Authors - Abir Paul, Priti Giri, Rajdeep Ghatak, Soumitra Sasmal, Mauparna Nandan, Partho Mallick Abstract - Accurate forecasting of drug demand is one of the challenging areas in the healthcare service to reduce waste as well as shortages. Some recent studies focused only on predicting drug use demand for regions and hospitals, missing an overall way to combine these forecasts. In this study, a multilevel machine learning framework is presented that merges regional tender demand predictions with monthly and seasonal order forecasting in hospitals and pharmacies. With historical drug usage, the system captures time-based changes, seasonal demands, and also location specific behaviors . Models for regional tenders predict yearly procurement, but models at hospitals and pharmacies try to tell the need of each month, allowing better resource distribution.The rigorous experimental process showed better estimates and forecasting with less error than just making a single-level prediction. This framework helps to make better purchasing decisions and ensures a stable drug supply across healthcare systems. Health departments, hospital chains, and pharmacy groups can benefit from using a model .
Authors - Gagandeep Malhotra, Dharm Singh Jat Abstract - Modern Electronic Warfare (EW) environments are very dynamic, crowded, and hostile, which makes static or centralised spectrum-allocation strategies useless. To tackle these issues, this paper introduces a completely adaptable Federated Deep Q-Network (A-FDQN) framework for each node, which is built onto a high-fidelity ns-3.40 EW simulation environment. In this simulation each tactical radio has been configured to work as an independent federated client which trains a local DQN within itself based on metrics obtained from SINR, congestion, delay, jitter, and interference caused by jamming. A federated server then periodically collects client models using Federated Averaging (FedAvg) or Median method. This lets global learning happen without needing centralised state visibility or constant connectivity, which is very important for networks on contested battlefields. Our framework is different from earlier RL and FL studies because it combines per-node reinforcement learning, periodic federated aggregation, partial client participation, tuneable synchronisation frequency, and realistic ns-3 modelling of mobility, sweep jamming, bursty traffic, congestion hotspots, and Wi-Fi PHY/MAC interactions. Our A-FDQN system dynamically changes the channel assignments at each node, which greatly improves the packet delivery ratio, minimum SINR, fairness, and delay when faced with challenging EW scenarios. This first of its kind end-to-end FRL architecture offers a decentralised, resilient, and operationally relevant way to manage the spectrum for next-generation military communications.
Authors - Harsh Vardhan, Harsh Vikramaditya, Doyelshree Bhui, Shilpi Basak, Soumitra Sasmal, Subhajit Bhowmick, Ishan Ghosh Abstract - Security audits present a unique and ever evolving challenge due to the dynamic nature of cyberthreats and complex regulations. Traditional compliance audits remain largely manual and labor inten sive, resulting in vast inconsistencies. This paper introduces a solution to make compliance audits easier and faster by proposing a framework that leverages the use of Natural Language Processing and Large Lan guage Models to map organizational policies to frameworks and allows for real-time data from security controls to be validated against these complex security frameworks. Through a hybrid multi-model architec ture, the solutions in this paper aim to enhance the accuracy and trans parency of compliance evaluations coupled with evidence-backed insights. The results demonstrate the potential of integrating intelligent auditing systems to deliver compliance assessments that are consistent, accurate, and rapid; streamlining governance and improving cyber security posture management.
Authors - Anjali Yawatkar, Hemlata Gaikwad Abstract - Contemporary customer support systems require processing a massive number of user queries with low latency and high semantic relevance. Rule-based systems fail to capture context, while fully LLM-based systems are computation ally expensive and suffer from high latency. This paper introduces an adaptive AI-assisted customer support automation system using an optimized Retrieval Augmented Generation (RAG) model. The proposed system combines Azure OpenAI embeddings, FAISS-based vector search, selective Cross-Encoder re ranking, and a Learning-to-Rank (LambdaMART) model for adaptive score fu sion. Unlike vanilla RAG models, the proposed system adaptively re-ranks only the top-k retrieved candidates, trading off ranking precision and latency. Experi ments were carried out on a 1,30,000-sample e-commerce customer support da taset with query-response pairs annotated with intent labels. Compared to rule based retrieval, embedding+FAISS, and vanilla RAG models, the proposed hy brid system showed improved top-1 retrieval precision with a concurrent reduc tion in end-to-end latency from 0.414s to 0.365s (≈11.8% relative improvement). The LambdaMART model adaptively learned weights from FAISS and Cross Encoder scores, improving ranking robustness and eliminating misranked top re sponses. The system was implemented on Azure Machine Learning with a cloud scale pipeline and interactive Streamlit web interface, showcasing the cost-effec tive inference capabilities of the proposed system via selective re-ranking.
