Authors - Yohan Ranasinghe, Janice Abeykoon, Samantha Kumara Senavirathna Abstract - Efficient blood supply chain management is a critical global impera tive in healthcare, yet it is consistently hampered by significant post-expiry blood wastage. This issue, prevalent across diverse healthcare systems, represents a considerable loss of a vital and non-substitutable resource, primarily stemming from challenges in accurate demand forecasting and dynamic inventory coordi nation. To address this pervasive problem, this research proposes and validates a novel data-driven framework. The approach leverages a multivariate deep learn ing forecasting model, specifically a Multivariate Long Short-Term Memory (LSTM) network, integrated into a comprehensive platform designed for proac tive inventory management. The model's development and empirical validation utilize historical blood collection and transfusion data (January 2020 – December 2024) from a cluster center of the National Blood Transfusion Service (NBTS) in Sri Lanka, serving as a representative case study to demonstrate real-world applicability. The framework incorporates multivariate factors such as historical transfusion patterns, seasonal variations, and interdependencies between blood groups to generate more accurate demand predictions. The integrated system, de signed to support real-time inventory monitoring, automated near-expiry track ing, and digital blood request and redistribution mechanisms, aims to align blood supply with anticipated demand. The findings of this research demonstrate that this integrated deep learning and inventory optimization framework significantly improves blood stock utilization, minimizes wastage, and enhances the overall efficiency of blood supply systems. It offers a scalable and ethically governed solution, contributing broadly to efforts in sustainable healthcare delivery world wide.
Authors - Rashmi Y Matt, Shreya Srinivasan, Venkata Sravani Revuri, Vismaya Murali, Chandravva Hebbi, Natarajan Abstract - Preparing for technical interviews has become very challenging for computer science students due to highly competitive hiring environments and the lack of company-specific practice resources. Existing resources and Generative platforms provide generic questions that do not reflect the specific patterns, technical focus areas, or expectations of different requirements.To address this gap, we present a system that combines a structured knowledge-graph-based retrieval module with a fine-tuned LLamA-2-7B model to generate company-specific technical interview questions. The data set contains 28,854 curated questions from 470 companies, which were cleaned and used for finetuning. The proposed framework also integrates an evaluation pipeline using both LLM-as-a-Judge and manual scoring to check validity, clarity, and technical correctness.The fine-tuned LLamA-2-7B model integrated with the knowledge graph retrieval achieved the best performance, which significantly outperformed other generative models in producing contextually appropriate and technically relevant questions. This approach aims to provide students with more targeted preparation resources aligned with real-world hiring expectations.
Authors - Halima Tuj Saydia, Partha Chakraborty Abstract - The mental health issues, such as stress and suicidal threats, have become a major public health concern for students and young adults. Early identification of such conditions is important for timely interventions and prevention. The study aims to develop a two-stage hierarchical framework to predict stress and suicide risk early. It is based on the questionnaire survey dataset of 1436 responses. The hierarchical method utilizes psychological and lifestyle characteristics gathered through surveys, thereby eliminating the need for physiological sensors. The first stage develops machine learning (ML) models, namely XGBoost, Random Forest (RF), and Support Vector Machine (SVM), to detect stress. These models have achieved an accuracy of 93%, 88%, and 83%, respectively. If the individual is detected as stressed, it moves to the second stage for suicide risk detection. Deep learning (DL) models, mainly Artificial Neural Network (ANN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), are developed in the second stage. They have achieved accuracy of 94%, 90%, and 89%, respectively. The study presents a scalable, data-driven framework that supports early mental health screening in resource-limited communities.
Authors - Satrasala Hari priya, Sabhya Kulkarni, Sindhu Baddela, Spoorthi Krishna Devadiga, Suja CM Abstract - This paper evaluates the quantum entanglement techniques for the detection of Parkinson’s disease using multimodal clinical data from the PPMI database. Four encoding techniques are evaluated: Amplitude Encoding, Dense Angle, IQP-based Pauli, and Hierarchical. The results of the analysis indicate that accuracy and the efficiency of the circuit are greatly impacted by the entanglement technique. Amplitude Encoding is the most efficient for NISQ computers (92.00% accuracy, 6-depth circuits), while Dense Angle provides the highest accuracy (92.59%). Hierarchical entanglement is the least efficient (80.86%), showing that too much depth causes optimization difficulties. These results provide practical recommendations for the design of quantum circuits for medical diagnosis.
Authors - Chandan Kumar, Supriya Narad Abstract - In the contemporary digital landscape, the proliferation of cyber threats has become a pervasive and escalating concern, posing imminent dangers to individuals, businesses, and entire nations. Cyber intelligence emerges as a critical component in the ongoing battle against these threats, involving the systematic gathering, analysis, and dissemination of information pertaining to cyber threats, actors, and vulnerabilities. This research paper aims to provide an insightful examination of the existing landscape of cyber intelligence, delineating its fundamental sub-domains and highlighting areas ripe for future research. The paper begins by delving into the current state of cyber intelligence, emphasizing the dynamic nature of the digital threat landscape. It elucidates the multifaceted challenges posed by cyber threats, underscoring the need for a proactive and adaptive approach to intelligence gathering and analysis. This section also explores contemporary technologies and methodologies employed in cyber intelligence, ranging from advanced analytics and machine learning to threat intelligence platforms.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak Sharma Abstract - Higher education in India is poised at a junction and change seems to be driven by the issues of quality, access and sustainable development. In this framework, the HR sustain ability is essential for recruiting, hiring and retaining competent employees. This paper discusses ICT enabled practices of Indian deemed universities in the direction of promoting HR sustaina bility. Drawing on Review of literature and theme analysis, it explores e-based practices such as e-recruitment, digital training, online performance management, wellness technologies digital knowledge collaborations platforms. The study reveals that adoption of ICTs promotes effective ness, transparency and inclusivity of HR functions through the maintenance continuous staff de velopment. Nonetheless, other contributors such as leadership support, digital literacy and policy environment were found to significantly influence implementation outcomes. Digital divides, lack of training, data privacy and cost are some of the other concerns highlighted by the review. An overview of future themes in which AI, personalized HR services, and eco- sustainable ICT platforms will play a significant role into developing Future-proofed University.
