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The technology-supported education systems seamlessly integrated throughout the globe in response to the demands of post COVID-19 pandemic. The swift developments of the digital tools with Artificial Intelligence (AI) support are also readily diffused among the educational communities. This research paper investigates the synergistic impact of digital literacy, the incorporation of AI tools, and Peer Supported Collaborative Learning (PSCL) on the learning perceptions of university students. The research aims to discern the implications of these technological and social facets on students' attitudes towards AI assisted learning process. Structured questionnaire-based survey among the University students were done for this descriptive research. 409 responses collected were analysed with SPSS, Excel and Process Macro. It is found that the students' Digital Literacy, Use of AI tools and PSCL on AI assisted learning were positively correlated. The partial mediatory path through the PSCL and AI tool usage has a significant positive influence on students learning process. The insights gathered from this study can inform educators, policymakers, and institutions on optimizing the amalgamation of digital literacy, AI tools and PSCL to enhance the contemporary learning environment. As universities navigate the digital age, this research provides a nuanced understanding of the dynamics shaping students' perceptions, offering valuable insights into the multifaceted aspects of AI influencing the educational landscape.

In today's scenario of India most of the youth is preparing for some competitive exam. If we think behind 15–20 years the number of candidates appearing for competitive exams were in thousands but as the population is increasing exponentially in India day by day the number of candidates is increasing in lacks. We can observe by daily newspapers that most of the competitive exams are facing paper leak problems. Although online examination systems are more effective and secure as compared to offline examination systems because it's not easy to open the question paper before the time starts. On the other hand there are also various consequences where the examination process can be hacked online. But the main issue with online examination process is to lack of resources to conduct parallel examination of millions of candidates, lack of techno enabled exam centers in small cities etc. Our focus is to propose a system in which offline examination can be conducted at most of the govt. and private centers in metro cities as well as small techno backward cities with reduced possibility to leak the paper before commencement of examination. [ABSTRACT FROM AUTHOR]

The adoption of large language models (LLMs) in education holds much promise. However, like many technological innovations before them, adoption and access can often be inequitable from the outset, creating more divides than they bridge. In this paper, we explore the magnitude of the country and language divide in the leading open-source and proprietary LLMs with respect to knowledge of K-12 taxonomies in a variety of countries and their performance on tagging problem content with the appropriate skill from a taxonomy, an important task for aligning open educational resources and tutoring content with state curricula. We also experiment with approaches to narrowing the performance divide by enhancing LLM skill tagging performance across four countries (the USA, Ireland, South Korea and India-Maharashtra) for more equitable outcomes. We observe considerable performance disparities not only with non-English languages but with English and non-US taxonomies. Our findings demonstrate that fine-tuning GPT-3.5 with a few labelled examples can improve its proficiency in tagging problems with relevant skills or standards, even for countries and languages that are underrepresented during training. Furthermore, the fine-tuning results show the potential viability of GPT as a multilingual skill classifier. Using both an open-source model, Llama2-13B, and a closed-source model, GPT-3.5, we also observe large disparities in tagging performance between the two and find that fine-tuning and skill information in the prompt improve both, but the closed-source model improves to a much greater extent. Our study contributes to the first empirical results on mitigating disparities across countries and languages with LLMs in an educational context.

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

Sign language is the natural way of communication of speech and hearing-impaired people. Using Indian Sign Language (ISL) interpretation system, hearing impaired people may interact with normal people with the help of Human Computer Interaction (HCI). This paper presents a method for automatic recognition of two-handed signs of Indian Sign language (ISL). The three phases of this work include preprocessing, feature extraction and classification. We trained a BPN with Histogram Oriented Gradient (HOG) features. The trained model is used for testing the real time gestures. The overall accuracy achieved was 89.5% with 5184 input features and 50 hidden neurons. A deep learning approach was also implemented using AlexNet, GoogleNet, VGG-16 and VGG-19 which gave accuracies of 99.11%, 95.84%, 98.42% and 99.11% respectively. MATLAB is used as the simulation platform. The proposed technology is used as a teaching assistant for specially abled persons and has demonstrated an increase in cognitive ability of 60-70% in children. This system demonstrates image processing and machine learning approaches to recognize alphabets from the Indian sign language, which can be used as an ICT (information and communication technology) tool to enhance their cognitive capability.

Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980-2015). The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R 2 ). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.

The purpose of this paper is to evaluate the levels of awareness, degree of familiarity, willingness of educators to embrace the Artificial Intelligence (AI) environment and to evaluate the potential benefits that they can have from AI in their teaching activities. Exploratory research was conducted at 14 business schools and to achieve the goals of the study, factor analysis was carried out. Four factors were identified from factor analysis which was given names based on the mean and standard deviation of factor scores. This can act as a reference for those business schools that have begun offering management education using AI or are planning to use AI in the future.

Concerns about the health effects of frequent exposure to electromagnetic fields (EMF) emitted from mobile towers and handsets have been raised because of the gradual increase in usage of cell phones and frequent setting up of mobile towers. Present study is targeted to detrimental effects of EMF radiation on various biological systems mainly due to online teaching and learning process by suppressing the immune system. During COVID-19 pandemic the increased usage of internet due to online education and online office leads to more detrimental effects of EMF radiation. Further inculcation of soft computing techniques in EMF radiation has been presented. A literature review focusing on the usage of soft computing techniques in the domain of EMF radiation has been presented in the article. An online survey has been conducted targeting Indian academic stakeholders' (Specially Teachers, Students and Parents termed as population in paper) for analyzing the awareness towards the bio hazards of EMF exposure.

Assessment plays an important role in education. Recently proposed machine learning-based systems for answer grading demand a large training data which is not available in many application areas. Creation of sufficient training data is costly and time-consuming. As a result, automatic long answer grading is still a challenge. In this paper, we propose a practical system for long or descriptive answer grading that can assess in a small class scenario. The system uses an expert-written reference answer and computes the similarity of a student answer with it. For the similarity computation, it uses several word level and sentence level similarity measures including TFIDF, Latent Semantic Indexing, Latent Dirichlet Analysis, TextRank summarizer, and neural sentence embedding-based InferSent. The student answer might contain certain facts that do not occur in the model answer. The system identifies such sentences, examine their relevance and correctness, and assigns extra marks accordingly. In the final phase, the system uses a clustering-based confidence analysis. The system is tested on an assessment of school-level social science answer books. The experimental results demonstrate that the system evaluates the answer books with high accuracy, the best root mean square error value is 0.59 on a 0-5 scoring scale.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

The financial landscape in India is undergoing a transformative shift propelled by the integration of Artificial Intelligence (AI) technologies within the Banking, Financial Services, and Insurance (BFSI) sector. This paper explores the multifaceted impact of AI on various facets of the industry, ranging from customer service and engagement to risk management and regulatory compliance. In the realm of customer service, AI-powered chatbots and virtual assistants have revolutionized interaction channels, providing instantaneous responses to customer queries and delivering personalized experiences. The paper discusses how latest technologies contribute to improved efficiency, reduced response times, and heightened customer satisfaction. Furthermore, the study investigates the significant contribution of AI in fortifying security measures within the BFSI sector. Machine learning algorithms are examined for their efficacy in fraud detection, leveraging vast datasets to identify anomalous transaction patterns and enhance the resilience of financial systems. The adoption of biometric authentication methods, such as facial recognition and fingerprint scanning, is explored as a means to bolster account security and mitigate risks associated with identity theft. The paper also elucidates the impact of AI on credit scoring, underwriting processes, and risk management strategies. Predictive analytics and automated underwriting systems are scrutinized for their role in expediting loan approvals, while AI-driven risk assessment models are discussed for their ability to analyze market trends and economic indicators, aiding in more informed decision-making. In the context of process automation, the integration of Robotic Process Automation (RPA) in routine tasks is highlighted for its potential to reduce operational costs and minimize errors. The study examines the deployment of AI in document processing, enhancing efficiency in document verification and compliance activities. Emerging trends such as voice banking, insurtech innovations, and the use of blockchain technology are also addressed in the paper. AI-powered voice recognition, telematics, roboadvisors, and smart contracts are explored for their contributions to enhancing accessibility, personalized financial advice, and security in transactions. As the BFSI sector in India continues to embrace AI-driven solutions, this research aims to provide a comprehensive overview of the evolving landscape, shedding light on the transformative potential of AI technologies and their implications for the future of financial services in the country. [ABSTRACT FROM AUTHOR]

As India moves toward enacting a comprehensive data protection legislation, it becomes essential to examine the possible application of India's proposed data protection law to the use of Artificial Intelligence (AI). The various challenges posed by AI to data protection principles and data principals' rights need to be examined. The need for data maximisation in the use of AI challenges the principle of collection limitation. The difficulty in anticipating the processing purposes of AI challenges the principle of purpose limitation. With a brief introduction to AI and data protection law in India, the paper examines the compatibility of various data protection provisions under India's Digital Personal Data Protection Act, 2023 with AI. The paper also provides recommendations for data protection regulation of AI. The paper proposes the need to hold data fiduciaries accountable using Data Protection Impact Assessments, Codes of Practice and Security Measures. Besides, there is a need to define the fiduciary duty of care between the data principal and data fiduciary. There is a need recognize data protection by design and default and the Right against automated decision making. Technical solutions need to be explored, but at the same time, AI must not be over-regulated. Lastly, there is a need for flexibly interpreting the provisions of the proposed data protection law. [ABSTRACT FROM AUTHOR]

