AI in creating inclusive work environments for neurodiverse employees
Artificial intelligence; Inclusivity; Neurodivergent; Neurodiversity; Workplace diversity
Purpose This study aims to examine the increased focus on neurodiversity in contemporary businesses. It shows how inclusive policies can capitalize on the special abilities of people with neurodiverse backgrounds, including their extraordinary problem-solving abilities, meticulous attention to detail and creative thinking. These policies benefit the individuals and contribute to a more diverse and innovative workplace. Design/methodology/approach Data was collected through semistructured interviews with HR experts and neurodivergent employees. The qualitative data were manually analyzed and coded, and themes were identified. Findings The results highlight the significant benefits of accepting neurodiversity in the workplace, enlightening the audience about its potential. For instance, artificial intelligence (AI) can be used to anonymize resumes, removing potential biases related to gender, ethnicity or age. In addition, AI can help in identifying the unique skills and strengths of neurodivergent employees, enhancing the fit between job responsibilities and their abilities. This study also emphasizes the wider effects of accepting neurodiversity on employee satisfaction, productivity and organizational innovation. This study promotes a deep learning framework that combines human-centered strategy with strategic methods to maximize the participation of neurodiverse workers and foster a more creative and dynamic corporate culture, convincing the audience of its benefits. Research limitations/implications This study is limited by its qualitative nature and relatively small sample size, comprising 15 HR professionals and 20 neurodivergent employees, which restricts generalizability. The sensitive nature of neurodiversity also made participant recruitment challenging, with some individuals hesitant to disclose their condition. In addition, companies were reluctant to share internal AI practices due to confidentiality concerns. The research focused on a select set of organizations, primarily from specific regions, limiting cross-cultural applicability. Furthermore, the absence of AI developers in the sample means insights into technical tool design and implementation remain unexplored, suggesting a gap for future multidisciplinary research. Practical implications This study provides actionable insights for HR professionals and organizational leaders aiming to improve neurodiverse hiring and support systems. It identifies specific AI tools such as Grammarly, Otter.ai and Pymetrics, that can be integrated into recruitment and workplace settings to enhance communication, reduce sensory overload and match roles to individual strengths. Organizations can use the deep learning framework proposed to design more inclusive policies and infrastructure. Training managers and customizing AI-driven accommodations can improve retention, engagement and performance among neurodiverse talent. This research supports firms in developing more equitable, adaptive and innovative environments aligned with diversity and inclusion goals. Social implications This study promotes a societal shift in how neurodivergent individuals are perceived and supported in the workforce. By emphasizing ability over deficit and proposing inclusive AI integration, it helps reduce stigma and encourages broader acceptance of cognitive diversity. The findings advocate for universal accommodations that do not require self-disclosure, promoting dignity and equity. Improved employment outcomes for neurodiverse individuals contribute to economic inclusion, reduce unemployment rates and challenge ableist norms. The research also aligns with broader Diversity Equity and Inclusion (DEI) movements, inspiring organizations and policymakers to build socially responsible frameworks that reflect the value of every individual, regardless of neurological difference. Originality/value This paper offers original value by exploring the underresearched intersection of AI and neurodiversity inclusion in the workplace. It contributes novel insights through qualitative analysis of HR professionals and neurodivergent employees, highlighting the role of AI in reducing hiring bias, customizing work environments and enhancing employee well-being. By proposing a deep learning framework and cataloging AI tools matched to neurodiverse conditions, this study bridges theory and practice. It uniquely positions AI as both a technological and ethical enabler for inclusive employment, making it highly relevant for scholars, practitioners and policymakers aiming to foster equitable, future-ready workplaces. 2025 Emerald Publishing Limited
George, Ginu; Kulkarni, Mugdha; Varghese, Bindi
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1108/AIA-05-2025-0046 https://www.scopus.com/pages/publications/105024714959?origin=resultslist
George G., Department of Academics, Westford University College, Sharjah, United Arab Emirates; Kulkarni M., Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India; Varghese B., Department of School of Business Management, CHRIST (Deemed to be University), Bangalore, India
The use of artificial intelligence (AI) techniques for data collection and analysis is examined in this chapter. It also looks at the benefits, challenges, and future directions. It provides a broad overview of AI techniques and illustrates the use of generative adversarial networks (GANs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. in this area. Data recovery is an essential process when trying to recover lost or damaged data. For AI methods like CNN, the retrieval of image and video data has shown great promise. Using the power of deep learning, CNNs can search for patterns in data, assisting in the reconstruction and restoration of lost information. On the other hand, RNNs excel at retrieving serial data, such as text or time series data. These networks can efficiently learn dependencies and contexts, which makes it possible to precisely reconstruct missing or imperfect sequences. AI-based data analytics provides businesses with insightful information and opportunities. GANs, for example, are increasingly being used to generate and improve data, enabling organizations to expand the size of their datasets and improve the efficacy of their analytical models. Large amounts of data can also be divided up using A-based clustering algorithms, which are also well classified and provide insightful analysis and interpretation. In the gathering and analysis of data, AI has many benefits. Businesses can process and analyze enormous amounts of data in a fraction of the time thanks to this productivity-boosting automation of challenging and time-consuming tasks. By reducing bias and human error, AI techniques also increase accuracy, resulting in results that are more dependable and consistent. Additionally, AI-driven insights assist businesses in spotting trends, uncovering buried patterns, and coming to wise decisions that may not be apparent using traditional analytics methods. Due to privacy concerns, ethical considerations, interpretability, transparency, and accountability, AI deployment in data recovery and analysis is difficult. Future directions include collaboration between humans and AI, edge computing integration, and privacy-preserving methods. In conclusion, organizations looking to maximize their data assets stand to benefit greatly from the application of AI techniques to data analytics and data retrieval. 2024 selection and editorial matter, Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, and Naveen Kumar.
