AI-driven analytical frameworks significantly enhance the precision, speed, and scalability of astronomical research by enabling automated interpretation of large and complex datasets. Deep learning models, particularly convolutional neural networks, can extract high-dimensional features from images and spectra that traditional methods often overlook. Machine learning algorithms further support clustering, anomaly detection, and predictive modelling, helping astronomers identify hidden structures and rare cosmic events. The integration of AI reduces manual effort, minimizes error rates, and accelerates data-to-discovery timelines. Moreover, AI-based systems support real-time monitoring and classification of dynamic celestial phenomena. These capabilities strengthen observational accuracy and promote timely scientific insights. The proposed framework demonstrates how AI can transform astronomical workflows. It provides a unified approach for data processing, model training, validation, and visualization. This contributes to establishing a scalable and efficient foundation for next-generation astronomical research. Modern astronomy relies heavily on the analysis of massive, complex, and continuously growing datasets produced by telescopes, sky surveys, and space missions. Traditional analytical techniques often fail to handle the scale, velocity, and heterogeneity of these data streams. Artificial Intelligence (AI), particularly machine learning and deep learning models, provides an efficient, scalable, and automated solution for processing astronomical data with enhanced accuracy and speed. This paper presents a framework that integrates convolutional neural networks, clustering algorithms, anomaly detection systems, and neural sequence models to classify celestial objects, identify rare astronomical phenomena, and reveal hidden structures in the universe. The study highlights the transformative impact of AI on data-driven astronomy and proposes an end-to-end architecture for large-scale astronomical data analysis. Modern astronomical surveys such as LSST, Gaia, Pan-STARRS, and SDSS generate petabyte-scale datasets that exceed the capability of traditional statistical and manual analysis. Artificial Intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers scalable, automated, and highly efficient mechanisms to handle the computational and analytical challenges associated with large astronomical data streams. This study investigates the implementation of convolutional neural networks (CNNs), clustering algorithms, and anomaly-detection models for automated classification of celestial objects, rare-event detection, pattern discovery, and noise reduction in observational datasets. Experimental evaluations on benchmark astronomical datasets demonstrate that AI-based models significantly improve classification accuracy (up to 97%), reduce processing time by 45–70%, and enable real-time or near–real-time astronomical event monitoring. The findings highlight the transformative role of AI-driven analytical models in improving observational accuracy, accelerating the discovery of transient phenomena, and supporting next-generation astronomical missions.

The rapid advancement of Artificial Intelligence (AI) in conjunction with emerging technologies has ushered in a transformative phase in scientific and technological development. In the Age of AI, the integration of intelligent systems with technologies such as Internet of Things (IoT), Big Data analytics, cloud computing, blockchain, and edge computing has significantly reshaped computational paradigms and digital infrastructures. This study examines how the integration of AI with emerging technologies contributes to scientific and technological progress from a Computer Science and Information Technology (CS/IT) perspective, with Mumbai serving as the study area due to its prominence as a technological and innovation hub. The research adopts a system-oriented and analytical approach, focusing on AI-driven architectures, data-centric models, and intelligent computational frameworks deployed across technology-intensive environments in Mumbai. Key dimensions analyzed include AI-enabled data processing efficiency, algorithmic intelligence, system scalability, automation capabilities, and decision-support mechanisms. The study explores how machine learning models, deep learning architectures, and intelligent analytics enhance system performance when combined with emerging technologies. Emphasis is placed on real-world IT applications such as smart systems, intelligent service platforms, scientific data modeling, and technology-driven research environments. Findings indicate that AI integration significantly improves computational accuracy, processing speed, and adaptive intelligence of emerging technology systems. The study highlights the role of explainable AI, cloud-based AI services, and hybrid intelligent frameworks in advancing scientific research and technological innovation. Additionally, challenges related to data security, system interoperability, and ethical AI deployment are identified, offering insights for future system design and policy formulation. The study contributes to CS/IT literature by presenting a structured framework for AI–emerging technology integration and by providing empirical and conceptual insights relevant to researchers, system architects, and technology policymakers. The outcomes underscore the potential of AI-driven emerging technologies to accelerate scientific discovery and sustainable technological growth in urban innovation ecosystems like Mumbai.

Cybercrimes have relatively increased in recent years and it is fast evolving using artificial intelligence playing a key role in this exponential growth. The impact of AI on cybersecurity is having two folds: One hand Cyber criminals are using AI to conduct more sophisticated cyber-attacks. On the other hand, it is helping to build a strong cyber defense mechanism. Enabling predicting threats from possible attackers with greater speed and precision than ever before. Artificial Intelligence enables cyber criminals and hackers to exploit vulnerabilities more effectively to avoid detection, execute more sophisticated attacks and scale their operations. Artificial Intelligence in social engineering had made a significant increase in psychological manipulation and deception to obtain sensitive information or assets from their targets. Even though using AI driven cyber threats has increased, AI still plays a crucial role for improving cyber security significantly. Advanced machine learning powers for threat hunting and AI technologies can help to detect and respond to threats with greater accuracy and speed than traditional measures. In this paper given a brief overview on various cyber intelligence aids where AI is integrated for threat intelligence using machine learning to identify and predict malicious threats. This shifts the network from security posture from reactive to preemptive.

