DSpace Collection:

DL-BASED INTRUSION DETECTION FOR IOMT: A COMPARATIVE ANALYSIS OF EMBED-NET, CONV-NET-SVM, AND DEEP-SVM-NET

2026-06-25T05:09:41Z

Title: DL-BASED INTRUSION DETECTION FOR IOMT: A COMPARATIVE ANALYSIS OF EMBED-NET, CONV-NET-SVM, AND DEEP-SVM-NET Authors: KUMAR, ASHISH; Kumar, Shailender (SUPERVISOR) Abstract: The swift development of IoMT technology has led to the ability of continuously track ing patients, automating clinical operations, and facilitating interaction between connected devices in contemporary healthcare settings. While these interrelated processes greatly im prove medical efficiency and accessibility, there are major cybersecurity risks present as well. Typically, medical devices have low computing power, run outdated firmware, and lack sophisticated protection systems; therefore, they become easy targets for diverse cy ber attacks such as DoS [7], reconnaissance, spoofing, and data manipulation attacks. It turns out to be hard to cope with dynamic and sophisticated IoMT traffic using common IDS technologies [8], like signature-based IDS and traditional machine learning techniques. The limitations of the existing systems in terms of poor adaptiveness, simplified feature representation, and high levels of false negatives prove the necessity of further research in creating smart IDS techniques. This study introduces a 3-part deep learning solution specifically for an IoMT IDS Embed-Net, Conv-Net-SVM, and Deep-SVM-Net. Each model is a solution to specific se curity problems with IoMT environment. Embed-Net is an approach that embeds device identifiers, flags, and protocol attributes into dense models that are learned from the data and enables these models to represent subtle interactions between heterogeneous network traffic. The Conv-Net-SVM performed convolutional feature extraction followed by a Sup port Vector Machine (SVM) classifier, fusing the advantages of CNN, which is capable of learning the structs in the flow with good margin-based decision boundaries of SVM. Deep SVM-Net is an output layer based on SVM that exploits a novel approach to deep neural net that functions well in separating benign and malicious traffic and simultaneously has a low computational load for resource constrained medical devices. Several IoT-related datasets that included the various cyber-attack patterns were first preprocessed for standardization of scaling, transformations to ordinal encoding, feature selection based on covariance, and SMOTE balancing before training and validation were performed on the datasets, each using a cross validation strategy that ensured stable and generalizable models. All three exhibited excellent intrusion detection results with par ticularly good overall accuracy and low false-negative rates from Embed-Net, outstanding capability in learning attack signatures with high spatial correlations from ConvNetSVM, and superior intrusion detection real-time efficiency with highly reliable binary separation from Deep-SVM-Net. The results collectively point to the superiority of the deep learn ing architecture for improving the security of IoMT, which efficiently adapts to mixed-type features, to an imbalanced dataset, and to medical operational constraints.

EXECUTION-AWARE SELF-REFINING TEXT-TO-SQL GENERATION USING LARGE LANGUAGE MODELS

2026-06-25T05:09:31Z

Title: EXECUTION-AWARE SELF-REFINING TEXT-TO-SQL GENERATION USING LARGE LANGUAGE MODELS Authors: SHARMA, ANKIT; KUMAR, VINOD ( SUPERVISOR ) Abstract: The increasing need for data-driven systems has resulted in the growth of user-friendly database interaction methods, especially for non-technical users. Natural Language to SQL, also known as Text-to-SQL is hence required for converting natural language queries from users into executable SQL statements, allowing users to fetch data from databases more efficiently. Although, it is a challenging task to generate accurate SQL queries due to the complexity of natural language understanding, schema linking and variety of database structures. Recent progress in Large Language Models (LLMs) have sig nificantly improved the traditional Text-to-SQL methods by enhancing their semantic understanding for generalized SQL generation. This thesis proposes an Execution-Aware Self-Refining Text-to-SQL Generation framework using Large Language Models, where generated SQL queries are refined iteratively through execution feedback and context awareness. This framework integrates prompt engineering, RAG based architectures and execution validation to identify syntax errors, logical disparities and failed query results. The study also presents an overview of benchmark Text-to-SQL datasets such as Spider, WikiSQL, BIRD, and CoSQL representing that execution-aware self-refinement enhances SQL generation performance compared to conventional approaches. Moreover, it high lights challenges related to scalability, explainability and computational efficiency towards making a domain-centric Text-to-SQL systems for a broad range of technical and non technical users.