The unprecedented growth of 'Artificial Intelligence' (hereinafter referred to as AI) has brought immense benefits but at the same time has posed complex challenges that has impacted users' lives, including privacy and data security, particularly, children who are vulnerable to these problems. This paper examines privacy of children in the era of AI and the legal framework's adequacy in protecting children's privacy, focusing on India, the world's most populous nation in 2024,1 with over 833.7 million² internet users, accounting for more than half of its population. With the advent of AI, unprecedented accumulation, processing, and analysis of massive datasets has become possible by algorithms applying predictive analytics on discrete datasets. Nevertheless, AI's pattern recognition ability has blurred privacy boundaries which has enabled it to feed on sensitive information such as that concerning health, emotions, interests, and behaviours. Due to innate curiosity and digital immersion, children are more susceptible to privacy violations in this 'AI-driven' digital era. Since children possess a limited understanding of privacy risks, they are more likely to share information online. Consequently, there is an urgent need to address the issue concerning the increased digital footprint of children and the associated conflict between the 'age of consent' and the 'age of contractual capacity' for the purpose of fixing the 'digital age' of the child. Such a requirement can be potentially addressed through legislative intervention by enacting a comprehensive piece of legislation to regulate the ubiquitous collection of data. Facial recognition, predictive analytics, autonomous systems, and other AI applications, could be the reason for the apprehensions that systemic discrimination could occur and governance is also at stake that points out the need for transparency and accountability. While AI brings with itself exponential growth, there is also a need to underscore the importance of protecting children's right to privacy, given their vulnerability. A comprehensive legislative framework, responsible corporate policies, and increased awareness can help strike a balance, allowing children to harness AI's benefits while safeguarding their fundamental rights. [ABSTRACT FROM AUTHOR]

Countries across the world are grappling with how to deal with the rapid developments in Artificial Intelligence (AI) and its uses. In this article we analyse three such jurisdictions: the UK, Singapore and India. A common theme that prevails across these jurisdictions is the focus on principles and guidelines instead of straitjacketed regulations that tend to be more inflexible. Another common theme is the reluctance to adopt AI tools that serve to replace human decision makers. Some of these approaches are still evolving – particularly in a country like India where the burgeoning case load may yet make way for automated resolution for small value claims. [ABSTRACT FROM AUTHOR]

Automation of power systems is gaining momentum across the world, and there is a need to expose graduate and undergraduate students to the latest developments in hardware, software, and related protocols for power automation. This paper presents the design, development, and commissioning of an automation lab to facilitate the understanding of substation automation (SA). The laboratory has relay intelligent electronic devices (IEDs) that support IEC 61850 protocol, a universal secondary test kit for testing the relays, a protocol converter that can be programmed and can handle a large number of power automation protocols, a Global Positioning Systems (GPS) clock for time synchronization, and all related software. In this paper, the retrofitting of the latest relay IEDs with the existing control center software has been demonstrated. The commissioning process and the experimental setup of the SA laboratory have also been described. A post-training evaluation emphasizes that the student learning experience is helping students to gain a better understanding of the theory and to have better career prospects in power automation. The SA laboratory, which demonstrates all aspects of substation automation, is being used extensively for research and training in this emerging field. (Contains 7 figures.)

Blasting constitutes an essential component of the mining and construction industries. However, the associated nuisances, particularly blast vibration, have emerged as significant concerns that pose threats to operational stability and the safety of the surrounding areas. Given the increasing emphasis on sustainability, ecological responsibility, safety, and geo-environmental practices, the impact of blast vibration has garnered heightened attention and scrutiny. Nevertheless, the field still lacks comprehensive phase analysis studies. Therefore, it is imperative to elucidate the research progress on blast vibration and discern its current frontiers of investigation. To address this need, this study employs bibliometric methods and the CiteSpace 6.1.R2 software to analyze 3093 papers from the Web of Science database. Through this comprehensive analysis, the study aims to chronicle the developmental trajectory, assess the present research status, and identify future trends in the field of blast vibration. The findings of this study reveal that research on "blasting vibration" is advancing rapidly, with the number of citations exhibiting a J-shaped growth curve over time. China emerges as the leading contributor to this research, followed by India, and the foremost institution in this field is Central South University in China. Cluster analysis identifies the effects of ground vibration, numerical simulation, blast load, blasting vibration and rockburst hazard as the most prominent research areas presently. The primary research directions in this domain revolve around the rock fragmentation, compressive strength, particle swarm optimization, and ann. The emergence of these keywords underscores a dynamic shift towards a more holistic and multidisciplinary approach in the field of blasting-induced ground vibration. Furthermore, this study provides a concise overview of blast vibration, discusses prediction techniques, and proposes measures for its control. Additionally, the discussion delves into the social significance of intelligent blasting systems within the context of artificial intelligence, aiming to address the hazards associated with blast-induced ground vibrations. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