Singh A.; Joshi A.; Sankhla M.S.; Saini K.; Choudhary S.K.
Artificial Intelligence in Forensic Science: an Emerging Technology in Criminal Investigation Systems, pp. 142-164.
Singh A., School of Forensic Science and Risk Management, Rashtriya Raksha University, Gujarat, India; Joshi A., Department of Forensic Science, School of Life Science, Christ Deemed-to-be University, Karnataka, Bangalore, India; Sankhla M.S., Department of Forensic Science, University Center for Research and Development (UCRD), Chandigarh University, Punjab, Mohali, India; Saini K., School of Computing Science & Engineering (SCSE), Galgotias University, Delhi, India; Choudhary S.K., Rashtriya Raksha University, Gujarat, India
This current research chapter focuses on the different areas of e-learning where AI can be implemented to make e-learning a better experience. E-learning is a 24/7 platform where learners can gain knowledge at the convenience of their home and timeframe. AI can help such learners with different adaptive technologies in clarifying the doubt, identifying the problem area of the learner and providing them a customized learning solution. Adaptive learning suggested that the learning pace is different for different learners. It must be made sure that the educational supplies and amenities provided must fit the requirement of each learner; else, it will lose its essence. There are different AI features to enhance the learning experience of e-learning. The providers must keep this in mind that the acquired information about learners must be wisely used while implementing the AI technology to e-learning mode so that the blended model can provide an enriching experience to the end-user. Cognitive learning can be a key to constructive, collaborative and contextualized execution of AI-enabled learning processes. Maximization of AI effectiveness as a tool of e-learning can be brought only when it is implemented to overall program pedagogy and is monitored for continuous improvement. The Institution of Engineering and Technology 2021.
E-learning Methodologies: Fundamentals, technologies and applications, pp. 107-131.
Institution of Engineering and Technology
Sinha M., CHRIST (Deemed to be University), Bengaluru, India; Fukey L.N., CHRIST (Deemed to be University), Bengaluru, India; Sinha A., Robonomics AI India Pvt Ltd, Bengaluru, India
AI in Financial Fraud Detection and Prevention
anomaly detection; Artificial intelligence (AI); behavioral biometrics; deep learning; fraud detection; machine learning; NLP; predictive analytics
Fraud has always posed problems to financial institutions and with the rapid growth of digital transactions, and its complexity has increased beyond detection. Normal methods of fraud detection that depend on rules only are severely outdated and ineffective against newer types of schemes. It is now imperative to employ more sophisticated mechanisms for fraud detection considering the evolvement of financial crimes. The massive amounts of transnational data that need to be analyzed to detect fraudulent patterns can now be processed with medium to high levels of accuracy using AI with the help of machine learning, deep learning, and natural language processing (NLP), and fraud detection and prevention have been transformed for the better. Algorithms of machine learning like supervised, unsupervised, and reinforcement learning are central to the features of fraud detection. Suspicious transactions are detected during supervised learning by using already existing fraudulent data, whereas unsupervised learning detects all anomalies without any prior defined labels. Through real-time data input, reinforcement learning adjusts its detection methodologies. Deep learning models such as convolutional neural networks and recurrent neural networks identify and process fraud indicators hidden within messages or intricate datasets. Moreover, through intricate analysis of customer interactions, NLP techniques detect fraudulent activities by identifying phishing attempts and deceptive communications. The chapter touches upon the issues of implementing AI oriented fraud detection in realms like e-commerce and entertainment. Identifying fraud from e-commerce is complicated by factors like high volume of transactions, false positives, privacy issues, and the endless frameworks of fraud. Finally, the chapter provides a summary of the main insights and makes recommendations for further investigation like incorporating blockchain, federated learning, and higher explainability to bolster AI powered fraud detection systems. 2026 Scrivener Publishing LLC.