Authors - Jaykumar Gandharva, Hardika Menghani, Tilak Brahmbhatt, Nischay Agrawal Abstract - Modern Electronic Warfare (EW) environments are very dynamic, crowded, and hostile, which makes static or centralised spectrum-allocation strategies useless. To tackle these issues, this paper introduces a completely adaptable Federated Deep Q-Network (A-FDQN) framework for each node, which is built onto a high-fidelity ns-3.40 EW simulation environment. In this simulation each tactical radio has been configured to work as an independent federated client which trains a local DQN within itself based on metrics obtained from SINR, congestion, delay, jitter, and interference caused by jamming. A federated server then periodically collects client models using Federated Averaging (FedAvg) or Median method. This lets global learning happen without needing centralised state visibility or constant connectivity, which is very important for networks on contested battlefields. Our framework is different from earlier RL and FL studies because it combines per-node reinforcement learning, periodic federated aggregation, partial client participation, tuneable synchronisation frequency, and realistic ns-3 modelling of mobility, sweep jamming, bursty traffic, congestion hotspots, and Wi-Fi PHY/MAC interactions. Our A-FDQN system dynamically changes the channel assignments at each node, which greatly improves the packet delivery ratio, minimum SINR, fairness, and delay when faced with challenging EW scenarios. This first of its kind end-to-end FRL architecture offers a decentralised, resilient, and operationally relevant way to manage the spectrum for next-generation military communications.
Authors - Sachin Kumar Abstract - E-commerce search engines rely on Query Expansion (QE) to bridge the semantic gap between user queries and product catalogs, but expansion can induce query drift, where retrieved results diverge from the user’s original intent. Evaluating QE on novel or out-of-distribution queries is fundamentally intractable under the standard Cranfield paradigm, which requires pre-compiled relevance judgments. This paper introduces the Generalized Authority-Hub Score (GAHS), an unsupervised evaluation metric that repurposes the product catalog’s relational structure— modeled as a product graph—as a dynamic proxy for retrieval quality. Drawing on the HITS algorithm, GAHS quantifies the topical coherence of a retrieved product set without requiring explicit relevance judgments. Using the Amazon ESCI dataset, we validate GAHS against MAP and nDCG@10 on a held-out seen query set, demonstrating strong rank-order agreement (Kendall’s τ = 1.0 with MAP, τ = 0.67 with nDCG@10). We further demonstrate its discriminative power on a disjoint unseen query set, and discuss an observed performance reversal between the two query sets and its implications for QE evaluation methodology.
Authors - K Devi Priya, P Saranya Durga, Y Sony, D Varun Sai Abstract - This paper presents a comprehensive implementation and evaluation of a secure electronic voting system built on the Ethereum blockchain platform. Proposing on Ethereum smart contracts, Proof of Stake consensus, and modern Web3 technologies and implemented the project. The implementation deals with key e-voting issues like voter authentication, ballot privacy, vote immutability and transparent auditability.We examine security threats, offer Layer2 scaling design, introduce concepts of zero-knowledge proofs in order to achieve higher privacy levels, and measure the economic benefit of deployment on different scales. In our results, we have shown that Ethereum has a significant basis to support decentralized voting systems, but scalability and cost reduction remain an important challenge to large-scale elections. The paper ends with a set of practical recommendations on the deployment of production and the main directions on the further research in the field of blockchain-based democratic systems.