Artificial Intelligence technologies have elicited a range of policy responses in India, particularly as the Government of India attempts to position and project the country as a global leader in the production of AI technologies. Policy responses have ranged from providing public infrastructure to enable market-led AI production, to nationalising datasets in an effort to enable Big Data analysis through AI. This paper examines the recent history of AI policy in India from a critical political economy perspective, and argues that AI policy and governance in India constructs and legitimises a globally-dominant paradigm of informational capitalism, based on the construction of data as a productive resource for an information-based economic production, and encouraging self-regulation of harmful impacts by firms, even as it attempts to secure a strong hand for the state to determine, both through law and infrastructure, how such a market is structured and to what ends. [ABSTRACT FROM AUTHOR]

[Purpose] How to prevent fake news without spoiling the freedom of speech is a growing concern among governments across the world. Some countries see legislation as being the best approach to counter fake news. In the legislation proposals, accountability is mostly placed on technology companies, but also individuals seem to have responsibility in the legislation of some countries. Some other governments see non-legislative means to counter fake news. But it's a fact that countering fake news without compromising free speech is a high priority across governments in the world and a challenging task too. This paper investigates the India scenario and tries to list out other than legislation what other measures are required. [Methodology] This paper takes a survey of mitigation efforts in select countries. This survey is used to testify against similar efforts in India, if any and adopts comparative approach to understand where Indian efforts stand at. [Findings] From using fact-checking tools available online, finding the source, locating how many people viewed a particular story to check grammar and spelling, and developing a critical mindset; plenty of things become a critical means in fighting down fake news. Legislation alone is insufficient. Media literacy, public scrutiny, good citizenship, and education along with sensitive civil society require playing its significant part in India to fight fake news. In India, the policy is vague. It gives the government enormous power to surveillance in the name of fake news. [ABSTRACT FROM AUTHOR]

This research paper examines the impact of an online webinar on domestic violence awareness and knowledge of the Domestic Violence Act 2005 in India. It employs a two-group post-test experimental design to compare the knowledge levels of participants who attended the webinar with those who did not. The webinar aims to raise awareness about domestic violence, educate participants about the DV Act 2005, and empower individuals to take action. The findings will provide insights into the effectiveness of the webinar in enhancing understanding and knowledge of legal measures to address domestic violence. This research contributes to the existing literature and informs future efforts in designing effective awareness campaigns and educational interventions to combat domestic violence In India. [ABSTRACT FROM AUTHOR]

The average annual income of farmers in India is well below the poverty line, primarily due to off-season agriculture and a lack of awareness about optimal farming techniques and crop management. This research paper presents an AI-based web application designed to address these challenges and improve farmers' financial and mental well-being. Our solution, comprised of a farming tools leverage system and a collaborative farming forum, empowers farmers with the knowledge and tools needed to make informed decisions and increase their income. The system leverages MongoDB, asynchronous calling, and Fast API for efficient data management and real-time interactions. AI/ML services assist with crop recommendations, crop disease detection, and price predictions. Load balancing ensures optimal performance, and Pusher JS enables real-time communication. Billing services and a dashboard provide income insights, while geographic data enhances machine learning recommendations. In conclusion, this research contributes to alleviating poverty and enhancing the livelihoods of farmers in India by providing a comprehensive solution to the challenges they face. [ABSTRACT FROM AUTHOR]

About one-fifth of all oral cancer cases reported globally are from India. The low-income groups in India are affected most due to the wide exposure to risk factors such as tobacco chewing and insufficient access to early diagnostic tools. Visual examination and histological study are the standard for oral cancer detection. This paper proposes the idea of using Autofluorescence-based imaging techniques to detect and classify oral cancer using AI algorithms. Various features of the images along with medical history, age, gender, and tobacco usage are considered as inputs to the proposed Mobilenet classification architecture. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores the current state of Robo-advisory services in India. This paper further highlights the problems experienced by the service providers in disseminating the innovative business model among the Indians. Design/methodology/approach: The study adopts a qualitative approach to investigate the industry experts by conducting semi-structured interviews. The data collected were transcripted and further analyzed using the content analysis technique. Finally, the authors utilized categorization and coding techniques to frame broad study themes. Findings: The study findings reveal that the three pillars of Robo-advisory are ease and convenience, the time factor and transparency in operations. Robo-advisory services are still at a nascent stage in India. Furthermore, keeping the sentiments of Indians in mind, FinTech companies could combine automated Robo-advisory with a human touch of a wealth manager for optimal advisory services. Research limitations/implications: Since the present study is qualitative, the authors cannot generalize the study results. Future research can focus on empirically proving the constructs of the study using quantitative methods. Practical implications: Robo-advisors have a well-established market in developed nations but are still nascent in developing countries like India. The current focus of service providers and regulatory authorities must be to increase awareness among investors by educating the investors and building trust. Originality/value: The present study is the first to qualitatively synthesize the challenges faced by the FinTech service providers in the Indian market. [ABSTRACT FROM AUTHOR]

