E-Book, Englisch, 210 Seiten
Singh / Kumar / Malhotra Artificial Intelligence, Big Data, and Internet of Things for Sustainable Industry and Infrastructure Development
1. Auflage 2026
ISBN: 978-981-5322-97-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
E-Book, Englisch, 210 Seiten
ISBN: 978-981-5322-97-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
Artificial Intelligence, Big Data, and Internet of Things for Sustainable Industry and Infrastructure Development explains how AI, IoT, Big Data, and Blockchain reshape industries by enabling real-time monitoring, improving resource efficiency, predicting climate risks, and strengthening ethical decision-making in sustainable infrastructure and smart industrial systems.
The book connects digital innovation with responsible business and healthcare practices, showing practical uses in waste reduction, supply chain sustainability, disaster forecasting, traffic automation, online safety, and trust-building systems for digital markets.
Key Features
Reduce production waste using big data analytics
Boost operational efficiency and lower industrial costs
Predict climate risks using AI and historical datasets
Authenticates e-commerce reviews using ML and NLP
Decentralised digital industries using blockchain systems
Optimises sustainable supply chain logistics with data science
Forecasts natural disasters for smart cities using AI models
Improves disaster resilience using fractal and fractal-based analysis
Automates adaptive traffic signalling using Arduino and IR sensors
Detects cyberbullying using AI for faster moderation
Autoren/Hrsg.
Weitere Infos & Material
PREFACE
The book delves into the convergence of Artificial Intelligence, Big Data, and the Internet of Things (IoT) in the development of sustainable industry, infrastructure, and economic. It focuses on how new technologies are reshaping industries and infrastructure, allowing for greater development, efficiency, and sustainability. The book will explore how artificial intelligence (AI) extends human skills. Big data allows informed decision-making, and the Internet of Things connects the digital and physical worlds for real-time monitoring and control. They are applied in industries such as energy, transportation, healthcare, and urban planning, demonstrating how these technologies may improve resource management, minimize environmental impact, increase safety, and improve quality of life. The book contains 10 chapters, each representing a different issue, yet they are all connected to the technology system with sustainable economic development.
Chapter 1 deals with employing big data analytics to track and minimize waste in production processes. It has reached the point that today, waste reduction methods and production are very key to companies' sustainability and efficiency in the world. This study seeks to investigate how BDA can be applied to production systems, utilizing vast quantities of operational data to identify patterns, trends, and anomalies that are often difficult to observe. The study demonstrates the tangible benefits of utilizing data to manage waste, including lower costs, improved operational efficiency, and a reduced environmental impact. This study contributes to the knowledge of environmentally friendly manufacturing methods and highlights the importance of BDA in transitioning to more eco-friendly and effective production processes.
Chapter 2 explores blockchain for decentralizing the internet, exploring possibilities, future trends, and challenges. The study examines scientific articles on the transformation of blockchain technology in industries such as banking, agriculture, finance, and transportation, identifying trends, new areas, and research problems. This study explores the potential of blockchain technology for decentralizing the internet, addressing privacy, security, and censorship concerns, while also exploring its trends and issues. This study also examines the potential of blockchain technology to revolutionize internet like data storage, content delivery, and identity management, while examining current acceptance patterns for decentralization. Smart contracts improve transaction efficiency and transparency, while interoperability standards enable seamless communication across blockchain networks, despite substantial obstacles in decentralising the internet. Scalability, regulatory uncertainty, interoperability, and environmental difficulties all pose challenges to blockchain networks, preventing widespread adoption and compromising the interoperability and energy usage of internet infrastructure.
Chapter 3 discovers the data science and big data analytics revolution for sustainable supply chains. Optimizing supply chain logistics has become crucial in an era where sustainability is of paramount importance. The confluence of environmental consciousness, economic viability, and technological advancements has paved the way for innovative solutions. At the forefront of this revolution is the integration of Data Science and Big Data Analytics (BDA), offering unprecedented opportunities to enhance efficiency, reduce environmental impact, and forge a more sustainable future for Supply Chain Management (SCM). This chapter delves into the intricate intersection of supply chain logistics, data science, and big data analytics, exploring the transformative potential of these technologies in optimizing sustainable practices. This study aims to highlight how businesses can use data-driven insights to transform their supply chain operations and promote a sustainable balance between economic growth and environmental responsibility by thoroughly examining important concepts, approaches, and practical applications. Further, attempts are made to explore the barriers and challenges, highlighting the future potential, and inspiring more research and pedagogical advancements in this domain.
