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Öğe A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets(Springer London Ltd, 2019) Ibrahim, Hadeel Tariq; Mazher, Wamidh Jalil; Uçan, Osman Nuri; Bayat, OğuzSupport vector machines (SVM) are one of the important techniques used to solve classifications problems efficiently. Setting support vector machine kernel factors affects the classification performance. Feature selection is a powerful technique to solve dimensionality problems. In this paper, we optimized SVM factors and chose features using a Grasshopper Optimization Algorithm (GOA). GOA is a new heuristic optimization algorithm inspired by grasshoppers searching for food. It approved its ability to solve real-world problems with anonymous search space. We applied the proposed GOA + SVM approach on biomedical data sets for Iraqi cancer patients in 2010-2012 and for University of California Irvine data sets.Öğe A heuristic approach for optimal planning and operation of distribution systems(Hindawi Ltd, 2018) Alzaidi, Khalid Mohammed Saffer; Bayat, Oğuz; Uçan, Osman NuriThe efficient planning and operation of power distribution systems are becoming increasingly significant with the integration of renewable energy options into power distribution networks. Keeping voltage magnitudes within permissible ranges is vital; hence, control devices, such as tap changers, voltage regulators, and capacitors, are used in power distribution systems. This study presents an optimization model that is based on three heuristic approaches, namely, particle swarm optimization, imperialist competitive algorithm, and moth flame optimization, for solving the voltage deviation problem. Two different load profiles are used to test the three modified algorithms on IEEE 123- and IEEE 13-bus test systems. The proposed optimization model uses three different cases: Case 1, changing the tap positions of the regulators; Case 2, changing the capacitor sizes; and Case 3, integrating Cases 1 and 2 and changing the locations of the capacitors. The numerical results of the optimization model using the three heuristic algorithms are given for the two specified load profiles.Öğe A hybrid local search-genetic algorithm for simultaneous placement of DG units and shunt capacitors in radial distribution systems(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Almabsout, Emad Ali; El-Sehiemy, Ragab A.; Uçan, Osman Nuri; Bayat, OğuzControlling active/reactive power in distribution systems has a great impact on its performance. The placement of distributed generators (DGs) and shunt capacitors (SCs) are the most popular mechanisms to improve the distribution system performance. In this line, this paper proposes an enhanced genetic algorithm (EGA) that combines the merits of genetic algorithm and local search to find the optimal placement and capacity of the simultaneous allocation of DGs/SCs in the radial systems. Incorporating local search scheme enhances the search space capability and increases the exploration rate for finding the global solution. The proposed procedure aims at minimizing both total real power losses and the total voltage deviation in order to enhance the distribution system performance. To prove the proposed algorithm ability and scalability, three standard test systems, IEEE 33 bus, 69 bus, and 119-bus test distribution networks, are considered. The simulation results show that the proposed EGA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literature. Moreover, an economic based cost analysis is provided for light, shoulder and heavy loading levels. It was proven, the proposed EGA leads to significant improvements in the technical and economic points of view.Öğe A naïve bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information(Springer, 2022) Gültekin, Günay; Bayat, OğuzIn recent years, researchers have been trying to create recommender systems. There are many diferent recommender systems. Point of Interest (POI) is a new type of recommender systems that focus on personalized and context-aware recommendations to improve user experience. Recommender systems use diferent types of recommendation methods to obtain information on POI. In this research paper, we introduced a Naïve Bayes Prediction Model based on Bayesian Theory for POI recommendation. Then, we used the Brightkite dataset to make predictions on POI recommendation and compared it with the other two diferent recommendation methods. Experimental results confrm that our proposed method outperforms on Location-based POI recommendation.Öğe A new novel optimization techniques implemented on the AVR control system using matlab-simulink(Science and Engineering Research Support Society, 2020) Mohammed, Yousif R.; Basil, Noorulden; Bayat, Oğuz; Mohammed, Alaa HamidThe Automatic Voltage Regulator (AVR) System that structured in MATLAB/Simulink for the generator of synchronous with Conventional Proportional Integral Derivative (PID) Controller. There are numerous Techniques were utilized with Automatic Voltage Regulator system as different Algorithms utilized for taking care of numerous issues that face the traditional PID Controller and Improving the overshoot, steady-state error, rising time, settling time and control of voltage (Step response of Change) in this paper two proposed strategies calculations that called Optimization Methods have been tested, such as, Equilibrium Optimizer (EO) and Rider Optimization Algorithm (ROA) and these procedures that contrasts and past investigations rely upon the parameters that set for PID Controller to improve the positions that got from these Novel Algorithms. © 2020 SERSC.