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The main motive of this research is to design an efficient image encryption method. To achieve this goal, the chaotic tent map algorithm is taken into consideration for secret key generation. In the tent map algorithm, the control parameter μ plays an important role for key generation. Therefore, the optimal value of μ parameter is determined using the JAYA algorithm. JAYA is a metaheuristic algorithm that determines the optimal value by putting the random value of μ parameter in the tent map algorithm based on the objective function. The beauty of the JAYA algorithm is that it does not require any parameter tuning, is simpler, and provides a better convergence rate to find the optimal solution. After determining the optimal parameter value, a secret key is generated. Further, exclusive-OR and circular shifting are performed for image encryption. The performance analysis is performed on different images. The subjective analysis provides a better noisy image, and the objective analysis shows a low PSNR, a high correlation coefficient, a high value of entropy, a maximum deviation, and a mean square error. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) have made substantial advances in the domains of computer vision and speech recognition in recent years. These deep learning architectures have shown exceptional ability in a variety of tasks. Evaluating the performance of such models is critical for comprehending their efficacy and directing future developments. In this study, we undertake a thorough comparison utilizing two essential evaluation metrics: Root Mean Square Error (RMSE) and Mean Average Precision (MAP). Our research intends to give light on the applicability of these metrics for analyzing the performance of CNNs and LSTMs, as well as their strengths and limitations. [ABSTRACT FROM AUTHOR]

Modern technological and scientific advancements have made it possible for devices to becoming smaller and more portable. Such devices' power electronics converters would be a key component in what appeals to users and makes them useful. Modern power electronics applications include DC-DC (chopper), AC-DC (rectifier), DC-AC (inverter), and AC-AC (conversion) (Cyclo-Converter) power electronics. To boost Wireless Transmission (WPT) capacity across a variable range, several solutions have been proposed. Some methods for transmitting wireless power include capacitive, inductive, and magnetic resonant coupling, although these couplings do have limits on the power transfer values and range, illustrating the inherent trade-off between distance & power transferred. The piece is divided into three parts: For low-power applications, the Active Clamp Forward Converter (ACFC) is a well-liked choice for power supply with one or more outputs. It is an isolated DC-DC converter with a power range of 50W to 500W. Comparatively speaking, active clamp forward converters are more compact and effective. Transformers are used in forward converters to achieve circuit isolation, energy transformation Using the reactive clamp technique, the transformer's stator current was also reset from the transmission line to the secondary windings. Although there are a number of ways to reset a transformer, the active clamp approach is both straightforward and efficient. A few advantages of active switching are ZVS (zero voltage changing), reduced switch energy stress, a wider range of duty cycles, and lower EMI. The relay is activated at zero voltage using the active clamp method. changing (ZVS) by using the inductor leakage currents (LL), parasitic capacitive, and/or switch outputting capacitance. This method employs a standard pulse width modulation (PWM) system to convey power (CDS). [ABSTRACT FROM AUTHOR]

The reliable operation of power systems is essential to prevent over-voltages, excessive currents, outages, and equipment damage, which can pose risks to users and lead to interruptions in the power supply. To address these challenges, a robust power safety system is required to swiftly identify, categorize, and pinpoint faults, ultimately reducing their impact. This paper presents a comprehensive approach for locating, classifying, and detecting faults within power systems. The primary objectives of this paper are to accurately identify and categorize different fault types occurring at various resistance levels and locations, gain insights into the causes of interruptions, expedite power restoration, and minimize recurrence. Additionally, this research aims to enhance understanding of the components of the protection system, enabling the implementation of preventive measures to reduce the likelihood of service interruptions and equipment damage. To achieve precise problem identification, classification, and fault localization, the proposed approach employs simple neural networks and efficient machine learning models. Leveraging the capabilities of neural networks, this technology yields precise results in power system fault analysis. [ABSTRACT FROM AUTHOR]