Faculty Publications
Permanent URI for this communityhttp://192.168.24.11:4000/handle/123456789/7
Scholarly Publications by Integral Academia
Browse
20 results
Search Results
Item AI for Public Good: Managing Health Risks and Opportunities(Nitya Publications, 2025) Tulika SinghArtificial Intelligence (AI) is reshaping public health paradigms, offering transformative benefits while posing novel legal and ethical challenges. This chapter explores AI’s role in advancing health as a global public good, emphasizing the need for a rights based, equity oriented governance framework. It redefines the concept of "public good" in the digital health era, analyzing how AI technologies intersect with the right to health and ethical imperatives outlined by WHO, UNESCO, and UNHRC. The chapter examines AI applications in disease surveillance, diagnostics, telemedicine, and precision medicine, highlighting both their promise and the risks of algorithmic bias, data inequities, and cybersecurity threats. It scrutinizes the Indian legal ecosystem including the IT Act 2000 and the Digital Personal Data Protection Act 2023 alongside global regulatory frameworks such as the EU Artificial Intelligence Act 2024, US FDA guidelines, and WHO's governance principles for health AI. Through a comparative legal lens, the chapter advocates for ethical AI deployment via transparency, explainability, and institutional capacity building. It proposes policy innovations like ethics by design, regulatory sandboxes, and public private collaboration to align AI with public health values. The chapter offers a roadmap for inclusive, accountable, and resilient AI governance that upholds human dignity in global health systems.Item Safeguarding Health Data: Navigating Legal Complexities in the Digital Age(Nitya Publications, Bhopal MP India, 2025) Wasim Ahmad, Mohd. Tahir HusainIn today’s rapidly evolving digital landscape, healthcare data emerges as both a catalyst for medical innovation and a source of significant privacy concerns. This chapter provides an in-depth legal examination of healthcare data protection, tracing the transition from conventional health records to intricate digital networks. As health data is inherently sensitive, the chapter scrutinizes international legal frameworks, including the EU’s GDPR, the US’s HIPAA, and India’s Digital Personal Data Protection Act, 2023, identifying both their robust features and existing gaps. Key legal concepts, such as informed consent, data ownership, confidentiality, and cross-border data transfers, are analyzed to understand their real-world implications. Moving beyond theoretical discussions, the chapter addresses practical risks—from cyberattacks to telemedicine vulnerabilities, and evaluates innovative technological defenses, including AI and blockchain. Furthermore, it considers regulatory practices, the involvement of data protection authorities, and enforcement tactics, ultimately reflecting on the challenging interplay between healthcare innovation and data privacy.Item Artificial Intelligence: A Socio Legal Paradigm(The Lawgical Junction and MJS Publishing House, 2024) Seema Siddiqui, S. Fatima Zahara Jafri, Sadaf KhanItem Integrating AI Approaches in Teaching-Learning Associated with the Mitigation of Air Pollution: A Comprehensive Analysis(Bentham Science Publishers, 2024) Rahila Rahman Khan, Ahmad Faiz Minai, Rushda SharfPollution is a major hazard to ecosystems, human health, and the stability of the global climate. Acknowledging the shortcomings of traditional methods, this thorough examination investigates the incorporation of Artificial Intelligence (AI) as a revolutionary instrument for reducing air pollution. A summary of the current situation of air pollution is given in this chapter, with a focus on its significant effects. It provides an overview of AI's ability to address environmental issues and lays the groundwork for a full investigation of its uses. This chapter uses satellite technology, sensor networks, and remote sensing to demonstrate how AI is revolutionising air quality monitoring, predictive modelling, and early warning systems. It also emphasizes AI's ability to identify pollution sources, presenting methods for measuring pollution sources and incorporating AI findings into urban planning. It clarifies AI's critical role in influencing public involvement, awareness, and evidence-based policymaking. It provides examples of AI-driven air pollution solutions from around the world, together with best practices and insights into successful projects. It discusses privacy and equality issues as well as ethical issues related to AI in environmental monitoring. It also points the way for upcoming discoveries and lines of inquiry, enabling ongoing progress.Item ARTIFICIAL INTELLIGENCE FOR EXTENSION ADVISORY SERVICE(DOORS PUBLICATION, 2024) Sunil Kumar, Mustfa HussainArtificial Intelligence (AI) is revolutionizing agricultural extension advisory services by providing precise, data-driven, and personalized recommendations to farmers. AI-based systems analyze weather patterns, soil conditions, and crop data