Haseena, Shaik Valli; Jasawani, Neha; Ayasha; Suresh, Gaikwad Beena; Suresh, Gaikwad Beena; Shanavas, Simna
Designing Inclusive Classrooms: Integrating Emerging Technologies for Equity and Social Justice;pp.295-327
https://doi.org/10.1002/9781394389223.ch14 https://www.scopus.com/pages/publications/105034021951?origin=resultslist
Haseena S.V., Christ University, Bengaluru, India, Department of Computer Applications, Presidency College, Bengaluru, India; Jasawani N., Department of Computer Applications, Presidency College, Bengaluru, India; Ayasha, Department of Computer Science, Christ College of Science and Management, Bengaluru, India; Suresh G.B., Department of Computer Applications, Presidency College, Bengaluru, India; Suresh G.B., Department of Computer Applications, CMR University, Presidency College, Bengaluru, India; Shanavas S., Department of Computer Applications, Presidency College, Bengaluru, India
AI in Forensics A Data Analytics Perspective
Artificial intelligence (AI) is rapidly becoming the most significant science in all areas of life, and forensic science is one of the fields benefiting from it. Forensics can be defined as a study of crime via the use of scientific methods and techniques. Around the globe, governments invest a large amount of money in developing forensics techniques to prove criminal activities and track criminals effectively. It is now becoming a practice to involve artificial intelligence in supporting the forensic application. It involves a smart and intelligent examination of massive volumes of very complicated data. As a result, AI is becoming an excellent solution for addressing many of the complicated issues that now exist in forensics. For example, AI proves more effective in skeleton-based human identification compared to the traditional skull/skeleton superimposition method. AI can be used to pool meta-data generated from multiple sources connected to forensic science and do a meta-analysis on it to simplify complex data. AI finds patterns and uses them to identify/recognize/predict something that is required in crime tracking or criminal/victim recognition. Complex analytics and probabilistic reasoning are used to recognize patterns. Among the most crucial things to forensic science is the identification of specific sorts of patterns in enormous amounts of data. This could include image pattern recognition, in which the program attempts to distinguish between distinct components of an image or a person. Other types of pattern recognition, such as finding patterns in text, may also exist. Artificial intelligence aids in the more accurate recognition of such patterns in complex data. This chapter introduces the reader to several aspects of artificial intelligence that can be used in forensics. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors.
Artificial Intelligence for Cyber Defense and Smart Policing, pp. 41-60.
Jacob L., Department of Data Science, Christ University, Pune, Lavasa, India; Thomas K.T., Department of Data Science, Christ University, Pune, Lavasa, India; Savithri M., Department of Data Science, Christ University, Pune, Lavasa, India
AI in IA: Impact of Artificial Intelligence in Internal Audit: A Qualitative Study
Artificial intelligence; Audit process; Internal audit; Technology
Internal auditing is becoming more crucial as businesses become more complex and extensive. Artificial intelligence (AI) in internal auditing is a trend change that promises to revolutionize how internal auditing functions are performed and delivered through significant improvements in audit quality and operational discipline. This paper reflects on many of the multifaceted impacts of AI on internal auditing functions. This paper intends to investigate how this AI will impact the audit profession. By interviewing ten individual internal audit experts qualitatively, the study shows that AIs implementation will impact the following six critical levels. AI makes it possible for an auditor to (1) spend less time and make the audit more productive, (2) increase coverage, (3) real-time auditing, (4) enhance decision-making, (5) risk assessment and management, and (6) create new advisory services. The findings thus imply a need for a well-defined and consistent audit structure that is flexible enough for auditors to improve their audits. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Contributions to Finance and Accounting, Vol-Part F3769, pp. 451-463.
