A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected artificial neurons that process and transmit information, enabling machine learning tasks such as pattern recognition, classification, and regression by learning from labeled data.
This study introduces an AI-based system predicting gait quality progression. Leveraging kinematic data from 734 patients with gait disorders, the researchers explore signal and image-based approaches, achieving promising results with neural networks. The study marks a pioneering application of AI in predicting gait variations, offering insights into future advancements in this critical domain of healthcare.
This study proposes an innovative approach to enhance road safety by introducing a CNN-LSTM model for driver sleepiness detection. Combining facial movement analysis and deep learning, the model outperforms existing methods, achieving over 98% accuracy in real-world scenarios, paving the way for effective implementation in smart vehicles to proactively prevent accidents caused by driver fatigue.
Researchers present an AI platform, Stochastic OnsagerNet (S-OnsagerNet), that autonomously learns clear thermodynamic descriptions of intricate non-equilibrium systems from microscopic trajectory observations. This innovative approach, rooted in the generalized Onsager principle, enables the interpretation of complex phenomena, showcasing its effectiveness in understanding polymer stretching dynamics and demonstrating potential applications in diverse dissipative processes like glassy systems and protein folding.
This paper unveils the Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system, a pioneering solution utilizing artificial intelligence, digital twins, and Wi-Sense for accurate activity recognition. Employing Deep Hybrid Convolutional Neural Networks on Wi-Fi Channel State Information data, the system achieves a remarkable 99% accuracy in identifying micro-Doppler fingerprints of activities, presenting a revolutionary advancement in elderly and visually impaired care through continuous monitoring and crisis intervention.
Researchers unveil a pioneering approach using a convolutional neural network (CNN) to analyze extreme precipitation patterns' link to climate shifts. This CNN-based method, trained with data from 10,000 precipitation stations, overcomes limitations of traditional analyses, providing high-resolution maps and nuanced insights into the sensitivity of extreme precipitation to climate change for North America, Europe, Australia, and New Zealand.
Researchers advocate for employing artificial neural networks (ANNs) as "artificial physics engines" to compute complex inverse dynamics in human arm and hand movements. The study showcases ANNs' potential in enhancing assistive technologies, such as prosthetics and exoskeletons, offering a detailed, customizable, and reactive approach for more natural movement in individuals with impaired motor function.
Researchers unveil RVTALL, a groundbreaking multimodal dataset for contactless speech recognition. Integrating data from UWB and mmWave radars, depth cameras, lasers, and audio-visual sources, the dataset aids in exploring non-invasive speech analysis. The study demonstrates applications in silent speech recognition, speech enhancement, analysis, and synthesis, though it acknowledges limitations in sample size and diversity. The dataset stands as a robust tool for advancing research in speech-related technologies.
The RefCap model pioneers visual-linguistic multi-modality in image captioning, incorporating user-specified object keywords. Comprising Visual Grounding, Referent Object Selection, and Image Captioning modules, the model demonstrates efficacy in producing tailored captions aligned with users' specific interests, validated across datasets like RefCOCO and COCO captioning.
Researchers proposed a hybrid optimization approach, combining Artificial Neural Network (ANN) and Genetic Algorithm (GA), to enhance plastic injection molding. Addressing quality, production efficiency, and sustainability, the method demonstrated effectiveness in achieving global multi-objective optimization, providing a valuable tool for smart, sustainable, and economically efficient production processes.
Researchers from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.
Researchers present a novel approach, the Dictionary-Based Matching Graph Network (DBGN), for Biomedical Named Entity Recognition (BioNER). By incorporating biomedical dictionaries and utilizing BiLSTM and BioBERT encoders, DBGN outperforms existing models across various biomedical datasets, demonstrating significant advancements in entity recognition with improved efficiency.
This article delves into bolstering Internet of Things (IoT) security, specifically countering botnet attacks that jeopardize IoT ecosystems. Employing tree-based algorithms, including Decision Trees, Random Forest, and boosting techniques, the researchers conduct a thorough empirical analysis, highlighting Random Forest's standout multi-class classification accuracy and superior computational efficiency.
This paper introduces SCANN, an interpretable deep learning architecture with attention mechanisms tailored for comprehending material structures and predicting properties. Utilizing iterative learning and global attention scores, SCANN excels in capturing complex structure-property relationships, outperforming traditional methods. The study demonstrates SCANN's robust predictive capabilities across diverse datasets, emphasizing its interpretative capacity to unveil how material properties correlate with specific structural features, thereby guiding future advancements in material design and discovery.
The article presents a groundbreaking approach for identifying sandflies, crucial vectors for various pathogens, using Wing Interferential Patterns (WIPs) and deep learning. Traditional methods are laborious, and this non-invasive technique offers efficient sandfly taxonomy, especially under field conditions. The study demonstrates exceptional accuracy in taxonomic classification at various levels, showcasing the potential of WIPs and deep learning for advancing entomological surveys in medical vector identification.
This article introduces an AI-based solution for real-time detection of safety helmets and face masks on municipal construction sites. The enhanced YOLOv5s model, leveraging ShuffleNetv2 and ECA mechanisms, demonstrates a 4.3% increase in mean Average Precision with significant resource savings. The study emphasizes the potential of AI-powered systems to improve worker safety, reduce accidents, and enhance efficiency in urban construction projects.
This research introduces FakeStack, a powerful deep learning model combining BERT embeddings, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for accurate fake news detection. Trained on diverse datasets, FakeStack outperforms benchmarks and alternative models across multiple metrics, demonstrating its efficacy in combating false news impact on public opinion.
Researchers developed a cutting-edge robot welding guidance system, integrating an enhanced YOLOv5 algorithm with a RealSense Depth Camera. Overcoming limitations of traditional sensors, the system enables precise weld groove detection, enhancing welding robot autonomy in complex industrial environments. The experiment showcased superior accuracy, reaching 90.8% mean average precision, and real-time performance at 20 FPS, marking a significant stride in welding automation and precision.
A groundbreaking study introduces the IGP-UHM AI v1.0 model, utilizing deep learning and XAI to enhance El Niño-Southern Oscillation (ENSO) prediction. The 2023–2024 forecast reveals sustained yet weakening EN conditions, emphasizing the model's credibility through Layerwise Relevance Propagation (LRP) explanations. The research underscores the need for ongoing refinement, human oversight, and raises crucial questions about ENSO predictability limits in the context of climate change.
Employing AI and ML, this study analyzed elite junior female tennis players' game statistics to predict tournament outcomes and understand career trajectories. While accurately forecasting junior tournament results, predicting future careers faced challenges, emphasizing the role of non-game factors and junior tournament participation in shaping successful careers. The study recommends refining models, emphasizing serve improvement, and supporting young talents through international tournaments for a nuanced understanding of tennis dynamics and enhanced training programs.
Researchers employ a Convolutional Neural Network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles, showcasing substantial accuracy in comparison to Computational Fluid Dynamics (CFD) simulations. The CNN's efficiency, reducing computational time by four orders of magnitude, suggests promising prospects for cost-effective and efficient aerodynamic field predictions in vehicle design, addressing challenges associated with CFD tools.
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