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Koleksiyon [5]
Tam Metin [2]
Yazar [20]
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Konu Başlıkları [20]
Yayın Yılı [4]
Yayıncı [20]
Dil [4]
Dergi [19]
Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm

Javad Rahebi

Makale | 2023 | DIAGNOSTICS 13 ( 10 ) , pp.1 - 14

This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm t . . .o select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others Daha fazlası Daha az

Radiomics method in the differential diagnosis of diabetic foot osteomyelitis and charcot neuroarthropathy

Gökalp Tulum

Makale | 2023 | The British journal of radiology , pp.1 - 11

Objectives: Our study used a radiomics method to differentiate bone marrow signal abnormality (BMSA) between Charcot neuroarthropathy (CN) and osteomyelitis (OM). Materials and Method: The records of 166 patients with diabetic foot suspected CN or OM between January 2020 and March 2022 were retrospectively examined. A total of 41 patients with BMSA on MRI were included in this study. The diagnosis of OM was confirmed histologically in 24 of 41 patients. We clinically followed 17 patients as CN with laboratory tests. We also included 29 nondiabetic patients with traumatic (TR) BMSA on MRI as the third group. Contours of all BMSA on T . . .1 and T2-weighted images in three patient groups were segmented semi-automatically on ManSeg (v.2.7d). The T1 and T2 features of three groups in radiomics were statistically evaluated. We applied multi-class classification (MCC) and binary-class classification (BCC) methodology to compare classification results. Results: For MCC, the accuracy of Multi-Layer Perceptron (MLP) was 76.92% and 84.38% for T1 and T2, respectively. According to BCC, for CN, OM and TR BMSA, the sensitivity of MLP is 74%, 89.23%, and 76.19% for T1, and 90.57%, 85.92%, 86.81% for T2, respectively. For CN, OM, and TR BMSA, the specificity of MLP is 89.16%, 87.57%, and 90.72% for T1 and 93.55%, 89.94%, and 90.48% for T2 images, respectively. Conclusion: In the diabetic foot, the radiomics method can differentiate the BMSA of CN and OM with high accuracy. Advances in knowledge: The radiomics method can differentiate the BMSA of CN and OM with high accuracy Daha fazlası Daha az

Fishier mantis optimiser: a swarm intelligence algorithm for clustering images of COVID-19 pandemic

Javad Rahebi

Makale | 2023 | International Journal of Nanotechnology20 ( 1-4 ) , pp.25 - 45

In this study, an automated segmentation method is used to increase the speed of diagnosis and reduce the segmentation error of CT scans of the lung. In the proposed technique, the fishier mantis optimiser (FMO) algorithm is modelling and formulated based on the intelligent behaviour of mantis insects for hunting to create an intelligent algorithm for image segmentation. In the second phase of the proposed method, the proposed algorithm is used to cluster scanned image images of COVID-19 patients. Implementation of the proposed technique on CT scan images of patients shows that the similarity index of the proposed method is 98.36%, . . . accuracy is 98.45%, and sensitivity is 98.37%. The proposed algorithm is more accurate in diagnosing COVID-19 patients than the falcon algorithm, the spotted hyena optimiser (SHO), the Grasshopper optimisation algorithm (GOA), the grey wolf optimisation algorithm (GWO), and the black widow optimisation algorithm (BWO) Daha fazlası Daha az

A memetic animal migration optimizer for multimodal optimization

Taymaz Rahkar Farshi


Unimodal optimization algorithms can find only one global optimum solution, while multimodal ones have the ability to detect all/most existing local/global optima in the problem space. Many practical scientific and engineering optimization problems have multiple optima to be located. There are a considerable number of optimization approaches in the literature to address the unimodal problems. Although multimodal optimization methods have not been studied as much as the unimodal ones, they have attracted an enormous amount of attention recently. However, most of them suffer from a common niching parameter problem. The main difficulty . . . faced by existing approaches is determining the proper niching radius. Determining the appropriate radius of the niche requires prior knowledge of the problem space. This paper proposes a novel multimodal optimization scheme that does not face the dilemma of having prior knowledge of the problem space as it does not require the niching parameter to be determined in advance. This scheme is the extended version of the unimodal animal migration optimization (AMO) algorithm that has the capability of taking advantage of finding multiple solutions. Like other multimodal optimization approaches, the proposed MAMO requires specific modifications to make it possible to locate multiple optima. The local neighborhood policy is modified to adapt the multimodal search by utilizing Coulomb's law. Also, Coulomb's law is also applied to decide the movement direction of the individuals. Hence, instead of moving an individual toward the two randomly chosen individuals, it moves toward the near and good enough two neighborhoods. Additionally, a further local search step is performed to improve the exploitation. To investigate the performance of the MAMO, the comparisons are conducted with five existing multi-modal optimization algorithms on nine benchmarks of the CEC 2013 competition. The experimental results reveal that the MAMO performs success in locating all or most of the local/global optima and outperforms other compared methods. Note that the source codes of the proposed MAMO algorithm are publicly available at Daha fazlası Daha az

