explainable ai

k-jinma/dogo-tutor: dogo-tutor v0.0.2 - Initial Release

This is the initial release of "dogo-tutor," an AI tutor designed for coding education. This release is created to archive the repository and obtain a DOI via Zenodo. Key Features Provides step-by-step hints (problem clarification, strategy, logic structuring, and partial code) rather than direct answers. Designed to o...

Neuro-Symbolic Reasoning via Temporal Logic Embedding

This paper introduces a novel approach to neuro-symbolic reasoning by embedding temporal logic rules directly into the weights of a neural network. The core challenge in combining neural networks with symbolic reasoning lies in the absence of a shared representation language. Our method addresses this by creating a mec...

Explainable AI-Driven Synthetic Data Generation

This paper explores the application of explainable artificial intelligence (XAI) techniques to the generation of synthetic data. The core claim is that XAI can significantly improve the quality and utility of synthetic data, ultimately facilitating the training of AI models while maintaining data privacy and ensuring d...

naganjaneyulu75/NeuroCareIoT: NeuroCareIoT v1.0.0

NeuroCareIoT v1.0.0 Initial release of NeuroCareIoT — Reliable Multimodal Edge-Based Alzheimer's Home Monitoring Using NeuroFuseNet. Features SafeFallNet for IMU-based fall detection WanderSenseNet for indoor localization and wandering-risk assessment DailyRoutineNet for temporal routine-deviation detection VitalRhythm...

Explainable Artificial Intelligence (AI) Modeling of Co-Exposure Synergies in Tobacco Farming Health Risk Assessment with Epidemiological Analysis

HOW DO INFORMATICS TEACHERS PERCEIVE THE CHANGING PURPOSES AND PRACTICES OF INFORMATICS EDUCATION IN THE ERA OF GENERATIVE AI?

AbstractGenAI is increasingly capable of performing activities central to Informatics learning, includinggenerating, explaining, modifying, and debugging code, raising questions about what studentsneed to learn and how teachers' roles may change. This study explored Informatics teachers'perceptions of the changing purp...

Pathway to achieve sustainable value through task-AI fit: the critical roles of green knowledge management capabilities, competitive advantage and financial resilience

This study aims to investigate how artificial intelligence (AI)-based technologies are associated with sustainable value intentions in green-oriented organisations operating under circular economy principles in China. Drawing on an adapted view of task-technology fit and the resource-based view (RBV), this study ex...

Using Artificial Intelligence to Achieve Inclusive and Effective Education

Artificial intelligence, as one of the transformative technologies of the contemporary era, has considerable capacity to respond to learners’ individual differences, reduce barriers to access to education, and improve the quality of the teaching–learning process. The present study was conducted with the aim of explaini...

Are Feature Selection and Feature Attribution the Same? A Comparative Survey -- Extended Version

Dynamic and Explainable Deep Learning Models

This paper presents a novel approach to developing dynamic and explainable deep learning models. The core challenge in deploying deep learning systems is often their "black box" nature, hindering trust and adoption. This work addresses this issue by integrating Explainable Artificial Intelligence (XAI) techniques with...

Intuitions and Directional Shifts in Minimal Computation Cosmology

This research genealogy traces the development of Minimal Computation Cosmology from its earliest prime-based intuitions to the separation of LAW and SOURCE, the Wonsik Reality-Renderer Architecture (WRRA), and a non-branching universe with a single accumulated history. Rather than presenting the model as a completed p...

AI-Based Phishing Detection: A Review and Practical Study

This paper presents a review and practical study of AI-based phishing detection. It explains how machine learning can be used to identify phishing websites using URL and webpage features. The paper discusses common machine learning models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine...