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Öğe A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model(Springer Heidelberg, 2020) Abdullah, Ahmad S.; Rahebi, Javad; Özok, Yasa Ekşioğlu; Aljanabi, MohanadIn this paper, a new approach is proposed for localization and segmentation of the optic disc in human retina images. This new approach can find the boundary of the optic disc by an initial fuzzy clustering means algorithm. The proposed approach uses active contour model evolution based on a fuzzy clustering algorithm. The robustness of the proposed approach was evaluated with retinal imaging medical databases, such as DRIVE, STARE, DIARETDB1, and DRIONS. These bases contained images affected by different abnormalities, for example, diabetes, retinitis pigmentosa, and age-related macular degeneration AMD. A success detection rate with 100% accuracy was achieved for the DRIVE, DIRATEDB1, and DRIONS-DB databases, and 97.53% for the STARE database. For the optic disc segmentation, the method achieved an average accuracy and overlap in the range of 97.01-99.46% and 78.35-84.56% in these four databases. The result was compared with various methods in the literature, and it was concluded that the proposed method is more accurate than the other existing methods.Öğe A new approach in energy consumption based on genetic algorithm and fuzzy logic for wsn(Springer, 2021) Alwafi, A.A.W.; Rahebi, Javad; Farzamnia, A.Although the sensor node is tiny, it covers large areas by connecting these nodes together wirelessly, it called wireless sensor network (WSN). WSNs are one of the common things that still evolving very fast nowadays. Routing protocols challenge the energy consumption of wireless sensor networks. In this paper, we proposed a new Fuzzy Logic and Genetic Algorithm based protocol (FL-GA) for WSNs, as follows, we used the fuzzy logic Mamdani method for finding the best cluster heads. We used two inputs for fuzzy, energy and distance, we used the Genetic Algorithm for optimization. Taking into account variable parameters, the choice of cluster heads will be more efficient and the cluster forming will be more accurate, all the nodes will almost die at the same time. One of the classic routing protocols is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. We compared our protocol to the LEACH protocol. Our network nods, still alive much more than the LEACH protocol nodes. The proposed method is more efficient in extending the network lifetime and maximizing the total number of data packets received in the sink. © Springer Nature Singapore Pte Ltd 2021.Öğe A novel method for retinal optic disc detection using bat meta-heuristic algorithm(Springer Heidelberg, 2018) Abdullah, Ahmad S.; Özok, Yasa Ekşioğlu; Rahebi, JavadNormally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5-88.2% and 96.6-99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time.Öğe A study of deep neural network controller-based power quality improvement of hybrid PV/Wind systems by using smart inverter(Hindawi Ltd, 2020) Ab-BelKhair, Adel; Rahebi, Javad; Abdulhamed Mohamed Nureddin, AbdulbasetPresently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.Öğe Blood vessel segmentation and extraction using H-minima method based on image processing techniques(Springer, 2021) Boubakar Khalifa Albargathe, Salma M.; Kamberli, Ersin; Kandemirli, Fatma; Rahebi, JavadIn this paper, the H-minima transform is used for blood vessel segmentation. The aim of this study is to get the high accuracy of blood vessel segmentation in retinal images. In this study the good result and good performance were got. We compared our result with other methods. Also for simulation result we implemented on DRIVE and STARE database. The proposed method shows very remarkable performance on pathological retinal images. For the implementing of the proposed method MATLAB 2019a software is used. The running time of this method was 1 s for each image and the average accuracy for STARE dataset and DRIVE dataset achieved to 0.9591 and 0.9672 respectively.Öğe Cancer cell detection through histological nuclei images applying the hybrid combination of artificial bee colony and particle swarm optimization algorithms(Atlantis Press, 2020) Alsarori, Faozia Ali; Kaya, Hilal; Rahebi, Javad; Popescu, Daniela E.; Hemanth, D. JudeCancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never been used in any relevant study; so, in this study we purposed using this algorithm for detecting the centers of the nuclei with the help of histological images to obtain accurate results. If we compare this algorithm with previously proposed algorithms, this algorithm doesn't require a lot of parameters, and besides, it is faster, simpler, and more flexible. This study has been carried out on histological images obtained from a database containing 810 microscopic slides of stained H&E samples from PSB-2015 crowd-sourced nuclei dataset. During the determination process, the noise on images was first eliminated using morphological techniques, and then, we used Hybrid PSO-ABC algorithm to for segmentation of the nucleic images and compared the results with other optimization algorithms to test its accuracy and efficiency. The average 99.38% accuracy rate was assured for cancer nuclei. To demonstrate the robustness of this experiment, the results were compared with other known cancer nuclei detection procedures, which are already mentioned in the literature. Using the new proposed algorithm showed the highest accuracy when it was compared to rest of the methods. The high value outcome confirms that the suggested method outperformed as compared to other algorithms because it shows a higher distinctive ability. (C) 2020 The Authors. Published by Atlantis Press B.V.Öğe Detection of attacks on wireless sensor network using genetic algorithms based on fuzzy(Diponegoro Univ, 2019) Al Hayali, Shaymaa; Uçan, Osman Nuri; Rahebi, Javad; Bayat, OğuzIn this paper an individual - suitable function calculating design for WSNs is conferred. A multi-agent-located construction for WSNs is planned and an analytical type of the active combination is built for the function appropriation difficulty. The purpose of this study is to identify the threats identified by clustering genetic algorithms in clustering networks, which will prolong network lifetime. In addition, optimal routing is done using the fuzzy function. Simulation results show that the simulated genetic algorithm improves diagnostic speed and improves energy consumption. (c) 2019. CBIORE-IJRED. All rights reservedÖğe Diagnosis of multiple sclerosis disease in brain magnetic resonance ımaging based on the harris hawks optimization algorithm(Hindawi, 2021) Iswisi, Amal F. A.; Rahebi, JavadThe damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.