This article reviews existing techniques and proposes new methods to evaluate credit risk in economies without formal credit systems but widespread mobile phone usage. Limited availability of formal financial data remains the primary drawback to credit access, yet AI-based analysis of digital behavior can help bridge this gap. Our review suggests that applying machine learning methods to classify behaviors such as gambling and alcohol-related spending as high risk may accurately capture credit risk, while expenditure on education, including school fee payments, may signal creditworthiness. Further, stable and long-term location patterns may serve as strong indicators of high credit quality. We propose using AI to discover hidden “non-linear” patterns, such as a combination of alcohol consumption and irregular phone charging, which may predict high credit risk but possibly escape human analysts. The proposed systems automate the search for these factors and create models that are robust to sparse data. Finally, we analyze the “Digital Utility Trap”, where the fear of losing essential mobile access motivates borrowers to pay, offering a safe and scalable path for lending to the unbanked and thin-filed.

The exponential growth of biomedical data and clinical information necessitates the transformation of traditional medical libraries into intelligent knowledge ecosystems. This study explores the integration of artificial intelligence (AI) technologies-including natural language processing, machine learning, metadata automation, and explainable AI-within smart medical libraries to enhance knowledge discovery, information retrieval, and clinical decision support. Through a qualitative review of recent literature in medical informatics and library sciences, this paper synthesizes current AI-driven applications and evaluates their impact on research acceleration, personalized education, and precision medicine. The findings highlight the transformative potential of AI-enabled libraries as intermediary infrastructures between raw biomedical data and actionable clinical insights. While significant benefits are identified, challenges related to data privacy, algorithmic transparency, financial investment, and technical expertise remain critical considerations. The study underscores the necessity of interdisciplinary collaboration to optimize AI deployment in medical knowledge management systems.

Digital transformation in business and commerce refers to the integration of technologies such as Artificial Intelligence (AI), cloud computing, Big Data, and the Internet of Things (IoT) into business operations. It improves efficiency, enhances customer experience, and supports global business growth. This study examines how data analysis and AI assist in better decision-making, while also exploring challenges such as cybersecurity risks, regulatory compliance, and resistance to change. The research uses methods including literature reviews, case studies, and quantitative analysis, supported by frameworks like the Technology Acceptance Model (TAM), Diffusion of Innovation Theory, and the TOE framework. Findings show that automation, AI tools, and cloud-based systems reduce costs, improve accuracy, and enable real-time decision-making. Businesses increasingly use CRM systems, chatbots, and predictive analytics to deliver personalized customer experiences. The study concludes that digital transformation is essential for business survival and long-term growth. Companies adopting scalable digital tools and data-driven strategies achieve improved productivity, operational efficiency, and customer satisfaction.

Technology has become an essential part of modern life and has significantly changed the way people live, work and learn. In the field of education, technological advancements have transformed traditional teaching and learning methods in classrooms. Teachers and students now use various digital tools that make the learning process more interactive, practical and effective. One of the most important recent technological developments is Artificial Intelligence (AI). AI has begun to play a vital role in higher education by supporting teaching activities, improving learning experiences, and assisting in academic tasks. In India, the growing demands of higher education require innovative approaches and creative learning methods. This study aims to examine the use of Artificial Intelligence (AI) in Indian higher education, particularly in the teaching and learning process. The paper is based on secondary data and focuses on the role of AI in higher education and the challenges faced in today’s competitive academic environment.

Artificial Intelligence (AI) is revolutionizing essential company activities in today’s era of digital innovation, and the accounting and finance industries are seeing significant change. This study looks at how Google has greatly improved its internal financial infrastructure by incorporating AI technologies into its financial systems. Using a case study approach, the paper focuses on how Google leverages cutting-edge AI tools like Vertex AI, BigQuery ML, and TensorFlow to enhance financial decision-making, streamline reporting procedures, and bolster adherence to international laws. Predictive forecasting, intelligent expense tracking, fraud detection, and compliance with foreign tax regulations are some of the real-world applications that have been investigated. The results show how AI can improve operational effectiveness and strategic insight, establishing Google as a leader in contemporary, AI-enabled corporate finance. This study provides useful frameworks that other businesses might use to move toward finance systems that are data-driven and sophisticated.

