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Meeting the energy needs of a growing population is of paramount importance in today’s society. The use of renewable energy sources, especially solar energy, can help reduce greenhouse gas emissions from traditional sources. Solar irradiance, which depends on several factors such as the availability of sunlight, cloud cover index, latitude, orientation and tilt of solar panels, and technical factors, plays a crucial role in the use of solar energy. Predicting solar irradiance is therefore essential for enhancing the reliability and efficiency of solar energy systems. Artificial intelligence (AI) and machine learning (ML) models are increasingly being used to predict solar irradiance forecasting. These models can learn from historical weather data and identify complex patterns to predict future solar irradiance with high accuracy. Techniques such as regression, neural networks and ensemble methods are frequently used and enable more efficient planning and operation of solar energy systems. This study provides a comprehensive analysis of the existing state-of-the-art models for solar irradiance forecasting. The study evaluates a variety of forecasting models, including machine learning, numerical weather prediction and hybrid approaches for assessing their accuracy, strengths and weaknesses. As an outcome of this study, potential future improvements in the prediction of solar irradiance are highlighted and the importance of interdisciplinary collaboration and emerging technologies is emphasized. To ensure a sustainable and resilient energy future, it is crucial to continue efforts to better integrate solar energy into mainstream electricity systems. This work can serve as a fundamental analysis for future researchers to identify the most appropriate approaches for medium and long-term solar irradiance forecasts.

In real time scenarios, the single image encryption schemes are not that efficient when a bunch of color images is to be encrypted. This problem should be addressed due to computational cost incurred by multiple executions of single color-image encryption scheme. The objective of this research work is to provide a new and efficient multiple color images cipher scheme. This cipher is based on chaotic system, circular shift operations and SHA-384 hash codes. First of all, an arbitrary number of RGB images are input. They are combined in a rectangular fashion to get their hash code. Then this big color-image is decomposed into its three basic components, i.e., red, green and blue. To scramble the pixels of these components, they are shifted circularly both the row-wise and column-wise. Further, XOR operation has been performed to realize the effects of diffusion in these components. Lastly, these components are joined together to get the final RGB cipher image. Apart from the 384-hash codes, a salt key of 384-bit has also been used in the proposed cipher to heighten the security effects. The SHA-384 hash codes render the required key space and plaintext sensitivity. The simulation results and the performance analyses carried out at the end clearly prove the efficiency, effectiveness, robustness and the real-world applicability of the proposed multiple RGB images cipher. Besides, the encryption throughput 7.716 Mb/s of the proposed scheme outperforms many of the existing ones.

This is the era of information and the amount of data has immensely increased since the last few years. This increase has resulted in dilemmas like the curse of dimensionality where we need a large number of resources to process the huge number of features. So far there are many tools available that process such large volumes of data among which Rough Set Theory is a prominent one. It provides the concept of Reducts which represent the set of attributes that provide the maximum of the information. However, the problem with Reducts is that they are not stable and keep on changing with the addition of more and more data. So, the concept of Dynamic Reducts is introduced in the literature to provide a more stable version of Reducts. There are many Dynamic Reduct finding algorithms available; however, these dynamic Reduct finding algorithms are computationally too expensive and inefficient for large datasets. In this research, we have presented an improved dynamic Reduct finding technique based on rough set theory. In this technique, Reducts are selected, optimized, and further generalized through Parallel Feature Sampling (PFS) algorithm. An in-depth investigation is performed using various benchmark datasets to justify the effectiveness of our proposed approach. Results have shown that the proposed approach outperforms the state-of-the-art approaches in terms of both efficiency and effectiveness. Overall, 96% average accuracy is achieved and 46.13% reduction in execution time is observed by the proposed algorithm against the compared contemporary approaches.