750 papers
This paper proposes a custom high-performance CNN architecture that achieves perfect 100% accuracy and state-of-the-art performance on the NEU-CLS dataset for classifying six types of steel surface defects, demonstrating the superiority of task-specific designs over general-purpose models in industrial quality control.
This paper presents a pilot study of the Center for Computational Research and Education (CCRE), a training initiative in Africa that successfully guided a small cohort of early-career researchers through a structured, mentored pathway to develop high-performance computing skills despite significant infrastructure and mentorship barriers.
The paper introduces InVAER, a novel AI framework that combines adaptive hyperparameter optimization and multi-cue spatio-temporal validation to achieve real-time, high-precision accident detection with automated geospatial emergency coordination, significantly reducing false positives and response delays on edge devices.
This study proposes and validates a novel five-layer AI–IoT–Digital Twin framework that integrates real-time data, predictive analytics, and augmented reality to achieve significant improvements in manufacturing efficiency, sustainability, and human-machine collaboration while aligning with Industry 5.0 values.
This study proposes a blockchain-integrated cybersecurity framework that utilizes a Convolutional Neural Network (CNN) model to achieve 98.3% detection accuracy and 100% prevention capability against Denial of Service (DoS) attacks in AI-based network systems.
This study demonstrates that AI-assisted prompting enhances visual complexity and behavioral engagement in digital drawing while revealing that machine learning models can better predict broad affective categories than fine-grained emotions based on these drawing features.
DIT is a decentralized, post-quantum secure authentication protocol that eliminates the traditional CA hierarchy by anchoring domain identities in DNSSEC-verified ML-DSA-65 keys and utilizing a hybrid X25519MLKEM768 handshake with Merkle transparency logs to provide efficient, revocable trust without centralized certificate authorities.
This paper proposes an uncertainty-guided triple-memory fusion framework that leverages probabilistic Mamba to quantify motion uncertainty and dynamically fuse motion, appearance, and category memories, achieving state-of-the-art performance in robust multi-object tracking across complex dynamic scenes.
This paper proposes a grayscale image encryption scheme that combines a cubic fractional transformation-generated S-box with a logistic map to perform substitution, diffusion, and XOR-based scrambling, demonstrating robust security through comprehensive statistical and key sensitivity analyses.
This study proposes a comprehensive AI framework for pharmaceutical manufacturing that integrates predictive modeling with explainability, fairness, calibration, drift monitoring, and regulatory traceability to ensure audit-ready, defensible quality-governance decisions.