Artificial intelligence (AI) has become a disruptive force in company operations and financial markets, changing conventional wisdom and opening up new avenues for growth. A comprehensive evaluation of the literature covering a broad range of research on AI applications in business and finance is presented in this study. The review looks at how AI may improve trading tactics, risk management, fraud detection, and financial forecasting. It covers a range of artificial intelligence (AI) methods, including machine learning, deep learning, and natural language processing, emphasizing how well they analyse large datasets and enhance decision-making. Additionally, the paper discusses how implementing AI may optimize corporate operations, including process automation, predictive analytics, and improving the customer experience. The advantages of AI-driven innovations, such as improved productivity, lower costs, and customized services, are highlighted, as are the drawbacks, such as job displacement, algorithmic bias, and regulatory frameworks. The study ends with suggestions for future lines of inquiry to improve AI’s interpretability, openness, and moral application in commercial and financial settings.

Artificial intelligence (AI) has rapidly become a transformative force in health care, enhancing diagnostic accuracy, treatment planning, patient monitoring, and operational efficiency. This article explores advanced and emerging AI techniques-such as deep learning, reinforcement learning, natural language processing, and federated learning-and evaluates their applications, benefits, and ethical challenges. Through a systematic review of recent literature, we identify current trends, limitations, and future research directions. Findings indicate that AI not only improves clinical outcomes and reduces costs but also poses significant concerns related to data privacy, bias, and integration in clinical workflows. Recommendations are offered to maximize clinical benefits while addressing associated risks. 

The rapid digital transformation of financial systems has significantly altered the way financial decisions are made and how markets function. Among emerging technologies, Artificial Intelligence (AI) has gained particular importance due to its ability to process large volumes of data, identify complex patterns, and support informed decision-making. AI-driven decision-making represents a conceptual shift from traditional, human-centered financial judgments toward data-driven and algorithm-supported analytical processes. Market efficiency, a fundamental concept in financial economics, refers to the extent to which asset prices reflect available information accurately and in a timely manner. The increasing integration of AI into financial systems raises important theoretical questions regarding its influence on information processing, price discovery, and overall market efficiency. While AI has the potential to enhance forecasting accuracy, reduce information asymmetry, and improve risk management, it also introduces challenges related to transparency, ethical concerns, regulatory readiness, and over-reliance on automated systems. This study adopts a purely conceptual and theoretical approach to examine the relationship between AI-driven decision-making and market efficiency in financial systems. By synthesizing existing literature and theoretical perspectives, the paper highlights key opportunities, challenges, and future implications of AI adoption in finance. The study concludes that AI can contribute positively to market efficiency when supported by responsible governance, transparency, and continued human oversight. 

Artificial intelligence is being used in financial systems to make tasks more efficient, detect fraud, and offer personalized services. It is changing industries like banking, trading, and insurance by making processes faster, cutting costs, and helping manage risks better. Some key uses include automated trading, financial advisors that operate on their own, and making sure companies follow financial rules. Objectives of Study: Automating repetitive and data-heavy tasks like entering data and processing invoices to cut down on mistakes, speed up processes, and save money. Research Methodology: Research on AI in financial systems uses different methods, like looking at past studies, examining real-life examples, and using data to see how AI affects areas such as trading, risk management, and compliance. Data Analysis: AI watches patterns in transactions in real time to spot suspicious behavior. This helps reduce false alarms and improves how risks are managed. High-frequency trading systems use machine learning to notice small price changes and make trades with very little delay. Finding: AI helps cut costs by automating tasks. In banking and capital markets, 32 to 39% of work could be done by AI. Mid-sized financial firms have already seen a 28% drop in their operating costs. Recommendations: Create complete frameworks that cover all parts of AI use, including regular checks, making sure data is accurate, and identifying bias. This is important for dealing with issues like false information and cyber threats. Conclusion: AI is not just a new tool but a big force that is shaping the future of finance. It’s important to use its benefits while being careful about the ethical and system-wide risks it can bring. 

Artificial Intelligence (AI) is increasingly shaping modern commerce by enhancing operational efficiency, improving customer engagement, and enabling data-driven decision-making. However, alongside these benefits arise ethical concerns such as privacy violations, algorithmic bias, lack of transparency, and accountability challenges. This study examines how ethical AI systems contribute to sustainable competitive advantage in commerce. Through a qualitative review of secondary sources and conceptual analysis, the article explores ethical frameworks, governance strategies, and business implications of responsible AI adoption. The findings indicate that organizations integrating ethical AI principles experience improved customer trust, stronger brand reputation, and reduced regulatory risks. Ethical AI is therefore positioned as a strategic asset rather than a limitation. The study concludes that embedding ethical standards in AI development is essential for long-term commercial sustainability. 