Manuel A., School of Commerce, Finance and Accountancy, Christ University, Bangalore Central Campus, Bangalore, India; Arumugam S.K., Department of Professional Studies, School of Commerce, Finance and Accountancy, Christ University, Bangalore Central Campus, Bangalore, India
Robotic engineering, with a focus on the combination of artificial intelligence (AI) together with robotics, computers, electronics, and mechanical systems, as well as control system implementations, allows for many inventions. Key applications of AI in mechatronics engineering practice will be advanced production, intelligent robotics, predictive maintenance, and design optimization control. Robotics engineers are able to incorporate AI into their systems such that data can be collected, analyzed, and modeled, then used to enhance the dependability, flexibility, as well as performance of the systems. This chapter researches the engineering integration of AI along with mechatronics and the industries it is disrupting. Moreover, it addresses the basic definition of AI and its main application areas within mechatronics and its prospects toward enabling enhanced control systems, predictive maintenance, design optimization, intelligent robotics, and improved production in any contemporary industry. Such systems may be developed by mechatronics engineers due to the enriched capabilities of AI in data analysis, recognition, and decision-making. This study also addresses the limits and moral issues to the ethics of combining artificial and human power and suggests ideal steps for more study and advancement in the areas outlined. 2026 selection and editorial matter, Pushpalatha Naveenkumar, Vandana Sharma, Gunapriya Devarajan, Azween Abdullah, and Ahmed A. Elngar.
Naveenkumar, Pushpalatha; Sharma, Vandana; Devarajan, Gunapriya
Handbook of AI-Based Mechatronics Systems and Smart Solutions in Industrial Automation;pp.1-20
https://doi.org/10.1201/9781032615790-1 https://www.scopus.com/pages/publications/105017364844?origin=resultslist
Naveenkumar P., Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, India; Sharma V., Computer Science Department, Christ University, Bengaluru, India; Devarajan G., Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, India
AI in Predictive HR Analytics for Talent Management
Employee Retention; Employee Turnover; Machine Learning; Predictive HR Analytics; Random Forest; Talent Management; Workforce Optimization
This paper presents the topic on how Machine Learning (ML) can be used to conduct Predictive HR Analytics to streamline Talent Management practices. The aim of the development of the project is mainly the application of Random Forest as a supervised learning model to forecast turnover of the employees, performance, and career-growth potential. With the historical employee data, such as performance reviews, tenure, and levels of engagement, Random Forest models would help determine the aspects that are significant factors to employee retention and performance. The model is incorporated with HR software solutions such as SAP SuccessFactors that help to gather information seamlessly to make predictions in real-time, and base decisions on data. It can be seen in the findings of this research that this approach to identifying the factors that influence the effort to retain employees based on the likelihood of them leaving was not only more accurate than other methods but much more effective in the retention efforts. Through predictive analytics, organizations are better placed to take the initiative of managing talent, minimizing turnover and streamline workforce productivity, which eventually lead to business success. This research demonstrates that such predictive models based on AI have a high potential to change HR practice. 2025 IEEE.
Rajagopal, Navaneetha Krishnan; Chib, Shiney; Singh, Anjali; Sasidharan, M.; Chacko, Elizabeth; Nargunde, Amarja Satish
Proceedings - 2025 International Conference on Recent Innovation in Science Engineering and Technology, ICRISET 2025;
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICRISET64803.2025.11254762 https://www.scopus.com/pages/publications/105031412748?origin=resultslist
Rajagopal N.K., University of Technology and Applied Sciences, College of Economics and Business Administration, P.O. Box 608, Salalah, 211, Oman; Chib S., Datta Meghe Institute of Management Studies, Rtmnu, Nagpur, India; Singh A., Gl Bajaj College of Technology and Management, Dms, Greater Noida, India; Sasidharan M., Ck College of Engineering and Technology, Department of Mba, Cuddalore, 607001, India; Chacko E., Christ University, Dharmaram College Post, Hosur Road, Karnataka, Bengaluru, 560029, India; Nargunde A.S., Bharati Vidyapeeth (Deemed to be University), Institute of Management and Rural Development Administration, Department of Management Studies, Maharashtra, Sangli, India
AI Meets the Edge: Optimizing Computation Through Intelligent Offloading
Artificial intelligence; Computation offloading; Edge computing; IoT; Reinforcement learning; Resource optimization; Smart cities
The chapter looks into the developing bond between artificial intelligence (AI) and edge computing. In particular, the idea of using AI to intelligently offload computations. As the number of latency-sensitive applications have increased and the use cases for smart devices has widened, resource allocation at the edge has become critical. We discuss AI-based methods that intelligently determine what and when to transfer compute-intense tasks from resource-constrained edge devices to nearby edge servers or cloud environments. Pragmatic methods, RL optimization procedures and ML research exercises are the main focus of the standardized testing. To illustrate real-world examples, smart cities, autonomous vehicles, and industrial IoT are further explored. This chapter focuses on the development of a new hybrid offloading framework, synthesizing some of the greatest qualities of the predictive analytic and real-time learning to put into practice. These challenges including device heterogeneity, network variability, privacy, etc., are elaborated. Finally, in the concluding chapter, we argue the need for open problems that inform the path toward a sustainable, secure, AI-enabled edge computing. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Studies in Computational Intelligence;Volume;1267;pp.149-161
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/978-3-032-16711-8_7 https://www.scopus.com/pages/publications/105038040683?origin=resultslist
Uppal K., Christ University, Bengaluru, India; Vidushi, Christ University, Bengaluru, India; Shoran P., Christ University, Bengaluru, India
AI Solutions for Complex Communication Network Challenges
As communication networks balloon in size and complexity, managing them effectively becomes a monumental task. This chapter explores how Artificial Intelligence (AI) offers a powerful toolkit for tackling the intricate challenges faced by these systems. By leveraging machine learning, deep learning, and neural networks, AI can significantly enhance network performance, optimize resource allocation, and bolster security. Outlining the major hurdles plaguing modern communication networks, such as scalability limitations, latency issues, congestion bottlenecks, and ever- evolving cybersecurity threats. The chapter also acknowledges the ethical considerations and potential risks associated with AI deployment, emphasizing the need for responsible practices. Ultimately, this chapter provides a comprehensive perspective on how AI can become the cornerstone of resilient and efficient communication networks, paving the way for future advancements in this critical field. 2025 by IGI Global Scientific Publishing. All rights reserved.
