A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm.

Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

Development of renewable energy fed three-level hybrid active filter for EV charging station load using Jaya grey wolf optimization.

This work develops a hybrid active power filter (HAPF) in this article to operate in conjunction with the energy storage system (ESS), wind power generation system (WPGS), and solar energy system (SES). It employs three level shunt voltage source converters (VSC) connected to the DC-bus. Optimization of the gain values of the fractional-order proportional integral derivative controller (FOPIDC) and parameter values of the HAPF is achieved using the Jaya grey wolf hybrid algorithm (GWJA). The primary objectives of this study, aimed at enhancing power quality (PQ), include: (1) ensuring swift stabilization of DC link capacitor voltage (DCLCV); (2) reducing harmonics and improving power factor (PF); (3) maintaining satisfactory performance under different combinations of loads like EV charging load, non linear load and solar irradiation conditions. The proposed controller's performance is evaluated through three test scenarios featuring different load configurations and irradiation levels. Additionally, the HAPF is subjected to design using other optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to assess their respective contributions to PQ improvement.

Power enhancement in PV arrays under partial shaded conditions with different array configuration.

Solar Photovoltaic systems are used for electrical power generation, and they provide an alternative source to non-renewable energy sources like coal, oil, natural gas and nuclear energy. Photovoltaic arrays used in PV systems may be subjected to partial shading conditions, thereby affecting power generation because of higher power mismatch losses. Due to an uneven distribution of irradiation condition, some of the bypass diodes turned on and affect the power generation in a photovoltaic array. The mismatch losses are due to the output from PV panels subjected to different irradiations because of non-uniform partial shading conditions. The power loss can be reduced by uniformly distributing the partially shaded condition over the entire PV array. In this work different shaped 4 × 4 array configuration is proposed to overcome the effect of partial shading condition, thereby providing lower mismatch losses. Simulations under different partial shading conditions are carried out using MATLAB Simulink, and the experimental setupis carried out for the proposed array configuration for 4 × 4 PV array and the results are discussed.

Solar photovoltaic converter controller using opposition-based reinforcement learning with butterfly optimization algorithm under partial shading conditions.

The major use of a power point tracking controller is to maximize or enhance the power generation in photovoltaic systems. These systems are steered to operate and maximize the power point. Under partial shading conditions, the power points may vary or fluctuate between global maxima and local maxima. This fluctuation leads to a decrease in energy or energy loss. Hence, to address the fluctuation issue and its variations, a new hybridized maximum power point tracking technique based on an opposition-based reinforcement learning approach with a butterfly optimization algorithm has been proposed. The proposed methodology has been tested on 6S, 3S2P and 2S3P photo-voltaic configurations under different shading conditions. Performance comparison and analysis have been presented with a butterfly optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and particle swarm optimization-based maximum power point tracking techniques. Experimental results show that the proposed method performs better adaptation than the conventional approaches and mitigates the load variation convergence and frequent exploration and exploitation patterns.

Power enhanced solar PV array configuration based on calcudoku puzzle pattern for partial shaded PV system.

The power output of solar photovoltaic systems can be affected by environmental factors, such as partial shading. This can lead to a decrease in the power conversion rate of the system. Although existing solutions for this issue are cost-effective and efficient, new solutions could further improve the system's performance by increasing consistency, power generation, and reducing mismatch loss and costs. To address this, a new method for configuring PV arrays was proposed using the calcudoku puzzle pattern. The performance of this new array configuration was evaluated in MATLAB/Simulink® for a 9 × 9 PV array and compared to conventional methods like Series-parallel, Total Cross Tied (TCT), and Sudoku array configurations. The performance was evaluated under eight different shading patterns based on power conversion rate and mismatch losses between the PV rows. The proposed array configuration resulted in 3.9%-13.3% of mismatch losses across the different shading patterns, while other configurations had a minimum of 13.8% to a maximum of 51.9% of mismatch losses. This reduction in mismatch losses directly improved the power conversion rate of the PV array.

Performance Analysis of PLA Material Based Micro-Turbines for Low Wind Speed Applications.

Various studies have been conducted in recent years to find solutions to the issues in wind energy conversion systems. A 100W horizontal axis micro wind turbine is built for low wind speed applications in this work. The Blade Element Momentum theory approach was used to design the 100W micro wind turbine blade. The wind turbine blade 3D model was created using the CREO CAD 3.0 software. Based on the aerodynamic studies, the airfoil S9000 is chosen among others for generating high power at low wind speed. The density, Young's modulus, and the Poisson ratio of the proposed wind turbine blade model with acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) materials were compared. ABS and PLA materials were investigated using a 0.33 mm layer of infill ranging from 10% to 100%. PLA and ABS output values were compared in terms of deformation, equivalent stress, and equivalent strain. PLA materials, on the other hand, have less deformation and greater structural properties than ABS materials. The wind blade structural analysis was performed in ANSYS 15 software, and the details of experimental and simulated results are presented in this paper.

Deep Learning Approaches for Prognosis of Automated Skin Disease.

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.