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Found 27 papers, 0 papers with code
The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation
Subsequently, we present the CHARME$^{2}$D Model to explore further the relationship between the AI Pentad and AI regulatory enablers.
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions.
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations.
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications.
Medical-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
This scarcity of annotated data impedes the development of effective machine learning models for cancer document classification.
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
The field of Computer Vision (CV) has faced challenges.
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Inside of the field of explainable artificial intelligence, counterfactual explanations can give that information in the form of what changes to make to put the data point into the opposing class, in this case "healthy".
A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics
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.
A Survey of Transformer Enabled Time Series Synthesis
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.
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources
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 of BARKPLUG V. 2 is to provide information to users about various campus resources, including academic departments, programs, campus facilities, and student resources at a university setting in an interactive fashion.
Generating Synthetic Time Series Data for Cyber-Physical Systems
Data augmentation is an important facilitator of deep learning applications in the time series domain.
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models
Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses.
Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System
This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images.
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
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.
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems
The contributions of this work include the hybrid X-IDS architecture, the eclectic rule extraction algorithm applicable to intrusion detection datasets, and a thorough analysis of performance and explainability, demonstrating the trade-offs involved in rule extraction speed and accuracy.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge
SoCs undertake a manual labor-intensive task of utilizing these global threat repositories and local knowledge databases to create both organization-specific threat intelligence and mitigation policies.
Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security.
Impacts and Risk of Generative AI Technology on Cyber Defense
To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense.
Explainable Intrusion Detection Systems Using Competitive Learning Techniques
Lastly, we analyze the statistical and visual explanations generated by our architecture, and we give a strategy that users could use to help navigate the set of explanations.
TwinExplainer: Explaining Predictions of an Automotive Digital Twin
Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety of environments, and the likelihood of failure of one or more subsystems, such as the engine, transmission, brakes, and fuel, can result in unscheduled downtime and incur high maintenance or repair costs.
Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges
Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades.
A White-Box Adversarial Attack Against a Digital Twin
Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier.
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels
A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs).
Designing an Artificial Immune System inspired Intrusion Detection System
The Human Immune System (HIS) works to protect a body from infection, illness, and disease.
Creating an Explainable Intrusion Detection System Using Self Organizing Maps
We leverage SOM's explainability to create both global and local explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions.
Explainable Artificial Intelligence (XAI)
Intrusion Detection