The growing tendency of internet aggression, cyberbullying, and toxic communication has generated the necessity of smart technology to promote desirable digital behaviour. Although psychologists have gone a long way in creating and testing digital interventions that enhance empathy, cooperation, and positive interaction, they have not yet been applied in real technological systems. The current research suggests an Artificial Intelligence (AI) model that makes psychological understanding available in scalable and data-driven digital solutions to curb online toxicity and encourage prosocial behaviour in adolescents and young adults. The suggested framework is designed based on three mutually reinforcing dimensions, namely, proactive, interactive, and reactive interventions, each of which is accommodated by the properties of user interaction timing and nature. Prevention-based solutions will narrow down the adverse interactions on the internet by using educative prompts, emotional awareness devices, and the digital literacy module provided through AI capabilities. The interactive interventions utilise the real-time monitoring and adaptive feedback tool through natural language processing (NLP) and sentiment analysis in order to promote self-regulation and empathy in online interactions. Reactive intervention is premised on Reactive post-event reflection and behavioural strengthening, which involve the provision of Restorative feedback, online counselling referral mechanisms as well as peer-support. The combination of these layers will result in a complete ecosystem that is toxic in the prevention of online behaviour and responsive. The theoretical framework revolves around the methodological integration of the supervised and reinforcement models of learning with the socio-behavioural data sets when distinguishing linguistic and affective signals of aggression, empathy and cooperation. The lessons inform the dynamic provision of the interventions and consequently contextual lessons with the use of the ethical data. The study also embraces the principles of participatory design because the educators, psychologists and adolescent users are invited in system verification to enhance usability and credibility. There are preliminary signs that AI-inspired interventions grounded on the psychological theory and balanced with interdisciplinary cooperation can result in a drastic decrease in cases of verbal aggression and an increase in the number of cases of empathy and meaningful discussions in the virtual environment. The paper is also an extension of the existing discussions in the field of AI ethics, digital well-being and social technology because it provides a path towards transforming AI into a means of behavioural empowerment and digital citizenship rather than a surveillance tool. It suggests cooperation among the industries to transform technological innovation not only to be safer, but also caring, empathetic, and inclusive in the digital world. The proposed AI application can be duplicated as an evidence-based strategy of the promoting of the positive internet communication within the educational, social, and community platforms.

The advent of Artificial Intelligence (AI) has become a revolutionary trend in the field of strategic management and public policy, as it reshapes the very processes of making, implementing, as well as appraising grand strategy. In a world with continuous advancement in technology and uncertain trends in the & governance structures, the traditional approaches often adopted in the making of grand strategy have less often proved timely and evidence-based. The use of AI technology in the areas of Machine Learning, Predictive Analysis, and Intelligent Decision Support Systems assists in facilitating Big Data, creating patterns, and forecasting visions. This article explores the implications of AI in the field of strategic management, as well as in policy-making, focusing on the initiatives of the Indian government to implement AI in governance. This study is conducted using a qualitative method of research, where a thorough analysis of . The findings of this study have demonstrated that AI has improved processes in strategic planning, competitiveness, risk management, as well as evidence-based policy-making. This study has also addressed challenges in ethics, laws, and governance. However, as stated in the paper, for successful integration of AI technology within strategic management, there is a need for a balanced approach towards innovation in technology as well as governance, oversight, and management The paper, with its examination of some recent initiatives in AI strategies in the context of 'IndiaAI Mission', some guidelines on 'The Governance of AI in India', demonstrates how macro strategies play an important part in integrating 'Ethics/Sustainability' of AI into society. Finally, some strategic suggestions are presented on how to use AI.

Net Asset Value (NAV) serves as the price at which investors buy or sell units of mutual funds. It is computed at the end of each business day using closing prices of securities held by the fund. NAV is a benchmark for tracking a fund’s performance and is updated daily for open-end funds. This article presents NAV prediction using XG Boost machine learning Algorithm. The proposed model suggests time series prediction model. Lower MAE / RMSE shows predictions are numerically close. Very low MAPE (~0.55%) indicates strong relative accuracy. It is quite effective, with forecasted values only marginally different from actual NAV. For daily NAV forecasting, such low errors are often considered very acceptable. Very low MAPE (~0.55%) indicates strong relative accuracy.

