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Ambulance Vehicle Routing in Smart Cities Using Artificial Neural Network

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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 c ...Daha fazlası

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Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)javascript:;

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Compressing the image causes less memory to be used to store the images. Compressing images increases the transmission speed of compressed images in the network. Vector quantization (VQ) is one of the image compression methods. The challenge of the vector quantization method for compression is the non-optimization of the codebooks. Codebook optimization increases the quality of compressed images and reduces the volume of compressed images. Various methods of swarm intelligence and meta-heuristics are used to improve the vector quantization algorithm, but using meta-heuristic methods based on m ...Daha fazlası

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

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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 ...Daha fazlası

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

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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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Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

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Abstract The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further s ...Daha fazlası

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Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey

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

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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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Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods

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Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were creat ...Daha fazlası

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Fault Classification for Protection in MMC-HVDC Using Machine Learning Algorithms

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The problems in MMC-HVDC protection systems are categorized in this study using machine learning algorithms. The voltage and current data were utilized to determine the classification's features. With the use of the features derived from the voltage and current, machine learning (ML) and artificial machine learning (ML) have produced a defect locator that is accurate enough. Using this data, simulations of various fault types and unknown locations at different system points were run to anticipate the outcomes. Metrics including specificity, accuracy, and sensitivity were used to evaluate the e ...Daha fazlası

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

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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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Bitterling fsh optimization (BFO) algorith

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The bitterling fsh is a prime example of intelligent behavior in nature for survival. The bitterling fsh uses the oyster spawning strategy as their babysitter. The female bitterling fsh looks for a male fsh stronger than other fsh to fnd the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fsh. The bitterling fsh optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfy optimization algorithm, Harris Hawks optim ...Daha fazlası

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

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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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