Maximizing energy storage in Microgrids with an amended multi-verse optimizer.

Microgrids have emerged as a possible alternative to overcome the difficulties of the combined cooling, heating, and power (CCHP) system in power networks. Energy storage devices are vital for the stable and effective functioning of Microgrids. In this paper, a new modified metaheuristic technique, called the Amended Multiverse Optimizer algorithm (AMVOA) is used to suggest a new method of Microgrid design with energy storage. The Multiverse theory notion served as the inspiration for the metaheuristic optimization method known as the AMVOA. The suggested strategy takes into account the load demand, energy storage technologies, and architecture of a Microgrid with renewable energy sources. The goal is to keep the Microgrid's overall cost as low as possible while preserving its dependability and sustainability. To validate the efficiency of the proposed method, two HRES scenarios are put out, the first of which relies on PV, wind, diesel, and battery power, and the second of which uses PV, diesel, and battery power. To validate the superiority of the proposed method, the method has been compared with five state-of-the-art algorithms, including the Evolutionary Algorithm (EA), Modified Grasshopper Optimization Algorithm (MGOA), Improved Gray Wolf Optimization Algorithm (IGWOA), Improved Arithmetic Optimization Algorithm (IAOA), and the original MVOA. The study compares two scenarios: one with wind, PV, diesel, and battery power and the other with only PV, diesel, and battery power. In scenario 1 (Wind/PV/DG/BESS), the AMVOA algorithm achieves optimal results, resulting in a Net Present Cost (NPC) of $299,010 and an energy cost of $0.2309 per kilowatt-hour. The proposed technique successfully integrates 84.86 % renewable energy sources while meeting defined limitations. The optimal sizing for scenario 2 (PV/DG/BESS) is $333,800 with an energy cost of $0.3451 per kilowatt-hour. The AMVOA algorithm outperforms other algorithms in convergence and provides efficient power management. However, further analysis and evaluation are necessary to assess the robustness, practicality, and reliability of the proposed Microgrid configurations. The outcomes show how the suggested AMVO-based strategy may be used to create the best Microgrid architecture with energy storage. The recommended method may be applied as a decision-making tool for Microgrid planning and design, especially for the integration of renewable energy.

A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model.

An intrusion detection system (IDS) is designed to detect and analyze network traffic for suspicious activity. Several methods have been introduced in the literature for IDSs; however, due to a large amount of data, these models have failed to achieve high accuracy. A statistical approach is proposed in this research due to the unsatisfactory results of traditional intrusion detection methods. The features are extracted and selected using a multilayer convolutional neural network, and a softmax classifier is employed to classify the network intrusions. To perform further analysis, a multilayer deep neural network is also applied to classify network intrusions. Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four performance metrics: accuracy, recall, F1-score, and precision. The experimental results show that the proposed approach achieved better accuracy (99%) compared with other IDSs.

Interval linear quadratic regulator and its application for speed control of DC motor in the presence of uncertainties.

An analytical investigation of a DC motor with interval uncertainties is performed in this study and a new approach by interval analysis is suggested for optimal control of the system. The main advantage of using an interval model for uncertainties is that makes the system independent from the probability distribution models of the system; therefore, it can be analyzed by only having information about minimum and maximum bounds. Here, the interval analysis deals with linear quadratic feedback control (LQR) to simulate and optimal control of the DC motor in the realistic state. To do this, the Pontryagins principle is used to solve the interval linear quadratic regulator to obtain the essential conditions, and thus, they have been reconstructed as ordinary differential equation by applying several algebraic manipulations. Afterward, by solving the interval nonlinear system of the ODE, the confidence interval for the feedback controller is achieved. The confidence interval is to guarantee the solution which is included in it. The Chebyshev inclusion approach is applied here to find solution for the ODE system with uncertainties. A comparison of the step response of the suggested approach with the centered approach and Monte Carlo methods a statistical approach is performed. The simulation results indicated that the suggested approach retains tighter and more sensible results than the Monte Carlo method.

Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm.

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.

Computer-aided diagnosis of skin cancer based on soft computing techniques.

Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

Computer-aided Diagnosis of Skin Cancer: A Review.

Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.

A Hybrid Neural Network - World Cup Optimization Algorithm for Melanoma Detection.

One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world's FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP) employs the problem's constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.