Some New Results on Bicomplex Bernstein Polynomials

Seda Karateke | Cigdem Atakut | Oezge Ozalp Guller | Carlo Cattani

Makale | 2021 | MDPI

The aim of this work is to consider bicomplex Bernstein polynomials attached to analytic functions on a compact C2-disk and to present some approximation properties extending known approximation results for the complex Bernstein polynomials. Furthermore, we obtain and present quantitative estimate inequalities and the Voronovskaja-type result for analytic functions by bicomplex Bernstein polynomials.

A multi-modal bacterial foraging optimization algorithm

Taymaz Rahkar Farshi | Mohanna Orujpour


In recent years, multi-modal optimization algorithms have attracted considerable attention, largely because many real-world problems have more than one solution. Multi-modal optimization algorithms are able to find multiple local/global optima (solutions), while unimodal optimization algorithms only find a single global optimum (solution) among the set of the solutions. Niche-based multi-modal optimization approaches have been widely used for solving multi-modal problems. These methods require a predefined niching parameter but estimating the proper value of the niching parameter is challenging without having prior knowledge of the . . .problem space. In this paper, a novel multi-modal optimization algorithm is proposed by extending the unimodal bacterial foraging optimization algorithm. The proposed multi-odal bacterial foraging optimization (MBFO) scheme does not require any additional parameter, including the niching parameter, to be determined in advance. Furthermore, the complexity of this new algorithm is less than its unimodal form because the elimination-dispersal step is excluded, as is any other phase, like a clustering or local search algorithm. The algorithm is compared with six multi-modal optimization algorithms on nine commonly used multi-modal benchmark functions. The experimental results demonstrate that the MBFO algorithm is useful in solving multi-modal optimization problems and outperforms other methods Daha fazlası Daha az

Multilevel image thresholding with multimodal optimization

Taymaz Rahkar Farshi | Recep Demirci

Makale | 2021 | SPRINGER

Thresholding method is one of the most popular approaches for image segmentation where an objective function is defined in terms of threshold numbers and their locations in a histogram. If only a single threshold is considered, a segmented image with two classes is achieved. On the other hand, multiple classes in the output image are created with multilevel thresholding. Otsu and Kapur's procedures have been conventional steps for defining objective functions. Nevertheless, the fundamental problem with thresholding techniques is the determination of threshold numbers, which must be selected by the user. In that respect, thresholding . . . methods with both techniques are user-dependent, and may not be practical for real-time image processing applications. In this study, a novel thresholding algorithm without any objective function has been proposed. Histogram curve was considered as an objective function. The peaks and valley in histogram have been detected by means of multimodal particle swarm optimization algorithms. Accordingly, valleys between two peaks have been assigned as thresholds. Consequently, the developed scheme does not need any user intervention and finds the number of thresholds automatically. Furthermore, computation time is independent of the number of thresholds, whereas computation time in Otsu and Kapur procedures depends on the number of thresholds Daha fazlası Daha az

Grid connected photovoltaic system design an example application for İstanbul province

Vedat Esen

Makale | 2023 | INTERNATIONAL JOURNAL OF ENERGY STUDIES8 ( 2 ) , pp.189 - 200

It is seen that the damage to the environment has increased with the use of fossil fuels around the world. It is known that studies continue to minimize the damage to the environment with alternative energy generation methods. Recently, it is seen that generating electrical energy using solar energy, known as clean energy, has an important place. With the developing semiconductor technologies, the use of photovoltaic systems is increasing day by day. The aim of this study is to estimate the amount of energy that will be produced by simulating and modeling the performance of PV (Photovoltaic) systems using PVsyst and PV*SOL programs . . .before the Photovoltaic systems are installed in the region. In the study, grid-connected roof system modeling was made in Bakırköy district of Istanbul province. In the modeling of the system, a total of 90 solar panels were placed on an area of 114.9 m2 , in East and West directions. In total, it is predicted that 17.1 kW of energy will be obtained when the system is used. In the system design, the avoided CO₂ emission is calculated as 8,856 kg/year and the amortization period is calculated as 7.2 years. When the programs are used, the analysis of the system is made before the implementation and it is seen that time and cost savings are achieved Daha fazlası Daha az