Rajendran, Rajesh Kanna; Priya, T. Mohana; Blessing, N. R. Wilfred
AI for Large Scale Communication Networks;pp.45-58
https://doi.org/10.4018/979-8-3693-6552-6.ch003 https://www.scopus.com/pages/publications/105007899875?origin=resultslist
Rajendran R.K., Christ University, India; Priya T.M., Christ University, India; Blessing N.R.W., University of Technology and Applied Sciences, Ibri, Oman
AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges
Autonomous car; Explainable artificial intelligence; Intelligent transport system
With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. 2023 IEEE.
Sinha K.; Misra B.; Chakraborty S.; Dey N.
Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023
Sinha K., Christ (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Misra B., Techno International New Town, Department of Computer Science Engineering, Kolkata, India; Chakraborty S., Techno International New Town, Department of Computer Science Engineering, Kolkata, India; Dey N., Techno International New Town, Department of Computer Science Engineering, Kolkata, India
AI that Understands Us: LLM-Based Emotion and Stress Insights from Online Communication
Classification; Emotion; Healthcare; LLM; Social Chat; Social Media; Stress
The large language models (LLMs) show more accurate interpretation of human emotion and psychology from text which is available on the social media chats. This study analyses emotion recognition and stress detection capabilities of fine-tuned LLMs (GPT-2, FLAN-T5 and LLaMA-7B) using text data from social media and conversation logs. The models were tested for performance using the publicly available emotion language datasets, DailyDialog, EmotionX and GoEmotions. The models were evaluated for performance and efficiency using classification accuracy, macro-F1 score, and inference time. The results identify the performance spectrum of the models and the large models' enhanced ability to recognize detailed emotional states. These results offer real world applicability of LLM methodologies to stress detecting and decision-support automated systems in mental healthcare. 2026 IEEE.
Rajak, Akash; Mishra, Siddheshwari Dutt; Vidushi; Bhardwaj, Shashank; Kumar, Amit; Kumar, Sunil
International Conference on Innovative Practices in Technology and Management, ICIPTM 2026;
https://doi.org/10.1109/ICIPTM69057.2026.11465340 https://www.scopus.com/pages/publications/105037801381?origin=resultslist
Rajak A., Krishna Institute of Engineering & Technology (KIET), Uttar Pradesh, Ghaziabad, India; Mishra S.D., School of Computer Science and Engineering, Galgotias University, Greater Noida, India; Vidushi, School of Sciences, Christ (Deemed To Be University), Karnataka, Bengaluru, India; Bhardwaj S., Krishna Institute of Engineering & Technology (KIET), Uttar Pradesh, Ghaziabad, India; Kumar A., Krishna Institute of Engineering & Technology (KIET), Uttar Pradesh, Ghaziabad, India; Kumar S., Krishna Institute of Engineering & Technology (KIET), Uttar Pradesh, Ghaziabad, India
AI tools for enhancing student engagement
Artificial Intelligence (AI) is revolutionizing the learning environment with adaptive technologies that offer new possibilities to engage, support, and empower learners. Throughout this chapter, we examine how AI technologies are transforming student engagement from the past to enable more personalized, responsive, and inclusive learning environments. From intelligent tutoring systems and adaptive learning systems to chatbots, game- based learning, and predictive analytics, AI offers teachers more and more tools to make behavioral, cognitive, and emotional connections with learners. Based on actual case studies of K- 12 and higher education, including DreamBox Learning and Civitas Learning, the chapter discusses real- world success with respectful consideration of the ethical, technical, and social issues of AI deployment. Issues from data privacy through algorithmic bias and student agency protection are discussed in detail. In the future, the chapter discusses briefly trends such as affective computing, AI- powered AR/VR environments, and human- AI collaboration construction for education's future. Rather than recommending AI as a replacement for teachers, the chapter argues for an equitable partnership-where AI as a system itself can be an effective collaborator to human teachers, enhancing their capacity to create rich learning environments. In the end, the chapter is appealing for an intentional, ethical, and equitable stance in adopting AI, so technology can be used in the name of education not only by facilitating engagement, but also in safeguarding human dignity, equity, and creativity. 2026, IGI Global Scientific Publishing. All rights reserved.