In this work, we have investigated text readability in Bangla language. Text readability is an indicator of the suitability of a given document with respect to a target reader group. Therefore, text readability has huge impact on educational content preparation. The advances in the field of natural language processing have enabled the automatic identification of reading difficulty of texts and contributed in the design and development of suitable educational materials. In spite of the fact that, Bangla is one of the major languages in India and the official language of Bangladesh, the research of text readability in Bangla is still in its nascent stage. In this paper, we have presented computational models to determine the readability of Bangla text documents based on syntactic properties. Since Bangla is a digital resource poor language, therefore, we were required to develop a novel dataset suitable for automatic identification of text properties. Our initial experiments have shown that existing English readability metrics are inapplicable for Bangla. Accordingly, we have proceeded towards new models for analyzing text readability in Bangla. We have considered language specific syntactic features of Bangla text in this work. We have identified major structural contributors responsible for text comprehensibility and subsequently developed readability models for Bangla texts. We have used different machine-learning methods such as regression, support vector machines (SVM) and support vector regression (SVR) to achieve our aim. The performance of the individual models has been compared against one another. We have conducted detailed user survey for data preparation, identification of important structural parameters of texts and validation of our proposed models. The work posses further implications in the field of educational research and in matching text to readers.

As documented in this special issue, all over the world hard choices must be made in education, government, business, and medicine. Intelligence tests, used intelligently and with appropriate ethical safeguards, are one tool of many that help make hard choices work out well, or at least better than the next-best alternative (Kaufman, Raiford, & Coalson, 2016). The reliability of intelligence tests is far from perfect. Complaining about IQ tests is the privilege of those who have them. It is probably no accident that intelligence tests were invented in France, not long after a series of reforms from 1881 to 1901 made education free and compulsory for all children (Harrigan, 2001). It is likewise probably not an accident that intelligence testing was then adopted most enthusiastically in the world's wealthiest countries in the midst of similar attempts to raise educational standards. Among the countries featured in this special issue, Greece, Japan, the Netherlands, and the United States achieved near-universal education decades ago; Brazil, India, Mexico, Oman, and Peru have done so only within the last 15 years. Once a government takes on the responsibility of educating all of its citizens and then successfully achieves near-universal school attendance, it is confronted with the magnitude of population-wide individual differences in academic aptitude. The most pressing concern has to do with vulnerable children with intellectual disabilities. It is true that every child can learn, but not every child learns best in regular education. In this commentary, the author addresses the relation between universal education and the need for intelligence testing. The article goes on to compare U.S. developments in intelligence testing with those in Oman, Greece, India, Japan, Brazil, Peru, and the Netherlands. It concludes with three themes that emerged among the articles in this issue.

