Reinventing Technological Innovations with Artificial Intelligence

Digitization has a substantial impact on almost all facets of fashion, starting from the designing of a fashion item to its production and its usage by consumers. Fashion has always been evolving with emerging technologies. With the beginning of Artificial Intelligent (AI) enabled technologies, the fashion industry has become as dynamic as technology, emerging as a forward-looking trend giant. The impact of AI on the augmentation of fashion trends is unquestionable and the industry has witnessed its fast move from 4.0 to 5.0 with the use of advanced technology. Although fashion is changing at a very fast pace with AI, fashion professionals have raised the socioeconomic impact of AI on the fashion industry, including the Green Economy (GE) issues, thus, making the exploration of the phenomenon essential. This chapter explores how AI-enabled technology in the fashion industry and fashion environment, is influencing the GE status of the fashion industry, especially in the post-COVID-19 era of innovative e-commerce fashion.

Artificial intelligence (AI) has gained enormous usage in business in recent years. Still, in regard to measuring business ethics and morality, otherwise called corporate social responsibility, the use of Artificial Intelligence is limited to a greater extent. In this regard, the purpose of the study is to conceptually formulate the implementation of AI in CSR programs. For gathering data, the study utilised a structured questionnaire. Employers from a range of governmental and commercial organisations provided the primary data for the study. Using AMOS 21's Structural Equation Modelling (SEM) and SPSS 21, the projected model was empirically tested. The Research concludes that AI can strengthen effective CSR practices. The research also uses SEM to establish a cause-and-effect connection between the research variables. 

Artificial intelligence (AI) has recently become one of the most heavily debated themes in the technological world. AI is active in numerous fields and now it has lately entered the healthcare sector. In addition to biomarkers, the use of AI is increasing in a variety of applications such as genetic editing, disease prediction and diagnostics, drug development, personalized treatment, and so on. Accuracy in disease diagnostics is essential for effective and efficient treatment as well as patient safety. Artificial intelligence is a wide and varied field of data, analytics and continuously evolving insights that meet the needs of the healthcare sector as well as patients. The purpose of the many subsections in this book chapter is to shed light on how AI integrated with machine learning (ML) & Deep-learning (DL) techniques operate in various disease diagnosis domains, medication discovery, medical visualization, digital health records, and electro-medical equipment. 

By combining information in the form of image alternatives with a software programme that stores knowledge on real images, augmented and virtual reality (AVR) technologies aid in the explanation of concepts. This methodology is developed to improve educational learning through two-dimensional media in education. In bloom taxonomy approach to teaching, integrating AR technology with academic content results in a new kind of automated application that serves to reinforce the usefulness of teaching-learning for learners in real-world situations. The learning outcome, which includes knowledge level and performance engagement, has a significant impact on all phases of higher education, from course planning to student evaluation and grading. AVR is a novel technique that combines elements of omnipresent computing, tangible computing, and social computing. This mode offers different affordances, combining the physical and virtual worlds, with continuous and implicit purposes of reading and interactivity. Digital resources are high-potential educational technology that enhances learning by supporting the learning environment through numerous e-resources. The various universities and Technical and Vocational Education Training (TVET) institutions give students an opportunity to complete an experiential learning component in their studies in order to complete their qualifications with the help of AVR implementation. This chapter provides an introduction to Augmented and Virtual reality (AVR) technology, the current status in education from different viewpoints, key technologies, and strategies mentioned in the context of higher educational learning output of students through these applications.

The crime scene, a place where a crime has been or is suspected to have occurred or where the evidence related to a crime was found, is a vital part of the investigation as it contains all the major information about a crime. A keen eye for the crime scene can determine the possible Modus Operandi of a crime and establish Corpus Delicti in a court of law. As per Indian law, we are allowed to visit a crime scene once only and if we want to visit again, we have to take permission but it is of no use. During a revisit to a crime scene, the chance or probability of finding any evidence is nearly 0%. Documenting a crime thus became a very crucial step. By using ordinary methods for documenting the crime scene we cannot give a visual or walk to the actual crime scene. It’s just a physical view of the documents. It is, therefore, critical to visually capture the crime scene and any potential evidence to aid the investigation. The current demand is for Augmented Reality and Virtual Reality. Virtual Reality is a wholly virtual view of the scenario, whereas Augmented Reality reflects a real-world context. VR is a type of advanced user interface that comprises a real-time simulation of a real-world environment with which the user interacts through numerous sensory channels: sight, hearing, touch, smell, and taste. The 3D reconstruction and visualization of crime situations such as criminal assaults, traffic accidents, and homicides is a new method of criminal investigation that has the potential to improve efficacy. To produce an accurate and immersive virtual environment, modern 3D recording and processing methods, such as AR and VR, are used. Immersion in a virtual environment, on the other hand, allows for various points of view.

