Brijendra Singh
@vit.ac.in
Assistant Professor Senior
Vellore Institute of Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science Applications, Health Informatics, Artificial Intelligence, Software
Scopus Publications
Scopus Publications
Aryan Chopra, Aditya Modi, and Brijendra Singh
IGI Global
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.
S. Sethuraman and Brijendra Singh
IGI Global
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.
Brijendra Singh, Daphne Lopez, and Rabie Ramadan
Springer Science and Business Media LLC
Ankita Mishra, Sourik Banerjee, and Brijendra Singh
IGI Global
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.
Ishan Jain and Brijendra Singh
IGI Global
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.
Tumburu Chandhana, Anuhya Balija, Siva R R Kumaran, and Brijendra Singh
IGI Global
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.
Brijendra Singh and Anbarasi Masilamani
IGI Global
Smart education derived from information communication technologies (ICT) has attracted various academicians towards it. The growth of multiple sensor devices and wireless networks has brought drastic changes in IoT in the education sector. Applications of IoT in the education sector can improve academicians' and learners' considerable skills. Therefore, this chapter analyses various applications, advantages, and challenges of IoT in the education sector. The multiple applications of IoT in the education sector are identified in terms of smart classroom management, student tracking and monitoring, campus energy management, and intelligent learning. IoT in education's significant advantages are an innovative teaching and learning process, cost reduction, and smart infrastructure development. Various challenges in developing IoT-based applications identify as designing a secure learning environment, efficient resource tracking, efficient access to information, and intellectual plan development.
Brijendra Singh and D. P. Acharjya
Springer Science and Business Media LLC
Computational intelligence innovation and the use of computers have changed the entire healthcare delivery system. Nurses are the leading crew of healthcare organization. But, these nurses are either lacking in computer usage or automated analysis generated by computers. Therefore, it motivates to study the use of computers and information technology by nurses in Indian healthcare system. Further, it is essential to identify the chief factors where these nurses are lacking while using computers and information technology. This will help the management to take necessary measure to train them and make the healthcare industry more productive in perception with usage of computer and information technology. To this end, data has collected from nurses in hospitals in the state of Tamilnadu, India. Data collection is not beneficial unless it is analyzed and meaningful information obtained from it. In this paper, we hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions. Rough set is used to analyze the data and to generate rules. These generated rules further passed into formal concept analysis to identify the chief characteristics affecting the decisions. This in turn help the organization to provide adequate training to the nurses and the healthcare system will move further to the next stage.
Deb Dutta Das, Sharan Sharma, Shubham Natani, Neelu Khare, and Brijendra Singh
IOP Publishing
Social Media has taken the world by surprise at a swift and commendable pace. With the advent of any kind of circumstances may it be related to social, political or current affairs the sentiments of people throughout the world are expressed through their help, making them suitable candidates for sentiment mining. Sentimental analysis becomes highly resourceful for any organization who wants to analyse and enhance their products and services. In the airline industries it is much easier to get feedback from astute data source such as Twitter, for conducting a sentiment analysis on their respective customers. The beneficial factors relating to twitter sentiment analysis cannot be impeded by the consumers who want to know the who's who and what's what in everyday life. In this paper we are classifying sentiment of Twitter messages by exhibiting results of a machine learning algorithm using R and Rapid Miner. The tweets are extracted and pre-processed and then categorizing them in neutral, negative and positive sentiments finally summarising the results as a whole. The Naive Bayes algorithm has been used for classifying the sentiments of recent tweets done on the different airlines.
Brijendra Singh
Diva Enterprises Private Limited
The study has been carried out in the medium Select hospitals at Tamil Nadu, India. The purpose of this study is to determine the attitude of nurses towards computerization and factors affecting the use of computers. Variables including gender, marital status, qualification, age and type of job have been studied. A random sample of 600 nurses formed the study population. Out of them 164 nurses responded to the survey. Analysis of Variance (ANOVA) test was done to analyze the data. Results show that gender has no impact on nurse’s attitude towards computerization. Marital status, qualification level, age and type of job have been found to affect the attitude of nurses in use of computers at their workplace. Unmarried nurses have more positive attitude than married nurse. Nurses possess bachelor degree got more positive attitude than diploma holder nurses towards computers. Nurses with age range 20-25 years had more positive attitude than nurses above 26 years. Full time working nurses have more positive attitude than part time working nurses towards computerization. Lack of computers is the major concern in use of computers at their work place. Results of the study may be useful for nurse administrators and educators to develop and design educational and training programs.
Brijendra Singh and J. Senthil
Richtmann Publishing
The aim of the study was to assess the comfort level and frequency in the use of information technology (IT) among nurses and analyze the relationship between various factors governing their use. A survey was conducted during 2013-14 at select Indian Hospitals of Tamil Nadu State. Correlation analysis and ANOVA test were used to analyze the data. The nurses working in the medium sized hospitals in the State are the study population. A random sample of 600 nurses was selected for the study. Gender, qualification and age level were analyzed to found the influence on use of computer hardware, communication and administrative related activities using computers. Effective and majority use of computers for information processing activities, communication, planning and policy development activities, finance, administration, education and research can lead healthcare organization to run their processes efficiently. Hospitals can provide appropriate computer training programs and computers access to nurses to realize the benefits from IT. Healthcare professionals may encourage nurses to use IT and computers at their work place by providing them attractive incentives. DOI: 10.5901/mjss.2015.v6n4s2p658