cs papers | Gist.Science

Mapping the Evolution of Artificial Intelligence in Healthcare, 2017–2025: An Integrated Bibliometric and Latent Dirichlet Allocation Topic Modelling Analysis

This study integrates standard bibliometric analysis with Latent Dirichlet Allocation (LDA) topic modelling on a curated corpus of 2,631 healthcare AI documents from 2017 to 2025 to overcome keyword limitations, revealing a maturation trajectory from deployment-ready applications to governance-dependent frontiers like large language models and explainable AI.

Piyusha Ambradkar, Vandana Tandon Khanna.2026-09-08

💻 computer science

Rendered-Egocentric Vision for Short-Horizon Body-Risk Prediction in Quadruped Command Selection

This paper presents a simulation-based command-selection layer for quadruped robots that uses rendered egocentric views and candidate commands to predict short-horizon body risks, significantly improving safety and command agreement while maintaining forward progress without relying on terrain maps or lookahead rollouts.

Yuxiang Tian2026-09-08

💻 computer science

Uncovering neural decisions: How textual interpretation bridges AI and clinical reasoning

The paper introduces CRysTal, a neuron-level post-hoc explanation framework that maps AI model activations to standardized clinical terminology, demonstrating superior alignment with radiology reports, enhanced diagnostic reliability, and improved efficiency for physicians compared to existing explanation methods across multiple medical imaging tasks.

Hui Lu2026-09-08

💻 computer science

Explainable and trustworthy AI-driven fuzzy consensus for secure data sharing in intelligent transportation systems

This paper proposes an Explainable and Trustworthy AI framework utilizing intuitionistic fuzzy sets to establish a transparent, trust-weighted voting consensus mechanism that enhances message verification efficiency, privacy protection, and security in dynamic intelligent transportation systems.

Guomin Gu, Guohui Zhang, Zhenxi Li, Yingqi Zhuo, Yinglong Li2026-09-08

💻 computer science

An Interpretable AI Framework for Multiclass Classification of Thalassemia Using Combined CBC and HPLC Biomarkers

This paper presents an interpretable multimodal machine learning framework that combines CBC and HPLC biomarkers to achieve highly accurate (99.89%) and clinically consistent multiclass classification of thalassemia using a stacking ensemble model, with SHAP analysis confirming the top predictive features align with standard diagnostic criteria.

Banoth Dinesh Nayak, Sridevi Chitti, R Jegadeesan2026-09-08

💻 computer science

Bilingual Retrieval-Augmented Access to ANVISA Regulatory Documents: A Technical Evaluation Using Drug-Development and Health-Product Questions

This study evaluates a bilingual retrieval-augmented generation system built on ANVISA regulatory documents, demonstrating its high accuracy in locating, citing, and summarizing Portuguese and English regulatory requirements for drug development while effectively handling unanswerable queries after calibration.

Carlos Victor Montefusco-Pereira, Howard Lopes Ribeiro Junior2026-09-08

💻 computer science

A security risk assessment for virtual reality learning system in the Unmanned Swarms Internet of Things

This paper proposes a CLCM model, integrating Conv1D-LSTM, CNN, and a multi-head attention mechanism, to effectively assess security risks in virtual reality learning systems within the Unmanned Swarms Internet of Things by accurately analyzing heterogeneous data modalities and overcoming limitations of existing models.

Chunlan Liu, Wei Xue, Lushui Zhou2026-09-08

💻 computer science

Tomato Leaf Disease Identification Using EfficientNetB3 Transfer Learning and Grad-CAM Explainable Analysis

This paper proposes an automated tomato leaf disease diagnosis framework using EfficientNetB3 transfer learning and Grad-CAM explainable analysis, which achieves high classification accuracy and interpretability for detecting Early Blight, Late Blight, Leaf Mold, and healthy leaves to support precision agriculture.

B. Bhagya Laxmi, Rupesh Kumar Mishra, Shree Harsh Attri, D. Gireesha2026-09-08

💻 computer science

DYNAMICS-WEB: A Pre-Registered Measurement of Web Content Character over the Link Graph, and a Powered Proof that its Sub-Perceptual Half Changes Conversion

This paper introduces DYNAMICS-WEB, a pre-registered measurement demonstrating that a distinct "character" signal within the web's link graph, which is largely independent of authority and topic, can subconsciously influence reader conversion rates when aligned with user preferences.

Jason Duke2026-09-08

💻 computer science

A Hybrid Multi-scale Convolutional Neural Network-Long Short-Term Memory Model Incorporating Squeeze Excitation Attention for Hourly Air Quality Index Prediction

This study proposes a hybrid MSCNN-LSTM model enhanced with Squeeze-and-Excitation attention to predict hourly Air Quality Index values, demonstrating superior accuracy and stability compared to traditional deep learning models through multi-scale feature extraction and adaptive channel weighting on data from Yiyang City.

Huawei Xu, Long Li, Qi Tang, Junhua Huang, Xin Fang2026-09-08

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