Book Chapters
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Item Futuristic AI convergence of megatrends: IoT and cloud computing(wiley, 2022) Pandey, C.; Sahu, Y.K.; Kannan, N.; Rashid Mahmood, M.R.; Sethy, P.K.; Behera, S.K.Recent years have seen increasing curiosity among users in migrating their cloud computing and internet-of-things apps. Cloud-based and internet-of-things infra-structures require specialized hardware to enable software and advanced manage-ment strategies to improve performance. Adaptability and autonomous learning capabilities are highly valuable in facilitating the configuration and complex transition of these infrastructures to customers' changing demands and designing adaptable applications. This capacity to self-adapt is increasingly essential, particularly for nonexpert managers and autonomous device applications. Cloud Networking (CN) and the Internet of Things (IoT) have arisen as modern outlets for the ICT movement of the 21st century. In this paper, we carry out a survey of nearly 183 articles on which the latest methodologies have been applied. Also, we discuss the proposed approaches and the reported advantages and limitations. The goal of this survey paper is to offer a brief idea to researchers working in this area. In order to consider the present and future challenges of such a framework, it is important to recognize critical innovations that will allow future implementa-tions. This article examines how three new paradigms (cloud computing, IoT, and artificial intelligence) can affect workspace and business. Also, we describe a range of innovations that propel these paradigms and encourage experts to address the current state and perspective directions. © 2023 Scrivener Publishing LLC.Item Hybrid Malicious Encrypted Network Traffic Flow Detection Model(Springer Science and Business Media Deutschland GmbH, 2023) Hublikar, S.; Shet, N.S.V.Encrypted communication technology has evolved as the network, and Internet applications have advanced. Malicious communication, on the other hand, employs encryption to bypass standard detection and security protection. The existing security prevention and detection technologies are unable to identify harmful communication that is encrypted. The growth of artificial intelligence (AI) in these days has enabled to employ machine learning (ML) as well as deep learning approaches to identify encrypted malicious communications without decryption, with remarkably precise detection outcomes. At this moment, research on detecting harmful encrypted traffic is mostly focused on analyzing the features of encrypted data and selecting neural network (NN) techniques. Hybrid ML is proposed in this study by merging two well-performing data mining algorithms with natural language processing tasks. Here, a new traffic flow detection method is performed by the hybrid ML technique. At first, the benchmark data is collected from public sources. The features are extracted using the convolutional layer of deep convolutional neural network (DCNN). Then, the weighted feature extraction is performed by grasshopper optimization algorithm (GOA). Employed the hybrid machine learning-based malicious detection with the “support vector machine (SVM) and neural network (NN)” is utilized in this model to detect the traffic affected by malicious activities, where the hidden neuron count of NN and kernel of SVM are tuning by the same GOA for increasing the accuracy and precision. This research provides findings from experiment, encouraging various researchers to develop the research as future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Li-ion Battery Energy Storage Management System for Solar PV(Springer, 2024) Chaitrashree, C.N.; Kashyap, Y.; Sidharthan, P.V.Battery storage has become the most extensively used Solar Photovoltaic (SPV) solution due to its versatile functionality. This chapter aims to review various energy storage technologies and battery management systems for solar PV with Battery Energy Storage Systems (BESS). Solar PV and BESS are key components of a sustainable energy system, offering a clean and efficient renewable energy source. A background study on existing ESS, its advantages, and issues are detailed with the vital role of battery energy storage technologies, specifically LiBs, their characteristics, and SoC estimation techniques. Further, the chapter highlights integrating Battery Management Systems (BMS) with PV and BESS to ensure the efficient and reliable operation of the energy storage system. The major research gap/challenge is related to the less consideration given in terms of power consumption reduction and cost minimization, which forms multiple objective problem-solving. Multi-objective optimization with type 2 fuzzy controllers can achieve these objectives. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Artificial Intelligence in Damage Detection of Concrete Structures: Techniques, Integration and Future Directions(Springer Science and Business Media Deutschland GmbH, 2025) Barbhuiya, S.; Das, B.B.The chapter thoroughly explores the pivotal role played by Artificial Intelligence (AI) in the identification of damages in concrete structures. It delves into conventional methods, their limitations, and how AI can effectively complement these approaches. The basics of AI, encompassing machine learning and deep learning, are elucidated within the specific context of damage detection. Additionally, the chapter examines data acquisition and pre-processing techniques tailored for AI models. It sheds light on AI-driven damage detection methodologies, such as the utilization of convolutional neural networks for image analysis, vibration analysis, and AI-enhanced non-destructive testing methods, highlighting their precision in identifying structural issues. Moreover, the chapter investigates the integration of AI into structural health monitoring systems, providing in-depth discussions on data fusion and real-time monitoring. Emphasis is placed on the significance of performance assessment and model validation to ensure the reliability of AI algorithms. The chapter also addresses future trends, including the integration of AI with the Internet of Things (IoT), and delves into ethical considerations in the sphere of infrastructure development. In summary, the chapter underscores AI's transformative potential in revolutionizing damage detection and structural health assessment, contributing to the creation of more resilient and sustainable concrete structures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.