Computer Science Applications, Health Informatics, Artificial Intelligence, Software
24
Scopus Publications
Scopus Publications
Cloud computing and cybersecurity in digital business environments Abhishek Basliyal, Brijendra Singh Fostering Economic Diversification and Sustainable Business Through Digital Intelligence, 2025 The rise of cloud computing paradigm eases business operations by enabling efficient data storage and processing in a cost-effective manner. At the same time, it also brings security challenges associated with it. In this book chapter, we have discussed major characteristic of cloud computing, various deployment models and types of cloud services. It discusses various cloud security challenges such as data security, data breaches, data loss, data theft, distributed Denial-of-Service attacks, insider threats, phishing and ransomware. To address these challenges, this book chapter depicts best cybersecurity practices for cloud computing, such as understanding the shared responsibility between cloud provider and user. Finally, this chapter depict cybersecurity concerns in the cloud and the importance of protecting sensitive data for seamless business operations.
Tangent sine search optimization enabled energy prediction and SWIPT-based power transfer for energy-aware communication in WSN Anbarasi Masilamani, Santhi Krishnan, Brijendra Singh, Chellatamilan T International Journal of Communication Systems, 2024 SummaryWith the rapid increase in energy utilization and the tremendously growing number of connected devices in wireless sensor networks (WSNs), alternate power transfer methods have not only become significant for scholastic purposes but also the growth of WSNs and the reduction of operational costs. Simultaneous wireless information and power transfer (SWIPT) is an emerging technology that has the potential to boost the lifespan of WSNs. This work presents an innovative approach to improving the energy efficiency of WSNs. Here, a WSN is considered, and energy‐aware communication is established in six phases, such as setup, steady state, energy prediction, SWIPT‐based power transfer, communication/route discovery, and route maintenance. Further, a hybrid approach named tangent sine search optimization (TSSO) is created to identify the ideal node as cluster head (CH). Later, the age of the neighboring nodes is forecasted utilizing the deep long short‐term memory (DLSTM), whose trainable parameters are selected using the proposed TSSO algorithm. A SWIPT‐based power transfer is also performed for harvesting the energy to avoid node failures. The proposed TSSO‐DLSTM+SWIPT is investigated given various metrics, like delay, residual energy, throughput, and trust, and the values attained are 0.784 s, 0.790 J, 0.970 Mbps, and 0.997, respectively without any attacks. The proposed TSSO‐DLSTM+SWIPT obtained performance improvement of 11.08%, 10.07%, 7.99%, and 5.83% than the Iterative algorithm in terms of delay, residual energy, throughput, and trust.
Applications of cloud computing in industrial robotics S. Sethuraman, S. Vikram, R. Dharma Surya, Brijendra Singh Shaping the Future of Automation with Cloud Enhanced Robotics, 2024 In the past few decades, robots have played a significant role in both industrial and technological advancements. Cloud-integrated industrial robotics provide a vast network of machines to industries. The integration of robotics with cloud computing offers increased computational power for various applications of industrial, communication, healthcare, disaster management, defence systems, and space exploration missions. It also provides reliable and extensive accessibility across a network of devices. In this chapter, the authors delve into the fundamentals of robotics and cloud computing. They introduce the core concepts of robotics and cloud computing, followed by their integration. Subsequently, they explore communication in cloud robotics, encompassing machine-to-machine (M2M) and machine-to-cloud (M2C) communication. They then examine the major applications in industrial robotics and types of robots that leverage cloud robotics technology. They conclude by addressing security issues and challenges in cloud robotics.
Applications of Artificial Intelligence in Education K. Aravindh, Brijendra Singh Next Generation AI Methodologies in Education, 2024 This chapter explores the transformative impact of artificial intelligence (AI) on the education sector, highlighting how AI technologies enhance teaching quality, streamline assessment processes, and support both physical and virtual learning environments. By facilitating innovative methods of lecture delivery and interactive learning, AI tools benefit educators and students alike, enabling a more engaging and efficient educational experience. The chapter provides a comprehensive review of various AI techniques and methodologies used within educational contexts, illustrating practical applications that support personalized learning, efficient evaluations, and adaptive teaching strategies. Additionally, it examines the benefits and challenges of AI from the perspectives of both educators and students, shedding light on the potential improvements AI can bring to learning outcomes. Future directions and opportunities for AI in education are also discussed, offering insights into the next steps for integrating AI into educational systems for enhanced adaptability and impact.
Machine learning algorithm with TensorFlow and SciKit for next generation systems Aryan Chopra, Aditya Modi, Brijendra Singh Machine Learning Algorithms Using Scikit and Tensorflow Environments, 2023 Machine learning plays a vital role in all major sectors like healthcare, banking, finance, and marketing. There is a need to understand the role and working of ML algorithms in a better way. Google also uses a learning algorithm to rank the web pages whenever we try to browse the internet to get the desired information. Understanding the platform and working of these algorithms is crucial for researchers. In this chapter, the authors have presented an overview of machine learning fundamentals and the working of these algorithms with suitable examples. They have also highlighted the importance of major machine learning libraries like TensorFlow and SciKit in developing and deploying vast applications. Finally, a case study of ML application is presented to better understand the concept. Future prospects of ML applications are also depicted in detail.