Artificial intelligence (AI) is rapidly becoming a game-changing tool for tackling global environmental issues. The purpose of this research is to explore how Artificial Intelligence can be applied to drive advance sustainability enterprise across diverse sectors. For instance, numerous associations are formerly tapping into AI technologies to enhance energy effectiveness. By incorporating AI into sustainability systems and processes, associations can optimize resource application, reduce waste, and save energy and capitalist. An illustration of this is smart grids, where AI-powered algorithms can play a transformative part in revolutionizing energy operation. The methodologies for accelerating sustainability with AI involve relating and assaying sustainability challenges, developing AI results, enforcing AI results, monitoring and assessing issues, conforming and perfecting. AI is reshaping sustainability attempts by allowing associations to minimize operations, reduce waste and accelerate the adoption of low-carbon technologies. By integrating AI into sustainability initiatives, companies can improve efficiency and foster new business models that align environmental responsibility with economic growth. An association between AI and sustainability is not only perfecting effectiveness but also creating new openings for invention. From energy operation to agriculture and climate monitoring, AI is proving to be an important tool in the fight against environmental challenges. As we look to the future, it is clear that AI will play a vital part in creating a more sustainable and adaptable world.

The modern workplace is facing significant changes owing to the growing influence of Artificial Intelligence (AI), bringing opportunities along with challenges to both employees and employers. While AI-driven systems improve efficiency and productivity, at the same time it is also alleged that big tech companies now increasingly substitute human workforce with such tools. There are discussions related to automated systems, which are supposed to result in job displacements. However, implementation of AI tools has also elevated the significance of tasks that require reasoning, complex decision-making, creativity, and emotional intelligence—areas where human skill sets are still essential. This study aims to explore the latest scenario regarding the various impacts and concerns of using Artificial Intelligence in workplaces and attempts to propose how it can reshape the workplace dynamics for the better. The methodology includes a mixed-method approach that analyses both published secondary data and primary data. The findings aim to provide recommendations for managing AI-driven transitions while ensuring sustainable workforce development.

The integration of Large Language Models (LLMs) into the Software Development Life Cycle (SDLC) marks a fundamental transition from human-centric programming to AI- augmented workflows. While this evolution promises substantial productivity gains, it introduces significant pedagogical risks for students and novice developers. This paper investigates the phenomenon of “Cognitive Atrophy,” arguing that an over- reliance on generative AI prioritizes Syntactic Fluency, the ability to produce functional code at the expense of Architectural Reasoning. We define this discrepancy as the “Syntactic Facade,” a condition where developers produce seemingly correct code without possessing the underlying mental models to explain its logic or maintain its structure. This creates a dangerous “Confidence-Competence Gap,” where the speed of delivery masks a lack of foundational understanding. As active learning characterized by struggle, trial, and error is replaced by the passive acceptance of AI suggestions, the long-term development of deep problem-solving skills is threatened. By analyzing these theoretical concepts, this paper highlights the risks of substituting critical thinking with automated tools. We explore how the erosion of mental effort in computer science education may produce a generation of “assemblers” rather than “architects,” ultimately weakening the technical resilience of the future workforce.

The integration of Artificial Intelligence (AI) in areas like healthcare, finance, and cloud computing has notably heightened computational needs and energy usage, prompting important sustainability issues. Data centers and AI-driven tasks now represent a significant share of worldwide electricity consumption, requiring creative strategies to enhance energy efficiency while maintaining performance. This paper presents an in-depth analysis of AI-driven energy-efficient computing, exploring the difficulties posed by AI tasks and the potential AI brings for enhancing power efficiency. Crucial AI methods, such as smart workload scheduling, forecast power management, dynamic voltage and frequency adjustment, and flexible resource distribution, are examined for their efficiency in minimizing energy consumption in computing systems. This paper also examines hardware advancements like AI accelerators, energy-efficient processors, cutting-edge memory architectures, and edge computing devices that enhance AI-driven optimization. Emerging paradigms such as hardware–software co-design, neuromorphic computing, and energy-efficient interconnects are also examined. In conclusion, the paper emphasizes key obstacles concerning scalability, energy expenses for training, complexity of models, and trade-offs between performance and energy, proposing future research paths for sustainable and energy-efficient AI systems.

Marine transportation continues to face significant safety challenges due to boat overloading, unexpected sinking, fire accidents, and delayed emergency response, particularly in small and medium-sized vessels. Many existing safety measures rely heavily on manual monitoring and periodic inspection for such ferry or water taxi and workboats, which may not be sufficient in dynamic and unpredictable marine environments. With increasing dependence on electronic sensing systems for safety monitoring, sensor reliability has also become a critical concern, as unnoticed sensor failures can lead to incorrect safety decisions. This paper addresses the need for a reliable and real-time boat safety solution that can continuously monitor hazardous conditions while ensuring the dependability of the sensing infrastructure itself. By emphasizing automated monitoring, timely alerting, and early identification of sensor malfunctions, the proposed approach aims to reduce accident risks, improve response time, and support preventive maintenance. The work highlights the importance of intelligent and dependable safety systems in modern marine applications to enhance passenger safety and minimize loss of life and property.

