M.K. Engineering College (RMKEC) is a private engineering college at Kavaraipettai, Gummidipoondi Taluk, Tamil Nadu, India, governed by the Lakshmikanthammal Educational Trust. It is affiliated to Anna University, Chennai, and accredited by All India Council for Technical Education(AICTE) with A+ Grade (3.52/4.00). All the seven departments of the college are accredited by the National Board of Accreditation(NBA). RMKEC is among the top engineering colleges of Anna University in Tamil Nadu and a Tier-I institution among self-financing colleges. It is a minority institution under language category.
The ever-evolving field of organised cybercrime now faces significant threats from synthetic media, deepfakes, and AI-enabled identity manipulation. Business email compromise, social manipulation, financial deceit, cyber extortion, political misinformation, and reputational assaults are some of the more common uses of these technologies. The use of synthetic media allows coordinated cybercriminal organisations to evade traditional authentication systems, enable deceit, and carry out complex schemes all over the globe. Now that generative AI tools are readily available, criminal groups may easily and cheaply produce realistic counterfeit stuff. Deepfakes and synthetic media are discussed in this chapter along with their technical foundations, new attack methodologies, integration into organised cybercriminal enterprises, ethical and legal concerns, methods of identity manipulation, detection strategies, and potential approaches to combating digital fraud enabled by artificial intelligence.
In order to coordinate EVs along with renewable energy, it is necessary to have accurate forecasting, adaptive control, and a secure energy exchange. The hybrid framework that integrates Transformer forecasting with multi-agent reinforcement learning (MARL) suggested in this paper appears to highly suitable for the intended application. In fact, the Transformer encoder is the one responsible for the accurate predictions of EV load and renewable generation, while MARL policies give the required decentralization and dynamic coordination. Blockchain acts as a safety net for the transactions and a sign of trustworthiness for the prosumers, with IoT-level compression playing the role of latency eliminator in densely packed EV networks. Testing results show that predictive reliability is 96.9%, balancing is 32.7%, efficiency is 26.4%, and latency is 24 ms. Based on the comparison with ML baselines, the framework is 6.8% more accurate, 7.5% more balancing is achieved, 5.9% of the cost optimization is improved, and therefore, the EV-renewable integration is not only scalable but also resilient.
In this paper, an sustainable imperfect production system for shortages and complementary products with advertisement-price- sales return and green degree dependent demand and carbon emission is considered. The green production lot size, shortages and optimal pricing are three decision variables in third order equation. A provision has been made to sell the defective items. In literature, only two decision variables are considered in imperfect production system. In this model, three decision variables that is green production lot size, shortages and pricing are considered in imperfect production system. Price-break-even point is determined, the law of demand is verified, and highest possible profit is identified from the three alternative prices. The price break-even of the product per unit is 3299.4834 and in this stage neither profit nor loss for the concern. Taking from a third-order equation including three price variables, optimum pricing solution shows that a price level of 4079.5108 yields the highest profit of 102184.49. The objective of this model is to determine the green production lot size, shortages, and pricing for overall maximum profitability. The mathematical formulation's validity is shown by a numerical experiment, and the effects of inventory components on overall profit and managerial insights are examined using sensitivity analysis. The validation of the model's results was programmed utilizing Microsoft Visual Basic 6.0.
The untreated natural fiber reinforced polymer composites limited their broader structural applications due to their limited mechanical performance and poor interfacial adhesion. To counteract this, the tensile strength of modified banana fiber reinforced epoxy composites incorporated with nano-silica (SiO2) at different concentrations of 0.5 to 3 wt
Perovskite-based tandem solar cells have become attractive options for effective photovoltaic applications due to their strong optical absorption and adjustable bandgap. Nonetheless, challenges like instability, scalability issues, and material degradation hinder their commercial deployment. This study proposes a new approach to develop the stability and efficiency of lead-free perovskite/silicon tandem solar cells by optimizing absorber layer thickness, material selection, and key performance parameters. The proposed method integrates the Quantum Kernel Self-Attention Network with the Narwhal Optimizer, where the neural network model predicts the perovskite bandgap and the algorithm optimizes absorber layer thickness for maximum performance. Implemented in MATLAB, the approach is benchmarked against existing techniques, such as the Artificial Neural Network and the Chaotic African Vultures Optimization Algorithm, and Genetic Algorithm-Particle Swarm Optimization, demonstrating superior outcomes in terms of power conversion efficiency and device reliability. The proposed approach achieves a conversion efficiency of 26.68 %, outperforming benchmark methods. By optimizing parameters such as bandgap alignment, doping, and thermal stability, the tandem configuration achieves a Voc of 1.87 V, a Jsc of 16.44 mA/cm2, and a fill factor of 86.8 %, indicating its potential for efficient and stable solar energy applications.
