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This research presents an integrative framework that utilizes Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) for generating reliable and transparent justifications in deep ensemble models tailored for pulmonary carcinoma classification. This research approach used three different pre trained models as ResNet50, DenseNet121, EfficientNetB0 and deep ensemble model. The paper details a methodological pipeline that begins with preprocessing high-resolution computed tomography (CT) images, followed by enhancement and feature extraction using convolutional neural network architectures. As there is no clear suggestion of which pre-trained model gives best performance as compared to others, an ensemble approach has introduced. The ensemble approach consolidates predictions from multiple deep learning models to improve overall accuracy and reduce variance among individual model predictions. To address the critical issue of interpretability in clinical settings, two prominent post-hoc explainability methods— Grad-CAM and LIME—are in use to elucidate the inner workings of the model. The experimental evaluation conducted on CT scan lung cancer imaging datasets shows that the combination of ensemble learning with these XAI techniques enhances both diagnostic performance and transparency. The deep ensemble model achieved 0.972 of accuracy while ResNet50, DenseNet121 and EfficientNetB0 achieved 0.94, 0.9358 and 0.94 of accuracy respectively. The results clearly identify the regions of interest and diagnostic features that contribute to lung cancer detection, thereby increasing clinical trust in the system. The paper also discusses potential challenges, such as the variability in imaging quality and the computational overhead of ensemble approaches, and proposes strategies for optimization and real-time application. Overall, the findings demonstrate that leveraging Grad-CAM and LIME in a deep ensemble framework not only improves predictive accuracy but also provides interpretable insights crucial for clinical decision-making and subsequent patient management.

With the tremendous expansion of generative artificial intelligence (GenAI) in the last months of 2022, artificial intelligence (AI) has seen a sudden surge in popularity. AI literacy has become crucial skill that must be acquired to succeed in both academic and workplace settings. Algorithmic prejudice within GenAI systems poses significant challenges that undermine equitable educational outcomes. The objective of the study is to investigate, assess and explore a strategic, market-oriented framework for integrating Generative AI (GAI) into educational systems, and explore strategies that mitigate ethical risks. This study adopts a quantitative approach using explanatory research design and cross-sectional survey method employed to collect primary data from both faculty members and students within higher education institutions located inside the Kathmandu Valley The study aims to identify the extent to which the LIPSAL dimensions, GAI integration, and ethical requirement contribute to institutional innovation and sustainability, and how academic learning mediates these relationships. Within each stratum, respondents has been selected using simple random samplings to minimize selection bias. The findings of the study revealed that GAI adoption is strongly aligned with learner behavior, academic skills, and institutional readiness. Overall, the results contribute meaningful evidence to the field of educational technology, importance of strategic AI integration, ethical governance, and continuous pedagogical support illustrating that generative AI is not only reshaping how students learn but also redefining how institutions evolve, innovate, and sustain their growth in an increasingly digital academic ecosystem.

In our rapidly changing world, where climate change, resource depletion, and socio-environmental challenges are becoming more pressing, the journey towards sustainability has transformed from a trend to an urgent global necessity. This article delves into the exciting ways in which Artificial Intelligence (AI) is driving sustainable development across economic, environmental, and social dimensions. By systematically exploring the emergence of AI applications, we unveil their incredible power to enhance efficiency, precision, and predictive intelligence in tackling some of the most intricate sustainability challenges we face today. This paper also illustrates real-world applications of AI-driven models across varying scenarios—from climate forecasting and optimizing waste management to creating circular economies and smart resource allocation in urban planning. With its robust predictive analytics and automation capabilities, AI is paving the way for real-time monitoring and management of ecological systems.

This study examines how Digital Finance and Technological Innovation can contribute to sustainable business practices and social enterprises while addressing the persistent challenge of Non-Performing Assets (NPAs). It explores whether digital financial systems can reduce credit risk, improve repayment behavior, and support financially vulnerable communities. The paper adopts an analytical approach using secondary data, policy reports, and empirical insights from financial inclusion and NPA-related studies. It evaluates digital tools such as mobile banking, digital credit scoring, AI-based risk assessment, fintech platforms, and data-driven monitoring systems in mitigating loan defaults and strengthening sustainable financial ecosystems. The study finds that digital finance enhances transparency, real-time monitoring, and borrower profiling, which significantly reduces information asymmetry—a major cause of NPAs. Digital payment systems promote disciplined repayment through automated reminders and structured repayment tracking. Social enterprises leveraging FinTech platforms improve access to responsible credit and financial literacy, thereby lowering default risk. However, digital exclusion, low financial literacy, cyber security threats, and algorithmic bias may create new vulnerabilities if not carefully regulated. Strengthening digital infrastructure, improving financial literacy, and implementing ethical AI frameworks are essential for reducing NPA risks while promoting inclusive and sustainable growth. The study links digital finance, sustainability, and NPA risk management within a unified framework, highlighting how innovation can transform credit ecosystems and build resilient, inclusive economies.