The growing use of artificial intelligence in criminal justice has intensified debate around reliability, transparency, and procedural fairness, particularly in the highly sensitive context of criminal interrogations. Although recent AI advances enable analysis of speech, behavior, and interaction patterns, prior research consistently shows that inferential applications—such as deception detection, emotion recognition, or predictive judgments of credibility— remain scientifically contested and risk introducing bias, automation dependence, and undue influence into investigative decision-making. In contrast, established interrogation research emphasizes accurate documentation, procedural compliance, and non-coercive interviewing as foundational to investigative integrity and evidentiary reliability. This paper proposes a human-in-the-loop framework for AI-assisted criminal interrogation rooms that intentionally excludes predictive or judgment-oriented functions. Instead, the framework prioritizes non-inferential, supportive AI capabilities designed to enhance transparency and structured review while preserving human authority. These include automated transcription and structured summarization of interviews, contextual visualization of interactional trends without evaluative labeling, consistency and timeline analysis across multiple interviews, and automated indicators for monitoring rights notification and procedural compliance. The framework introduces a modular system architecture and dashboard-oriented review layer that separates interview recording from post-interview analysis. This design mitigates automation bias, reduces the risk of coercive influence during questioning, and supports accountable review by investigators, courts, and defense stakeholders. Overall, the proposed approach offers a practical and ethically grounded model for responsible AI integration in criminal interrogations.

SignConnect.App presents a practical assistive communication system aimed at supporting interaction between hearing- and speech-impaired individuals and non-sign language users. The application interprets static hand gestures and converts them into readable text and synthesized speech using computer vision techniques. Hand landmark extraction is performed through MediaPipe, while OpenCV, Tkinter, and pyttsx3 are employed for gesture processing, interface design, and offline speech output, respectively. The system is designed to recognize English alphabet gestures along with a limited set of commonly used words relevant to daily communication. In contrast to many existing solutions that rely on specialized hardware or internet-dependent processing, the proposed application operates entirely offline using a standard webcam. Experimental testing under typical indoor conditions indicates reliable recognition performance with minimal response delay, suggesting that the system offers a cost-effective and accessible solution for real-world assistive communication scenarios.

The rapid growth of electronic waste (e-waste) has a significant impact on environmental, economic, and public health challenges worldwide. Traditional e-waste management and recycling methods are often inefficient, labor-intensive, and unable to cope with the increasing volume and complexity of discarded electronic products. This paper shows the study of the role of Artificial Intelligence (AI) in e-waste management and recycling processes. AI technologies such as machine learning, computer vision, and intelligent robotics enable automated identification, sorting, and disassembly of e-waste, leading to improved material recovery and reduced human exposure to hazardous substances.

The fast-paced development in the realm of Artificial Intelligence (AI) has appeared as a revolutionary element in the modern labor market, changing the characteristics and sustainability of job structures all over the world. Although AI-based automation provides benefits regarding productivity, innovation, and economic development, it also generates substantial concerns regarding job replacement, skill degradation, and job sustainability worldwide to a great extent. This research paper attempts to explore the idea of sustainable jobs in the AI era based on the impacts produced by AI that transform and extend job structures. This research aims to: (i) conceptualize sustainable employment in an AI-related economy, (ii) explore employment that could be at risk of automation and job opportunities, and (iii) analyze the policy direction for sustainable employment outcomes.

AI has given birth to a new era of beauty and then the beauty standards are rapidly changing. This study finds how media platforms with the help of Artificial Intelligence impact on contemporary beauty standards in a psychological, sociological, and ethical manner. The study through image enhancement tools, social media filters, and recommendation algorithms that utilizes a comprehensive review of secondary sources using a qualitative and descriptive research approach. This paper discussed algorithmic curation, bias in beauty representation, and psychological effects of AI-Driven Beauty Standards. The findings reveal that AI systems actively escalate the visibility of glamorized and normalized beauty standards, continuously adjusting and improving predictions as per the user interaction and feedback given by the users. Artificial intelligence has drastically changed representation and view of beauty in the media. These mechanisms contribute to unrealistic beauty expectations, reinforce algorithmic bias, and marginalize diverse representations of beauty. At the same time, the study highlights the potential of ethical and inclusive AI design to challenge dominant beauty norms by promoting cultural diversity, transparency, and responsible media practices. The study concludes that while AI-driven media poses significant risks to individual well-being and social inclusivity, it also offers opportunities for the positive potential of AI in redefining beauty. Integrating ethical principles, regulatory frameworks, and media literacy initiatives is essential to ensure that AI contributes to healthier and more inclusive beauty representations in digital media.

