Browse Items (14421 total)
Sort by:
-
AI as sustainable and eco-friendly environment for climate change
[No abstract available] -
AI Avatars in Immersive Environments for Communication Skill Training
In the contemporary landscape, communication skills are essential across different spheres, from professional spaces to personal engagements. AI-generated content is gaining popularity in education, which assists students in their assignments and other activities. With the advent and development of technology, AI avatars incorporated with virtual reality environments present a novel and promising pathway for cultivating communication abilities. VR environments are experimenting with AI technology that can create a more realistic environment and provide users with interactive characters around them. The education sector is widely using AI technology for interactive classrooms and presentations, which can improve students' creative communication skills. It is assumed that the AI sector is expected to grow by 48% in the future. The promising potentials of the technology attract more users into the medium as it allows for customization, dynamic assessment patterns, a hybrid learning mode with meaningful interactions, and blended learning experiences. This chapter delves into the potential of AI avatars in virtual reality environments, especially communication skill training applications that help improve communication skills. AI avatars create an interactive space for the users in VR environments that creates an engaged space for the users to be involved in conversations and activities without the fear of being judged. The present study takes an experimental approach with the social learning theory to test whether these AI avatars help users improve communication skills, mainly interpersonal communication skills and public speaking. A mixed methodology is adopted for the research, where data collected from the training and questionnaire data will undergo triangulation to get the appropriate results. The research also addresses ethical considerations like privacy and inclusivity in AI avatar interactions. Overall, the study highlights the effectiveness of incorporating AI avatars in VR environments for improved communication skill development. 2026 Scrivener Publishing LLC. All rights reserved. -
AI Based Non-invasive Glucose Detection Using Urine
This proposed device uses urine to predict the glucose level present in the patient using non-invasive technique with a high level of accuracy for detection of diabetes. The paper presents a urine glucose level diagnosing and prediction using a computer-based polarimeter held in a portable device, to provide a fast and accurate on-field result. The instrument consists of an LCD screen, optical sensor, Benedicts reagent, a detachable tank, and an embedded system-on-chip (SoC). Springer Nature Singapore Pte Ltd 2020. -
AI Based Seamless Vehicle License Plate Recognition Using Raspberry Pi Technology
This research presents the implementation of an innovative Vehicle Management System designed specifically for the Christ University Project 'CampusWheels.' The system incorporates cutting-edge technologies, including YOLOv8 and Tesseract OCR, for robust license plate recognition. Addressing the unique challenges faced by Christ University in managing and securing vehicular movements within the campus, this project becomes crucial as the number of vehicles on campuses continues to grow. It not only provides an effective solution to these challenges but also introduces innovative methodologies, marking a significant departure from conventional campus management practices. The paramount importance of this project lies in its ability to enhance campus security through real-time vehicle monitoring and identification. The utilization of YOLOv8 for vehicle detection and Tesseract OCR for license plate recognition ensures a high level of accuracy in identifying and tracking vehicles entering and leaving the campus. This precision significantly contributes to the prevention of unauthorized vehicle access, a common security concern on educational campuses. Moreover, the system's ability to streamline traffic flow and improve efficiency in parking and access control addresses practical issues faced by campus administrators and security personnel. 2024 IEEE. -
AI Based Technologies for Digital and Banking Fraud During Covid-19
The only viral thing today is the Covid 19 virus, which has severely disrupted all the economic activity around globe because of which all the businesses are experiencing irrespective of its domain or country of origin. One such major paradigm shift is contactless business, which has increased digital transaction. This in turn has given hackers and fraudsters a lot of space to perform digital scams line phishing, spurious links, malware downloads etc. These frauds have become undesirable part of increased digital transactions, which needs immediate attention and eradication from the system with instant results. In this pandemic situation where, social distancing is key to restrict the spread of the virus, digital payments are the safest and most appropriate payment method, and it needs to be safe and secure for both the parties. Artificial intelligence can be a saviour in this situation, which can help combat the digital frauds. The present study will focus on the different kinds of frauds which customers and facing, and most possible ways Artificial intelligence can be incorporated to identify and eliminate such kind of frauds to make digital payments more secure. Findings of the study suggest that inclusion of AI did bring a change in the business environment. AI used for entertainment has become an essential part in business. Transfiguration from process to platform focused business. The primary requirement of AI is to study the customer experience and how to give a better response for improving the satisfaction. But recently AIs are used not only for customer support, but its been observed that businesses have taken it as marketing strategy to increase demand and sales. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System
