cs papers | Gist.Science

Towards Trustworthy Breast Cancer Diagnosis: A Comparative Explainability Stability Study of DenseNet121 and Vision Transformers

This study compares the classification accuracy and explainability stability of DenseNet121 and Vision Transformer models on breast histopathology images, revealing a critical trade-off where DenseNet121 achieves higher accuracy and localized explanations while the Vision Transformer demonstrates greater robustness of explanations under input perturbations.

Harsh Verma, Harish Kumar Shakya2026-07-03

💻 computer science

A Three-Phase Deep Learning Framework for Mine Reclamation: LSTM Prediction and DRL Control with Synthetic Data Validation Against African Soil Profiles

This study presents a three-phase deep learning framework that combines LSTM-based pH prediction with Deep Reinforcement Learning control, validated against African soil profiles using synthetic data, to provide a scalable and transferable solution for optimizing mine reclamation in data-scarce Sub-Saharan regions.

Daniel Agyekum Amakye2026-07-03✓ Author reviewed ⓘ

💻 computer science

A Deep Set-Based Aggregation Approach for Repeated Measurements: Insights from Variable-Length Wearable Device-Measured Physical Activity Data

This study demonstrates that Deep Set-based aggregation leveraging self-attention outperforms traditional methods in modeling variable-length, high-frequency wearable physical activity data, while suggesting that simpler aggregation strategies remain sufficient for more stable, low-frequency measurements.

Jaeyoung Park, Suyeon Kang, Ramakanth Yakkanti, Ausberto Velasquez Garcia2026-07-03

💻 computer science

AdapNet-IDS: A Self-Adapting, Interpretable Deep Learning Framework for Real-Time Intrusion Detection

This paper presents AdapNet-IDS, a self-adapting and interpretable deep learning framework that integrates BiLSTM, self-attention, and Elastic Weight Consolidation to achieve high-accuracy, real-time intrusion detection with continuous learning capabilities and transparent decision-making across diverse network environments.

Nandhini Pari2026-07-03

💻 computer science

Closed-Loop Fairness and Privacy Optimization for Federated Text Classification under Non-IID Data

This paper proposes FedMOJO, a closed-loop collaborative framework that integrates low-rank representation learning, differential privacy, and fairness-privacy optimization to effectively balance utility, group fairness, and privacy preservation in federated text classification under non-IID data.

Lihong Guo, Mingkai Dai, Luogang Zhang, Chao Ma2026-07-02

💻 computer science

Node-Level Fault Monitoring in WSN using Deep Neural Networks with Real-Time Graph Mapping and Performance Analysis

This paper proposes a deep learning framework utilizing CNN, RNN, and LSTM models to detect node-level faults in Wireless Sensor Networks based on temporal features, validated through performance metrics and visualized via a real-time graph-mapping interface.

Manjunatha A S, Venkatramana Bhat P, Ramakrishna M2026-07-02

💻 computer science

A transferable explainable and uncertainty-aware machine learning framework for water quality classification in data-scarce regions

This study proposes a transferable, explainable, and uncertainty-aware machine learning framework for water quality classification in data-scarce regions that combines distinct task families and robust modeling to provide a cautious, reproducible decision-support tool rather than a blind automatic classifier.

Mahoudo Fidèle ASSOGBA, Papin Sourou MONTCHO, Alhassane Diami DIALLO, Kossoko Babatoundé Audace DIDAVI, Adama Moussa SAK (…)2026-07-02

💻 computer science

A Hybrid Metaheuristic for the Family Capacitated Vehicle Routing Problem

This paper introduces ILS+SP, a hybrid metaheuristic combining Iterated Local Search with Set Partitioning post-optimization, which significantly outperforms existing state-of-the-art methods in solving the Family Capacitated Vehicle Routing Problem by achieving near-optimal solutions on large-scale benchmark instances.

Bruno Oliveira, Diogo Lima, Marcos Roboredo2026-07-02

💻 computer science

When Pooling Fails: An Information-Theoretic Ceiling on Object Grounding in Aggregated Planning Agents

This paper proves that pooling-based planning agents face an irreducible, computable ceiling on object grounding determined solely by embedding dimension and object count, a structural limitation that causes identity loss regardless of model capacity or training.

Shoryavardhaan Gupta2026-07-02✓ Author reviewed ⓘ

💻 computer science

Entropy-resolved sensor selection for compact and interpretable temporal multisensor monitoring

This study proposes the Entropy-Resolved Sensor Actionability (ESA) framework, a unified selection method that integrates information relevance, temporal stability, non-redundancy, and completeness to identify compact, interpretable sensor subsets that maintain near-perfect predictive performance under varying field conditions and missing data.

Faris A. Kateb, Adel Aboud Bahaddad2026-07-02

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