cs papers | Gist.Science

Implementing Random Forest Method for Healthcare Provider Fraud Detection Framework to Mitigate Financial Risk and Cost Optimization in Healthcare Management

This paper proposes a Random Forest-based machine learning framework, enhanced with SMOTE-Tomek class balancing, to effectively detect healthcare provider fraud and mitigate financial risks by outperforming traditional models like Decision Trees, Logistic Regression, SVM, and Naive Bayes in accuracy and key performance metrics.

Jenny Patel2026-07-13

💻 computer science

MSAL-YOLO: a YOLOv8-based detector for small and densely distributed object detection in UAV aerial imagery

The paper proposes MSAL-YOLO, an enhanced YOLOv8 detector that integrates a Spatial–Channel Mixed Convolution module, a Multi-Branch Enhanced Coordinate Attention mechanism, and a Region-Adaptive Wise-IoU loss to effectively address the challenges of detecting small and densely distributed objects in UAV aerial imagery, achieving significant performance improvements on the VisDrone2019 and DOTAv1 datasets.

Fei Ding, Xiufu Du, Haining Zhang, Haibin Liu, Liguo Han2026-07-13

💻 computer science

TRAUMA: A Machine Learning–Based Record Linkage Method for Health Databases

This study validates the TRAUMA method, a supervised machine learning-based record linkage approach using LightGBM, demonstrating its superior ability to recover true matches with high precision and specificity compared to the traditional CIDACS-RL tool in Brazilian health databases.

Daniel Scaldaferri Lages, Thayna Karoline Sousa Silva, Patricia Bartholomay Oliveira, Dayan Carvalho Ramos Salles de Oli (…)2026-07-13

💻 computer science

Diamojism: Semantic-Perceptual Tile Selection for Large-Format Emoji Mosaic Rendering

This paper introduces Diamojism, a novel photomosaic rendering architecture that enhances large-format emoji mosaics by integrating semantic theme scoring and iterative palette correction into a diamond tile grid system, thereby improving thematic relevance and color accuracy without relying on generative models.

Michael Jacobs2026-07-13

💻 computer science

An (n, n) Threshold Color QR Code-based Visual Secret Sharing Scheme for Privacy Images

This paper proposes a novel (n, n) threshold visual secret sharing scheme that utilizes color QR codes as meaningful shares to securely encrypt and losslessly recover privacy images by leveraging color redundancy and XOR operations.

Tao Liu, Yongjie Wang, Xuehu Yan, Yanlin Huo2026-07-13

💻 computer science

What Makes a Programming Problem Hard for a Language Model? An Empirical Study of Item Difficulty Across Code LLMs on Two Benchmarks

This paper presents an empirical study demonstrating that problem difficulty in code generation benchmarks is a stable, transferable metric driven by specification features (like examples and prompt length) on HumanEval and solution complexity on MBPP, offering critical insights for improving benchmarking, autograding, and educational tool design as aggregate model scores saturate.

TANZIM ISLAM KHAN2026-07-13

💻 computer science

Detecting ovarian endometriomas from ultrasound using vision transformers and cross-modality transfer learning

This paper presents a pioneering machine learning model that detects ovarian endometriomas from ultrasound images using vision transformers and cross-modality transfer learning, achieving high performance despite data scarcity and imbalance while demonstrating the transferability of low-level features across medical imaging modalities.

Matthew Watson, Miliani Fraser-Fletcher, Tom Willshare, Molly Jowsey, Noura Al Moubayed2026-07-13

💻 computer science

Turbulence Is Not What You Need to Detect Hallucinations

Despite the theoretical appeal of viewing hallucinations as dynamical instabilities in large language models, this paper demonstrates that static representations are far more effective for detection, as adding dynamical features provides no statistically significant improvement over simple linear probes.

Igor Itkin2026-07-13

💻 computer science

Intelligent Root Cause Analysis and Incident Diagnostics for Microsoft Fabric and Power BI through Telemetry Correlation and Kusto Query Language

This paper presents an intelligent root cause analysis framework for Microsoft Fabric and Power BI that utilizes multi-layer telemetry correlation and Kusto Query Language-driven inference to significantly improve incident diagnosis accuracy and speed compared to traditional manual methods.

Shujath Baig Mirza2026-07-13

💻 computer science

Human–AI collaboration in deductive coding of classroom dialogue: prompt engineering and collaboration patterns

This study employs design-based research to demonstrate that a customised GPT coding assistant, when integrated with structured prompts and a collaborative human-first workflow, effectively supports deductive coding of classroom dialogue while highlighting that successful human–AI collaboration depends on the dynamic interplay between prompt architecture, workflow sequencing, and the researcher's stance toward AI.

Luwei Bai, Dongkeun Han, Sara Hennessy2026-07-13

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