to offer timely advice on pest control, irrigation, and nutrient management. Chatbots, expert systems, and predictive models enhance communication between farmers and experts, ensuring rapid problem-solving and informed decision-making. By automating advisory services, AI improves efficiency, accuracy, and scalability of agricultural extension. It supports sustainable farming, reduces risks, and bridges the knowledge gap in rural areas.Item Role of Artificial Intelligence in Hydrological Modelling(Multi Spectrum Publications, 2025) Nida Fatma, Amina Jafri, Monowar Alam Khalid, Arpit ChoukseyHydrological modelling involves simulating the movement, distribution and behaviour of water within a watershed or catchment area using mathematical equations and computational tools. It uses simplified representations of realworld systems to understand, predict, and manage water resources, encompassing processes like precipitation, runoff, river discharge, evaporation, infiltration, flood forecasting and groundwater flow. These models are useful in water resource management, flood control, drought assessment, and climate change impact studies. These models are useful in predicting the quantity and quality of water in a watershed necessary for the effective management of water resources [Kambarbekov and Baimaganbetov, 2024]. There are many traditional hydrological models developed to apply on numerous watersheds to find out the impact of climate and soil properties on hydrology and water resources [Devia et al., 2015]. The best model is the one that gives close to reality results with the use of the least parameters and the complexity of the model [Devia et al., 2015]. These models are classified into the following types:Item Role of Artificial Intelligence in Teaching and Learning Chemical Sciences(Bentham Science Publishers Pte. Ltd. Singapore, 2024) Shahla Tanveer, Mariyam Tanveer, Ayesha TanveerArtificial Intelligence (AI) is revolutionizing our everyday tasks, and education has certainly not been left behind. AI harnesses technologies such as machine learning, natural language processing, and deep learning, to execute tasks and elevate our problem-solving capabilities. The infinite possibilities that arise due to interactions between atoms and molecules further leading to bond formation are nearly impossible for a human to comprehend. Thus, AI is playing a vital role in understanding chemistry by accelerating research, designing novel molecules, and optimizing processes. AI plays a diverse role, from assisting in drug discovery research to identifying new drug targets to supporting personalized learning experiences that aid students in their learning journeys. AI-powered adaptive learning system identifies a student’s performance and tailor the learning requirements accordingly. Students receive real-time feedback and personalised content helping them to understand the concepts more easily. AI is being used to develop interactive simulations and customized learning programs to help students learn chemistry more efficiently. Virtual laboratories driven by AI provide a safe and reachable environment for hands-on experience. This allows students to be inquisitive about chemical reactions, molecular structures, and their spectroscopic analysis in a risk-free environment. Some examples include Chat GPT, which helps create a customized learning experience for students while helping them answer their queries, an AI-powered tutoring system known as Socratic, which helps the students learn chemistry concepts, and Molecules in Motion (an AI-powered simulation) to inspect the behaviour of molecules. This chapter discusses how the union of AI and chemical sciences has accelerated innovation in the field of chemistry and can further improve learning outcomes.Item AI Tools for Teaching-Learning Chemistry(Bentham Science, 2024) Saman Raza, Satya, Tahmeena Khan, Manisha SinghArtificial Intelligence (AI) is quickly becoming ubiquitous, with applications in all spheres of life. The education sector is also not untouched, in fact students are now relying on AI tools for studying, doing homework, making assignments and reports, and preparing for exams. Teachers are also using AI tools to enhance classwork and assessments. The use of AI in chemistry education is rapidly growing and many AI tools are proving to be quite useful in this regard. However, chemistry being a vast subject with lots of concepts, laws, formulae, reactions, and applications, requires deep understanding and comprehension, which is a challenge for these tools as they are not always accurate and consistent in providing answers. The present chapter gives a brief account of the uses of AI in chemistry, with teaching-learning chemistry, in particular. It explores the advantages and disadvantages of using AI in cAbstract: Artificial Intelligence (AI) is quickly becoming ubiquitous, with applications in all spheres of life. The education sector is also not untouched, in fact students are now relying on AI tools for studying, doing homework, making assignments and reports, and preparing for exams. Teachers