DSpace 9

This site is running DSpace 9. For more information, see the DSpace 9 Release Notes.

DSpace is the world leading open source repository platform that enables organisations to:

  • easily ingest documents, audio, video, datasets and their corresponding Dublin Core metadata
  • open up this content to local and global audiences, thanks to the OAI-PMH interface and Google Scholar optimizations
  • issue permanent urls and trustworthy identifiers, including optional integrations with handle.net and DataCite DOI

Join an international community of leading institutions using DSpace.

The test user accounts below have their password set to the name of this software in lowercase.

  • Demo Site Administrator = dspacedemo+admin@gmail.com
  • Demo Community Administrator = dspacedemo+commadmin@gmail.com
  • Demo Collection Administrator = dspacedemo+colladmin@gmail.com
  • Demo Submitter = dspacedemo+submit@gmail.com
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Now showing 1 - 5 of 6

Recent Submissions

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    Cybersecurity Challenges in FinTech – Identifying Threats and Proposing Robust Defences
    (2025 International Conference on Emerging Trends in Industry 4.0 Technologies (ICETI4T), 2025-08) Madan, Suman; Mehndiratta, Jyoti; Chouhan, Sarika
    The rapid growth of the "Smart-everything" movement, alongside advancements in Artificial Intelligence (AI), has ushered in an era of highly sophisticated cyber threats. Unfortunately, traditional security measures often struggle to keep pace with these emerging challenges. This issue is particularly concerning in the realm of financial technology (FinTech), which relies heavily on data and operates around the clock. In this paper, we introduce a fresh and refined classification of security threats specific to FinTech. By highlighting the differences from existing frameworks, we aim to emphasize the novelty of our approach. Additionally, we conduct a thorough systematic review of the strategies currently employed to counter these threats. Using the PRISMA methodology and topic modeling, we identified key cyber threats discussed in recent research and paired them with the most effective defense strategies. Our review offers valuable insights for various stakeholders ranging from financial institutions to global regulators and sheds light on both the challenges facing FinTech today and the countermeasures available, while suggesting directions for future research.
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    Transforming Data to Decisions: AI-Driven Smart Governance and Digital literacy in India’s Public Policy Ecosystem
    (Journal of Informatics Education and Research, 2025-11) Verma, Shilpa; Madan, Suman
    This article discusses how Data Science and artificial intelligence (AI) are redefining public policy architecture in India. It looks at how technologies such as real-time analytics, predictive modelling, and adaptive decision-making are being integrated into governance by initiatives such as the India AI Mission, Digital India, and the National Data and Analytics Platform. These programs mark a substantial shift towards evidence-based policymaking, in which huge amount of data are used not just to improve the quality of services but also to forecast demand from the public and assess the actual impact of policy decisions.The paper focuses on how AI applications are influencing and shaping major sectors. In sector like healthcare, AI facilitates early disease detection and online diagnosis in remote areas. In field of education, data-intensive analysis helps in the identification of students who are prone of drop-out and also in the development of personalized learning programs. The agricultural sector benefits from the use of satellite imagery and machine learning to manage crop cycles and reduce climate-related hazards. Simultaneously, in urban areas, government employs modern data technologies to improve traffic management, public safety, and environmental monitoring.While these developments show the potential of digital governance, the article emphasizes the significance of ethical and legal safeguards. The implementation of the Digital Personal Data Protection Act (DPDPA 2023) is an important step toward tackling data privacy, algorithmic unfairness, and uneven access to digital infrastructure. Without rigorous regulation and inclusion, the technologies that empower citizens may exacerbate existing inequalities.
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    Adapting Corporate Valuation Models to the Technology Sector A Sector-Specific Framework Integrating Intangibles and User-Based Metrics
    (Journal of Theoretical Accounting Research, 2025) Deshmukh, Sanika; Bathia, Amit; Mehta, Apurva; Kappal, Jyoti Mehndiratta; Desai, Bineet; Thakker, Hemal
