In recent years, many image encryption approaches have been proposed on the basis of chaotic maps. The various types of chaotic maps such as one‐dimensional and multi‐dimensional have been used to generate the secret keys. Chaotic maps require some parameters and value assignment to these parameters is very crucial. Because, poor value assignments may make the chaotic map un‐chaotic. Therefore, hyper‐parameter tuning of chaotic maps is required. Recently, meta‐heuristic based image encryption approaches have been designed by researchers to resolve this issue. However, the majority of the techniques suffer from poor computational speed and stuck in local optima problems. Therefore, in this study, a strength Pareto evolutionary algorithm‐II based meta‐heuristic approach is proposed to tune the hyper‐parameters of the four‐dimensional chaotic map. The proposed approach is also implemented in a parallel fashion to enhance the computational speed. The effectiveness of the proposed approach is evaluated through extensive experiments. Comparative analyses show that the proposed approach outperforms the competitive approaches in terms of entropy, NPCR, UACI, and PSNR by 0.9834, 1.0728, 0.9134, and 0.8971normal%, respectively.

Nowadays, multimedia applications are extensively utilized and communicated over Internet. Due to the use of public networks for communication, the multimedia data are prone to various security attacks. In the past few decades, image watermarking has been extensively utilized to handle this issue. Its main objective is to embed a watermark into a host multimedia data without affecting its presentation. However, the existing methods are not so effective against multiplicative attacks. Therefore, in this paper, a novel quantum-based image watermarking technique is proposed. It initially computes the dual-tree complex wavelet transform coefficients of an input cover image. The watermark image is then scrambled using Arnold transform. Thereafter, in the lower coefficient input the watermark image is embedded using quantum-based singular value decomposition (SVD). Finally, the covered image is obtained by applying the inverse dual-tree complex wavelet transform on the obtained coefficients. Comparative analyses are carried out by considering the proposed and the existing watermarking techniques. It has been found that the proposed technique outperforms existing watermarking techniques in terms of various performance metrics.

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.

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Radio resources in wireless communication systems, implementing different multiple access techniques, must be wisely managed. This perspective is pivotal since the variations in propagation channel are very fast. This complexity in the cellular system periodically contributes to different interference levels, high or low, resulting in the degradation of the system capacity. Transmitter power control is an efficient technique to mitigate the effect of interference under fading conditions, combat the Near-Far problem and conserve the battery life. Thus, an effective implementation of different power control algorithms in cellular radio communication systems can offer a significant improvement in the Quality of Service (QoS) to all the users. Choice of an appropriate power control algorithm is of prime importance, as it should aim at increasing the overall efficiency of the system. In this paper different distributed power control algorithms, each suited for implementation under different cellular technologies, were studied extensively. Specifically, three distributed power control algorithms are compared through simulations on the basis of performance metrics like Carrier to Interference Ratio (CIR) and Outage for the downlink case.