898 papers
This paper addresses the fragmentation in Augmented Workplace design by conducting a PRISMA-driven systematic review of 122 studies to consolidate 151 guidelines into a structured, transferable knowledge base that supports the systematic development and deployment of AR systems in professional environments.
This paper proposes a hybrid NSGA-II algorithm (HENSGA-DRL) that integrates a critical-path graph-guided deep reinforcement learning operator and a multi-metric state-aware hierarchical intervention strategy to overcome the structural perception and stagnation limitations of existing evolutionary methods in solving the multi-objective distributed flexible job shop scheduling problem.
This paper proposes a secure, AI-driven, voice-based email system designed for visually impaired users that eliminates graphical interfaces by integrating FaceNet for biometric authentication, Whisper for high-accuracy speech recognition, and transformer models for email summarization and translation via Gmail API.
This study demonstrates that 3D Swin Transformers significantly outperform convolutional neural networks in cross-scanner generalization for hippocampal segmentation, achieving near-invariant performance across diverse multi-center datasets without fine-tuning and establishing a new standard for clinical deployment.
This paper demonstrates that while both opinion and structural similarity drive polarization in social networks, strategically combining these mechanisms in link-recommendation algorithms can prevent network fragmentation and foster moderate opinion coexistence.
This paper proposes an explainable multimodal hybrid deep learning framework that integrates smartphone and wearable sensor data to achieve high-accuracy early depression detection by leveraging advanced feature engineering and attention mechanisms to identify key behavioral indicators such as sleep irregularity, decreased mobility, social withdrawal, and increased nighttime smartphone use.
This paper presents a Karakalpak automatic speech recognition system based on the wav2vec 2.0 XLS-R architecture, which leverages a newly created 50-hour Karakalpak Speech Corpus to achieve a 21.10% word error rate and demonstrates the impact of labeled data volume on low-resource language recognition performance.
This paper proposes an AI-guided assembly framework that integrates real-time, context-aware instructions directly into product design to replace static manuals, demonstrating through a user study that this approach significantly reduces assembly time, errors, hesitation, and cognitive load compared to traditional manual guidance.
This study proposes a Time-Decay Gated Recurrent Unit (TD-GRU) model that confines temporal decay to the hidden state to preserve raw clinical values, demonstrating statistically credible net clinical benefit and superior performance over several baselines for early Sepsis-3 prediction in sparse, irregularly sampled ICU trajectories.
This paper proposes CCPP-LPC, a complete-coverage path-planning algorithm that utilizes a local path cost evaluation model and an adaptive dual-guided perturbation strategy to overcome the limitations of existing heuristic methods, thereby achieving superior computational efficiency and path optimization in complex environments.