are also using AI tools to enhance classwork and assessments. The use of AI in chemistry education is rapidly growing and many AI tools are proving to be quite useful in this regard. However, chemistry being a vast subject with lots of concepts, laws, formulae, reactions, and applications, requires deep understanding and comprehension, which is a challenge for these tools as they are not always accurate and consistent in providing answers. The present chapter gives a brief account of the uses of AI in chemistry, with teaching-learning chemistry, in particular. It explores the advantages and disadvantages of using AI in cAbstract: Artificial Intelligence (AI) is quickly becoming ubiquitous, with applications in all spheres of life. The education sector is also not untouched, in fact students are now relying on AI tools for studying, doing homework, making assignments and reports, and preparing for exams. Teachers are also using AI tools to enhance classwork and assessments. The use of AI in chemistry education is rapidly growing and many AI tools are proving to be quite useful in this regard. However, chemistry being a vast subject with lots of concepts, laws, formulae, reactions, and applications, requires deep understanding and comprehension, which is a challenge for these tools as they are not always accurate and consistent in providing answers. The present chapter gives a brief account of the uses of AI in chemistry, with teaching-learning chemistry, in particular. It explores the advantages and disadvantages of using AI in chemistry education and how AI can be incorporated in classroomItem AI-DRIVEN STRATEGIES FOR MODERN AGRIBUSINESS(Stella International Publication, 2024) Srishti ThakurThe integration of Artificial Intelligence (AI) into agriculture is revolutionizing the agribusiness sector, enabling smarter, more efficient farming practices. AI technologies such as machine learning, deep learning, computer vision, and natural language processing are transforming key areas like crop monitoring, precision farming, supply chain optimization, and farm automation. By automating tasks, optimizing resource use, and improving decision-making, AI helps address the growing global food demand while minimizing environmental impact and enhancing sustainability. However, challenges such as data quality, high initial investment, and workforce skill gaps remain significant barriers. Despite these hurdles, AI-driven solutions are proving to be instrumental in enhancing productivity, profitability, and environmental stewardship in agriculture. Looking forward, the combination of AI with other emerging technologies like blockchain promises to further enhance the transparency, efficiency, and adaptability of agricultural systems, shaping the future of agribusiness in an era of climate change and resource scarcity.Item Role of Emotional Intelligence in Next Generation Artificial Intelligence System(Book Rivers, HN 22 Kanchan Nagar Kalyanpur Lucknow UP, 2024) Huma NazThe combination of emotional intelligence with next-gen artificial intelligence users in a new era of human-machine collaboration. These systems are able to interact more naturally - and empathetically - with their users by enabling artificial intelligence to recognize, understand, and respond to human emotions. This in turn is expected to improve user experience in different kinds of applications ranging from customer service to healthcare, education and personal virtual agents. Awareness of user emotions: Emotionally intelligent artificial intelligence can understand the emotional cues of their users using various technologies such as Natural Language, facial recognition and voice analysis to naturally respond with the right emotion and ensuring trust and delight. Customer service is an example where emotionally smart Artificial Intelligence leads to more successful resolution of queries by being able to adjust to the emotional tone of the user, which translates to improved customer loyalty and satisfaction. In healthcare, this could mean Artificial Intelligence system could act as a first point of mental health support detecting distress and triggering relevant interventions. Within education, Artificial Intelligence with emotional intelligence can better hold students’ attention by being able to adjust the interaction if the student shows an emotional response and in turn, support the improvement of learning results. Integrating Emotional Intelligence into Artificial Intelligence has its own issues such as preserving privacy, avoiding biases, and maintaining transparency. However, the potential benefits, including improved communication, better decision-making, and increased resilience, underscore the significance of this advancement. As Artificial Intelligence systems become more emotionally aware, they will not only execute tasks more efficiently but also build deeper, more meaningful connections with humans. This evolution marks a crucial step forward in the development of Artificial Intelligence, highlighting the importance of emotional intelligence in the future of technology.