Authors - Manav Thakar, Nischay Agrawal, Jaykumar Gandharva, Manish Singh Abstract - Predicting and understanding the inhibitory activity associated with Breast Cancer resistance protein can assist in the drug discovery process by anticipating the potential drug resistance and drug-drug interactions. Prediction of BCRP inhibitors using machine learning can accelerate the identification of BCRP inhibitors by analyzing large datasets, finding patterns in molecular structures, and predicting interactions that would be time-consuming and expensive through traditional methods like high-throughput screening or trial-and-error experimentation. In the literature, machine learning has been employed to develop techniques for predicting BCRP inhibition. However, these methods often exhibit low prediction accuracy, highlighting the need for improved prediction techniques with enhanced accuracy. In this research, BCRP inhibition prediction has been carried out using features spaces fusion to enhance the features information with richer representation of data incorporating complementary aspects of molecule to get the increased accuracy for discovery of inhibitors for drugs of breast cancer. The experimental results show that the proposed technique has increased accuracy and precision for the discovery of BCRP inhibitors. The accuracy of the proposed technique is 97% which is higher than the techniques developed in literature. The study demonstrates that enhancing the features information by combining various compound properties creates a more richer and comprehensive feature space. This enhanced feature representation can significantly help in identifying BCRP inhibitors specifically and contribute to advancements in drug discovery overall.
Authors - Shaik Sohail Ahammed, B. Rohan Teja, R. Naga Sumithra, D. Manasa, T. N. V. D. Sai Krishna Abstract - Privilege Escalation is a major issue for securing Linux sys tems. When a user gains unauthorized root access he has the ability to access all system resources and manipulate them at will. In the past, Linux has used Static Access Control Policies and User Space Monitoring Tools to secure system access. However, these methods provide little in sight into how the kernel is modifying users credentials when permissions are changed. In this paper we propose a Kernel-Level solution to detect and prevent unauthorized privilege escalations. This detection/ preven tion occurs in real time via a Credential Transition Monitoring Mecha nism within the kernel layer, which prevents the elevation of privileges by illegal means. To create the functionality necessary for the above, a Linux Kernel Module (LKM) was created which utilizes kprobes to in tercept calls to the commit creds() function, which is used to update a processes credentials in the kernel. To evaluate if the privilege escalation being requested is legitimate or malicious, the LKM contains a Policy Based Evaluation Mechanism which evaluates each request to modify a process’s credentials. We tested our proposed solution using a con trolled test environment composed of a Virtual Machine (VM) running the Ubuntu Operating System. We ran two types of tests, first were Le gitimate Administrative Operations utilizing the ”sudo” utility, second were Simulated Privilege Escalation Attacks based upon SetUID Vul nerabilities. Our results show that the system effectively detected and blocked malicious privilege escalations, while providing minimal over head to normal system operation.
Authors - Menna Elgabry, Ali Hamdi Abstract - Mortality prediction for intensive care unit (ICU) patients with alcohol-related disorders remains insufficiently explored despite the distinct clinical characteristics and elevated risk profile of this population. Unlike general ICU cohorts, these patients often present with impaired physiological function, frequent complications, and poorer overall outcomes. However, few research works have taken this patient group into account for mortality prediction. This study addresses the gap by developing mortality prediction models specifically for ICU patients with alcohol-related disorders using multimodal electronic health record data. To capture the complex clinical status of patients, we integrate six major data modalities in the first 24 hours after admission, including demographics, diagnoses, medications, procedures, laboratory results/vital signs, and patient outputs. A refined preprocessing pipeline was used to harmonize and process heterogeneous input data. In addition, severe class imbalance is another challenging issue in resolving this mortality predict task. Therefore, our work examines systematically several rebalancing strategies: no resampling, oversampling, undersampling, and SMOTENC. Evaluated on both MIMIC-III and MIMIC-IV databases, our proposed rebalanced multimodal data approach is effective for tackling the task. Indeed, the experimental results show that CatBoost with random undersampling provides the most consistent and balanced effectiveness. Furthermore, multimodal analysis demonstrates that combining diagnoses, laboratory results/vital signs, and medications substantially improves prediction, while integrating all modalities achieves the best overall performance.