Online business has become common, with every field engaged in it. Post-COVID, 99% of business sectors have adopted GPS location applications. Every business app cannot operate without a GPS location feature. Preparing and consuming food at home is a thing of the past, as the focus has shifted to consuming food prepared elsewhere. This is the reason for the necessity of GPS location apps. The present paper focuses on the GPS location applications used by food delivery personnel with reference to Vijayapur City. It presents a comparative study between the GPS location apps used by Zomato and Swiggy’s food delivery personnel. In Vijayapur City, there are more than 200 online food delivery personnel. In this paper, responses were collected from 50 respondents and analyzed using the statistical tool of a five-point Likert scale. The research covers questions regarding the accuracy of customer locations while using GPS, buffering problems, instances of the app showing shorter routes than the actual distance, issues of getting stuck while reaching a destination, and discrepancies in the location displayed by the app compared to previous information, along with additional research questions. The paper presents conclusions regarding the GPS applications used by Zomato and Swiggy.

In modern business and trade, digital transformation (DT) has become a key factor in gaining a competitive edge. Rapid developments in blockchain, big data analytics, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) are changing company models, value generation workflows, and organizational strategies. By combining organizational, strategic, and technological viewpoints, this study offers a multifaceted examination of digital transformation. Secondary data from peer-reviewed literature, international industry publications, and corporate disclosures of top companies, such as Amazon, Alibaba Group, Microsoft, and Tesla, Inc., were analyzed using a descriptive and analytical research design. The study creates a conceptual framework that connects performance results, transformation processes, and digital drivers. The results indicate that ecosystem integration, organizational agility, digital capability development, and strategic alignment are necessary for a successful digital transformation. The paper contributes to digital transformation literature by combining findings from several sectors and putting forth an integrated strategic model that can be empirically validated in further studies; the paper adds to the body of knowledge on digital transformation. 

Artificial intelligence driven supply chain and inventory management enhances accuracy, reduces costs, and improves efficiency by leveraging machine learning, predictive analytics, and computer vision.
The artificial intelligence optimizes stock levels, reduces forecasting errors by up to 50%, improves warehouse efficiency through automated monitoring, and enables proactive, data-driven decisions for demand planning and logistics. Supply chains are complex, and managing them requires significant time and effort from different teams within a business, including procurement, and production.
But with the increasing availability of artificial intelligence enabled supply chain management solutions, businesses of all sizes now have access to transformative tools to both improve their processes and gain deeper insights into their supply chains data.
While some artificial intelligence applications are trained on extensive datasets from various supply chain stages, others use predefined rules or mathematical models.
Recently, this technology gained popularity as further advancements such as generative artificial intelligence and tools such as chatbots, robots and artificial intelligence assistants demonstrate the value artificial intelligence brings to risk mitigation and supply chain resilience.
Meanwhile, the COVID-19 pandemic illustrated just how fragile the global supply chain can be, highlighting the need for smarter tools to reduce delivery times and cut costs.
Once implemented, these systems can analyze patterns, optimize processes, and provide insights to enhance decision-making. Analyzing sensor data from critical equipment like trucks and drills, artificial intelligence can learn from historical data to predict potential equipment failures, enabling maintenance teams to intervene before breakdowns occur.

A society management system is an all-embracing software solution aimed at managing and automating the processes of residential societies, complexes, and gated communities. This system allows for effective handling of daily running of the estate’s operations, for instance, accounting, maintenance, security, voice and video communication, and management of community activities. Some of the core features common in such platforms are payment solutions for dues and fees, request for maintenance, rooms and facility bookings, visitors’ management, and efficient means of communication for residents and management teams. It also usually contains the event modules, for documents storage, and for voting on the society decisions, using exemplary democratic means. 

The rapid growth of aging populations across the world has intensified the demand for sustainable and compassionate elderly care solutions. Artificial Intelligence (AI) is increasingly being adopted to support older adults through intelligent health monitoring, fall detection systems, virtual assistants, and assistive technologies. This paper examines the technological foundations of AI-driven elderly care and critically evaluates its ethical and societal implications. While AI systems enhance independence, safety, and healthcare accessibility, they also raise concerns related to privacy, informed consent, trust, and the potential reduction of human interaction. The study analyzes both opportunities and limitations associated with AI integration in elderly support systems and emphasizes the importance of human-centered design principles. It argues that responsible deployment, regulatory oversight, and ethical safeguards are essential to ensure that AI complements rather than replaces human empathy. The paper concludes that AI-enabled elderly care can significantly improve quality of life when implemented with transparency, inclusivity, and social awareness.

