Sudip Mittal
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View article: IRSDA: An Agent-Orchestrated Framework for Enterprise Intrusion Response
IRSDA: An Agent-Orchestrated Framework for Enterprise Intrusion Response Open
Modern enterprise systems face escalating cyber threats that are increasingly dynamic, distributed, and multi-stage in nature. Traditional intrusion detection and response systems often rely on static rules and manual workflows, which limi…
View article: Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy
Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy Open
This paper addresses the critical challenge of estimating the reliability of an Electric Vehicle (EV) charging systems when facing risks such as overheating, unpredictable, weather, and cyberattacks. Traditional methods for predicting fail…
View article: Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges Open
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged …
View article: Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers
Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers Open
The adoption of Machine Learning Operations (MLOps) enables automation and reliable model deployments across industries. However, differing MLOps lifecycle frameworks and maturity models proposed by industry, academia, and organizations ha…
View article: From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction
From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction Open
The shift toward patient-centric healthcare requires understanding comprehensive patient journeys. Current healthcare data systems often fail to provide holistic representations, hindering coordinated care. Patient Journey Knowledge Graphs…
View article: Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Open
The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification, their utility is …
View article: Towards a HIPAA Compliant Agentic AI System in Healthcare
Towards a HIPAA Compliant Agentic AI System in Healthcare Open
Agentic AI systems powered by Large Language Models (LLMs) as their foundational reasoning engine, are transforming clinical workflows such as medical report generation and clinical summarization by autonomously analyzing sensitive healthc…
View article: RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration Open
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies …
View article: MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses Using Large Language Models
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses Using Large Language Models Open
Providing contextual and comprehensive medical information tailored to individual patients is critical for enabling effective care in the healthcare domain. However, existing approaches often struggle to deliver personalized responses due …
View article: CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations Open
This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, O…
View article: IRSKG: Unified Intrusion Response System Knowledge Graph Ontology for Cyber Defense
IRSKG: Unified Intrusion Response System Knowledge Graph Ontology for Cyber Defense Open
Cyberattacks are becoming increasingly difficult to detect and prevent due to their sophistication. In response, Autonomous Intelligent Cyber-defense Agents (AICAs) are emerging as crucial solutions. One prominent AICA agent is the Intrusi…
View article: Multivariate Data Augmentation for Predictive Maintenance using Diffusion
Multivariate Data Augmentation for Predictive Maintenance using Diffusion Open
Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been tra…
View article: Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant
Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant Open
Recent research has shown that large language models (LLMs) are particularly vulnerable to adversarial attacks. Since the release of ChatGPT, various industries are adopting LLM-based chatbots and virtual assistants in their data workflows…
View article: AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models Open
For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically,…
View article: Patient-centric knowledge graphs: a survey of current methods, challenges, and applications
Patient-centric knowledge graphs: a survey of current methods, challenges, and applications Open
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient’s health information holistically and multi-dimensionally. PCKGs integrate various types…
View article: R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data Open
Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often constrained by limited availability of …
View article: Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities Open
The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Tra…
View article: Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Explainable Anomaly Detection: Counterfactual driven What-If Analysis Open
There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection alerts the stakeholder that an anomaly is occurring.…
View article: Bark Plug: The ChatGPT of the Bagley College of Engineering at Mississippi State University
Bark Plug: The ChatGPT of the Bagley College of Engineering at Mississippi State University Open
Higher education has been caught by storm through the advent of artificial intelligence (AI) and large language models (LLM) such as chatGPT. Clearly, there are multiple roles for these sorts of tools to play in higher education, and in pa…
View article: A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics
A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics Open
Industry 4.0 has witnessed the rise of complex robots fueled by the integration of Artificial Intelligence/Machine Learning (AI/ML) and Digital Twin (DT) technologies. While these technologies offer numerous benefits, they also introduce p…
View article: Defending Multi-Cloud Applications Against Man-in-the-Middle Attacks
Defending Multi-Cloud Applications Against Man-in-the-Middle Attacks Open
Multi-cloud applications have become ubiquitous in today's organizations. Multi-cloud applications are being deployed across cloud service provider platforms to deliver services to all aspects of business. With the expansive use of multi-c…
View article: A Survey of Transformer Enabled Time Series Synthesis
A Survey of Transformer Enabled Time Series Synthesis Open
Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these models to time series generation is less explored, however, …
View article: From Questions to Insightful Answers: Building an Informed Chatbot for University Resources
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources Open
This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot system built using Retrieval Augmented Generation (RAG) pipelines to enhance the user experience and access to information within academic settings.The objective …
View article: Generating Synthetic Time Series Data for Cyber-Physical Systems
Generating Synthetic Time Series Data for Cyber-Physical Systems Open
Data augmentation is an important facilitator of deep learning applications in the time series domain. A gap is identified in the literature, demonstrating sparse exploration of the transformer, the dominant sequence model, for data augmen…
View article: MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models Open
Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive resp…
View article: An Analysis of Prerequisites for Artificial Intelligence / Machine Learning-Assisted Malware Analysis Learning Modules
An Analysis of Prerequisites for Artificial Intelligence / Machine Learning-Assisted Malware Analysis Learning Modules Open
This paper presents the findings of action research conducted to evaluate new modules created to teach learners how to apply machine learning (ML) and artificial intelligence (AI) techniques to malware data sets. The trend in the data sugg…
View article: AI Ethics
AI Ethics Open
Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a…
View article: Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications Open
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various ty…
View article: A Bibliometric View of AI Ethics Development
A Bibliometric View of AI Ethics Development Open
Artificial Intelligence (AI) Ethics is a nascent yet critical research field. Recent developments in generative AI and foundational models necessitate a renewed look at the problem of AI Ethics. In this study, we perform a bibliometric ana…
View article: Utilizing Large Language Models to Translate RFC Protocol Specifications to CPSA Definitions
Utilizing Large Language Models to Translate RFC Protocol Specifications to CPSA Definitions Open
This paper proposes the use of Large Language Models (LLMs) for translating Request for Comments (RFC) protocol specifications into a format compatible with the Cryptographic Protocol Shapes Analyzer (CPSA). This novel approach aims to red…