Most of the Active Noise Cancellation (ANC) systems working properly in low-frequency noises only. To make it suitable for isolating high-frequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods. 2023 IEEE. -
AI Chatbots and Market Dynamics: Analyzing the Impact of AI on the Indian Stock Market
This study examines the potential of ChatGPT v4.0 for stock market analysis, particularly within the Indian stock market. The research uses prompt engineering to assess ChatGPT's ability to recommend stocks, construct portfolios, provide fundamental analysis, and understand price movements. The study combines sentiment analysis of news data with historical price data to forecast future price movements. Results demonstrate ChatGPT's effectiveness in identifying investment opportunities and providing insights for decision- making. The study also evaluates the accuracy of ChatGPTs predictions, finding an 88.89% accuracy rate based on news sentiment analysis and a 94% accuracy rate when integrating sentiment analysis with historical price data. Despite limitations, including constraints in handling large datasets, ChatGPT exhibits promise for supporting investors in the Indian stock market. Future research should focus on refining models and addressing ethical considerations to ensure responsible AI integration in finance. 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI Chatbots in Financial Services: The Game Changer for Personalized Policy Solutions
The rapid evolution of artificial intelligence (AI) has transformed customer interactions in financial services, with AI-driven chatbots emerging as a pivotal tool for enhancing personalized policy recommendations. These intelligent systems leverage machine learning algorithms, natural language processing (NLP), and real-time data analytics to understand customer preferences and provide tailored financial solutions. By automating policy advisory and customer support, AI chatbots improve service efficiency, reduce response times, and enhance user satisfaction. This article explores the role of AI chatbots in delivering personalized policy solutions, their impact on customer experience, and the challenges associated with their adoption in financial institutions. Additionally, it highlights emerging trends and future possibilities in AI-driven financial advisory services. 2026 by IGI Global Scientific Publishing. All rights reserved. -
AI Diagnosis: Rise of AI-Powered Assessments in Modern Education Systems
The literature on the limitations on the current archaic education system is limitless, the consequences of which have only been exacerbated in the current lockdown scenario. The timed evaluations have not only failed as an assessment tool during these times but research has shown there are increased rates of using unfair means and proctoring as a result. Not only was the system faulty to begin with, it is failing miserably under current lockdown situations. Simultaneously the current literature keeps positing that since technology has become an integral part of our life already, it would not be long before technology integrates with education and assessments. Taking into consideration the need and potential of an integrative system, this paper aims to explore how artificial intelligence can be effectively introduced into education and improve learning outcomes. The paper performs a Comprehensive Literature Review (CLR), and analyses data based on the framework developed by Onwuegbuzie and Frels (2015). The paper thus reviews literature with the aim to explore current models of AIEd and relevant psychological concepts relating to learning and career outcomes. The evidence is consistence with the rationale for research problem: current AI methodologies in education focus only on delivering learning material, using AI as a means, instead of taking into other factors improving learning and education outcomes. The subsequent literature review on the factors influencing learning outcomes establish that there are two main thematic influences on students learning and behavioral outcomes: inside-school and out of school factors, which have been further implored in context of technological advancements. 2021. Transnational Press London. All Rights Reserved. -
AI Driven Air Quality Analysis for Health: An Experimental Review
Air pollution, both indoor and outdoor, was linked to 6.7 million premature deaths in 2020, including over 237,000 children under the age of 5, according to WHO. Indoor Air Pollution (IAP) is a crisis of public health that affects billions of people by exposing them to IAP pollutants like particulate matter (PM2.5), volatile organic compounds(VOCs), polycyclic aromatic hydrocarbons (PAHs), and carbon monoxide (CO). The most common cause of IAP varies from incense burning and biomass fuel to ventilation, leading to a horrific human health effect by causing respiratory disease, cardiovascular disease, sick building syndrome, and mental impairment. This review brings together evidence from various studies on the effects of indoor air quality on the environment, health, and productivity. Apart from pollutant exposure, determinants of well-being, i.e., thermal, acoustic, and visual comfort, are the subject of this article. Developments in artificial intelligence (AI), the Internet of Things (IoT), and computational modeling have revolutionized Indoor Air Quality monitoring to detect pollutants and exposures in real-time. All these technologies have the potential to intervene effectively but are intimidating through the prism of high cost, sensor calibration, and the need for large-scale epidemiological studies. To restrict indoor air pollution risks, inter-disciplinary studies need to be adopted to combine effective ventilation technologies and advanced pollutant control systems. Large-scale applications of clean fuel like solar, biogas, electricity, liquefied petroleum gas (LPG), and efficient biomass stoves need to be employed to restrict home air pollution. The present review calls for an emergent public campaign and policy intervention to enhance indoor air quality, health, and well-being. 2025 IEEE. -
AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*
The major goal of the current research is to carry out mathematical and finite element analysis on spur gear assemblage to improve fatigue life as well as minimize contact pressure among contact teeth by modifying the face width of spur gear. AI automates FEA simulations and analyses, speeding up the design process. The investigation presented above was conducted using three separate 3d models of driving gear. The equivalent stress for the spur gear assembly of design-3 has decreased up to 13.45% in comparison to design-1, and the fatigue life has increased up to 81.59% at 600 N m, according to the results. Further AI models shall predict stress distribution, contact pressure, and other relevant factors in spur gear assemblies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
AI enabled applications towards intelligent transportation
Artificial intelligence (AI) is the ability of a machine to perform cognitive functions like perceiving, reasoning, learning and problem-solving which humans are capable of performing at ease. AI has gained traction since the past two decades across the globe due to availability of huge volume of data generated through Internet. There has been a huge benefit to governments and businesses by processing this data using advanced algorithms in the recent past. The robust growth of machine learning algorithms supported by various technologies like Internet of Things, Robotic Process Automation, Computer Vision, Natural Language Processing have enabled the growth of AI. This article is a compilation of various issues plaguing Transport Industry classified under Intelligent Transportation Systems. Some of the sub-systems considered are related to Traffic Management, Public Transport, Safety Management, Manufacturing & Logistics from Intelligent Transportation Systems where AI benefits are put into use. The study takes up specific areas of concern in transport industry and its related issues that have possible solutions using AI. The approach involves a secondary study based on the country-wise data available from various sources. Further, discussions on AI solutions to resolve issues in transport industry across various countries in the globe and in Indian states is taken up. 2021 -
AI Enhanced Global Economic Resilience: Predicting and Mitigating Financial Crises
Global economic resilience relies on our ability to predict and mitigate financial crises, especially for small and medium-sized enterprises (SMEs)vital drivers of economic growth. These SMEs are particularly susceptible to market fluctuations in business-to-business or consumer-focused sectors. Organizations integration of big data technologies has revolutionized global financial data management, enhancing our resilience. In our interconnected world, the timely identification of impending financial crises is crucial. It's the linchpin to prevent catastrophic collapses that could send shockwaves through the global economy and societies. To address this challenge, we introduce the Nature-inspired Red-optimized Stochastic Artificial Neural Network (NRFO-SANN), a powerful instrument for detecting global financial crises and anomalies. Our approach leverages a diverse array of financial data collected worldwide. Employing Minmax normalization, we meticulously pre-process the data, ensuring its readiness for analysis. Principal Component Analysis (PCA) extracts the core features crucial for crisis identification. These insights fuel the implementation of the NRFO-SANN method, unlocking the potential of AI-driven prediction. The results are remarkable. Our NRFO-SANN model not only outperforms its peers but does so resoundingly. With an impressive 96% accuracy rate, it operates efficiently, taking just 1s for computations. It boasts an F-score of 96.5%, a sensitivity of 94% and a specificity of 95%. This model equips us with a robust tool for anticipating and responding to global financial crises, ultimately reinforcing the stability and resilience of the global economy and societies. In this era of AI-empowered global economic resilience, we possess enhanced capabilities to navigate the intricacies of our interconnected world. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AI for Fair Recruitment: Balancing Tech and Ethics