Öğe A new precoding scheme for spectral efficient optical OFDM systems(Elsevier Science Bv, 2018) Hardan, Saad Mshhain; Bayat, Oğuz; Abdulkafi, Ayad AtiyahAchieving high spectral efficiency is the key requirement of 5G and optical wireless communication systems and has recently attracted much attention, aiming to satisfy the ever increasing demand for high data rates in communications systems. In this paper, we propose a new precoding/decoding algorithm for spectral efficient optical orthogonal frequency division multiplexing (OFDM) scheme based visible light communication (VLC) systems. The proposed coded modulated optical (CMO) based OFDM system can be applied for both single input single output (SISO) and multiple input multiple-output (MIMO) architectures. Firstly, the real OFDM time domain signal is obtained through invoking the precoding/decoding algorithm without the Hermitian symmetry. After that, the positive signal is achieved either by adding a DC-bias or by using the spatial multiplexing technique. The proposed CMO-OFDM scheme efficiently improves the spectral efficiency of the VLC system as it does not require the Hermitian symmetry constraint to yield real signals. A comparison of the performance improvement of the proposed scheme with other OFDM approaches is also presented in this work. Simulation results show that the proposed CMO-OFDM scheme can not only enhance the spectral efficiency of OFDM-based VLC systems but also improve bit error rate (BER) performance compared with other optical OFDM schemes.Öğe A novel approach for ensuring location privacy using sentiment analysis and analysis for health-care and Its effects on humans health(Amer Scientific Publishers, 2020) Ali, Bassam S.; Uçan, Osman Nuri; Bayat, OğuzTwitter is the free report benefit for given important to as far as possible to 150 typescripts that may include script, pictures, videotapes and hyper-links. Persons sharing news, ideas and info to supports or agonists media. The most petrify theme is the persecution of ladies occurring a rounds the world. Individual's persecution of women of the web-based life to dependably impart utilizing coded words or to setting up their indirections nearness. This paper offering a novel procedure for feelings research on the maltreatment of women connected tweets and to organizing the suppositions with their geo-zones. The information mining calculations, for example, Bolster Vector Machine, Irregular Woodland, Sacking, Choice Trees and Most extreme Entropy are applying for extremity's based characterization of mistreatment of ladies related Tweets. The outcomes are looking at and exhibiting.Öğe A novel software engineering approach toward using machine learning for improving the efficiency of health systems(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Moreb, Mohammed; Mohammed, Tareq Abed; Bayat, OğuzRecently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications.Öğe A robust hybrid control model implementation for autonomous vehicles(Institute of Electrical and Electronics Engineers Inc., 2024) Al-Jumaili, Mustafa Hamid; Özok, Yasa Ekşioğlu; Ibrahim, Abdullahi Abdu; Bayat, OğuzThis work presents a robust control strategy for controlling autonomous vehicles under various conditions. This approach makes use of two controllers to guarantee excellent performance and a few faults when the car is traveling. Model Predictive and Stanley based controller (MPS) is the name of the new control system. This combines the functionality of a Stanley controller with a model predictive controller. The suggested approach tries to address these issues and provides a high-performance control system. Utilizing the finest aspects of both controllers and attempting to improve the other, this hybrid approach to integrating two well-known controllers provides advantages. The MPS is put to the test on straight and curvy roads in a variety of scenarios for both path-following and vehicle control. This controller has demonstrated excellent performance and adaptability to handle various autonomous driving conditions. When the findings are compared to earlier controller kinds, the suggested system performs better.Öğe A systematic mapping study on touch classification(Int Journal Computer Science & Network Security-Ijcsns, 2018) Fleh, Saad Q.; Bayat, Oğuz; Al-Azawi, Saad; Uçan, Osman NuriOne of the basic interpersonal methods to communicate emotions is through touch. Social touch classification is one of the leading research which has great potential for more improvement. Social touch classification can be beneficial in the much scientific application such as robotics, human-robot interaction, etc.. Each person has the ability to interact with the environment and with other people via touch sensors that are speared over human soma. These touch sensors provide us with the important information about objects such as size, shape, position, surface and their movement. Therefore, the touch system plays the main role in human life from early days. The small gesture can express strong emotion, from the comforting experience of being touched by one's spouse, to the discomfort caused by a touch from a stranger. This paper presents and explains a systematic mapping study on social touch gesture recognition. From various digital libraries, 938 papers in total are collected. After applying three filters, 49 papers as primary studies related to the main topic are selected as listed in Appendix (A). The selected papers classified with respect to several facets. The results provide an overview of the existing relevant studies that are reported in the literature, highlight the focused areas and research gaps.