This study aims to integrate an Explainable Artificial Intelligence (XAI) approach using SHapley Additive exPlanations (SHAP) into an XGBoost model developed in Google Colab and deployed as an interactive web dashboard via Streamlit, providing intuitive clinical and managerial transparency for public health planning.
The Judicial Relativity Framework is proposed, an AI-assisted decision-support methodology inspired by Einstein's concept of multiple frames of reference and by dimensionality-reduction principles from machine learning that offers augmented rather than automated justice, improving consistency and transparency subject t...
Prabhat Kumar· International Journal of Adv...· 0 citations
XSec is introduced, a self-explainable deep architecture developed for security applications that produces deterministic explanations for a fixed trained model and input and substantially reduces explanation latency compared with approximation-based and perturbation-based post-hoc methods.
Ananth Shreekumar, Jyun-Jhu Syu, Muslum Ozgur Ozmen et al.· Lecture notes in computer sc...· 0 citations
The paper discusses a dynamic stress test using Generative Adversarial Networks (GANs), answers key questions in the context of Explainable AI (XAI) and data privacy, and offers a recommended course of action in implementing and standardised RegTech/SupTech systems to transition regulatory control to an active and proa...
Elizabeth Ope, Yejide R. Alli, Ifeoluwa A. Ojo et al.· International Journal of Sci...· 0 citations
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The widespread use of pesticides in modern agriculture has substantially improved food production while raising serious concerns regarding contamination and food safety. Considerable scientific effort has focused on improving methods for monitoring and detecting pesticide residues in food to enhance safety assurance. A...
B. C. Ezenwanne, C. Okoye, S. E. Abhadiomhen et al.· Food Research International· 1 citation
A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out...
Harsh Verma· International Journal of Sci...· 1 citation
ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in N...
Yundian Zeng, Qing Ye, Jike Wang et al.· Journal of Chemical Informat...· 0 citations
Background: Large language models (LLMs) are increasingly used to automate code review, but the reasoning behind their decisions remains hard to understand. Developers struggle to assess the validity of LLM-generated reviews, making it difficult to gauge how much trust to place in them. The role of Explainable AI (XAI)...
Zhenhan Gao, Marvin Muñoz Barón, Umm E. Habiba et al.· arXiv.org· 0 citations
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soi...
Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou· Journal of computing in civi...· 0 citations
These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.
Shahbaz Siddeeq, Muhammad Waseem, Umar Subhan Malhi et al.· 0 citations
The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScor...
Purpose: This paper develops a theory-informed conceptual framework for responsible AI entrepreneurship ecosystems in higher education. It addresses the limited integration of AI capabilities, entrepreneurship, institutional readiness, and governance in existing scholarship, particularly amid increasing AI adoption by...
Oluwatosin Omosolape Omodewu, M. Shokunbi· Elicit Journal of Economics...· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