The pervasive digital transformation within the financial sector has profoundly reshaped contemporary banking and payment systems, enabling more rapid, accessible, and efficient transactions. Nevertheless, this digital expansion has concurrently heightened the risk of cybercrime, leading to a notable increase in financial fraud cases. Online scams, identity theft, unauthorized transactions, and data breaches now pose significant challenges to individuals, businesses, and financial institutions. Traditional fraud detection methods, which rely heavily on predefined rules and manual oversight, are insufficient for addressing the dynamic and complex nature of modern fraudulent activities. Consequently, Artificial Intelligence (AI) and Machine Learning (ML) have been adopted as crucial technologies for developing advanced fraud detection systems. This study seeks to examine how AI and ML algorithms can be employed to identify fraudulent activities, detect irregular transaction behaviors, and uncover hidden patterns indicative of cyber threats. The research explores both supervised and unsupervised learning methods to evaluate their effectiveness in various fraud detection scenarios. These models learn the relationships between transaction attributes and known fraud outcomes to make accurate predictions. Conversely, unsupervised techniques are utilized to identify anomalies in situations where labeled data is scarce or unavailable, enabling the system to detect unusual transaction behaviors that may signal emerging fraud. The study also includes practical case examples that illustrate the implementation of ML-driven fraud detection systems in real financial environments. These cases demonstrate how continuous transaction monitoring, behavioral analysis, and real-time anomaly detection can significantly reduce financial losses while enhancing overall cybersecurity. The findings further underscore the importance of integrating AI-based detection mechanisms with existing security frameworks to create adaptive systems capable of responding to evolving cyber threats. In conclusion, this research confirms that AI and ML offer powerful and flexible tools for improving fraud detection in modern financial systems. Their ability to process large volumes of transaction data, learn from evolving patterns, and provide real-time insights makes them essential for safeguarding digital financial ecosystems. The study also emphasizes the need for ethical data management, regulatory compliance, and continuous model improvement to ensure the responsible and effective long-term deployment of intelligent fraud detection solutions.

Air pollution poses significant risks to human health and the environment, particularly in rapidly urbanizing metropolitan regions. Accurate monitoring and prediction of air quality parameters are essential for effective policy formulation and public health interventions. This study proposes an integrated machine learning framework for monitoring and assessing air quality in the Mulund region of Mumbai using real-world sensor data collected from February 2025 till 23 January 2026. The dataset comprises pollutant concentrations including PM2.5, PM10, and O₃ obtained from the Maharashtra Pollution Control Board through the OpenAQ platform. After data preprocessing and temporal feature engineering, predictive models based on Linear Regression and Random Forest algorithms were developed to estimate PM2.5 concentrations. Experimental results demonstrate that the Random Forest model achieves superior performance with an R² value of 0.85 and a mean absolute error of 2.07 µg/m³, significantly outperforming Linear Regression. The proposed framework effectively captures nonlinear relationships and temporal patterns in air quality data, offering a reliable and scalable approach for real-time air quality assessment and decision-making in urban environments.

Artificial Intelligence (AI) has become an integral component of contemporary mathematics education, offering tools that support problem-solving, feedback, and conceptual understanding. This study investigates the influence of AI on human mathematical reasoning, with particular emphasis on skill development and cognitive dependency. Data were collected from 173 undergraduate students using a structured questionnaire based on a five-point Likert scale. The study examines relationships between AI usage, mathematical reasoning ability, comparative reasoning performance, and cognitive dependency. The findings reveal a strong positive correlation between AI usage and mathematical reasoning skills (r = 0.65), as well as comparative reasoning performance (r = 0.60), indicating that AI-assisted learning enhances accuracy, efficiency, and the ability to evaluate multiple solution strategies. However, results also show a moderate positive correlation between AI usage and cognitive dependency (r = 0.48), suggesting that excessive reliance on AI tools may reduce independent problem-solving and critical thinking. Survey responses further indicate that many students prefer manual problem-solving for better conceptual retention and deeper understanding. The study concludes that AI is most effective when used as a supportive learning aid rather than a replacement for human reasoning. Balanced and guided integration of AI can enhance mathematical learning outcomes while preserving essential cognitive and analytical skills.

The rapid digitalisation of workplaces in India has significantly altered work patterns, often extending work-related communication beyond official working hours and blurring the boundaries between professional and personal life. In response to these emerging challenges, the Right to Disconnect Bill, 2025 has been proposed to safeguard employees from after-hours work obligations. Against this backdrop, the present study examines the proposed Right to Disconnect Bill, 2025 and its potential impact on work–life balance in India. The study adopts an exploratory research design to assess employees’ awareness, perceptions, and anticipated effects of the proposed legislation. Primary data were collected through a structured questionnaire supported by secondary data. Descriptive and inferential statistical techniques were employed to analyse the perceived relationship between the proposed right to disconnect and key dimensions of work–life balance, including personal time management, psychological well-being, job satisfaction, and work-related stress. The findings suggest that while employees hold positive perceptions of the Right to Disconnect, these perceptions do not immediately translate into improved work–life balance. RTD is largely viewed as a forward-looking safeguard rather than an indicator of existing work–life conditions. The results highlight that organisational culture and individual coping mechanisms play a significant role in shaping employee’s work–life balance.