Makale | 2022 | Applied Computer Science , pp.189 - 200

Increasing dependence on Information and Communication Technologies (ICT) and especially on the Internet in Industrial Control Systems (ICS) has made these systems the primary target of cyber-attacks. As ICS are extensively used in Critical Infrastructures (CI), this makes CI more vulnerable to cyber-attacks and their protection becomes an important issue. On the other hand, cyberattacks can exploit not only software but also physics; that is, they can target the fundamental physical aspects of computation. The newly discovered RowHammer (RH) fault injection attack is a serious vulnerability targeting hardware on reliability and sec . . .urity of DRAM (Dynamic Random Access Memory). Studies on this vulnerability issue raise serious security concerns. The purpose of this study was to overview the RH phenomenon in DRAMs and its possible security risks on ICSs and to discuss a few possible realistic RH attack scenarios for ICSs. The results of the study revealed that RH is a serious security threat to any computerbased system having DRAMs, and this also applies to ICS Daha fazlası Daha az

Vector quantization using whale optimization algorithm for digital image compression

Javad Rahebi

Makale | 2022 | SPRINGER

Today, much of the information is storing in images. To transfer information in the form of images, image compression is required. Compressing images reduces the size of images and sends them faster over the network. One of the most methods of image compression is the vector quantization. For vector quantization compression, the codebook is using in cryptography and decryption. The vector quantization compression method typically uses codebooks that are not optimized, which reduces the compression quality of the images. Choosing the optimal codebook makes compression of images with higher quality. Choosing the optimal codebook is a . . .difficult optimization problem and therefore requires intelligent algorithms to solve it. In this paper, the whale optimization algorithm is used to find the optimal codebook in image compression. Whale Optimization Algorithm has different search strategies and is an ideal algorithm for finding the optimal codebook in images compression. Implementation of the proposed algorithm for compression on several standard images shows that the proposed method compresses images with appropriate quality. The proposed method performs more efficient compression than the proposed algorithms such as particle swarm optimization, bat, and firefly algorithms. The signal-to-noise ratio of the proposed method is higher than the compared methods. Experiments on a set of standard images show the proposed method compared to the Fire Fly, Bat, and Differential evolution, Improved Particle Swarm Optimization, and Improved Differential Evolution methods with a compression execution time of 60.48 and 10.21, respectively. , 4.79, 5.09 and 3.94 decreased. The proposed method in compression has a higher PSNR index of about 17 than the Linde-Buzo-Gray method Daha fazlası Daha az

Otomatik gerilim regülatörü için hibrit bir denetleyici tasarımı

Güngör BAL

Makale | 2023 | Gazi Üniversitesi POLİTEKNİK DERGİSİ JOURNAL of POLYTECHNIC26 ( 1 ) , pp.199 - 207

Senkron generatörler elektrik enerjisinin üretiminde temel makina olma görevini sürdürmektedir. Senkron generatörlerin çıkış gerilimi ve frekansı uyartım akımı ve devir sayısı değiştirilerek kolayca ayarlanabilmektedir. Genellikle senkron generatörler şebekeye bağlı olarak çalıştıkları için çıkış gerilimi ve frekansının sabit olması gerekmektedir. Gerilimin ayarlanması için kullanılan Otomatik Gerilim Regülatörlerinde çeşitli denetleyici sistemler kullanılmaktadır. Bu çalışmada MATLAB/Simulink programında bulanık mantık tabanlı anahtarlamalı bir hibrit denetleyici yapısı önerilmiştir. Önerilen hibrit denetleyici, Yapay Arı Kolonisi . . .Algoritması kullanılarak optimize edilen PID denetleyiciyle ve Ziegler-Nichols yöntemine dayalı farklı denetleyicilerden elde edilen sonuçlarla karşılaştırılmıştır. Karşılaştırma kriterleri maksimum aşım miktarı, yükselme zamanı ve oturma zamanı olarak belirlenmiştir. Karşılaştırmanın sonuçları da çok kriterli karar verme tekniklerinden biri olan TOPSIS metodu ile analiz edilerek değerlendirilmiş ve sunulmuştur. Synchronous generators are still the main machines in the production of electrical energy. Changing the excitation current and the rotation speed easily adjust their output voltage and frequency. Since synchronous generators are operated with the grid, the output voltage and frequency must be fixed. Various control systems are used in Automatic Voltage Regulators for adjusting the voltage. In this study, a hybrid controller structure proposed and it operates in MATLAB / Simulink program. The simulation results of the proposed AVR is compared with PID adjusted by Artificial Bee Colonies algorithm and Ziegler-Nichols based different controllers. The comparison was made on maximum overshoot, rise and settling time. According to the comparison made, it was seen that the proposed hybrid controller has better response than the other controllers in terms of maximum overshoot, rise and settling Daha fazlası Daha az

Human retinal optic disc detection with grasshopper optimization algorithm

Javad Rahebi

Makale | 2022 | Multimedia Tools and Applications

A growing number of qualified ophthalmologists are promoting the need to use computer-based retinal eye processing image recognition technologies. There are differ- ent 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 grass . . .hoppers, 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. Accord- ing 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 Daha fazlası Daha az

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