, theoretical analyses show that the proposed DC-PAM based OFDM scheme is more energy efficient than the PAM-DMT. Furthermore, simulation results show that the proposed scheme has better bit error rate performance and reduces the Peak-to-Average Power Ratio when compared with the existing methods under same spectral efficiency conditions.Öğe Lightweight offline authentication scheme for secure remote working environment(IEEE, 2021) Coruh, Ugur; Khan, MansoorDuring pandemic situations, remote work security and reliability become more important for sustainability. The main problem in remote working is providing a reliable internet connection between remote working employees and remote systems. Lack of internet connection issues cause remote working problems, and some companies allow employees to work on untrusted personal computers, which causes untrusted and unmanageable IT systems. According to these problems, this study proposed a lightweight, offline authentication scheme for the secure remote work environment. The proposed model is designed for LPS (Lightweight Portable Security) devices, and these LPS devices provide portable USB bootable images with a secure environment for this connection and trust problems. LPS devices are single USB dongles to make users more dependent on these LPS devices. This design provides mobile application-dependent authentication to provide strong user authentication with 2FA (2 Factor Authentication), which is something you have (mobile phone and USB dongle) and something you know (mobile PIN). Also, the proposed model provides geofencing between USB dongles and Mobile phones to offer robust security.Öğe Hovering drones-based FSO technology in weak atmospheric turbulence with pointing error(IEEE, 2021) Mahdi, Abdullah Jameel; Mazher, Jalil; Uçan, Osman NuriThe rapid development of drones that are a type of Unmanned Arial Vehicles (UAV) in different fields encourages researchers and institutes concern with wireless communication to use this technology. As wireless optical communication (OWC) has been applied and has many advantages over Radio Frequency (RF), this enables using the optical beam in UAV-based Free-space optical (FSO) technology as an alternative to RF technology. In this paper, the proposed system configuration has two subsystems: Single Input-Single Output (SISO) and Multiple Input-Single Output (MISO). The system was simulated using MATLAB software 2020. The optical signal was modulated using Pulse Position Modulation (PPM) and transmitted in a weak turbulence regime. The Average Bit error Rate (ABER) was measured depending on the pointing error HP factor. The idea was to find the specific values of the pointing error angle θr and link distance Z that are the related parameters of HP. The results have clarified that for each pointing error angle θr value, there was a specific path length Z, which can keep the system at high performance, for example, θr= rad, the applicable link distance Z=1000 m, and the ABER≈ .