Pandey, Vaibhav Kumar; Poonia, Ramesh Chandra
AI Applications and Pedagogical Innovation;pp.201-230
https://doi.org/10.4018/979-8-3373-5092-9.ch008 https://www.scopus.com/pages/publications/105013535380?origin=resultslist
Pandey V.K., Christ University, Delhi, India; Poonia R.C., Christ University, Delhi, India
AI Trust, Risk, and Security Management: Framework, Principles, and Practices
For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success-and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on; Addresses security threats, bias, and compliance gaps to avoid costly AI failures; Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform; Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management. 2026 Scrivener Publishing LLC.
Manoj, R. Karthick; Senthilnathan, S.; Selvi, S. Arunmozhi; Kumar, T. Ananth; Balamurugan, S.
AI Trust, Risk, and Security Management: Framework, Principles, and Practices;pp.1-377
https://doi.org/10.1002/9781394393022 https://www.scopus.com/pages/publications/105032868588?origin=resultslist
Manoj R.K., Department of Electrical and Electronics Engineering, Academy of Maritime Education and Training, Deemed to be University, Tamil Nadu, India; Senthilnathan S., Department of Electronics and Communication Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Selvi S.A., Computer Science and Engineering, Holy Cross Engineering College, Tamil Nadu, India; Kumar T.A., Computer Science and Engineering, IFET College of Engineering (Autonomous), Tamil Nadu, India; Balamurugan S., iRCS, an Indian Technological Research & Consulting Firm, Coimbatore, Tamil Nadu, Coimbatorex, India
AI vs. traditional portfolio management: A study on Indian investors
This research chapter investigates the dynamics between artificial intelligence (AI) and traditional portfolio management strategies, specifically focusing on the attitudes and preferences of investors in the Indian market. The study aims to elucidate the comparative performance, risk-adjusted returns, and behavioral aspects associated with AI-driven portfolio management as opposed to traditional methods. Utilizing a methodology tailored to the unique characteristics of the Indian investment landscape, this research engages investors with varying degrees of experience in the stock market. Through a meticulous collection of data during October and November 2023, employing convenience sampling, the authors explore the factors influencing investor perceptions and decisions in adopting AI-based portfolio management strategies. These findings contribute to the existing discourse by shedding light on the role of trust, subjective norms, perceived usefulness, perceived ease of use, and attitudes as critical variables shaping the adoption of AI in portfolio management. 2024, IGI Global. All rights reserved.
AI-Driven Decentralized Finance and the Future of Finance, pp. 325-342.