    Valuing technology firms poses challenges that are hard for multiples and customary models, such as for example Discounted Cash Flow (DCF), to address also. These approaches are widely used in practice while they rely heavily on concrete assets and short-term cash flows since much of the value embedded in intangibles and user ecosystems is underrepresented. Current practice remains fragmented. Analysts often use extra commentary on user metrics to help DCF or multiples while venture capitalists depend on exit models or scorecards, like monthly active users, ARPU, churn, or SaaS-specific measures like ARR multiples. Yet even these adjustments still lack any standardization. Furthermore, no one systematically integrates these adjustments within valuation models. This study proposes a programmable valuation framework specific for sectors embedding intangible-based measures (R&D capitalization, patent intensity, intangible-to-concrete ratios) and user-based metrics (MAUs, ARPU, CAC, churn) into valuation methodologies. Startups like UiPath and Palantir, growth firms like Shopify and Zoom, and mature giants like Apple and Microsoft show through tests of this framework how integrating the variables bridges the persistent gap from market capitalization to customary valuation outputs. The contribution represents a reproducible structured model as it moves past fragmented adjustments; it gives analysts, investors, plus policymakers a stronger tool for valuing technology-driven enterprises and valuation frameworks.
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    Student–teacher model based breast cancer classification approach with depthwise separable layers
    (Expert Systems with Applications, 2025) Jadhav, Yogesh; Unhelkar, Bhuvan; Kshirsagar, Pravin R.; Thiagarajan, R.
    Breast cancer is among the most prevalent diseases affecting women worldwide and remains the leading cause of cancer-related mortality in women. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Existing automated approaches, including Convolutional Neural Network (CNN)-AlexNet, CNN-Residual Network (ResNet), CNN-GoogleNet, Attention U-Net (AUNet), Multi-Task Learning Network (MTLNet), and Deep Supervision (DS) U-Net, face challenges such as limited data availability, overfitting, high computational requirements, and long training times. To address these limitations, a novel Residual connection assisted student–teacher distilled dual bidirectional vision transformer (Res-STdVT) is proposed for breast cancer detection and classification. The method begins with image acquisition from the Mini- Mammographic Image Analysis Society (MIAS) and CBIS-DDSM datasets, followed by effective pre-processing using an improved weighted guided filter (Imp-WeGF). The region of interest (ROI) is then extracted via upgraded fuzzy c-means clustering (Up-FCM). High-level and deep features are captured from the ROI using a Depthwise Separable Convolutional Inception Network (DSC-Inception Net), which are subsequently classified into benign and malignant categories by the proposed Res-STdVT model. Experimental results demonstrate that the model achieves superior performance, with accuracy, precision, recall, Intersection over Union (IOU) score, and dice score of 96.29 %, 98.88 %, 95.19 %, 98.91 %, and 98.23 % on Mini-MIAS, and 98.66 %, 96.25 %, 98.75 %, 96.43 %, and 98.84 % on Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), respectively. These findings suggest that the proposed method can assist radiologists and clinicians in reliable, automated breast cancer diagnosis, reducing workload and enhancing early detection.
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    Sectoral Analysis Of Key Sectors Of Indian Markets Using Discounted Cashflow Valuations
    (Journal of Theoretical Accounting Research, 2025) Bhatia, Amit; Jhaveri, Rushil; Baid,Sakhi; Bist,Drishti; Shelke, Gauri
    This study looks into the useand application ofthe discountedcash flow (DCF) method for valuingfirmsacross various industries.Whenvaluingacompany usingtheDCFmethod,futurecashflowsarepredictedandarisk-adjusteddiscount rateisappliedtoestimateintrinsicvalue.However,theeffectivenessofthismethodishighlydependentontheindustryin question.ThestudyanalyzesDCFvaluationsandmarketpricecomparisonsacrosssectorsincludingBankingandFinancial Services, Healthcare and Pharmaceuticals, Information Technology, Fast-Moving Consumer Goods (FMCGs), and Automobiles.ItisalsoworthmentioningthatHealthcareandespeciallytheITindustryaresectorswithstablerevenuesand, thus, more predictable cash flows. In contrast, the FMCG and Automobile industries contain more intangible assets and exhibitmorevolatilecashflowsonacyclicalbasis.TheFinancialServicesindustryalsohascashflowvolatilityduetodirect dependenceonregulatoryandeconomiccycles.Theprimary conclusionofsuchstudiespublishedisthatwhileDCF can capture value, the precision with which they do so is industry specific and in certain industries, a multi-model approach includingothermodelswillbe necessary tocapturethevalueofthefirm