In India, newborn screening (NBS) is essential for detecting health problems in infants. Despite significant progress, significant gaps and challenges persist. India has made great strides in genomics dueto the existence of the National Institute of Biomedical Genomics in West Bengal. The work emphasizes the challenges NBS programs confront with technology, budgetary constraints, insufficient counseling, inequality in illness panels, and a lack of awareness. Advancements in technology, such as genetic testing and next-generation sequencing, are expected to significantly transform the process. The integration of analytical tools, artificial intelligence, and machine learning algorithms could improve the efficiency of newborn screening programs, offering a personalized healthcare approach. It is critical to address gaps in information, inequities in illness incidence, budgetary restrictions, and inadequate counseling. Strengthening national NBS programs requires increased public awareness and coordinated efforts between state and central agencies. Quality control procedures must be used at every level for implementation to be successful. Additional studies endeavor to enhance NBS in India through public education, illness screening expansion, enhanced quality control, government incentive implementation, partnership promotion, and expert training. Improved neonatal health outcomes and the viability of the program across the country will depend heavily on new technology and counseling techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This work introduces a novel approach to remotely count and monitor potato plants in high-altitude regions of India using an unmanned aerial vehicle (UAV) and an artificial intelligence (AI)-based deep learning (DL) network. The proposed methodology involves the use of a self-created AI model called PlantSegNet, which is based on VGG-16 and U-Net architectures, to analyze aerial RGB images captured by a UAV. To evaluate the proposed approach, a self-created dataset of aerial images from different planting blocks is used to train and test the PlantSegNet model. The experimental results demonstrate the effectiveness and validity of the proposed method in challenging environmental conditions. The proposed approach achieves pixel accuracy of 98.65%, a loss of 0.004, an Intersection over Union (IoU) of 0.95, and an F1-Score of 0.94. Comparing the proposed model with existing models, such as Mask-RCNN and U-NET, demonstrates that PlantSegNet outperforms both models in terms of performance parameters. The proposed methodology provides a reliable solution for remote crop counting in challenging terrain, which can be beneficial for farmers in the Himalayan regions of India. The methods and results presented in this paper offer a promising foundation for the development of advanced decision support systems for planning planting operations., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Large language models (LLMs) have revolutionized natural language processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 (OpenAI), and Bard (Google LLC), find applications beyond natural language processing, attracting interest from academia and industry. Students are actively leveraging LLMs to enhance learning experiences and prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India., Objective: This comparative analysis aims to evaluate the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions., Methods: In this paper, we evaluated the performance of the 3 mainstream LLMs, namely GPT-3.5, GPT-4, and Google Bard, in answering questions related to the NEET-2023 exam. The questions of the NEET were provided to these artificial intelligence models, and the responses were recorded and compared against the correct answers from the official answer key. Consensus was used to evaluate the performance of all 3 models., Results: It was evident that GPT-4 passed the entrance test with flying colors (300/700, 42.9%), showcasing exceptional performance. On the other hand, GPT-3.5 managed to meet the qualifying criteria, but with a substantially lower score (145/700, 20.7%). However, Bard (115/700, 16.4%) failed to meet the qualifying criteria and did not pass the test. GPT-4 demonstrated consistent superiority over Bard and GPT-3.5 in all 3 subjects. Specifically, GPT-4 achieved accuracy rates of 73% (29/40) in physics, 44% (16/36) in chemistry, and 51% (50/99) in biology. Conversely, GPT-3.5 attained an accuracy rate of 45% (18/40) in physics, 33% (13/26) in chemistry, and 34% (34/99) in biology. The accuracy consensus metric showed that the matching responses between GPT-4 and Bard, as well as GPT-4 and GPT-3.5, had higher incidences of being correct, at 0.56 and 0.57, respectively, compared to the matching responses between Bard and GPT-3.5, which stood at 0.42. When all 3 models were considered together, their matching responses reached the highest accuracy consensus of 0.59., Conclusions: The study's findings provide valuable insights into the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. GPT-4 emerged as the most accurate model, highlighting its potential for educational applications. Cross-checking responses across models may result in confusion as the compared models (as duos or a trio) tend to agree on only a little over half of the correct responses. Using GPT-4 as one of the compared models will result in higher accuracy consensus. The results underscore the suitability of LLMs for high-stakes exams and their positive impact on education. Additionally, the study establishes a benchmark for evaluating and enhancing LLMs' performance in educational tasks, promoting responsible and informed use of these models in diverse learning environments., (©Faiza Farhat, Beenish Moalla Chaudhry, Mohammad Nadeem, Shahab Saquib Sohail, Dag Øivind Madsen. Originally published in JMIR Medical Education (https://mededu.jmir.org), 21.02.2024.)

Purpose: This paper aims to introduce the background with the theme issue question: How is the hospitality and tourism industry in India responding to the dynamic digital era? Design/methodology/approach: The paper has discussed the importance of digital technologies and its scope in customer engagement and marketing of hospitality and tourism products; nevertheless, it also identifies the role of human touch and traditional marketing by suggesting the appropriate mix. This paper has examined the role of influencers and online reviews in impacting the purchase tech decisions related to travel and tourism. Findings: This paper highlights the current digital trends in hospitality and tourism of India and highlights the contribution of authors toward the strategic question. Practical implications: The theme issues draw extensively from industry leaders, digital agencies, restaurant owners and tech consumers to offer relevant and varied perspectives. Originality/value: India is making significant progress in the adoption of digital technologies; yet, there is limited research in providing insights and barriers about hospitality and tourism services. This theme issue will identify the untapped potential and issues with respect to the Indian context. [ABSTRACT FROM AUTHOR]

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards. [ABSTRACT FROM AUTHOR]

This paper considers three moments in the treatment of data about race and identity in India. Many elements go into the development of data imaginaries as these change over time. A complete history is beyond the scope of this paper, but I develop three key episodes to explore critical but changing features of interrelations between race, identity and statistical arguments historically. One aim is to explore key features of the argument developed by two significant individuals -- Thomas Nelson Annadale and P.C. Mahalanobis -- as they sought to develop databases that could answer questions about race formation and, in the case of Mahalanobis, might also be used to develop statistical methods on the one hand and aid governance on the other hand. A second aim is to use this historically based but highly selective investigation to uncover key features of the ideology with which the government of India has presented Aadhaar, its vast biometric identification system powered by authentication technologies afforded by artificial intelligence. This enables me to identify different forms of racial or ethnic identity that could be -- and in one or two cases actually have been -- implicated in the way Aadhaar has been used in practice. [ABSTRACT FROM AUTHOR]