Doctoral research in Information Science (IS) in India has grown substantially over the past four decades, propelled by the rapid digital transformation of libraries, the advancement of information and communication technologies, and the emergence of nationwide open-access scholarly ecosystems such as Shodhganga, the reservoir of Indian theses hosted by INFLIBNET. This paper presents an expanded, data-driven examination of the evolution of IS doctoral research in India, drawing on metadata relating to year of submission, thematic keywords, institutional affiliation, geographical distribution, and supervisory patterns across four historical phases. Employing descriptive bibliometric indicators, keyword clustering, and network-mapping techniques, the study traces the discipline’s transition from classical librarianship toward digital, data-intensive, and interdisciplinary domains. Findings indicate a marked rise in research productivity since the late 1990s, a pronounced shift toward artificial intelligence, big data, and research-data-management themes after 2016, growing co-supervision and inter-institutional collaboration, and persistent regional imbalances favouring southern and northern India. The revised methodology strengthens data validation, triangulates multiple repositories, and clarifies analytical procedures. The study offers evidence-based insights that can inform curriculum redesign, funding policy, supervisory planning, and strategies for balanced regional development, contributing to a richer understanding of how Information Science is maturing in India.

Digital transformation has become a defining force shaping modern organizations, economies, and societies. Initially focused on digitization and process automation, digital transformation has evolved into a broader strategic phenomenon encompassing artificial intelligence (AI), advanced analytics; cloud computing, Internet of Things (IoT), blockchain, and platform ecosystems. As digital technologies become ubiquitous, organizations are entering a post-digital era where competitive advantage depends not merely on technology adoption but on the integration of digital capabilities with human-centered innovation, sustainability, ethical governance, and organizational resilience. This article examines the evolution of digital transformation beyond the traditional digital age, explores emerging technological and managerial paradigms, and discusses future directions for organizations navigating increasingly complex digital ecosystems. The study concludes that successful transformation in the post-digital era requires balancing technological innovation with human values, social responsibility, and sustainable development objectives.

The rapid advancement of digital technology has transformed the sector of higher Education with the Internet of Things (IoT). IoT refers to interconnected devices and sensor that collect exchange and analyze data in real time. It offers enhanced opportunities for automation in educational system. The significance of IoT in higher education is analyzing its major applications, benefits and challenges. IoT enables smart classrooms improve teaching effectiveness through interactive tools, automated attendance, adoptive learning systems and real time performance monitoring. University deploy surveys Smart campus infrastructure including intelligent lighting, security, and energy management systems and this helps educational cost & reduce its cost. IoT Support research and Laboratory activities, enables data–driven decision-making and collaboration with other institutions. IoT provides personalized learning support, and improves engagement through technology. But there are also disadvantages of applying IoT in higher Education, Such as high implementation cost, concern about data privacy, cybersecurity risks and the need for digital readiness among teachers and students. The review emphasizes that these challenges can be overcome through proper policy framework, skill development, robust cybersecurity measures, can be taken for better accessed. IoT plays crucial role in modern higher education, enabling smart academic environment that fosters innovation and improves learning  outcomes.

Doctoral theses constitute a significant yet systematically underutilized category of scholarly output. Despite their intellectual depth and methodological rigour, most dissertations remain poorly indexed, rarely cited in commercial databases, and largely invisible to mainstream bibliometric evaluation. This paper presents a structured articulation of the research objectives and analytical scope of an AI-Based Bibliometric Framework designed to address this invisibility—with particular focus on the interdisciplinary domain of Library and Information Science (LIS) and Computer and Information Science (CIS). Six specific research objectives are defined: conceptualizing the AI-driven framework, incorporating citation context analysis using NLP, addressing limitations of traditional bibliometric indicators, designing metadata validation mechanisms, conducting comparative India–Global analysis, and contributing theoretically to AI-enabled scientometrics. The scope encompasses doctoral theses from national repositories (Shodhganga), international databases (ProQuest, EThOS, Scopus), and LIS-specific archives, with explicit AI compatibility criteria. A comparative citation analysis of 284 doctoral theses from five Indian universities (2015–2023) empirically grounds the study, revealing that 18–35% of theses carry zero recorded citations—reflecting a discoverability gap rather than low quality. The framework integrates PageRank-based importance scoring, TF-IDF and LDA-based semantic classification, context-aware citation impact metrics, and altmetric aggregation. By bridging LIS bibliometric tradition with CIS computational methodology, the proposed framework advances both the theory and practice of research evaluation in the era of AI-augmented scholarship.