Driver Distraction Identification with an Ensemble of Convolutional Neural Networks by Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad, Mohamed N. Moustafa

“…In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. …”
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Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications by Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain

“…The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. …”
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A Comprehensive Review of Facial Beauty Prediction Using Multi-task Learning and Facial Attributes by Ali H. Ibrahem, Adnan M. Abdulazeez

“…This review addresses the pressing need to develop robust and fair predictive models for facial beauty assessments by leveraging deep learning techniques. Using facial attributes such as symmetry, skin complexion, and hairstyle, we explore how these features influence perceptions of attractiveness. …”
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A Novel Approach to Discriminate Between Structural and Non-Structural Post-Earthquake Damage in RC Structures by Beyza Gultekin, Gamze Dogan

“…For the damage classification model, a deep learning algorithm was developed using the 9680 damage images obtained from field studies after the recent earthquakes of Mw ≥ 5; Istanbul-Silivri (Mw: 5.8), Elazığ-Sivrice (Mw: 6.8) and Izmir-Seferihisar (Mw: 6.6) in Turkey. …”
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PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement by Huidi Jia, Qiang Wang, Bo Fu, Zhimin Zheng, Yandong Tang

“…Light scattering and attenuation in water degrade underwater images with low visibility and color distortion, which often interfere with the high-level visual tasks of underwater autonomous robots. Most existing deep learning methods for underwater image enhancement only supervise the final output of network and ignore the promotion effect of the intermediate results on the final feature representation. …”
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Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector by Inés Romero, Alberto Ochoa-Zezzati

“…In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. …”
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Investigation of Coal Preparation for Life Cycle by Using Building Information Modeling (BIM): A Case Study by Jian Chen, Da-Lin Guo, Xiao-Long Dong, Xiao-Bo Hou, Zhi-Feng Wang

“…In this paper, the kappa big data processing architecture is used to realize the integration and unification of stream data and batch data processing process. By using deep learning method and multimodal data fusion method, the multimodal data association fusion is realized, and Bentley software is adopted for verification and integration. …”
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PDE-Based Physics Guided Neural Network for SAR Image Segmentation by Rachana Rao, B. Roja Reddy, M. Uttara Kumari

“…By harnessing the synergy between deep learning and physics-based knowledge, this work not only improves segmentation accuracy but also facilitates a deeper understanding of SAR data, paving the way for more reliable and insightful remote sensing applications.…”
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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…Traditional scale-based depression diagnosis methods often have problems of strong subjectivity and high misdiagnosis rate, so it is particularly important to develop automatic diagnostic tools based on objective indicators.MethodsThis study proposes a deep learning method that fuses multimodal data to automatically diagnose depression using facial video and audio data. …”
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Modeling of Hyperparameter Tuned Fuzzy Deep Neural Network–Based Human Activity Recognition for Disabled People by Faiz Abdullah Alotaibi, Mrim M. Alnfiai, Fahd N. Al-Wesabi, Mesfer Alduhayyem, Anwer Mustafa Hilal, Manar Ahmed Hamza

“…HAR involves using technology, typically wearable devices or sensors, to automatically identify and classify human activities and movements. HAR using deep learning (DL) is an effective and popular method to automatically classify and identify human activities based on sensor information. …”
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TIE‐GCM ROPE ‐ Dimensionality Reduction: Part I by Piyush M. Mehta, Richard J. Licata

“…This work focuses on the dimensionality reduction step of the ROPE development process and addresses three limitations of the proof‐of‐concept: (a) extending the altitude upper boundary from 450 km to nearly 1000 km, (b) employing deep learning for nonlinear dimensionality reduction over principal component analysis (PCA) for improved performance during storm periods, and (c) maintaining the spatial resolution of the physical TIE‐GCM model, without down‐sampling, to preserve the spatial scales and variations. …”
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An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model by Fengying Ma, Jingyao Zhang, Wei Chen, Wei Liang, Wenjia Yang

“…In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. …”
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Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization by murtadha ali

“…Made for network security by combining deep learning and optimization, tests reached 99.5% accurate. …”
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Secure UAV-Based System to Detect Small Boats Using Neural Networks by Moisés Lodeiro-Santiago, Pino Caballero-Gil, Ricardo Aguasca-Colomo, Cándido Caballero-Gil

“…The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. …”
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Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach by Yang Liu, Qingguo Zeng, Joaquín Ordieres Meré, Huanrui Yang

“…In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. …”
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Alzheimer’s disease diagnosis by 3D-SEConvNeXt by Zhongyi Hu, Yuhang Wang, Lei Xiao

“…Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3D) convolution and incorporates a 3D Squeeze-and-Excitation (3D-SE) attention mechanism to enhance early classification of AD. …”
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Belt conveyor idler fault detection algorithm based on improved YOLOv5 by Cen Pan, Qing Tao, Hao Pei, Biao Wang, Wei Liu

“…Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults. …”
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ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network by Jingming Xia, Dawei Xuan, Ling Tan, Luping Xing

“…With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. …”
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Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks by Xuan Wu, Wenlin Pan

“…This research not only highlights the potential of deep learning technologies in enhancing traffic safety but also provides a novel and efficient approach for law enforcement agencies to monitor and address this growing issue effectively. …”
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Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples by Yao Li, Liyi Zhang, Lei Chen, Yunpeng Ma

“…Abstract Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. …”
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