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 Adaptive video transmission over communication networks(Altınbaş Üniversitesi, 2019) Göse, Ersin; Uçan, Osman Nuri; Nafea, Ghaidq Nassr; Bayat, OğuzThis paper is to minimize video bitrate and keeping the high resolution video file manageable. This process is achieved by using new generation of Advanced Video Coders (AVC). Such process can be done at one level encoder or multi levels of encoders by means of transcoding, where reformatting the content to be streamed on channel is called transcoding. The goal is concerned of encoder processes that can be used to achieve transcoding. The codec used for compression of video is H.264, a standard for providing high definition video at substantially low complicity and lower bit rates. The x264 Library is used for encoding H.264 AVC, undergirds some of the most profiles for broadcasting and streaming operations over wired and wireless channels, including different applications. When a technique of bit-rate control is incorporated with the encoder, more reliable and qualified system for low bitrate video streaming over constant bit rate communication channel is achieved, where output rate of the video encoder is controlled by feedback based on the buffer level. Where the most effective parameters such as skip frame, QP, cycle length (Gop), etc. are configured and it used as a rate control tools to test the streaming coded bit rate and the decoded video quality. Testing scenarios use many different videos with QCIF, CIF, and HD formats encoded under main profile. JM19 reference software is used for implementing and testing the standards.Öğe An adapting soft computing model for intrusion detection system(Wiley, 2021) Alsaadi, Husam Ibrahiem Husain; ALmuttari, Rafah M.; Uçan, Osman Nuri; Bayat, OğuzNetwork security in smart cities has become a key problem in the rapid development of computer networks over the past few years. Intrusion detection systems play a fundamental part in the integrity, confidentiality, and resource accessibility among the multiple network security policies. The classification of the genuineness of packets is main object of the presents research work, the soft computing has applied to classify the genuineness of packets. The complexity of soft computing is greatly reduced if the numbers of features in a dataset are reduced. Managing and analysis of the dimensionality reduction is novelty of the proposed model. The existence of uncertainty and the imprecise nature of the intrusions appear to create suitable fuzzy logic systems for such structures. The neural-fuzzy algorithm is one of the effective methods that incorporate fuzzy logic systems into adaptive and analysis capacities. In this research work, soft computing fuzzy logic system is proposed to enhance network security through intrusion detection. Three network datasets are demonstrated to test and estimate the proposed system. Feature selection has used to remove irrelevant features from entire network data which are obstacle classification processes. The Information Gain method was applied to select importance features for detection intrusion. Adaptive Neuro-Fuzzy Inference System (ANFIS) is further used to process the significant features of the classification network data as normal or attacks packets. Two functions named Jang's Neuro-fuzzy and faster-scaled conjugate gradient (SCG) based on the ANFIS system. Obviously, the experimental results demonstrate the proposed system has attained higher precision in detecting normal or attack. The experimental results have suggested that the proposed system results are better in accuracy and time process for classification compared with the existing models. The Overall Results show that the proposed system can be able to detect various intrusions efficiently and effectively.Öğe An efficient many-objective evolutionary algorithm for ecological optimization problems(Universidad del Zulia, 2018) Safi, H.H.; Ucan, Osman Nuri; Bayat, OğuzEcological problems are becoming more and more important in the engineering society and their importance are expected to be increased in the next century. Many ecological problems have more than one objective function in nature which makes it difficult to solve such problems using traditional single objective optimization algorithms. In this paper, we propose a new evolutionary algorithm based on the NSGA-II algorithm to efficiently solve the many-objective ecological optimization problems. The proposed algorithm uses three schemes to enhance the performance of NSGA-II algorithm to solve Many-objective ecological optimization problems. A new efficient sorting method, smart archive and simple local search are used to speed up the solutions convergence process to the POF and enhance the diversity of the solutions. The proposed method is general and can be adapted to a wide range of ecological optimization problems. The proposed algorithm is compared with the state-of-the-art multi-objective optimization algorithms using five DTLZ test problems. The results show that our proposed algorithm significantly outperforms the other algorithms when the number of objective functions is high. © 2018, Universidad del Zulia. All rights reserved.