Holding AI-Based Systems Accountable in the Public Sector: A Systematic Review

Public Performance & Management Review, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 34

Published: Feb. 23, 2025

Language: Английский

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Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124908 - 124908

Published: July 30, 2024

The rising prevalence of gastrointestinal (GI) tract disorders worldwide highlights the urgent need for precise diagnosis, as these diseases greatly affect human life and contribute to high mortality rates. Fast identification, accurate classification, efficient treatment approaches are essential addressing this critical health issue. Common side effects include abdominal pain, bloating, discomfort, which can be chronic debilitating. Nausea vomiting also frequent, leading difficulties in maintaining adequate nutrition hydration. current study intends develop a deep learning (DL)-based approach that automatically classifies GI diseases. For first time, GastroVision dataset with 8000 images 27 different was utilized work design computer-aided diagnosis (CAD) system. This presents novel lightweight feature extractor compact size minimum number layers named Parallel Depthwise Separable Convolutional Neural Network (PD-CNN) Pearson Correlation Coefficient (PCC) selector. Furthermore, robust classifier Ensemble Extreme Learning Machine (EELM), combined pseudo inverse ELM (ELM) L1 Regularized (RELM), has been proposed identify more precisely. A hybrid preprocessing technique, including scaling, normalization, image enhancement techniques such erosion, CLAHE, sharpening, Gaussian filtering, employed enhance representation improve classification performance. consists twenty-four only 0.815 million parameters 9.79 MB model size. PD-CNN-PCC-EELM extracts features, reduces computational overhead, achieves excellent performance on multiclass images. achieved highest precision, recall, f1, accuracy, ROC-AUC, AUC-PR values 88.12 ± 0.332 %, 87.75 0.348 87.12 0.324 98.89 92 respectively, while testing time 0.000001 s. comparative utilizes 10-fold cross-validation, ablation various state-of-the-art (SOTA) transfer (TL) models extractors. Then, PCC EELM integrated TL generate predictions, notably terms real-time processing capability; significantly outperforms other models. Moreover, explainable AI (XAI) methods, SHAP (Shapley Additive Explanations), heatmap, guided Grad-Cam (Gradient-weighted Class Activation Mapping), Grad-CAM, Saliency mapping, have explore interpretability decision-making capability model. Therefore, provides practical intelligence increasing confidence diagnosing real-world scenarios.

Language: Английский

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Automated and explainable machine learning for monitoring lipid and protein oxidative damage in mutton using hyperspectral imaging

Food Research International, Journal Year: 2025, Volume and Issue: 203, P. 115905 - 115905

Published: Feb. 1, 2025

Language: Английский

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Clever Hans in the Loop? A Critical Examination of ChatGPT in a Human-in-the-Loop Framework for Machinery Functional Safety Risk Analysis

Eng—Advances in Engineering, Journal Year: 2025, Volume and Issue: 6(2), P. 31 - 31

Published: Feb. 7, 2025

This paper presents a first-of-its-kind evaluation of integrating Large Language Models (LLMs) within Human-In-The-Loop (HITL) framework for risk analysis in machinery functional safety, adhering to ISO 12100. The methodology systematically addresses LLM limitations, such as hallucinations and lack domain-specific expertise, by embedding expert oversight ensure reliable compliant outputs. Applied four diverse industrial case studies—motorized gates, autonomous transport vehicles, weaving machines, rotary printing presses—this study assesses the applicability ChatGPT routine tasks central safety workflows, hazard identification assessment. results demonstrated substantial improvements: during HITL involvement subsequent iterations assessment with feedback, complete agreement ground truth was achieved across all use cases. also identified additional scenarios edge cases, enriching analysis. Efficiency gains were notable, time efficiency rated at 4.95 out 5, on average, studies. Overall accuracy (4.7 5) usability (4.8 ratings robustness ensuring practical Likert scale evaluations reflected high confidence refined outputs, emphasizing critical role enhancing both trust usability. highlights importance prompt design, revealing that longer initial prompts improve accuracy, while shorter iterative maintain without compromising efficiency. process further ensures outputs align standards requirements. underscores transformative potential generative AI activities rigorous human safety-critical, regulated industries.

Language: Английский

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Machine learning approach for analyzing feature importance in alternative fuel vehicle selection

Travel Behaviour and Society, Journal Year: 2025, Volume and Issue: 39, P. 100987 - 100987

Published: Jan. 22, 2025

Language: Английский

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AI-Driven Design Optimization for Sustainable Buildings: A Systematic Review

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115440 - 115440

Published: Feb. 1, 2025

Language: Английский

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Local Universal Rule-based eXplainer (LUX)

SoftwareX, Journal Year: 2025, Volume and Issue: 30, P. 102102 - 102102

Published: Feb. 27, 2025

Language: Английский

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The paradigm of digital health: AI applications and transformative trends

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Language: Английский

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Research overview and prospect in condition monitoring of compressors

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127284 - 127284

Published: March 1, 2025

Language: Английский

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Navigating the CISO’s Mind by Integrating GenAI for Strategic Cyber Resilience

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1342 - 1342

Published: March 27, 2025

AI-driven cyber threats are evolving faster than current defense mechanisms, complicating forensic investigations. As attacks grow more sophisticated, methods struggle to analyze vast wearable device data, highlighting the need for an advanced framework improve threat detection and responses. This paper presents a generative artificial intelligence (GenAI)-assisted that enhances cyberforensics strengthens strategic resilience, particularly chief information security officers (CISOs). It addresses three key challenges: inefficient incident reconstruction, open-source (OSINT) limitations, real-time decision-making difficulties. The integrates GenAI automate routine tasks, cross-layering of digital attributes from devices provide comprehensive understanding malicious incidents. By synthesizing applying 5W approach, facilitates accurate enabling CISOs respond with improved precision. proposed is validated through experimental testing involving publicly available datasets (e.g., GPS pairing activity logs). results show increasing accuracy speed CISOs’ responses threats. evaluation demonstrates our improves efficiency by streamlining integration attributes, reducing reconstruction time enhancing cybersecurity resilience in critical infrastructures, although challenges remain regarding data privacy, scalability.

Language: Английский

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