Overview

Experts continue to struggle with developing methods to effectively navigate the intricate landscape of the Internet of Things (IoT). As the IoT landscape continues to expand and influence various industries, from healthcare to smart cities and beyond, scholars often find themselves facing an absence of comprehensive guidance in navigating this evolving technological landscape. The challenges are multifaceted and include the need for intelligent modeling techniques, the intricacies of managing IoT applications, and the relentless pace of technological advancements. This issue of staying well-informed and equipped to address these challenges demands an insightful solution. To tackle these challenges, Scalable Modeling and Efficient Management of IoT Applications emerges as a valuable resource, offering a multitude of effective solutions to address these concerns. This is a book that was meticulously crafted to empower scholars with the knowledge and tools they need. By tackling the scarcity of guidance on intelligent modeling techniques, the book equips readers with a profound understanding of the fundamental concepts, algorithms, and methodologies crucial for designing and managing intelligent IoT systems. Furthermore, the book extends its guiding hand to conquer the complex terrain of managing IoT applications. It offers practical insights, strategies, and real-world case studies, providing scholars with the means to tackle implementation challenges encompassing scalability, interoperability, security, and data management. Rather than merely addressing existing challenges, the book also ensures that scholars remain at the forefront of IoT knowledge, offering insights into the latest research trends and emerging technologies. Scalable Modeling and Efficient Management of IoT Applications is the compass that academic scholars need, helping them navigate the complexities of IoT and bridging the gap between theory and practical implementation.

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Harshita Patel had a Ph.D. awarded entitled 'Efficient Computational Models for Classification of Imbalanced Data' in Dept. of Computer Applications, Maulana Azad National Institute of Technology Bhopal (M.P.) in November 2017. Self-motivated learner gives emphasis on conceptual teaching and fundamentals. Generates subject interest in students through motivation, creative thinking, positive thinking with CAN DO approach and good verbal & communication skills. Impart theoretical knowledge with emphasis on application knowledge associated with lab learning, experimental and innovative approach in teaching with success records.

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