Öğe An efficient multi-objective memetic genetic algorithm for medical image handling and health safety to support systems in medical internet of things(Amer Scientific Publishers, 2020) Safi, Hayder H.; Uçan, Osman Nuri; Bayat, OğuzOver the most recent couple of decades, the Evolutionary Algorithms (EA) have been considered as a typical dynamic research zone in the field of medicinal picture preparing and wellbeing administrations. This is a direct result of the presence of numerous streamlining issues in this field which can be comprehended utilizing developmental calculations. Besides, numerous certifiable streamlining issues in the restorative picture handling and wellbeing administrations have more than one target work, and for the most part the issue goals are in struggle with one another. The traditional multi-objective transformative calculations perform well when the streamlining issue has less than three goal functions, where the execution of these calculations altogether degrades when the improvement issue has high number of destinations. To deal with this issue, there is a requirement for growing new versatile transformative streamlining mechanisms which can handle the high target capacities in the medicinal picture preparing and wellbeing administrations advancement issues. In this paper, we propose a new developmental calculation dependent on the NSGA-II calculation to productively take care of the many-target enhancement issues. The proposed calculation adds three plans to improve the capacity of NSGA-II calculation when managing with high objective dimensional streamlining issues. Another productive arranging strategy, savvy file and straightforward nearby pursuit are utilized to accelerate the solutions combination procedure to the POF and upgrade the decent variety of the arrangements. The proposed calculation is contrasted and the cutting edge multi-target advancement calculations utilizing five DTLZ test issues. The outcomes demonstrate that our proposed calculation essentially beats alternate calculations when the quantity of target capacities is high. Moreover, we applied our proposed algorithm on medical imaging health problem which is the melanoma recognition problem to enhance the early detection of melanoma disease.Öğe An intelligent fuzzy-induced recommender system for cloud-based cultural communities(Inderscience Publishers, 2019) Ravi, Logesh; Devarajan, Malathi; Jeon, Gwanggil; Bayat, Oğuz; Subramaniyaswamy, V.The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services. Copyright © 2019 Inderscience Enterprises Ltd.Öğe Bit error rate performance of in-vivo radio channel using maximum likelihood sequence estimation(Institute of Electrical and Electronics Engineers Inc., 2020) Mezher, Mohad; Ilyas, Muhammad; Bayat, Oğuz; Abbasi, Qammer H.In this paper we present the Bit Error Rate (BER) performance of equalizers using in-vivo channel response measured using Vector Network Analyzer (VNA). Including the use of a Bandwidth (BW) of 50 MHz in the simulations, the results are compared with multiple equalizers and it is shown that Maximum Likelihood Sequence Estimation (MLSE) equalizer outperformed the rest of the equalizers including linear equalizers Least Mean Square (LMS) and Recursive least sequence (RLS) and non-linear equalizer Decision Feedback Equalizer (DFE). The BER performance using MLSE showed significant improvement by improving the BER and outperforming the linear equalizer from 10-2 to 10-6 and DFE from 10-4 to 10-6 at text{Eb}/ text{No}= 14 dB for in vivo radio communication channel at ultra wideband (UWB) frequencies. Furthermore, the un-equalized and equalized channel frequency response spectrum is also part of this article which presents the overall improvement between the two spectrums. © 2020 IEEE.Öğe Brain tumor segmentation and classification approach for MR images based on convolutional neural networks(Institute of Electrical and Electronics Engineers Inc., 2020) Sameer, Mustafa A.; Bayat, Oğuz; Mohammed, Hussam J.Brain tumor considers one of the most dangerous types of cancers as it affects the main nervous system of the human body. Therefore, several techniques using computer vision has been proposed for early diagnoses and avoiding surgical intervention. However, these techniques face challenges in terms of segmentation and classification processes to detect the brain tumor within Magnetic resonance images (MRI). This paper proposes an automated system for detecting and classifying the brain tumor. This system composed of three phases is including enhancement, segmentation, and classification. The enhancement phase utilizes the Adaptive Histogram Equalization (AHE) in order to adjust the MRI images. The segmentation process was achieved using U-NET to segment the abnormal cells from normal brain tissue. The classification phase was conducted using 3D-CNN to classify the brain tumor into a High-Grade Glioma (HGG) and a Low-Grade-Glioma (LGG). Several experiments were conducted to validate the developed system using Brats-2015 dataset. The system achieved 99.7% (Dice Similarity Coefficient) DSC as an accurate rate for segmentation, and 96% and 98.5% an accurate rate using 5-fold and 10-fold, respectively. © 2020 IEEE.Öğe Breast sentinel lymph node cancer detection from mammographic images based on quantum wavelet transform and an atrous pyramid convolutional neural network(Hindawi Limited, 2022) Qasim, Mohammed N.; Mohammed, Tareq Abed; Bayat, OğuzThis study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.Öğe Classification of resting-state status based on sample entropy and power spectrum of electroencephalography (EEG)(Hindawi Ltd, 2020) Mohamed, Ahmed M. A.; Uçan, Osman Nuri; Bayat, Oğuz; Duru, Adil DenizAn electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.