Authors - Mr. Shubham Kishor Kadam, chhitij Raj Abstract - Increasing demands of universities to become sustainable in their practice and the necessity to compete in the global arena have compelled higher education to the implementation of green communication infrastructure and smart ICT solutions in every facet of the university practice. As an ingredient of this change, there is the HR sustainability: that we will go digital faculty and staff, and at the same time retain them in friendly and efficient and inclusive systems that are environmentally friendly. The emergence of the green communication systems, intelli gent ICT infrastructures, and green HR practices is helping the higher education sector to fund their future in this paper. The article is narrowed down to new practices, such as the hiring without paper, the use of mobile based performance management and virtual training, that is generated under the secondary research and conceptual framework. It also talks about the benefits, chal lenges and opportunities of such system in higher learning institutions. The findings suggest that the effective adoption of the sustainable ICT will help improve the performance of the organiza tions, reducing the impact on the environment to a minimum and being part of the creation of the digitally resilient human resources.
Authors - Lakshmi BV, Anupriya S, Ningappa B, Diganth SD, RoopaRavish, Prasad B Honnavalli Abstract - Modern car infotainment head unit has become a highly connected cyber-physical system, incorporating Wi-Fi, Bluetooth, USB ports, and the Controller Area Network (CAN) bus. While such capabilities enhance the user experience, they also raise the susceptibility of the vehicle to attacks, and hence there is a need to assess the security of the vehicle. This paper performs a comprehensive penetration test on an infotainment system, examining wireless, wired, and in-car communication channels. For the Wi-Fi component, we performed a series of attacks such as Distributed Denial-of-Service (DDoS), deauthentication, MAC and IP spoofing attacks, creating fake access points, and WPA-based attacks to determine the robustness of the system against network-level threats. Bluetooth attacks included device snarfing, replay attacks, manual packet injection attacks, and unauthorized access to data. USB attacks were employed to analyze the dangers posed by connected devices, including the extraction of GPS information, log files, SMS messages, and access to the microphone and camera. For the CAN bus, we performed replay attacks, flooding attacks, manual frame injection attacks, and manipulation of sensor information such as humidity and temperature readings. The outcome of each of these attacks indicates that the infotainment system can serve as a means through which attackers gain access to the vehicle's network, and hence the need for enhanced authentication, improved security for the interfaces, and real-time monitoring for security breaches. This paper provides valuable information for enhancing the security of modern car infotainment systems and contributes to the efforts being made in the field of automotive cybersecurity.
Authors - Tejaswini Borkar, Kajal Salampuriya Abstract - This paper focuses on the product of state of the art artificial intelligence (AI) language models (that is, ChatGPT, Perplexity, and Grok) to generate and test algorithmic trading strategies in financial markets. With such AI tools in the field, the study examines the success of the tools in cases of generating trading signals, synthesizing market sentiment, and helping manage risks both through quantitative backtesting and through qualitative analysis. The conclusion is that though the procedures performed using AI-assisted tactics may be comparable to the findings of the use of conventional algorithmic processes and will outline beneficial information, the findings should undergo tangible verification and cautious human interventions to establish dependability and applicability. Our findings are indicators of the potential of the large language models as an addition to assist traders and researchers and indicate that caution is still necessary to integrate with the long-established quantitative methods and risk management functions.
Authors - Niraja Jain, Rajeev Kumar, Golnoosh Manteghi Abstract - Medical negligence litigation in India poses significant challenges to the justice delivery system due to the complexity of clinical evidence, fragmented legal documentation, and limited availability of structured decision-support mechanisms for legal practitioners. These challenges often result in delays, inconsistent legal reasoning, and increased cognitive burden on judges and lawyers handling medico-legal disputes. This paper presents the design and preliminary validation of a Judicial Decision Support System (JDSS) tailored specifically for medical negligence litigation in the Indian legal context. The proposed JDSS leverages advanced Natural Language Processing (NLP) techniques and supervised machine learning models to assist early-stage legal triage through automated case summarization, statutory section prediction, and precedent recommendation. Transformer-based language models are fine-tuned on publicly available Indian legal judgments and augmented with a domain-specific legal–medical ontology to bridge semantic gaps between clinical narratives and legal reasoning. Explainability is embedded at both the model and user-interface levels through attention visualization and feature attribution mechanisms, addressing transparency requirements critical for high-stakes judicial applications. The system has undergone formative evaluation through an exploratory stakeholder survey involving participants from legal, academic, and higher-education ecosystems in India. This evaluation focuses on perceived usefulness, trust, explainability expectations, and institutional readiness for AI-assisted judicial tools, rather than predictive performance. Findings from the survey informed key design choices, particularly the emphasis on explainable AI and modular deployment. While large-scale retrospective evaluation on real-world court data remains part of future work, the current study establishes a methodologically grounded and ethically aligned foundation for AI-assisted judicial decision support in resource-constrained legal environments, with scope for integration into India’s evolving digital judiciary infrastructure.