Chapter 4 explores Artificial Intelligence and Machine Learning for predicting natural disasters in smart city infrastructure. As smart cities emerge worldwide, the integration of real-time data in multiple sectors, including transportation, energy, and public services, is starting to surface. Among the unexplored frontiers within this data landscape, vital for fortifying resilient and efficient urban services, lies the realm of natural disaster prediction and mitigation. This chapter aims to address the first phase in developing intelligent, data-driven tools for natural disaster prediction in smart city infrastructure. The integration of Artificial Intelligence (AI) and Machine Learning (ML) models within smart city infrastructure stands as a pivotal advancement, offering an array of benefits in disaster prediction and management. Techniques such as neural networks, decision trees, random forests, and support vector machines have proven instrumental in this regard. This chapter is highly relevant to the field currently, due to the increasing sophistication and range of AI technologies, as well as the growing capacity for real-time data collection facilitated by smart city technology. The focus on combining AI approaches with the unique challenges and opportunities presented by smart city infrastructure makes this a timely and essential project for the field, as well as an excellent driver for knowledge exchange between the academic community and industry in both AI and smart city development. Despite facing obstacles such as legal and social concerns, the ongoing progress and widespread acceptance of AI-based solutions offer significant potential for strengthening disaster preparedness and improving the overall quality of life in cities.
Chapter 5 is focused on utilizing fractal analysis and machine learning to enhance natural disaster prediction and foster resilient smart city development. Disasters, which occur at irregular intervals, are natural phenomena such as floods, landslides, etc. Nowadays, they are more frequent due to the effects of global warming. These natural phenomena possess complex dynamics, making their precise prediction a difficult task. The intention of smart cities is to utilize advanced technologies for flexible and sustainable solutions to such threats. In this chapter, a new and efficient approach is presented for covering the natural threat predictions by using mathematical fractal analysis to identify self-similar, multi-scale information. This information is put into machine learning prediction models for predicting natural disasters. In addition, fractal generation techniques are employed to create virtual replicas of the metropolitan infrastructure in order to model reactions to extreme event forecasts. The proposed multimodal method at the nexus of urban modeling, machine learning, and fractal mathematics offers a ground-breaking way to improve climate preparedness for smart cities. Adaptive resilience planning and early warning systems can benefit from the application of data-driven infrastructure response techniques and catastrophe prediction methodology.
Chapter 6 deals with the design and analysis of an adaptive traffic signal control system. In many major cities across the world, traffic congestion is a huge issue that makes commuting a misery. One may manage traffic at several significant crossroads by incorporating either an autonomous traffic signal controller or a traffic police. Traditional traffic signal systems, however, are not adaptable to varying traffic intensities since they are predicated on the idea of predetermined hours allotted on each side of the intersection. Based on the number of cars parked on the same street, including VIP vehicles and ambulances, the priority of the traffic light may need to be adjusted. We suggest creating an adaptive-based traffic signal system where the traffic light automatically changes when there is traffic at a junction. Chapter 7 deals with harnessing AI for proactive cybercrime detection. Within the swiftly evolving landscape of the virtual age, the pervasive nature of technology has revolutionized the way we speak, join, and share statistics. At the same time as the internet has introduced remarkable possibilities for worldwide interaction, but it has also given upward thrust to new challenges, with cyberbullying rising as an important subject. The increasing incidence of online harassment, intimidation, and abuse necessitates innovative solutions to ensure people’s safety in the virtual realm. AI can play a vital role in cyberbullying prevention by supporting the identification, monitoring, and response to instances of online harassment. The objective of this chapter is to automate the identification of cyberbullying on social networks; therefore, a system is presented to automatically detect cyberbullying on social networks. This chapter delves into the transformative role of Artificial Intelligence (AI) in preventing and mitigating cyberbullying, exploring the potential of cutting-edge technologies to create safer online spaces utilizing natural language processing (NLP), Recurrent Neural Networks (RNN), among various methodologies.
Chapter 8 presents and explores some cutting-edge spam email detection techniques. Unsolicited emails, often known as spam, pose a persistent and serious problem in today’s digital world and have become a matter of concern for every email user over the past few years....
