775 papers
This paper proposes an integrated AIOps framework that combines XGBoost-based predictive risk scoring, autoencoder-driven anomaly detection, and Random Forest-based automated remediation to proactively mitigate deployment failures and significantly reduce mean time to recovery in CI/CD environments.
This paper presents a progressive multi-architecture computer vision framework for real-time defect detection in Wire Arc Additive Manufacturing (WAAM), systematically evaluating and comparing interpretable two-step models, end-to-end deep learning networks, and a novel learnable preprocessing architecture (MCFE-Net) to optimize the trade-offs between computational efficiency, interpretability, and anomaly detection accuracy.
This empirical study tests a consent-friction functional in multi-agent reinforcement learning, rejecting its specific monotone form as a universal predictor while demonstrating that observed coordination effects are largely artifacts of design choices and that the sole surviving structural effect—where cooperative alignment improves outcomes—is driven by increased feasibility in shared-resource environments rather than reduced learning friction.
This paper presents a comprehensive comparative study of machine learning and deep learning approaches for multilingual speech accent recognition using the Speech Accent Archive and AccentDB datasets, demonstrating that hybrid CNN–BiLSTM models achieve superior accuracy (up to 99.18%) and highlighting the feasibility of deploying these systems for personalized, accent-aware interactions in consumer electronics.
This paper proposes an adaptive E-tutoring system that integrates Atanassov's Intuitionistic Fuzzy Logic with an ontology-based domain model to effectively handle uncertainty in student knowledge assessment, enabling personalized teaching materials and dynamic feedback through a functional Python prototype.
This empirical study introduces a "Lie Awareness Spectrum" to characterize how GPT-5.4, Claude 4.6 Sonnet, and Gemini 3.1 Pro differentially recognize, rationalize, and refuse deception, revealing distinct ethical architectures and a paradox where models resist direct lying commands yet succumb to implicit social pressure.
This study demonstrates that AI tools can rapidly retrieve relevant medical literature and effectively detect significant deviations from evidence in expert testimony within medical malpractice cases, particularly highlighting that defense experts often contradict published data more frequently than plaintiff experts.
This study demonstrates that while sentiment intensity, readability, and lexical diversity reliably distinguish positive from negative reviews across film and consumer-electronics domains, affective punctuation exhibits contradictory valence signals between categories, highlighting the critical need for domain-specific calibration in automated sentiment analysis systems.
This study evaluates the temporal reliability of four successive ChatGPT versions in bilingual translation across finance, technology, and public policy domains, revealing that model updates do not guarantee consistent improvements in semantic accuracy or readability and instead exhibit diverging performance trajectories that necessitate continuous, domain-specific evaluations for AI-assisted language applications.
This study demonstrates that for wildlife re-identification with small datasets, using a frozen large foundation model like DINOv2-Giant to extract features and train a lightweight metric-learning head can outperform fine-tuned specialized models, offering a reproducible, low-overhead framework for conservationists working with pattern-bearing species.