Öğe Human retinal optic disc detection with grasshopper optimization algorithm(2022) Rahebi, JavadA growing number of qualified ophthalmologists are promoting the need to use computer-based retinal eye processing image recognition technologies. There are different methods and algorithms in retinal images for detecting optic discs. Much attention has been paid in recent years using intelligent algorithms. In this paper, in the human retinal images, we used the Grasshopper optimization algorithm to implement a new automated method for detecting an optic disc. The clever algorithm is influenced by the social nature of the grasshopper, the intelligent Grasshopper algorithm. Include this algorithm; the population contains the grasshoppers, each of which has a common luminance or exercise score. In this method, two-by-two insects are compared, so it could be shown that less attractive insects shift towards more attractive insects. Finally, one of the most attractive insects is selected, and this insect gives an optimum solution to the problem. Here, we used the light intensity of the retinal pixels instead of grasshopper illuminations. According to local variations, the effect of these insects also indicates different light intensity values in images. Since the brightest area "represents the optic disc in retinal images, all insects travel to the brightest area, which leads to the determined position for an optic disc in the image. The performance was evaluated on 210 images, reflecting three Open to the public and sequentially distributed datasets DIARETDB1 89 images, STARE 81 images, and DRIVE 40 images. The results of the proposed algorithm implementation give a 99.51% accuracy rate in the DiaRetDB1 dataset, 99.67% in the STARE dataset, and 99.62% in the DRIVE dataset. The results of the implementation show the strong capacity and accuracy of the proposed algorithm for detecting the optic disc from retinal images. Also, the recorded time required for (OD) detection in these images is180.14 s for the DiaRetDB1, 65.13s for STARE, and 80.64s for DRIVE, respectively. These are average values for the times.Öğe Increasing energy efficiency in wireless sensor networks using GA-ANFIS to choose a cluster head and Assess Routing and Weighted Trusts to demodulate attacker nodes(Springer, 2020) Al Hayali, Shaymaa; Rahebi, Javad; Uçan, Osman Nuri; Bayat, OğuzDemodulating harmful nodes and diminishing the energy waste in sensor nodes can prolong the lifespan of wireless sensor networks (WSNs). In this study, a genetic algorithm (GA) and an adaptive neuro fuzzy inference system were used to diminish the energy waste of sensors. Weighted trust evaluation was applied to search for harmful nodes in the network to prolong the lifespan of WSNs. A low-energy adaptive clustering hierarchy method was used to analyze the results. It was discovered that searching for harmful nodes with GA-ANFIS using weighted trust evaluation significantly increased the lifespan of WSNs. For evaluation of the proposed method we used the mean of energy of all sensors against of the round, data packets received in base station, minimum energy versus rounds and number of alive sensors versus rounds. Also, in this paper we compared the proposed method results with LEACH, LEACH-DT, Random, SIF and GA-Fuzzy methods. As results the proposed method has high life time than other methods. A representation of the overall system was implemented using MATLAB software.Öğe Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm(Springer, 2021) Al-Rahlawee, Anfal Thaer Hussein; Rahebi, JavadOne of the most important methods of image processing is image thresholding, which is based on image histogram analysis. These methods analyze the image histogram diagram and try to present optimal values for the image thresholds so that the image regions can be distinguished by these thresholds. Thresholding is a popular method in image processing and is used in most research related to image segmentation due to its accuracy and efficiency. Multi-level thresholding, such as the Otsu method, is one of the most common methods of thresholding image processing. These methods have high computational complexity despite their accuracy and efficiency. When the number of thresholds used increases, these methods lose their efficiency due to increased complexity and execution time. One of the ways to find thresholds in the Otsu threshold method is to use metaheuristic algorithms such as the Black Widow Spider Optimization Algorithm. These algorithms can find the appropriate thresholds for the image at the logical time. In the proposed method, each threshold is a component or one dimension of a solution of the Black Widow Spider Optimization Algorithm, and an attempt is made to calculate the optimal threshold value without high complexity by this algorithm. Experiments on several standard images show that the proposed algorithm finds better thresholds than the particle swarm optimization algorithm, the firefly algorithm, the genetic algorithm, and the gray wolf optimization algorithm. The analysis shows that the proposed method in the PSNR index has a better value in 83.33% of the experiments than other algorithms and also in 80% of the experiments the proposed method has a better SSIM index than these methods. Analysis of the proposed algorithm on several pertussis images also shows that the proposed method has a good ability to threshold medical images such as brain tumors and optic disc detection in human retinal images.Öğe A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model (December, 10.1007/S11517-019-02032-8, 2019)(Springer Heidelberg, 2020) Abdullah, Ahmad S.; Rahebi, Javad; Ozok, Yasa Eksioglu; Aljanabi, Mohanad[No abstract available]Öğe New auxiliary function with properties in nonsmooth global optimization for melanoma skin cancer segmentation(Hindawi Ltd, 2020) Masoud Abdulhamid, Idris A.; Sahiner, Ahmet; Rahebi, JavadIn this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.Öğe Power management controller for microgrid integration of hybrid PV/Fuel cell system based on artificial deep neural network(Hindawi Ltd, 2020) Nureddin, Abdulbaset Abdulhamed Mohamed; Rahebi, Javad; Ab-BelKhair, AdelNowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system's effectiveness.Öğe Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm(Mdpi, 2018) Aljanabi, Mohanad; Özok, Yasa Ekşioğlu; Rahebi, Javad; Abdullah, Ahmad S.The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95.24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.