Studies have found that artificial intelligence (AI) bots and cookies automate fake news in zones of social conflict such as race, religion, gender, and class. In this background, this paper investigates whether fake news is automated with the social structure unique to India. The research collected campaigning activities of political parties and politicians on the Internet but was limited to a select number of Facebook profiles, websites, hashtags, and Twitter profiles during India's 2014 and 2019 general elections. Politicians and political parties on Twitter, Facebook and other websites formed the contact points where empirical data were collected in the research design. By reviewing hashtags such as #Nationwantsrammandir; #NaamVaapsi; #RamMandir; #AntiNationals; #caste; and #Hindutva, as well as fake social media accounts; discussion forums; and profiles of followers of politicians, the paper corroborated that bots, AI, and trolls serve fake news in the conflict zones of India and some forces are using it to perpetuate social divisions based on caste, class, religion, gender, and region. This paper argues that automated social media accounts spread false information that likely polarizes social conflicts in India. [ABSTRACT FROM AUTHOR]

The growing incorporation of Artificial Intelligence (AI) in healthcare presents significant opportunities for transforming medical diagnosis, treatment, and accessibility, especially in countries with diverse populations such as India. Nevertheless, there are significant ethical concerns that accompany these commitments, specifically related to data privacy, algorithmic bias, and the potential erosion of human agency. This paper addresses these challenges by presenting a comprehensive policy framework for the responsible implementation of AI in the healthcare sector in India. The framework prioritizes values such as transparency, inclusivity, and public trust. The framework promotes the use of strong data governance mechanisms, such as informed consent, data anonymization, and responsible data sharing practices, based on the most favorable outcomes for ethical AI. In order to address algorithmic bias, it is crucial to conduct regular audits, employ bias mitigation techniques, and implement explainable AI models. Moreover, it advocates for a methodology that prioritizes the needs and well-being of individuals, fostering cooperation between AI and healthcare experts, while also valuing the independence of patients through transparent communication and honoring their decisions. The framework suggests the creation of a specialized regulatory entity with a diverse composition to formulate and enforce ethical standards, oversee adherence, and promptly address complaints. Lastly, recognizing the vital importance of public awareness, it underscores the need for extensive training and development of healthcare professionals, policymakers, and the general public. This will promote well-informed discussions and help alleviate any potential societal concerns. This paper seeks to establish a clear ethical framework and propose a detailed policy structure to enable the use of AI in Indian healthcare. The objective is to create a future where AI can effectively improve healthcare access, reduce disparities, enhance medical results, and ultimately prioritize the well being of patients and society. [ABSTRACT FROM AUTHOR]

The government of India is the final authority for country-wide policy making and policy implementation in the country. Recent times have witnessed increasing use of data-driven approaches in policy and decision making. This is true for public policy formulation as well. This paper is an effort to highlight how data science and artificial intelligence have the potential to revolutionize the field of public policy in India. The paper discusses some such applications and lists down the likely challenges. An extensive review of the research activities in this field has been undertaken. Cases of data science and artificial intelligence reviewed in this paper span over various sectors of public policy such as- policy making process, health, education, environment, agriculture, economy, and security. This paper, thus, aims to facilitate collaboration in the fields of public policy and data technology, in order to enable higher levels of public service in India. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Rapid advancement in Artificial Intelligence (AI) has transformed the dynamics of interaction between organizations and consumers. The rapid emergence and adoption of AI chatbots have ushered in a new era of convenient and efficient customer service. This paper addresses the gap of how Gen Z perceives chatbots as an alternative for service interaction, considering that this sample of the population is relatively more tech savvy and understands technology better. Utilizing semi-structured interviews for in-depth interaction, a thematic analysis reveals six key themes: trust and reliability, nature of interaction, perceived usefulness/ease of use, advantages, disadvantages, and areas of improvement. Gen Z generally views chatbots as limited in handling complex queries, highlighting the importance of human intervention and database expansion. The identified themes provide valuable insights for organizations to highlight strengths and address weaknesses in AI chatbots' interactions with customers. The findings assist managers responsible for technology implementation in understanding customer pain points, fostering enhanced value for both users and organizations leveraging chatbots. This paper offers a comprehensive analysis of user experiences to illuminate the advantages and shortcomings of chatbots as service agents. [ABSTRACT FROM AUTHOR]

