On the design and evaluation of generative models in high energy density physics by Ankita Shukla, Yamen Mubarka, Rushil Anirudh, Eugene Kur, Derek Mariscal, Blagoje Djordjevic, Bogdan Kustowski, Kelly Swanson, Brian Spears, Peer-Timo Bremer, Tammy Ma, Pavan Turaga, Jayaraman J. Thiagarajan

“…The computational demands of the computer models used for HEDP studies have led researchers to explore deep learning methods to enhance simulation efficiency. …”
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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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EITNet: An IoT-enhanced framework for real-time basketball action recognition by Jingyu Liu, Xinyu Liu, Mingzhe Qu, Tianyi Lyu

“…To overcome these challenges, we propose the EITNet model, a deep learning framework that combines EfficientDet for object detection, I3D for spatiotemporal feature extraction, and TimeSformer for temporal analysis, all integrated with IoT technology for seamless real-time data collection and processing. …”
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Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer by J.-S. Chou, H.-M. Nguyen, H.-P. Phan, K.-L. Wang

“…The novel framework evaluates machine learning, time series deep learning, and convolutional neural networks (CNNs), identifying the most effective models to be enhanced by the Age of Exploration-Inspired Optimizer (AEIO) algorithm. …”
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The Theranostic Genome by Xiaoying Xu, Pablo Jané, Vincent Taelman, Eduardo Jané, Rebecca A. Dumont, Yonathan Garama, Francisco Kim, María del Val Gómez, Karim Gariani, Martin A. Walter

“…To overcome these bottlenecks, we present the Theranostic Genome, the part of the human genome whose expression can be utilized to combine therapeutic and diagnostic applications. Using a deep learning-based hybrid human-AI pipeline that cross-references PubMed, the Gene Expression Omnibus, DisGeNET, The Cancer Genome Atlas and the NIH Molecular Imaging and Contrast Agent Database, we bridge individual genes in human cancers with respective theranostic compounds. …”
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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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Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization by Xiaoxuan Zhang, Meile Zhang, Yihao Li, Ping Deng

“…We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. …”
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Forecasting of Ionospheric Total Electron Content Data Using Multivariate Deep LSTM Model for Different Latitudes and Solar Activity by Nayana Shenvi, Hassanali Virani

“…In this study, we have investigated and tested a multivariate long short-term memory (LSTM) deep learning model for its forecasting accuracy over different latitudinal regions during the solar quiet and solar active years. …”
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Research on TCN Model Based on SSARF Feature Selection in the Field of Human Behavior Recognition by Wei Zhang, Guibo Yu, Shijie Deng

“…In addition, we compared the TCN classification model with other deep learning models in terms of evaluation metrics such as F1 score, recall, precision, and accuracy, and the results showed that the TCN model outperformed the other control models in all four metrics. …”
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