Anand Y., Christ University, India; Irfan M., Christ University, India; Hasan M., University of Information Technology and Sciences, Dhaka, Bangladesh
AI- and ML-driven intelligent design of digital twins
Artificial intelligence; Data integration; Digital twins,; Machine learning,; Predictive maintenance
Digital twins (DTs), or virtual copies of real-world systems, have changed and improved many industries in terms of monitoring, analysis, and optimization in real time. Artificial intelligence (AI) and machine learning (ML) together have significantly enhanced the functionalities of DTs so that they become more efficient and versatile decision-making and process improvement tools. The production and application of DTs most importantly rely on AI and ML. Such technologies allow integration and analysis of very large amounts of data from various sources and provide an overview of the physical system. The personnel involved in the company may gain deeper insights into overall business processes and identify changes that would remain unknown when applying the traditional methods, based on the employment of the capabilities of AI-based integration and data analysis. An essential example of ML use cases in the framework of DTs is predictive maintenance. Any ML algorithm can resort to historical data and immediate sensor data to predict potential failures of application equipment and propose a repair schedule, significantly reducing operational downtime and refining the distribution of resources. The AI-powered optimization and simulation methods can give organizations the possibility to consider numerous scenarios and identify the most effective ways to resolve complex issues. The DTs are AI-enabled and can detect and decide on the fly, which allows them to react to changing conditions instantly and prevent some of the issues before they happen. In addition, AI-powered predictive analysis and risk management allow the firms to go a step ahead and address the potential problems in advance by developing effective risk reduction strategies. DTs are mainly constructed with AI and ML in various industries. In the context of manufacturing and Industry 4.0, DTs play an important role in optimizing production and increasing the quality control standards. Urban planners use the DTs to strategize building smart cities, while healthcare professionals use them for medical diagnosis and planning. In the aerospace and auto industries, DTs are beneficial in improving the product development, testing, and other maintenance processes. This chapter focuses on the smart creation of DTs with the help of AI and ML technology. The discussion will also dive into the complex mechanism behind building advanced, digital replicas of physical systems, particularly the support of the AI and ML in the advancement of their usefulness and precision. The chapter begins with the discussion of the role of data integration and analysis in the creation of a DT. This section shows how AI and ML algorithms facilitate the seamless combination of different sources of data into one and reach a much more dynamic and detailed similitude of the physical member. The chapter illustrates how these technologies can convert raw information into valuable information, which makes the DT capable of replicating the real-world situations and behaviors quite dramatically. Moreover, the chapter addresses the profound role of AI and ML in the optimization and simulation of DTs. We observe how these advanced technologies are able to give more precise predictions and process the decision-making and testing of even complex scenarios. The chapter focuses on how AI-enabled optimization methodologies and AI-based simulations driven by ML are broadening the opportunities of DTs, thus driving innovation in a number of verticals. 2026 Elsevier Inc. All rights reserved.
Manasa, R.; Navya, K.; Keshavamurthy, R.; Madhura, R.; Mahesh Kumar, N.
Digital Twins for Sustainable Development;pp.1-33
https://doi.org/10.1016/B978-0-443-27388-9.00011-8 https://www.scopus.com/pages/publications/105036915188?origin=resultslist
Manasa R., Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India; Navya K., Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India; Keshavamurthy R., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to be University), Bengaluru, India; Madhura R., Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India; Mahesh Kumar N., Department of Information Science and Engineering, Global Academy of Technology, Bengaluru, India
Ai-assisted models for dyslexia and dysgraphia: Revolutionizing language learning for children
Dyslexia and dysgraphia are two common learning disabilities that affect children's ability to read, write, and spell accurately. These disabilities can significantly impede a child's academic performance, leading to lack of self-confidence, anxiety, and frustration. Traditional approaches to address these disabilities often involve one-on-one sessions with a tutor or special education teacher, which can be time-consuming and expensive. Artificial intelligence (AI) language learning models have shown tremendous potential in assisting children with dyslexia and dysgraphia. These models can provide real-time feedback and personalized instruction to help children overcome learning difficulties. This chapter highlights the importance of addressing these challenges and proposes a solution that leverages AI language learning models to assist children with dyslexia and dysgraphia. By embracing AI language learning models, educators and parents can empower children with dyslexia and dysgraphia, providing them with the necessary tools and support to overcome their learning challenges. 2023, IGI Global. All rights reserved.
Iyer L.S.; Chakraborty T.; Reddy K.N.; Jyothish K.; Krishnaswami M.
AI-Assisted Special Education for Students With Exceptional Needs, pp. 186-207.
Iyer L.S., CHRIST University (Deemed), India; Chakraborty T., CHRIST University (Deemed), India; Reddy K.N., CHRIST University (Deemed), India; Jyothish K., CHRIST University (Deemed), India; Krishnaswami M., CHRIST University (Deemed), India
AI-Augmented FinTech Platforms for Real-Time Credit Risk and Supply Chain Financing in Smart Industries
Artificial Intelligence; Credit Risk; FinTech; Smart Industries; Supply Chain
A combination of Artificial Intelligence (AI) and FinTech platforms has transformed the financial services sector, specifically, real-time credit risk evaluation and supply chain financing of smart industries. The conventional models of credit assessment, based on the use of fixed financial information and manual processing, are ineffective in capturing the dynamic nature of the modern-day industrial process. This paper conducts empirical research on AI-enhanced FinTech applications and utilizes machine learning (ML), natural language processing (NLP), and multi-modal industrial data, such as financial data, IoT sensor data, and supply chain data, to enable better predictive models and decision processes. The study compares several models, such as ensemble learning and deep neural networks, to predict credit risk and maximize the financing. Findings show that this is highly improved with AUC scores more than 0.88 and reduction in decision latency up to 70 percent showing quicker more information-oriented and context-sensitive risk management. It offers practical implications in the design of AI-based financial solutions, which will allow making smarter credit decisions and allocating working capital in intelligent industries more effectively, and it also notes that AI can transform industrial FinTech ecosystems. 2026 IEEE.
