High-dimensional data analysis has become increasingly important in machine learning, computational intelligence, bioinformatics, finance, and image processing due to the rapid growth of large-scale datasets generated by IoT, cloud computing, and digital technologies. These datasets often contain thousands of features with limited samples, creating challenges such as overfitting, high computational complexity, redundancy, and the curse of dimensionality. Sparse machine learning models address these issues by using sparsity constraints and regularization techniques to select the most relevant features while eliminating irrelevant data. Popular methods include LASSO, Elastic Net, Sparse PCA, Sparse Autoencoders, and Sparse Support Vector Machines. These approaches improve interpretability, scalability, computational efficiency, and predictive performance. Sparse learning is widely applied in genomics, cybersecurity, medical diagnosis, recommender systems, and industrial automation. This study reviews sparse optimization, feature selection, and sparse representation learning techniques for high-dimensional data analysis. Experimental findings show that sparse models provide better robustness, faster convergence, lower memory usage, and reduced training complexity than traditional dense models. Future research focuses on integrating sparse learning with deep learning, federated learning, reinforcement learning, and explainable AI systems.
Rajesh K. Sharma, Priya Natarajan· International Journal of Mac...· 0 citations
Artificial Intelligence (AI), machine learning, and adaptive control technologies have greatly improved industrial automation by enabling intelligent and flexible manufacturing systems. Traditional control methods, such as PID controllers, are less effective in handling dynamic industrial environments and complex production conditions. AI-based adaptive control systems use technologies like neural networks, fuzzy logic, reinforcement learning, and deep learning to optimize industrial processes in real time. These systems dynamically adjust control parameters using sensor feedback, improving efficiency, process stability, predictive maintenance, fault detection, and product quality.This research proposes an AI-driven adaptive control architecture that includes intelligent sensing, machine learning optimization, predictive analytics, reinforcement learning, and fault diagnosis modules for smart industrial automation. The system is evaluated using performance metrics such as response time, energy efficiency, production throughput, and operational reliability. The results show that AI-based adaptive control systems outperform traditional industrial controllers in adaptability, robustness, and intelligent decision-making. The study concludes that AI-powered adaptive control is essential for future Industry 4.0 and Industry 5.0 applications, supporting autonomous operations, smart manufacturing, and resilient industrial ecosystems.
Rajesh K. Sharma, Priya Natarajan· International Journal of Int...· 0 citations
Artificial Intelligence (AI), cloud computing, IoT, big data analytics, and automation are driving the evolution of digital transformation toward integrated and intelligent enterprise ecosystems. This research proposes an AI Orchestrated Digital Ecosystem (AIODE) framework that combines data acquisition, integration, AI intelligence, digital orchestration, process automation, and decision support layers. The framework enables intelligent decision-making, predictive analytics, resource optimization, and adaptive business operations through AI and machine learning. Performance evaluation using key indicators demonstrates significant improvements in operational efficiency, automation, customer satisfaction, predictive accuracy, and resource utilization compared to traditional enterprise systems. The proposed approach supports scalable, agile, and sustainable enterprise transformation, providing valuable insights for researchers and practitioners developing next-generation intelligent enterprises.
Rajesh Sharma· International Journal of Art...· 0 citations
Autonomous mobile robotic platforms operating in unstructured environments face significant challenges due to unpredictable terrain topologies, varying soil properties, and geometric obstacles. Traditional reactive motion planners often fail or suffer severe efficiency losses when transitioning between highly distinct surfaces such as sand, gravel, mud, and solid rock. This paper introduces a comprehensive framework for intelligent terrain adaptation that combines exteroceptive perception and proprioceptive feedback to dynamically optimize robotic locomotion parameters. By leveraging deep learning-based visual-tactile classification alongside a real-time predictive control system, the proposed mobile robotic architecture achieves autonomous adaptation of wheel torque, suspension geometry, and path selection. Experimental validations conducted across four distinct simulated and real-world testing grounds demonstrate substantial improvements in energy efficiency, slip reduction, and stability control compared to traditional static locomotion algorithms. The results show that multi-modal sensor fusion provides the reliable situational awareness necessary for long-term robot autonomy in search-and-rescue, planetary exploration, and agricultural operations.