Wound organization is a vital characteristic of healthcare that stresses fortunate and culminate organization to guarantee fitting treatment and decrease complications. This paper presents Wound Detox, a machine learning-based application created to classify wounds into five categories: Scraped spot, Bruise, Cut, Puncture, and Slash. These are gathered into three seriousness levels: low (Scraped area, Bruise), medium (Cut, Puncture), and high (Gash). The framework utilizes a Convolutional Neural Network (CNN) prepared on an assorted dataset of wound pictures for solid picture classification. Users can transfer wound pictures by means of a portable application built with React Native, which communicates with a Java-based backend. Upon classification, the app gives first-aid direction for low and medium-level wounds and appears adjacent clinics for medium cases. For high-severity wounds, the system automatically initiates an emergency call to the nearest ambulance service. The backend uses MySQL for secure data storage and efficient handling of user inputs and classification outcomes. User-friendly features such as registration, login, and image submission enhance usability. Wound Detox has been rigorously tested, demonstrating high accuracy and fast response times. By integrating AI with practical healthcare tools, the application offers accessible, real-time wound assessment and potentially life-saving support.

Artificial Intelligence (AI) is rapidly evolving beyond narrow applications toward transparent, ethical, and generalized systems. As AI increasingly impacts critical sectors, the research focus is shifting from mere computational accuracy to Explainable AI (XAI), responsible governance, and algorithmic bias mitigation. Emerging paradigms like Edge AI and federated learning prioritize data privacy and real-time processing, while the theoretical pursuit of Artificial General Intelligence (AGI) raises profound interdisciplinary questions. Furthermore, integrating AI with quantum computing, IoT, and green technologies highlights the critical need for sustainable, human-centered development. Balancing technological innovation with robust ethical frameworks is essential for empowering global society.

In the speculative but probable future stage of AI development known as artificial super intelligence (ASI), computational systems will surpass humans in all domains of cognition, including creativity, emotional intelligence, and new scientific discoveries, in addition to achieving general intelligence (AGI) comparable to humans. In this study, which examines the profound, complex impacts of ASI on humanity, the transition is examined as a potential “technological singularity” that will change labour, economics, ethics, and the human condition. Based on our findings, Artificial Super Intelligence (ASI) might be the crucial factor in addressing major global challenges such as mitigating climate change, curing severe illnesses, and enhancing resource management on a global scale. One of the most significant dangers associated with this extraordinary advancement is known as the “alignment problem,” which refers to situations where ASI goals diverge from human values, potentially leading to existential threats. Although ASI offers potential solutions to complex issues like disease and climate change, improper oversight could result in heightened inequality and scenarios of “charismatic extinction.” This study examines the economic, social, and ethical implications of the displacement of vast numbers of workers, the emergence of an “economy of radical abundance,” and the philosophical conundrums surrounding human control and purpose. We argue that aggressive governance, rigorous international safety standards, and comprehensive ASI alignment research are necessary to ensure that ASI serves as a tool for enhancing human well-being rather than a source of unmanageable systemic risk. 

In today’s world, students are more comfortable with technology than ever before. To keep education engaging and relevant, it’s essential to use innovative methods that incorporate technology. This is where Information and Communication Technology (ICT) steps in, offering a dynamic way to teach that goes beyond the traditional chalk-and-blackboard approach. ICT not only makes learning more interesting but also equips students with the crucial 21st-century skills they need to thrive.
The COVID-19 pandemic underscored the importance of ICT in education. With schools closed, both teachers and students relied heavily on digital tools to continue the learning process. This shift highlighted ICT’s potential to transform education, making it more accessible and flexible even in challenging times.

Artificial Intelligence (AI) is reshaping contemporary fashion by transforming design processes, production systems, and sustainability strategies in the textile and apparel sector. Amid growing concerns over overconsumption, environmental degradation, carbon emissions, and social inequities, AI has emerged as both a technological enabler and a subject of ethical scrutiny. This study examines the influence of AI on creative practice, circular design implementation, and responsible innovation in fashion. Drawing on a qualitative synthesis of systematic literature, design theory, and industry case analyses, this study proposes a framework that situates AI within sustainable fashion discourse. Findings indicate that generative design tools, virtual prototyping, digital twins, and predictive analytics support waste reduction, virtual sampling, demand-responsive production, and informed material selection. AI-enabled resale systems, automated textile sorting, and blockchain-based traceability strengthen circular economy initiatives by extending product lifecycles and improving transparency. However, algorithmic decision-making challenges authorship, craftsmanship, dataset neutrality, and labor structures. Concerns over bias, intellectual property ambiguity, digital energy consumption, and workforce displacement complicate sustainability narratives. The study argues that sustainable transformation requires a human-centered governance approach in which AI augments rather than replaces creative agency and is supported by ethical regulation and critical design education. By integrating sustainability theory, computational creativity, and AI ethics, this research contributes a holistic framework for responsible AI adoption in fashion systems.