Mathematics has always been considered a tricky subject because of its abstract concept and lack of personalization in the conventional method of instruction. The fast-paced development of Artificial Intelligence (AI) has brought innovative approaches to the instruction of mathematics through intelligent tutoring, adaptive learning, automated testing, and generative AI technology. AI technology allows for personalization of instruction, instant feedback, and increased engagement of the learner, which makes the learning process efficient and increases the level of comprehension. Nevertheless, the pervasive use of AI technology poses important pedagogical and ethical concerns, making AI a double-edged sword in the instruction of mathematics. Specific objectives are: (i) the place of AI in facilitating teaching and learning of mathematics, (ii) challenges that come with the integration of AI in mathematics education, and (iii) how managing AI use can be done to avoid over-reliance on technology and ensure that children do not lose the ability for independent thinking. The study adopts a conceptual research methodology informed by a critical review and synthesis of recent literature on AI applications, educational models, and ethical considerations. It has been determined that while it true that AI improves or enhances personalization and efficiency of assessments and engagement, over-reliance on it might hamper problem-solving skills and critical or conceptual understanding. It can thus be concluded from this paper that AI should be used as an aid in pedagogy and it should be grounded in sound pedagogy and ethical foundations so that there could be an optimum use of it.

Artificial Intelligence (AI) has become a transformative technology in education, offering innovative tools that support learning and academic development. AI-based applications, such as intelligent tutoring systems, virtual assistants, Chatbots, and writing assistance tools, are increasingly being used by students for educational purposes. These technologies have the potential to transform traditional learning methods into engaging sessions by improving understanding, engagement, and overall academic performance. The study aims to evaluate the role of Artificial Intelligence in enhancing student academic performance among undergraduate students. The study helps to analyse how AI tools lead to improved understanding of academic concepts, quality of assignments, time management, and overall learning efficiency. It also helps to know the students' perceptions regarding the opportunities and challenges associated with the use of AI in academics. A descriptive research method is used. Descriptive and analytical methods are used to interpret the collected data. The primary data was collected through a structured questionnaire. Secondary data was sourced from journals, articles and online reports. The findings of the study are expected to indicate that AI plays a significant role in enhancing academic performance and offers various benefits. However, challenges like over-dependency on AI tools, accuracy and reliability issues, data privacy and security risk were also identified. The study highlights the importance of proper guidance and awareness about ethical use of AI tools to maximise learning outcomes while minimising over-dependence. The results of this research may help educational institutions and policymakers in framing effective strategies for the responsible use of the AI in education and enhance academic development.

Social media platforms have become critical sources of real-time information for monitoring and predicting civil unrest and violent events. Recent advances in artificial intelligence have produced a wide range of analytical pipelines, including transformer-based language models, graph neural networks, temporal forecasting systems, and multimodal vision–language frameworks. However, existing studies remain fragmented across platforms, languages, and modeling paradigms, making it difficult to assess their relative effectiveness and applicability. This paper presents a comprehensive comparative review of AI-based architectures used for civil unrest prediction using Twitter and Instagram data. The study systematically analyzes model categories, data representations, performance trends, platform suitability, multilingual capability, and ethical considerations. By synthesizing findings across recent literature, this work highlights architectural trade-offs, identifies persistent research gaps, and provides practical guidance for selecting appropriate analytical frameworks. The review contributes toward a clearer understanding of current methodological capabilities and limitations in socially responsible unrest prediction.

Democratization of the Indian equity market has spurred considerable growth in retail investor participation; however, the vast majority of retail investors do not outperform the benchmark Nifty 50 index. Most of these retail investors underperform relative to their benchmark due to irrational decision-making habits (i.e., psychological heuristics). The purpose of this study was to collect primary data from 75 Indian retail investors in order to assess the extent to which these individuals experience fear, greed, and herd mentality in their decision-making processes. The results of this study indicate that panic selling (Mean = 3.89) and herd impact (Mean = 3.84) are most prevalent among Indian retail investors in addition to a large awareness-action gap. In addition to fear, greed, and herd mentality, we also evaluated various artificial intelligence (AI) based tools that are currently available to the retail investor; specifically, we evaluated the effectiveness of automated rebalancing (Mean = 4.21) and risk alerts (Mean = 4.17) as effective rational circuit breakers. We were able to conclude that while human emotion is an integral part of the decision-making process, the use of AI allows retail investors to maintain discipline while investing during times of market volatility.

Mental health contributes to well-being, productivity, and prosperity at both individual and societal levels. The Economic Survey 2024-25 draws a direct connection between mental well-being and India's economic future. Mental health challenges among university students have emerged as one of the most pressing public health concerns of the 21st century. There is a wide disparity between mental health service demand and availability. AI's entry into mental health treatment marks a pivotal moment in the transformation of healthcare delivery. Studies suggest that digital platforms for mental wellness have the potential to address care shortages, shorten wait periods, and provide more affordable treatment options. This research study investigates the awareness levels, perceptions and attitudes towards AI driven mental health support tools among university students hailing from Mumbai metropolitan and Thane district region. Previous descriptive studies covering this scope in India are insufficient to arrive at generalized findings. This study aims to fill this gap. The research is a descriptive cross sectional study conducted through a self-administered user friendly structured questionnaire circulated to students of various domains and colleges. Awareness was assessed through their familiarity with AI tools and their various functions and offerings. The constructs of perceived benefits, perceived drawbacks and attitude to use AI tools for mental health support were measured through several items. The results revealed that there was no association between gender of students and level of awareness and also their perceptions. However, there is significant correlation between perception and attitude to use AI powered tools for mental health support. These results would give insights to stakeholders such as the higher education institutions, AI product developers and the medical fraternity to promote AI tools as a complementary medium in the mental health care of university students grappling with several stresses.