Rapid advances in artificial intelligence (AI) are enhancing the performance of many sectors and enterprises, including green supply chain management. Innovative technologies include machine learning, IoT, and big data. AI in the manufacturing industry aims to utilise automation in production processes, better planning and forecasting, and quality products. Small and medium enterprises play a significant role in reducing carbon emissions, which has turned out to be an even more vital factor for the manufacturing industry in the past two decades. Supply chain management is one of the manufacturing’s utmost areas demanding a change. Sustainable procurement enables firms to access resource recycling, efficient production, channel distribution, and end consumption to lessen their environmental impact. The 2030 Agenda for Sustainable Development (2015) is a well-thought-out synthesis of discussion that establishes sustainable growth as a critical issue for the global community. The accomplishment of sustainable goals makes it essential to develop a system of practice. This is especially important for India, which has a history of high labour intensity and industrialization. This review paper will analyse the future outlook of the market for Artificial Intelligence (AI) in GSCM and green sustainability.

Multi-Agent-Based Decision Support Systems (MADSS) have emerged as powerful tools for facilitating decision-making in complex and dynamic environments. This chapter provides an overview of MADSS, highlighting their fundamental concepts, key components, and applications. MADSS leverage the principles of multi-agent systems, artificial intelligence, and decision support systems to enable collaborative decision-making among multiple autonomous agents. The chapter begins by introducing the concept of multi-agent systems, emphasizing the advantages they offer in terms of adaptability, flexibility, and scalability. It then explores the integration of decision support systems within this framework, enabling agents to make informed decisions by analyzing vast amounts of data, evaluating various alternatives, and considering multiple criteria. The architecture of MADSS is discussed, focusing on the interactions among agents, the coordination mechanisms employed, and the information exchange protocols utilized. Various agent types, such as user agents, decision agents, and knowledge agents, are described, along with their roles and responsibilities within the system. The chapter further explores the different approaches and techniques used in MADSS, including rule-based systems, expert systems, machine learning, and optimization algorithms. It highlights the importance of agent learning and adaptation to improve decision-making capabilities over time. The applications of MADSS across various domains are presented, including finance, supply chain management, healthcare, and transportation. Case studies illustrate how MADSS can enhance decision-making processes, improve efficiency, and optimize resource allocation in complex real-world scenarios. Lastly, the chapter discusses the challenges and future directions of MADSS. Issues such as agent coordination, trust among agents, and handling uncertainty are addressed. The potential of integrating emerging technologies like blockchain, the Internet of Things (IoT), and big data analytics is also explored, envisioning more sophisticated MADSS capable of handling larger-scale problems.

The farming sector is considered the backbone of the Indian economy. The demand for water is continuously increasing with rising population density. Water is frequently wasted on the land due to unscientific irrigation techniques and unpredictable weather conditions. The efficiency of irrigation networks is challenged by the extremely variable and farmer-dependent irrigation water demand. Each farm's irrigation intensity is influenced by both accurate and inaccurate variables, as well as the farmer's behaviour. Accurate and inaccurate variables include soil moisture, crop’s water requirement, and climate conditions. An auto solar-powered smart irrigation system enables users to accurately time watering cycles by tracking the soil moisture at numerous sites across the field. This system also brings down the utilization of grid power to save electricity as per the energy crisis for Indian farmers. The objective of our work is to use an automated watering system to reduce the farmer's manual involvement in the field at an effective cost. The artificial intelligence (AI) system is based on sensing a control mechanism with required correction for the maximum yielding of irrigation. It also optimizes the water requirement of a variety of crops. A more accessible and more affordable solution to this issue is provided by the present work. The conventional methods of irrigation used in India are sprinklers and floodtype systems. A large amount of water gets wasted, and crops are destroyed due to the uneven slopes of the field. These problems can be resolved by incorporating an intelligent automated irrigation system with an AI.

With the fast spread of connectivity via 5G and IoT (Internet of Things), the Smart City Artificial Intelligence (AI) software industry is expected to reach a massive value of $ 4.9 billion by 2025 globally [1]. In India, the AI market is slated to reach $ 7.8 billion by 2025 at a CAGR of 20.2% as per an International Data Corporation (IDC) report [2]. This chapter explains how AI can be used in the ambitious Smart Cities Mission (SCM) announced by the Government of India in 2015 [3]. Beginning with the conceptual understanding of the SCM, the chapter introduces AI as a useful aid to urban planning thereby creating a safer and sustainable future for its citizens. Applications of AI in Smart cities are then discussed followed by a brief discussion on the prevailing best practices. Challenges in creating AI-enabled smart cities in India are outlined followed by the conclusion which chalks out the road ahead for AI-enabled smart cities in India

Digitization has a substantial impact on almost all facets of fashion, starting from the designing of a fashion item to its production and its usage by consumers. Fashion has always been evolving with emerging technologies. With the beginning of Artificial Intelligent (AI) enabled technologies, the fashion industry has become as dynamic as technology, emerging as a forward-looking trend giant. The impact of AI on the augmentation of fashion trends is unquestionable and the industry has witnessed its fast move from 4.0 to 5.0 with the use of advanced technology. Although fashion is changing at a very fast pace with AI, fashion professionals have raised the socioeconomic impact of AI on the fashion industry, including the Green Economy (GE) issues, thus, making the exploration of the phenomenon essential. This chapter explores how AI-enabled technology in the fashion industry and fashion environment, is influencing the GE status of the fashion industry, especially in the post-COVID-19 era of innovative e-commerce fashion.