Smart agriculture applications using internet of things S. Sethuraman, Brijendra Singh Sustainable Science and Intelligent Technologies for Societal Development, 2023 Smart agriculture applications using IoT (Internet of Things) is getting popular in recent years. IoT-based smart agriculture applications involve the use of various sensor devices, such as soil sensors, weather stations, and drones, to collect data on environmental factors such as temperature, humidity, rainfall, soil moisture, and nutrient levels. This data is transmitted to the cloud where it is processed and analyzed to provide insights and recommendations to farmers. Smart agriculture applications using IoT can help farmers make data-driven decisions, reduce costs, and improve crop yields. This book chapter discusses the architecture and technologies used in IoT, then the three layers of IoT-based smart agriculture applications namely the physical layer, edge layer, and cloud layer. It also highlights the potential benefits of IoT-based smart agriculture applications and discusses the challenges of implementing IoT-based agriculture to improve farming practices.
Deep learning techniques for smart agriculture applications Ankita Mishra, Sourik Banerjee, Brijendra Singh Machine Learning and Deep Learning for Smart Agriculture and Applications, 2023 With an emphasis on the rapid and accurate diagnosis of plant and fruit diseases, researchers have been looking into sustainable agriculture utilizing cutting-edge deep learning techniques. The objective is to show how effective deep learning algorithms can revolutionize the agricultural industry. Automated illness detection is the main area of focus, where advances in image processing and computer vision techniques enable precise and quick identification while lowering labor requirements and associated costs. In order to identify plant and fruit diseases in a sustainable manner, this project intends to explore the possibilities of deep learning algorithms in detecting diseases from the leaves of agricultural plants using pre-trained deep convolutional neural network. This book chapter provides informative information on the use of deep learning in smart agriculture and a significant resource for researchers, professionals, and students interested in sustainable farming and intelligent agricultural systems.
Analysing the applications of cloud computing in smart agriculture Ishan Jain, Brijendra Singh Convergence of Cloud Computing AI and Agricultural Science, 2023 Agriculture has been the backbone of human civilization. For ages, humans have been using traditional farming techniques to produce high-quality crops, but with the era of digitalization and the challenges of these methods, there has been a shift from traditional farming techniques to smarter farming techniques, which utilise the latest technologies to reduce labour and maximise the yield in a given scenario. Smart agriculture produces a large amount of data from sensors and IoT devices; this is where cloud computing comes into play. Cloud computing-based technology provides storage and analysis facilities to deal with huge amounts of data and produces real-time insights that can help the farmer make better decisions. Cloud computing also helps in reducing the cost and resources incurred in smart agriculture techniques, thus making the model more efficient and useful for farmers. This chapter will analyse various applications in which innovative cloud computing technologies can be used in smart agriculture and the drawbacks that should be considered while adopting the cloud model.
Digital twins-enabling technologies including AI, sensors, cloud, and edge computing Tumburu Chandhana, Anuhya Balija, Siva R R Kumaran, Brijendra Singh Handbook of Research on Applications of AI Digital Twin and Internet of Things for Sustainable Development, 2023 Digital twin technology is starting to receive interest in the industry and, more recently, in academics. The digital twin is best described as the seamless integration of data between a physical and virtual system in either direction. The internet of things (IoT), cloud computing, edge computing, digital twins, and artificial intelligence all bring challenges, applications, and enabling technologies. Despite the fact that the idea of the “digital twin” has been well established over the past few years, there are still many different interpretations that result from varied professional viewpoints. The digital twin is primarily introduced in this chapter, along with its advantages and practical applications in different sectors. The authors have presented a detailed review of the artificial intelligence-driven digital twin, sensor-driven digital twin, cloud-driven digital twin, and edge computing-driven digital twin. It looks at the architectures, enabling technologies, potential obstacles, and challenges of current research on digital twins.
Sentimental Analysis for Airline Twitter data Deb Dutta Das, Sharan Sharma, Shubham Natani, Neelu Khare, Brijendra Singh Iop Conference Series Materials Science and Engineering, 2017
Analytical hierarchical process (AHP) and fuzzy AHP applications-A review paper International Journal of Pharmacy and Technology, 2016
Job type influence in the use of information technologyby nurses in private hospitals in the state of tamilnadu in India International Journal of Pharmacy and Technology, 2016
Information technology training needs and health awareness among nurses in india International Journal of Pharmacy and Technology, 2016
IOT: Framework for smart city International Journal of Pharmacy and Technology, 2016
Comparitive study on big data architectures proposed for smart cities context International Journal of Pharmacy and Technology, 2016
Use of Artificial Intelligence in automation of sequential steps of software development / production Journal of Theoretical and Applied Information Technology, 2013