Rajesh Sharma, Priya Natarajan· International Journal of Int...· 0 citations
Gamification has emerged as an innovative pedagogical approach that applies game design elements in non-game educational environments to improve learner motivation, engagement, and knowledge retention. This paper explores the theoretical foundations, technological frameworks, and measurable outcomes of gamification across multiple educational domains, including STEM, medical, management, language, and professional education. With the growth of digital learning platforms such as Learning Management Systems (LMS), mobile learning, augmented reality (AR), virtual reality (VR), and artificial intelligence (AI)-based analytics, scalable gamified learning environments have become possible. Gamification incorporates elements such as points, badges, leaderboards, narratives, quests, and feedback mechanisms to enhance both intrinsic and extrinsic motivation. The study draws on theories including Self-Determination Theory, Flow Theory, and Constructivist Learning Theory to explain learner engagement. Using experimental and quasi-experimental methods across five educational domains, the research evaluates metrics such as engagement rate, course completion, academic performance, and learner satisfaction. Results show significant improvements, including increased engagement (35%), retention (28%), collaboration (32%), and academic performance (24%). Despite these benefits, challenges such as digital access inequality and long-term engagement remain. The study proposes future integration of AI-driven personalization and competency-based assessment for scalable and ethical gamification in education.
Rajesh Sharma, Priya Natarajan· International Journal of Eme...· 0 citations
Quantum communication is an emerging field that promises to revolutionize data transmission by leveraging the principles of quantum mechanics. This paper explores the fundamental concepts, underlying principles, and potential impact of quantum communication on future data transmission systems. The study delves into quantum key distribution (QKD), quantum teleportation, and quantum networks, highlighting their advantages over classical communication systems. A comparative analysis of classical cryptographic methods and quantum-enhanced security mechanisms is provided. Furthermore, the paper discusses the challenges associated with implementing quantum communication, such as decoherence, quantum error correction, and scalability. The methodology section outlines experimental setups, simulations, and practical implementations of quantum communication networks. The results emphasize the benefits of quantum encryption and the potential of quantum internet. The discussion explores real-world applications in banking, defense, and cloud computing. Finally, the paper concludes with future perspectives, emphasizing the necessity for ongoing research and technological advancements to achieve a fully functional quantum communication infrastructure.
Rajesh Sharma, Priya Natarajan· International Journal of Mod...· 0 citations
Disaster Risk Reduction (DRR) has emerged as a critical global priority in response to the increasing frequency, intensity, and complexity of natural and human-induced disasters. Climate change, rapid urbanization, environmental degradation, and socio-economic inequalities have significantly amplified disaster risks, particularly in developing and vulnerable regions. This paper presents a comprehensive multidisciplinary analysis of disaster risk reduction strategies by integrating perspectives from engineering, environmental science, social sciences, economics, public policy, and information technology. The study emphasizes that effective DRR cannot be achieved through single-discipline interventions but requires a holistic framework that combines structural and non-structural measures, community participation, governance mechanisms, and technological innovation. The paper systematically reviews existing DRR frameworks and international agreements, including the Sendai Framework for Disaster Risk Reduction, highlighting their strengths and limitations. A detailed literature survey examines recent research on hazard assessment, vulnerability analysis, resilience building, early warning systems, and post-disaster recovery. The proposed methodology adopts a systems-based approach, integrating qualitative and quantitative methods such as risk modeling, stakeholder analysis, and multi-criteria decision-making. Key performance indicators are used to evaluate the effectiveness of DRR strategies across different hazard contexts. Results indicate that multidisciplinary DRR strategies significantly enhance preparedness, reduce disaster losses, and improve recovery outcomes when compared to sector-specific approaches. The discussion underscores the importance of adaptive governance, data-driven decision-making, and inclusive community engagement. The paper concludes by proposing a scalable and adaptable DRR framework suitable for policymakers, practitioners, and researchers, contributing to sustainable development and resilience-building efforts worldwide.
Rajesh Sharma· International Journal of Eme...· 0 citations
Reinforcement learning-based adaptive resource management framework is proposed that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment and significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability.
Rajesh Sharma, Priya Natarajan· International Journal of Mac...· 0 citations
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