Suzon Islam

This work aims at analyzing the part of AI given its capability to improve reliability, efficiency, and flexibility of renewable energy integration. The growing world demand for energy requires the incorporation of renewable energy into smart grids to create effective and efficient power systems. Through the utilization of sophisticated machine learning, predictive analytical tools, and real-time calculated data, we implemented forecasts, schedules, and management of renewable power within the grid. Our methods included establishing models that predict renewable power generation, demand, and real-time operations of the grid. According to the findings, the power oscillation range has been decreased by 30%, the use of renewable energy generation has been increased by 25%, and the dependence of fossil fuel backup generation has been decreased by 40% based on the usage of AI-enabled systems. Moreover, several of the AI-fortified systems were much more capable of maintaining the stability of the grid, cutting energy costs and CO₂ emissions by 20 on average. These insights demonstrate AI’s capability to facilitate smarter and more antifragile energy grids by navigating renewable energy and supply-demand Plexus effectively. Overall, our findings support the statement that AI-based renewable energy systems can help integrate the transition to more sustainable energy resources by enhancing grid performance, reducing carbon footprints, and improving energy access. This study also reveals the significant reality of AI to enhance global sustainable goals for energy systems.

This study examines the rationale and perceptions surrounding AI in military technologies, focusing on its application in autonomous weapons systems (AWS), intelligence, surveillance, and reconnaissance (ISR), and decision-support systems (DSS). AI models, such as unmanned aerial and ground platforms, showed high mission success rates, with 92% mission accomplishment, 95% true positive threat identification, and low false alarms. AI's effectiveness surpassed human operators, particularly in response time and threat detection. A global survey revealed mixed opinions: two-thirds believed AI enhances national security, yet 70% opposed autonomous weapons due to ethical concerns, particularly civilian casualties. The study highlights AI's advantages in improving decision-making speed and accuracy but acknowledges challenges such as ethical considerations and public trust in autonomous systems. Future research should address ethical AI models, international humanitarian law (IHL) standards in military operations, human-AI interaction in hazardous environments, and policies related to military robotics and accountability. Understanding and enhancing public awareness of AI in military contexts is also essential.

Space exploration has progressed significantly, largely due to innovations in rocket technology. Artificial Intelligence (AI) has become crucial in enhancing both rocket launch and landing systems. This paper offers a detailed analysis of the integration of AI into these systems, focusing on its role in improving operational efficiency, safety, and mission success rates. AI-powered algorithms enhance real-time decision-making during launch phases, optimize trajectories, and streamline autonomous landing procedures. By utilizing machine learning (ML) and neural networks, predictive system health analysis is possible, which improves maintenance schedules and reduces the likelihood of failures. Furthermore, AI is vital in managing reusable rocket systems, reducing human error and increasing cost-effectiveness. The analysis reveals that AI-driven algorithms have improved launch accuracy by up to 15% and decreased system malfunctions by 10%. Autonomous landing systems powered by AI have demonstrated a 20% increase in landing precision, which minimizes the risk of damage and enhances the reusability of rocket stages. The study highlights that AI integration enables predictive diagnostics, leading to a 25% improvement in overall system reliability. Additionally, AI's contribution to fuel optimization has resulted in a 12% increase in fuel efficiency, further promoting mission sustainability.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.