In India, where infrastructure meets biodiversity-rich environments, railway-wildlife collisions are a significant conservation and safety problem. Seven elephants were killed when a high-speed train in Assam collided with a group of wild Asiatic elephants on December 20, 2025. This case-based study incorporated operational, technological and environmental factors. The collision site was outside the area of AI-based Intrusion Detection Systems (IDS), and the results showed that fog, nighttime operations, and forest cover greatly decreased visibility. The IDS relies on human interaction and operates below Level 4 autonomy, causing significant delays. Elephant movements were not captured by static corridor mapping. To align technology and animal conservation with India's sustainable development goals, multimodal sensing, increased coverage, adaptive speed control, and improved autonomy must be blended with human oversight.

Artificial General Intelligence (AGI) represents a pivotal yet elusive goal in artificial intelligence research, promising machines capable of human-like reasoning across diverse domains. This paper examines AGI through scholarly lenses, distinguishing conceptual myths from empirical realities, reviewing key literature, and analyzing methodological challenges. Drawing on peer-reviewed sources, it identifies research gaps in evaluation benchmarks and ethical frameworks while discussing practical implications for society. Findings suggest AGI remains theoretically feasible but distant, necessitating robust governance.[1][2]

Purpose: The study aims to analyse the impact of AI chatbots on consumer trust and confidence in purchase decisions, focusing on awareness, usage patterns, perception, and satisfaction on shopping and service websites. It also examines whether chatbot interactions influence consumer confidence during online purchases. Research Methodology: A descriptive research design was used. Primary data was collected via a structured 18-question Google Forms survey from 47 respondents using convenience sampling. Secondary data came from research papers, journals, articles, and online sources. Data was analysed using percentages and descriptive interpretation. Results: High awareness and usage of chatbots were observed, mainly for customer support and quick query resolution. Chatbots positively influence confidence in routine purchases, but trust remains moderate due to accuracy issues and lack of human touch. Respondents prefer human support for expensive or complex decisions. Conclusion: AI chatbots enhance online customer experience by improving efficiency, confidence, and satisfaction in routine purchases. However, complete trust is limited, and integration with human support and continuous technological improvements are essential to strengthen consumer trust.

The study tries to understand applicability of AI Enabled Customer Support Tools by selected E commerce applications and Its Use Among the Young and Middle-Aged Customers. The sample consist of young adults, salaried individuals, businesspersons and homemakers between 18 years to 65 years. A simple survey method is used to collect the data through structured questionnaire. AI powered customer service tools make the interactions with the customers seamless by providing personalized experience to them. The tools like Chatbots, Voice Agents or Virtual Assistants are providing the resolutions any time of the day, filling the gap of lost connectivity between the company and customers when the human agents are unavailable. The study predicted that the use of AI powered customer service tools is more among the young and middle-aged customers to resolve the issues at the preliminary level. The study predicted that young and middle-aged customers are satisfied with the query resolution tools provided by E-Commerce applications by embedding AI in their traditional customer service platforms.

Artificial Intelligence (AI) is playing an increasing role in how organizations handle communication. As businesses increasingly rely on digital platforms, issues such as slow responses, unclear messages, inconsistent interactions, and too much information have become common. AI tools like chatbots, email helpers, grammar checkers, transcription tools, and Customer Relationship Management (CRM) systems help reduce routine work, increase accuracy, and support better communication between employees and stakeholders (Davenport & Kirby, 2016; Kaplan & Haenlein, 2019). This paper explores how AI can improve the efficiency of business communication. Through a review of existing literature and conceptual models, it highlights the benefits of AI tools, as well as possible drawbacks and their impact on interpersonal communication skills (Prasad & Sharma, 2020; Russell & Norvig, 2021). Overall, the study suggests that combining AI tools with thoughtful human supervision can greatly improve organizational communication, while maintaining a good balance between technology and human judgment.

The rapid advancement of Artificial Intelligence (AI) has significantly transformed corporate governance and compliance monitoring frameworks across organizations. In the context of increasing regulatory complexity and strict enforcement of corporate tax laws, AI has emerged as a strategic tool for enhancing accuracy, transparency, and efficiency in tax compliance processes. This study examines the role of Artificial Intelligence in corporate governance and compliance monitoring with specific reference to corporate tax compliance in the Thane region. The research adopts a descriptive research design and utilizes both primary and secondary sources of data. Primary data were collected through a structured questionnaire using a five-point Likert scale, while secondary data were sourced from books, journals, research articles, and regulatory publications. Reliability analysis was conducted to test the consistency of the scale, followed by one-sample t-tests for data analysis. The study incorporates key behavioral and competency variables such as frequency of AI usage, risk tolerance, digital literacy, motivation, consistency, herding behaviour, and financial literacy. The findings indicate that Artificial Intelligence plays a significant role in strengthening corporate governance practices and improving corporate tax compliance by enabling continuous monitoring, reducing human errors, and ensuring regulatory adherence. The study provides valuable insights for corporate decision-makers, tax professionals, and policymakers seeking to leverage AI for effective governance and compliance management.