Law is a global phenomenon and one of the highest revenue-generating industries. Due to its slow pace of development, it becomes difficult to adopt new technologies and tools for the better administration of the law. In the legal profession, researching is a requisite skill for lawyers. Even though legal research skills vary from lawyer to lawyer, even in the same case, every lawyer must engage in legal research to solve legal problems. Artificial Intelligence (AI) is a computer system that does tasks effectively and efficiently without any need for human intelligence. Much has been propounded on (AI) and the law in recent times, this paper focuses on elucidating AI and its relation to the practice and administration of the law, by addressing key issues on these topics. The paper aims to showcase that although the law is rigid, there is a very real possibility that it might change shortly with the help of artificial intelligence interference, which would change the working of the law in the country. It demonstrates the effects of artificial intelligence, both advantages and disadvantages and examines how they affect other areas of the law while using doctrinal methods of research. In India, there is no specific legislation governing artificial intelligence; the authors have looked into the laws of the US, and UK governing AI and have examined the Personal Data Protection Bill, 2019, and stated challenges that will be faced with the takeover of artificial intelligence. [ABSTRACT FROM AUTHOR]

Transliteration is the process of mapping the character of one language to the character of some other language based on its phonetics. India is very much diverse in languages where people speak different languages. Though they speak different languages, it might be difficult for them to read the script of those many languages. In a situation like this, transliteration process plays a major role. It helps in various Natural Language Processing applications such as Information retrieval, Machine translation, Speech recognition. These are NLP applications which make the computer understand the natural language as to how human being interprets. It helps in translating technical terms and proper names from one language to another language. Moreover, transliteration works have been carried out in languages such as Japanese, Chinese and English. But when considering Indian languages, especially Tamil language, very few recognizable works have been carried out. In this paper, transliteration process is carried out on Unicode Tamil characters. The phonetics-based forward list processing is implemented for transliterating from English language to Tamil language which yields promising results. [ABSTRACT FROM AUTHOR]

The COVID-19 has affected and threatened the world health system very critically throughout the globe. In order to take preventive actions by the agencies in dealing with such a pandemic situation, it becomes very necessary to develop a system to analyze the impact of environmental parameters on the spread of this virus. Machine learning algorithms and artificial Intelligence may play an important role in the detection and analysis of the spread of COVID-19. This paper proposed a twinned gradient boosting machine (GBM) to analyze the impact of environmental parameters on the spread, recovery, and mortality rate of this virus in India. The proposed paper exploited the four weather parameters (temperature, humidity, atmospheric pressure, and wind speed) and two air pollution parameters (PM2.5 and PM10) as input to predict the infection, recovery, and mortality rate of its spread. The algorithm of the GBM model has been optimized in its four distributions for best performance by tuning its parameters. The performance of the GBM is reported as excellent (where R2 = 0.99) in training for the combined dataset comprises all three outcomes i.e. infection, recovery and mortality rates. The proposed approach achieved the best prediction results for the state, which is worst affected and highest variation in the atmospheric factors and air pollution level. [ABSTRACT FROM AUTHOR]

Purpose: India is a rapidly developing economy with a rapidly expending internet infrastructure and among the largest Generation Z population. This generation is tech savvy and the access to technology and network creates a conducive environment for such usage. Internet banking for the same reasons is growing leaps and bounds. The introduction of artificial intelligence (AI) has created disruptions in the traditional banking also. This paper aims to analyze the comfort level and usage of AI-enabled banking services by Generation Z. Design/methodology/approach: The data is collected from 272 Generation Z members. The differential aspects, that is, the relationship of independent variables with dependent variables (AI-enabled internet banking), were analyzed using the structural equation modeling approach. Findings: Defining factors for AI-enabled internet banking were identified. The results of factors were consistent with previous studies. It was found that the usage of AI-enabled internet banking services is insignificant, indicating that Generation Z does not perceive any advantage in using AI-enabled internet banking services. Research limitations/implications: This paper does not incorporate age groups other than Generation Z. Further research could throw light on the difference based on age groups. Further research is required to deeply understand why Generation Z does not perceive AI-enabled internet services as very important. Practical implications: It has been observed that internet banking is important for Generation Z, but they also place greater importance on interpersonal communication. Banks need to consider this in designing their internet banking services. Originality/value: This paper addresses the gap between comfort with and usage of AI-enabled internet banking services, by Generation Z. This paper indicates that the comfort with AI-enabled internet banking services does not translate to usage. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs). Design/methodology/approach: Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique. Findings: Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs. Research limitations/implications: This study adds to the literature by understanding how customers' technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct. Practical implications: The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs. Originality/value: The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables. Design/methodology/approach: A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Findings: The T-bot's ADI's antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots. Practical implications: The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education. Originality/value: T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots. [ABSTRACT FROM AUTHOR]