Purpose: This chapter's objective is to examine how artificial intelligence (AI) is influencing the development of human resource management (HRM) systems, focusing on recruiting, developing employees, and increasing the diversity of the workforce by reducing bias. Design-methodology-approach: The investigation adopted a two-phase research strategy. First, bibliometric analysis of AI-HRM evidence received attention from most scholars indicating major topics of concern within the AI and HRM corpus. Second, a systematic literature review (SLR) based on these themes and applied focus key strings and PRISMA protocols to ensure satisfactory efforts in locating and discussing the relevant literature. Findings: The bibliometric analysis suggested three points of interest that are popular in the literature: AI in recruitment and diversity, employee development, and bias reduction. These programs can be used from mere training employees to including operational supervision and engagement. The case studies featured some well-known brands such as Unilever, Accenture, and IBM. The results indicate a possibility that AI will assist in the advancement of HRM processes, foster diversity, and inclusiveness and even bias-free recruitment and development of employees. Practical implications: The chapter proposes guidelines for the ethical application of AI in HR, including meticulous data collection, algorithmic design, and routine supervision. It emphasizes that AI possesses transformative potential for achieving diversity and inclusion in workplaces. Originality: This chapter expands on the ongoing discussion of AI in HRM by providing a bibliometric approach and SLR, making a new and substantiated claim on Al's role in promoting diversity and reducing biases. 2025 by Arun Kumar P, Rekha Aranha and Delma Thaliyan. All rights reserved. -
AI for Optimization of Farming Resources and their Management
The chapter explores the incorporation of artificial intelligence (AI) into framework strategies aimed at addressing the dynamic challenges confronting the agricultural industry. It focuses on issues like resource depletion, escalating labor costs, and the impacts of climate change, emphasizing the necessity for inventive solutions. The proposed framework adopts a comprehensive approach that integrates farm-to-fork strategies, smart agricultural practices, and advanced crop planning. Its primary objectives are to enhance crop yields, establish transparent supply chains, and optimize resource allocation. The chapter underscores the potential synergies associated with contextual understanding, efficient communication, and personalized user experiences, anticipating a transformative impact on agriculture. The integration of AI is anticipated to yield unprecedented benefits, paving the way for a more technologically advanced, sustainable, and productive future. Despite these promising prospects, challenges emerge during the integration process, manifesting as regulatory hurdles, infrastructure deficiencies, and inherent complexities. The chapter acknowledges these obstacles and asserts that overcoming them is crucial for realizing the full transformative potential of AI in agriculture. Looking ahead, the convergence of AI and framing strategies is poised to revolutionize the agricultural landscape, ushering in increased efficiency and sustainability. This innovative partnership holds the promise of building a resilient foundation for agriculture, ensuring its adaptability to changing needs and contributing to a greener and more productive future. 2025 selection and editorial matter, Sirisha Potluri, Suneeta Satpathy, Santi Swarup Basa, and Antonio Zuorro; individual chapters, the contributors. All rights reserved. -
AI Healthcare Industry in Life Science Industry: A Perspective View
The main goal of this study is to look at how well the innovation system for AI healthcare technology is working in the life science business and find things that are getting in the way of progress. A lot of different types of research were used for this study. It combines both quantitative and qualitative data from tertiary studies, business-related written sources, and conversations with 21 experts and 25 life science management leaders to get new ideas. The results make it clear that innovation system performance is being held back by a lack of resources and poor communication from top healthcare experts about what they need to improve healthcare with AI technology innovations. The study says that to deal with these problems, policymakers need to make changes that increase the resources that are available and come up with clear goals and visions for how AI technology can improve healthcare. Using the socio-technical technological advancement System (TIS) approach in the healthcare setting, the study adds to our knowledge of how the innovation system works and how different parts of it affect each other. Overall, this study throws light on the complicated ways that innovation works in the life science field. It gives lawmakers, industry workers, and other interested parties useful information for pushing AI healthcare technology forward in a sociotechnical framework. 2024 IEEE. -
AI in academia: Balancing integrity, ethics, and learning amid evolving norms of authorship and scholarship
The integration of AI in academic and publication content generation is a recent development, significantly altering policies on citation and authorship, which were previously designed for human-generated work. While AI tools have eased administrative and academic workloads, their rapid adoption raises concerns about ethics and academic integrity. This chapter explores the role of AI as a transformative force in academia, highlighting both its benefits and potential downsides. A key concern is the potential erosion of critical thinking among researchers and scholars due to overreliance on AI, which could impact the quality of research. Despite being a game-changer for students, educators, and administrative staff, the academic community must address the ethical implications and develop new policies to ensure that AI enhances rather than undermines scholarly work. This chapter aims to foster dialogue on how academia can coexist with advancing AI innovations while maintaining research integrity and quality. 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI in creating inclusive work environments for neurodiverse employees
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 -
AI in Data Recovery and Data Analysis
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. -
AI in e-learning
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.