EXPLAINABLE TEMPORAL TRANSFORMER FOR DISEASE PROGRESSION PREDICTION USING ATTENTION AND SHAP ANALYSIS

2026-06-25T05:09:07Z

Title: EXPLAINABLE TEMPORAL TRANSFORMER FOR DISEASE PROGRESSION PREDICTION USING ATTENTION AND SHAP ANALYSIS Authors: CHHIBBER, AJITESH; KUMAR, VINOD ( SUPERVISOR ) Abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the gradual deterioration of motor functions, significantly affecting the quality of life of patients. Accurate prediction of disease progression is essential for timely clinical inter vention, treatment planning, and personalized patient management. Traditional machine learning approaches often treat clinical observations as independent samples, limiting their ability to capture the temporal dynamics inherent in longitudinal patient data. Al though deep learning models such as Long Short-Term Memory (LSTM) networks have demonstrated improved temporal modeling capabilities, they may struggle to effectively capture long-range dependencies present in disease progression trajectories. This thesis presents an Explainable Feature-Aware Enhanced Temporal Transformer (FAETT) framework for predicting Parkinson’s disease progression using longitudinal voice-based biomarkers from the Parkinson Telemonitoring dataset obtained from the UCI Machine Learning Repository. The proposed framework integrates temporal sequence modeling with self-attention mechanisms to learn complex relationships across historical patient ob servations. A comprehensive preprocessing pipeline involving data cleaning, feature scal ing, temporal sequence generation, and patient-wise train-test partitioning is employed to ensure robust model development and unbiased evaluation. To assess the effectiveness of the proposed approach, FAETT is compared against three baseline models: Random Forest (RF), Long Short-Term Memory (LSTM), and a stan dard Transformer architecture. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Experimental results demonstrate that the proposed FAETT model achieves supe rior predictive performance, attaining an MAE of 1.2688, RMSE of 1.6457, and R² score of 0.9754, outperforming the baseline approaches. The findings indicate that the incor poration of feature-aware temporal attention significantly enhances the model’s ability to capture disease progression patterns. iii To address the interpretability requirements of clinical decision-support systems, SHAP (SHapley Additive exPlanations) analysis is integrated into the framework. The explain ability analysis identifies the most influential vocal biomarkers contributing to disease progression prediction and provides transparent insights into model behavior. Correlation analysis, residual diagnostics, and prediction-performance visualizations further validate the robustness and reliability of the proposed framework. The results demonstrate that the proposed FAETT architecture effectively combines pre dictive accuracy with model interpretability, making it a promising tool for explainable disease progression forecasting in Parkinson’s disease. The study highlights the potential of attention-based temporal learning and explainable artificial intelligence techniques for advancing data-driven healthcare applications.

ADAPTIVE CONTEXT COMPRESSION TECHNIQUES FOR EFFICIENT LARGE LANGUAGE MODEL INFERENCE: A QUERY-COMPLEXITY-AWARE APPROACH

2026-06-25T04:57:03Z

Title: ADAPTIVE CONTEXT COMPRESSION TECHNIQUES FOR EFFICIENT LARGE LANGUAGE MODEL INFERENCE: A QUERY-COMPLEXITY-AWARE APPROACH Authors: HAQ, INJAMAMUL; Bansal, Nipun (SUPERVISOR) Abstract: The exponential growth of large-scale neural language models has opened up transforma tive possibilities across a broad range of natural language understanding problems. Sys tems built on the Transformer architecture [1] have demonstrated remarkable proficiency on tasks ranging from reading comprehension and open-domain question answering to code synthesis and multi-step reasoning. Yet the self-attention operation sitting at the heart of every Transformer block carries a computational burden that scales quadratically with input length, denoted O(n2). As contexts grow beyond a few thousand tokens — a routine re quirement in legal document analysis, multi-document question answering, and long-form summarisation — this quadratic growth translates into sluggish response times, swollen GPU memory footprints, and deployment costs that place the technology beyond reach for many organisations. Prior work on context compression has made genuine progress by shortening the input before it reaches the model [7, 8]. The unifying flaw across all fourteen methods examined in this thesis, however, is a deceptively simple assumption: that every incoming query deserves the same compression budget. A simple lookup question — “When was BERT published?” — can be answered from a single sentence. A comparative multi-hop question — “How do the pre-training strategies of BERT and GPT-3 differ in their downstream effect on reasoning tasks?” — may need a dozen passages spread across an entire document collection. Treating both with the same 40% removal rate is not an approximation; it is a category error that systematically harms the harder queries and wastes capacity on the easier ones. This thesis introduces the Query-Complexity-Aware Adaptive Context Compression (QCAC) framework, a model-agnostic preprocessing system that measures how demand ing an incoming question is and adjusts the compression ratio accordingly. QCAC com putes a per-query complexity score C(q) ∈ [0, 1] from three lightweight surface features of the question — its normalised length, vocabulary entropy, and the presence of multi-hop syntactic cues — and derives a per-query removal ratio r(q) = rmax − (rmax − rmin) · C(q). Every sentence in the document is then ranked using a weighted combination of three signals extracted from BERT [2]: the attention-magnitude score A(si), the attentional entropy score H(si), and the cosine similarity between the sentence and query embeddings sim(si, q), inspired by dense retrieval research [19]. Experiments conducted on SQuAD v1.1 [22] and HotpotQA [23] (300 samples each) using BERT-base-uncased [2] as the scorer and RoBERTa-base [5] as the downstream QA model on NVIDIA Tesla T4 hardware show that QCAC with weights (α = 0.10, β = 0.30, γ = 0.60) achieves 70.4% F1 on SQuAD at 36.8% sentence removal — a +16.2 percentage- iii point improvement over the prior Attn+Entropy baseline at equivalent compression. Adap tive behaviour is statistically validated without per-dataset tuning: HotpotQA queries re ceive a higher mean C(q) score (0.587 vs. 0.539, p < 0.001, t = 11.2) and automatically receive less compression. A seven-variant ablation study reveals that query-semantic simi larity is the strongest individual signal (58.0% F1 in isolation), while attention-only scoring actively underperforms random pruning (44.9%), consistent with the findings of Clark et al. [21]. End-to-end inference latency stands at 89.7 ms per sample — the lowest among all BERT-based compression methods evaluated — and the framework requires no modifi cation or retraining of the downstream language model.