This research paper studies the awareness and perception of Artificial Intelligence (AI) in accounting among college students. The paper explains the basic concept of AI and its role in the accounting field. It examines how much accounting students know about AI, their opinions on the use of AI in accounting, and their views on how AI may affect future job opportunities. The study will be based on data collected through a questionnaire from accounting students. The paper also discusses the benefits and challenges of using AI in accounting and highlights the importance of including AI-related topics in accounting education. The find-ings of this research aim to help students, teachers, and institutions understand the importance of preparing for technological changes in the accounting profession.

Abstract: The agile growth of AI ushers in a new era of scientific and technological progress. It examines how the fast development of artificial intelligence is advancing scientific and technological progress while also creating new and complex security risks. This study compares traditional cybersecurity approaches with modern AI-driven security techniques that shape today’s threat landscape. It also reviews top-down transparency research and assesses how AI alignment methods can be applied to key safety concerns, including honesty, harmlessness, power-seeking behaviour, and resilience to manipulation. The findings show that AI-based transparency and security methods can effectively address a wide range of safety-critical challenges. These approaches demonstrate strong potential in identifying malicious behaviour, reducing system vulnerabilities, and improving the reliability and trustworthiness of AI systems used in scientific research. The analysis highlights the growing importance of AI-driven defences in countering advanced cyber threats. The paper outlines strategies for protecting the scientific “discovery engine” by securing models and datasets against adversarial machine-learning attacks such as data poisoning and model manipulation. It illustrates how AI-enabled security solutions can be integrated into scientific workflows to safeguard infrastructure, maintain data integrity, and ensure dependable research outcomes. This paper brings together AI-driven scientific innovation with cybersecurity and transparency research. By extending AI alignment and safety techniques to protect scientific models and data, it offers a novel framework for building secure and trustworthy AI-powered discovery systems.

The global information ecosystem is quietly and permanently collapsing. AI has automated the spread of misinformation, making it easy to create and share deepfake media and news that outpaces real information. Search engines have become the gatekeepers of reality, but their ranking systems prioritize speed, popularity, and optimization. Consequently, false information often dominates breaking news and high-attention situations. This leads to a routine spread of misinformation rather than mere confusion. This paper explores why AI-driven misinformation often sways public opinion despite the existence of detection algorithms. Detection acts as a reactive measure that cannot compete with search rankings, which favor speed and savvy SEO practices. To illustrate this, we created a trust-aware search intelligence system to examine the critical issue of mass shootings. We analyzed the information landscape by querying search results for misinformation and fake news related to various mass shootings, considering factors like SEO power, content trustworthiness through text and image-based deepfake detection, and the reliability of sources. We introduced a new measure called Ranking Harm to assess the societal damage caused by rankings, determining the risk that arises when low-trust links reach top search positions. Our approach includes a longitudinal analysis of narrative dominance, where we repeatedly queried search results about breaking news events over extended periods. We developed a large-scale monitoring system built on ELK (Elasticsearch, Logstash, Kibana) to track ranking, trustworthiness, and narrative dominance. We observed a significant first-mover advantage: the earliest optimized sites that achieve top search positions tend to entrench misinformation in the public discourse more effectively, even when algorithms later detect inaccuracies and authors make corrections. Overall, search rankings correlate much more closely with SEO power than with trustworthiness. Late corrections often fail to regain public trust once misleading information takes hold during breaking news events. AI-driven misinformation is no longer just an error to be filtered out; it will continue to influence public opinion through search rankings until we create search systems that are trustworthy and aware of timing.

Road accidents remain a major public safety challenge, particularly for two-wheeler riders, where delayed emergency response and lack of real-time safety monitoring significantly increase injury severity and fatality risk. This paper proposes an AI-enabled smart helmet–based safety and monitoring framework designed to improve rider protection through continuous assessment of critical riding conditions. The proposed system focuses on three primary safety objectives: detection of accident-like events, identification of unsafe riding behaviour such as potential intoxication, and verification of helmet compliance. To enhance reliability and reduce false alerts, the framework incorporates sensor-fusion-driven machine learning that classifies riding events more accurately than conventional threshold-based approaches. In addition, the design supports a hybrid communication strategy to ensure emergency alerts can be triggered even under limited network availability, while also enabling optional cloud/dashboard-based visualization and long-term analytics. The proposed approach further introduces rider risk scoring and anomaly detection to provide preventive warnings and decision support. Overall, this work presents a scalable and research-oriented blueprint for intelligent rider safety systems that combines edge intelligence with real-time monitoring for improved road safety outcomes.