Sistla, Swetha; Sankaran, Mohan; Jooluri, Nagaraju; Fikri, Yahya; Shruthaalaxmi; Devesh, Sonal
2026 Innovations in Machine, Engineering, and Digital Conference, IMED 2026;
https://doi.org/10.1109/IMED68921.2026.11484223 https://www.scopus.com/pages/publications/105038347293?origin=resultslist
Sistla S., FinTech & Ai Solutions Infosys Limited, Richmond, 23060, VA, United States; Sankaran M., Software Engineering PayPal Inc, 94555, CA, United States; Jooluri N., Software Engineering Incode Technologies, 20148, VA, United States; Fikri Y., Abdelmalek Essaadi University, Department of Governance And Performance of Organizations, Tetouan, Morocco; Shruthaalaxmi, Statistics University of Toronto, Department of Public Policy, Toronto, Canada; Devesh S., Christ University (Deemed To Be University), School of Business And Management, Karnataka, Bangalore, 560073, India
AI-Based Chatbots for Education: A Framework for Ethical and Social Perspectives
AI-based chatbots in education are extensively used. This chapter discusses the challenges and benefits of AI-based chatbots for students, teachers, and administrators. The use cases of AI-based chatbots reveal benefits and concerns. This chapter provides ethical and social implications and discusses the framework for the effective implementation of AI-based chatbots in education. The framework offers ethical implications such as fairness, accountability, transparency, accuracy, and autonomy. Further, it mentions the social implications such as equity, mental health, social interaction, assessment and feedback, and privacy and security. Lastly, it gives the policy implications and future research directions. 2026, IGI Global Scientific Publishing. All rights reserved.
Sivathanu, Brijesh; Nannaware, Shatabdi; Pillai, Rajasshrie; Abidi, Naseem
Transforming Education With Data Science in the AI Era;pp.167-190
https://doi.org/10.4018/979-8-3373-2185-1.ch007 https://www.scopus.com/pages/publications/105021208231?origin=resultslist
Sivathanu B., School of Business and Management, Christ University, Bangalore, India; Nannaware S., Pune Institute of Business Management, Pune, India; Pillai R., Chandragupt Institute of Management, Patna, India; Abidi N., Skyline University College, University City of Sharjah, United Arab Emirates
AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages
Artificial Intelligence; Autism Spectrum Disorder; Feature Extraction; Functional MRI; MRI; Structural MRI
High-dimensional data, lower detection accuracy, susceptibility to manual errors, and the requirement of clinical experts are some drawbacks of conventional classification models available for Autism Spectrum Disorder (ASD) detection. To address these challenges and explore the affiliated information from advanced imaging modalities such as Magnetic Resonance Imaging (MRI) in structural MRI (sMRI) and resting state-functional MRI (rs-fMRI), the study applied an Artificial Intelligence (AI) approach. In this context, AI is used to automate the feature extraction process, which is crucial in the interpretation of medical images for diagnosis. The work aims to apply AI-based techniques to extract the features and identify the impact of each feature in the Autism diagnosis. The morphometric features were extracted using sMRI images and rs-fMRI scans were employed to fetch functional connectivity features. Surface-based, region-based, and seed-based analyses are performed for the whole brain, followed by feature selection techniques such as Recursive Feature Elimination (RFE) with correlation, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and graph theory are implemented to extract and distinguish features. The effectiveness of the extracted features was measured as classification accuracy. Support Vector Machine (SVM) with RFE is the best classification model, with 88.67% accuracy for high-dimensional data. SVM is a supervised learning model that outperforms other classification models due to its capability to handle high-dimensional data with a larger feature set. Medical imaging modalities provide detailed insights and visual differences related to various cognitive conditions that must be recognized accurately for efficient diagnosis. The study presented an empirical analysis of various Feature extraction approaches and the significance of the extracted features in high-dimensional data scenarios for Autism classification. 2024 Meenakshi Malviya Chandra J and Nagendra N. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Journal of Computer Science, Vol-20, No. 11, pp. 1495-1503.
https://doi.org/10.3844/jcssp.2024.1495.1503 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205241551&doi=10.3844%2fjcssp.2024.1495.1503&partnerID=40&md5=1ca6595019a37f260bae244ce847e7d8
Malviya M., Department of Computer Science Christ, Deemed to be University, Bangalore, India; Chandra J., Department of Computer Science Christ, Deemed to be University, Bangalore, India; Nagendra N., Department of Computer Science Christ, Deemed to be University, Bangalore, India