The role of agriculture in providing food security globally cannot be overstated, but it is associated with various complex issues, and agricultural researchers have found that Machine Learning (ML), as a subset of Artificial Intelligence (AI), is helpful in precision decision-making through learning from multiple agricultural data types. This paper reviews various state-of-the-art ML algorithms applied to crop yield, disease identification, soil/water management, and decision support systems. The paper summarizes various ML models, applications, and trends, as well as research challenges and gaps, aiming to offer insights to guide future research and applications.

Personalized learning is increasingly essential in higher education due to variations in student abilities, learning pace, and academic preparedness. This paper presents EduMentor-AI, a hybrid adaptive intelligence model designed to support personalized learning through the integration of machine learning, learning analytics, and intelligent mentoring mechanisms. The proposed framework constructs dynamic learner profiles by continuously analyzing academic performance, engagement patterns, and interaction behavior. Based on these profiles, EduMentor-AI adaptively recommends learning resources, adjusts content difficulty, and delivers timely feedback via a virtual mentoring interface. In addition to learner support, the model provides educators with predictive analytics to identify at-risk students at an early stage and enable data-driven instructional planning. The hybrid architecture combines automated intelligence with human supervision to ensure transparency, fairness, and pedagogical effectiveness. Experimental observations indicate improvements in learner engagement, academic performance, and intervention timeliness when compared with conventional instructional approaches. Furthermore, the system reduces manual workload for instructors while enhancing individualized student support. The results demonstrate that EduMentor-AI offers a scalable and learner-centric framework capable of enhancing teaching and learning processes in higher education environments. By acting as an intelligent virtual mentor, the proposed model contributes toward inclusive education, improved academic outcomes, and sustainable digital transformation in universities.

Accurate wood species identification is crucial for biodiversity preservation and forest management. Because traditional identification methods are time-consuming and heavily rely on expert knowledge, automated image-based solutions have become more and more important. This research suggests a hierarchical framework for identifying wood species that makes use of both machine learning (ML) and deep learning (DL) approaches. The dataset of bark images utilized in the testing includes 22 distinct wood species. The ML-based approach evaluates classifiers such as Random Forest, Support Vector Machine, XGBoost, and ensemble models following extensive preprocessing and manually created feature extraction using statistical, color, and texture descriptors. The DL-based technique uses a specially designed CNN architecture in combination with convolutional neural networks that apply transfer learning models, such as MobileNetV2, DenseNet121, and ResNet50. The short dataset size is addressed using data augmentation and fine-tuning techniques. The experimental results demonstrate that Deep Learning models achieve greater classification performance and robustness when compared to Machine Learning models. This study provides a detailed comparison of complex DL algorithms with conventional ML to show the effectiveness of transfer learning for automated wood species identification.

The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep learning (DL) and hybrid algorithms of artificial intelligence to predict heart diseases has enjoyed a widespread use in the recent years with different levels of success. The current paper provides a systematized review of the latest ML-, DL-, and hybrid-based systems to predict heart disease that include ensemble model, deep learning model, explainable artificial intelligence (XAI), and privacy-preserving model. The analyzed studies are evaluated based on datasets, classifiers, validation methods, data balancing and evaluation measures methods. It is illustrated in the analysis that the traditional ML models are most likely to have the prediction accuracy in between 80-90, the ensemble and hybrid models will most likely have the prediction accuracy in between 90-98. Recent explainable and fine-tuned ensemble methods show an accuracy of a level of 99, however, frequently under controlled conditions. Irrespective of the developments made, the issues include low interpretability, excessive benchmark data, imbalance in classes, and computational complexity. According to the research gaps that are mentioned, the given review reflects the necessity of clarifiable hybrid models that include high performing ensemble models like XGBoost and Deep learning-based feature fusion to increase the predictive quality, transparency and clinical usability. 

Military operations, urban planning, environmental monitoring, and disaster management all benefit from modern aerial observation. In order to identify critical infrastructure, including airports, highways, ports, railroad stations, and defense zones, our work focuses on deep learning-based classification of high-resolution aerial photos. We train and assess three CNN models: MobileNetV2, VGG16, and DenseNet121. VGG16 enhances feature extraction, DenseNet121 increases accuracy by effective feature reuse, and MobileNetV2 offers a lightweight solution for real-time applications. To increase robustness and generalization, data augmentation is used. Accuracy, precision, recall, and F1-score are used to assess model performance. According to experimental results, all models function well, with MobileNetV2 being appropriate for real-time aerial surveillance applications and DenseNet121 offering the highest accuracy.