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Fakültelerİstanbul Topkapı Üniversitesi Kurum Koleksiyonu
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Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Cevat Rahebi

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efciency, reducing costs, and ensuring grid reliability. Tis research aims to develop a cost-efective and high-performance fault detection solution for HVDC systems. Te primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifer and inverter sides of the HVDC transmission system and their associated alternating cur ...Daha fazlası

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MOBRO: multi-objective battle royale optimizer

Taymaz Akan

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of B ...Daha fazlası

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Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

Buket İşler

The coronavirus (COVID-19) is a disease declared a global pan-demic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML)-based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K -nearest neighbor (KN ...Daha fazlası

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Hybrid model-based prediction of biomass density in case studies in Turkiye

Buket İşler

Growing global concern over natural resource degradation due to urbanisation and population growth emphasizes the critical need for innovative solutions. Addressing this imperative, our study pioneers the integration of cutting-edge artificial intelligence (AI) methods to investigate crucial changes in vegetation density. In this context, a hybrid model, which harmoniously integrates conventional artificial neural network (ANN) models with the innovative Wavelet-ANN (W-ANN) approach, was employed in two case pilot areas, namely on Alanya in Antalya and Iznik in Bursa, Turkiye, renowned for the ...Daha fazlası

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Voltage Dependent Profiles of the Surface States and Series Resistance (Rs) in the Al-(Cd:ZnO)-pSi Schottky Diodes (SDs) Utilizing Voltage-Current (IV) Characteristics

İlke Taşçıoğlu

In this work, the main electronic parameters of the performed Al-(CdxZn1-xO)-pSi Metal/Interface-layer/Semiconductor (MIS) type Schottky Diodes (SDs) were investigated by utilizing IV characteristics at 300 K. The (CdxZn1-xO) interfacial layer was grown on the pSi wafer by utilizing the sol -gel technique. Ideality-factor(n), potential barrier 0Bo, Rs, shunt resistance (Rsh), and rectification rate (RR) (Iforward/Ireverse) values were calculated based on thermionic emission (TE) theory and Cheung function between -4.5V and 4.5V. There parameters also varied for the samples with different dopin ...Daha fazlası

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Smart energy meter with GSM card recharge

Cevat Rahebi

The objective of the project are to reduce delays at energy metre billing counters and automatically limit energy meter usage, if the bill is not paid. The study also seeks to provide a mechanism that will lessen the amount of money lost to power theft and other illicit activities. The method of work takes a completely novel idea for a prepaid energy meter. GSM technology is employed in order for the customer to get notifications regarding your power usage (measured in watts); if it hits the minimum, it would immediately notify the user that they require a recharge. This method works well for ...Daha fazlası

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Frequency-dependent dielectric, electric modulus, and ac conductivity features of Au/n-Si Schottky diodes (SDs) with PVC and (PVC:Graphite/Graphene-Oxide) interlayer

İlke Taşçıoğlu

To determine the interlayer effect on dielectric features and conductivity, Au/n-Si (S-0), Au/PVC/p-Si (S-1), and Au/PVC:Gt-GO/p-Si (S-2) type SDs were grown onto the same n-Si wafer and their admittance measurements performed between 100 Hz and 1 MHz. The observed decrease in C and G/omega values as frequency increases shows that the charges at the interface-states (N-ss) can easily follow ac-signal and supply an excess capacitance and conductance at lower frequencies. Using C and G/omega data at 1.5 V, the dielectric-constant ('), dielectric-loss (''), and loss-tangent (tan delta) were obtai ...Daha fazlası

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Effective test-data generation using the modified black widow optimization algorithm

Mahsa Torkamanıan Afshar

Software testing is one of the software development activities and is used to identify and remove software bugs. Most small-sized projects may be manually tested to find and fix any bugs. In large and real-world software products, manual testing is thought to be a time and money-consuming process. Finding a minimal subset of input data in the shortest amount of time (as test data) to obtain the maximal branch coverage is an NP-complete problem in the field. Different heuristic-based methods have been used to generate test data. In this paper, for addressing and solving the test data generation ...Daha fazlası

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Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey

Cevat Rahebi

Abstract: Alzheimer’s is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual’s cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer’s disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The u ...Daha fazlası

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Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

Cevat Rahebi

Abstract Palm print identification is a biometric technique that relies on the distinctive characteristics of a person’s palm print to distinguish and authenticate their identity. The unique pattern of ridges, lines, and other features present on the palm allows for the identification of an individual. The ridges and lines on the palm are formed during embryonic development and remain relatively unchanged throughout a person’s lifetime, making palm prints an ideal candidate for biometric identification. Using deep learning networks, such as GoogLeNet, SqueezeNet, and AlexNet combined with gray ...Daha fazlası

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Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Cevat Rahebi

This study introduces an innovative smart grid (SG) intrusion detection system, integrating Game Theory, swarm intelligence, and deep learning (DL) to protect against complex cyber-attacks. This method balances training samples by employing conditional DL using Game Theory and CGAN. The Aquila optimizer (AO) algorithm selects features, mapping them onto the dataset and converting them into RGB color images for training a VGG19 neural network. AO optimizes meta-parameters, enhancing VGG19 accuracy. Testing on the NSL-KDD dataset generates remarkable results: 99.82% accuracy, 99.69% sensitivity, ...Daha fazlası

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Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Cevat Rahebi

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The imple ...Daha fazlası

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