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The Leukemia Healthy and Unhealthy Detection with Wavelet Transform Based On Co-Occurrence Matrix and Support Vector Machine

Javad Rahebi

Makale | 2021 | Avrupa Bilim ve Teknoloji Dergisi

Leukemia is a malignant disease and belongs in a broader sense to Cancers. There are many types of leukemia, each of which requires specific treatment. Leukemia is almost one-third of all cancer deaths in children and young people. The most common type of leukemia in children is acute lymphoblastic leukemia (ALL). In this paper, a new approach is implanted on Leukemia ALL database. For the method the wavelet transform is used for feature extraction, the gray level co-occurrence matrix is used. Also, for classification, the SVM (Support Vector Machine) method is used. The proposed method is the best in applying the system designed to . . . the Local Binary Pattern (LBP) and Histogram of Orientation (HOG) methods. This system aims to detect, diagnose, and verify leukemia cells from microscopic images to get high accuracy, efficiency, reliability, less processing time, smaller error, not complexity, fast, and easy to work. The system was built using microscopic images by examining changes in texture, colors, and statistical analysis. The success rate was 96.1667% for cancer data and 99.8833% for non-cancer data 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

Multi-Controller Model for Improving the Performance of IoT Networks

Javad Rahebi

Makale | 2022 | Energies

: Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with . . .multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall Daha fazlası Daha az

BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

Javad Rahebi


This article proposes an ambulance vehicle routing approach in smart cities. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). It aims to take transfer the patients confidentially, accurately, and quickly. The type of CNN used in this research is a residual network (ResNet). The node method is responsible for creating the city map. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The driver feeds the data that contain the ambulance vehicle's node position and the accident location to the BA-CNN vehicle r . . .outing algorithm. The algorithm then obtains the shortest path to reach the location of the accident by the driver. When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. The offline data are then trained by utilizing a neural network. The neural network is used for finding the shortest routes between source and destination. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV Daha fazlası Daha az

Ambulance Vehicle Routing in Smart Cities Using Artificial Neural Network

Javad Rahebi

Kitap Bölümü | 2022 | International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

This work proposes a routing ambulance vehicles method that uses the neural network. For the input of the neural network, eight features are selected. These features depend on the time, the position of the accident, ambulance and hospital, number of streets and injured person, type of accident, and age of the patient. With these features, the Ambulance can be decided to select the minimum route to find the nearest hospital. In this paper, we evaluate crucial metrics in responding to the accident, such as establishing temporary emergency units, the number of available ambulance units, and the city’s response and resources.

Harris Hawks Optimization Method based on Convolutional Neural Network for Face Recognition Systems

Javad Rahebi

Kitap Bölümü | 2022 | HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings

This paper discusses the momentum gradient dependent on the convolutional neural organization’s strong point. It is a new methodology introduced to detect evenness in the data set of faces. The proposed face recognition framework was created for various purposes. Through Gabor wavelet change, facial evenness was extracted from the face-preparing information. After that, we applied a profound learning process to carry out verification. After applying the proposed method to YALE and ORL data sets, we simulated them using MATLAB 2021a. Before this, similar trials were directly applied through Harris Hawks Optimization (HHO) for includi . . .ng the determination approach. The extraction process was conducted with many picture tests to execute the Gabor wavelet method, which proved more viable than other strategies applied in our examination. When we applied the HHO on the ORL dataset, the acknowledgment rate was 93.63%. It was 94.26% when the three techniques were applied to the YALE dataset. It shows that the HHO calculation improved the exactness rate to 96.44% in the case of the YALE dataset and 95.88% in the ORL datase Daha fazlası Daha az

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

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

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

Sulayman Ahmed | Mondher Frikha | Taha Darwassh Hanawy Hussein | Javad Rahebi

Makale | 2021 | HINDAWI LTD

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 implementation of Gabor wavelet feature extra . . .ction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42 while it is 92 with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22 for the ORL database and 94.66 for the YALE database Daha fazlası Daha az

Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for Heart Disease Diagnosis

Javad Rahebi

Bildiri | 2021 | 2021 IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2021

Signal processing methods usually diagnose heart disease, and the diagnosis of this type of disease by signal processing sometimes encounters many difficulties. To reduce diagnostic problems, careful feature selection and training are needed to analyze these signals. In this study, an attempt has been made to combine machine learning skills, such as neural network learning, with the Harris Hawks Optimization method to diagnose heart disease. In this paper, the heart disease diagnosis is analyzed with the feature selection method. For feature selection, the Harris Hawks Optimization Algorithm based on a fitting neural network is used . . .. First, the Harris Hawks Optimization algorithm was implemented on the data, and the sample features were randomly selected. Then the sample features are trained by a neural network, and the best features are selected. Results show that the proposed method's accuracy, sensitivity, and precision for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from MLP, SVM, RF, and AdaBoost. Daha fazlası Daha az

Hybrid Gray Wolf Optimization–Proportional Integral Based Speed Controllers for Brush-Less DC Motor

Javad Rahebi

Makale | 2023 | Article,Energies , pp.1 - 18

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading situations, and variable set speed situations, speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations and this is mostly because the characteristics of a Brush-less DC motor drive exhibit non linearity. This work proposes a hybrid gray wolf optimization and proportional integral controller for management of the speed in Brush-less DC motors to address this issue. For constant load conditions, varying load . . .situations and varying set speed situations, the proposed controller’s efficiency is evaluated and contrasted with that of PID controller, PSO-PI controller, and ANFIS. In this study, two PI controller are used to get the more stability of the system based on tuning of their coefficients with meta heuristic method. The simulation findings show that Hybrid GWO-PI-based controllers are in every way superior to other controllers under consideration. In this study, four case studies are presented, and the best-case study was obtained 0.18619, 0.01928, 0.00030, and 0.01233 for RMSE, IAE, ITAE, and ISE respectively Daha fazlası Daha az

Ambulance Vehicle Routing using BAT Algorithm

Javad Rahebi

Bildiri | 2021 | 3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021

In this paper, ambulance vehicles are routed using BAT algorithm. The city map is created by node method. The control station receives the information about accident place and then this information is communicated to the ambulance and hospital. The drive feed the data i.e., node position of the accident and ambulance vehicle in the bat algorithm based vehicle routing method and it provides shortest path for reaching accident place to driver. After reaching accident place, drive feed the position of the accident place and hospital position in the bat algorithm vehicle routing method and it provide shortest path for reaching hospital . . .to driver. Shortest bath and quick reach time is generated using this algorithm. Daha fazlası Daha az

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