This research makes use of two different approaches to compress medical images for long-term purposes. In the first method, images are compressed using the Huffman cipher and then simplified using a hierarchical modeling based on a neural network-designed categorization More
This research makes use of two different approaches to compress medical images for long-term purposes. In the first method, images are compressed using the Huffman cipher and then simplified using a hierarchical modeling based on a neural network-designed categorization. A prediction strategy based on deep neural network training is employed in the second method. This technique uses a trained neural network to infer the locations of individual pixels, hence reducing the amount of data required to describe a picture. Huffman compression encryption is used on the leftover data. An enhanced spatial filtering technique is used to decode the picture data, and the wild horse optimization and gray wolf optimization meta-heuristic algorithms are then used to produce a rebuilt image. Without compromising compression efficiency, this allows for a more realistic application of the suggested solutions in non-deterministic contexts. The suggested approaches allow for picture simplification, which has resulted in faster decoding. Structural similarity index modulation, time and peak signal-to-noise ratio have been improved by an average of 2, 30.1 and 15.15%, respectively. The suggested algorithms were able to compress medical photos with very high quality level, as compared to the current deep learning-based methods.
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In this paper, a dynamic mutant version of the gray wolf optimization algorithm (MGWO) is proposed to solve the economic-environmental dispatch (E-ED) problem of a standard 40-unit power system with two wind farms. Thus, a comprehensive objective function of operating c More
In this paper, a dynamic mutant version of the gray wolf optimization algorithm (MGWO) is proposed to solve the economic-environmental dispatch (E-ED) problem of a standard 40-unit power system with two wind farms. Thus, a comprehensive objective function of operating costs is presented, which is a combination of wind energy costs, over-estimated penalty costs, under-estimated penalty costs, thermal unit costs and emission costs. Due to the random nature of wind speed, the power generated by wind turbines is unpredictable. Therefore, the Weibull probability distribution function has been used to model the wind farm power in this paper. The cost of operating a wind farm is considered probabilistic so that low-probability wind scenarios have less effect on the total operation cost. The simulations are performed in the form of three section and the optimization results are compared with several meta-heuristic algorithm results for validation. The results of the optimizations in all three scenarios and its comparison with other algorithms confirm the better performance and higher accuracy of the proposed MGWO algorithm than the original version of the gray wolf algorithm (GWO) as well as other algorithms.
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One of the most important challenges of the expansion of the Internet and virtual space is cyber-attacks. These attacks are becoming new every day and it is becoming more difficult to deal with them. As a result, methods should be used to detect them, which can detect a More
One of the most important challenges of the expansion of the Internet and virtual space is cyber-attacks. These attacks are becoming new every day and it is becoming more difficult to deal with them. As a result, methods should be used to detect them, which can detect all types of cyber-attacks in the shortest possible time and with proper accuracy. Nowadays, machine learning methods are usually used to detect cyber-attacks. But since the data related to cyber-attacks have many characteristics and are kind of bulky data, as a result, the accuracy of conventional machine learning methods to detect them is usually low. In this research, we have used a hybrid feature selection method to select optimal features from the database related to cyber-attacks, which increases the accuracy of attack detection by classification models. In the proposed feature selection method, first the features that have the least redundancy with each other and at the same time are most related to the category variables (labels) are selected by the MRMR algorithm. Then, using a wrapper feature selection method based on the gray wolf optimization (GWO) algorithm to select a subset of the features selected from the previous step, which maximizes the accuracy of the SVM classifier model, is used this subset has optimal features by which the SVM model is trained. As a result, the accuracy of detecting cyber-attacks by the SVM model increases. According to the simulation results, the average accuracy of the proposed method for detecting cyber-attacks is 99.84%, which has improved compared to the intrusion detection methods of the reference article.
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