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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Predicting EV battery state of health using long short term degradation feature extraction and FEA TimeMixer by Weijie Tang, Jiayan Chen, Dongjiao Chen

“…Experimental results show that the proposed method achieves a Mean Absolute Error of less than 0.0219 for short-term SOH predictions and less than 0.1007 for long-term SOH predictions, outperforming other deep learning models in prediction accuracy over multiple prediction lengths.…”
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Topic Detection and Tracking Techniques on Twitter: A Systematic Review by Meysam Asgari-Chenaghlu, Mohammad-Reza Feizi-Derakhshi, Leili Farzinvash, Mohammad-Ali Balafar, Cina Motamed

“…In this review article, we cover more than 50 research articles in the scope of topic detection from Twitter. We also address deep learning-based methods.…”
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Efficient anomaly detection in tabular cybersecurity data using large language models by Xiaoyong Zhao, Xingxin Leng, Lei Wang, Ningning Wang, Yanqiong Liu

“…While traditional machine learning and deep learning methods have shown some success, they continue to face significant challenges in terms of generalization. …”
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Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) by Emmanuel Gbenga Dada, David Opeoluwa Oyewola, Stephen Bassi Joseph, Onyeka Emebo, Olugbenga Oluseun Oluwagbemi

“…Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. …”
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Autonomous Landing Guidance for Quad-UAVs Based on Visual Image and Altitude Estimation by Lingxia Mu, Shaowei Cao, Youmin Zhang, Xielong Zhang, Nan Feng, Yuan Zhang

“…A double-layered nested marker is designed to ensure that the marker can be captured both in high and low altitudes. A deep learning-based marker detection method is designed where the intersection of union is replaced by the normalized Wasserstein distance in the computation of non-maximum suppression to improve the detection accuracy. …”
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A Hardware Accelerator for the Inference of a Convolutional Neural network by Edwin González, Walter D. Villamizar Luna, Carlos Augusto Fajardo Ariza

“… Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications, e.g. image classification, speech recognition, medicine, to name a few. …”
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GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach by Diego Bravo, Juan Frias, Felipe Vera, Juan Trejos, Carlos Martínez, Martín Gómez, Fabio González, Eduardo Romero

“…By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.…”
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Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network by Hao Han, Kejie Li, Yi Li

“…A recognition model of different fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. …”
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Classroom Behavior Recognition Using Computer Vision: A Systematic Review by Qingtang Liu, Xinyu Jiang, Ruyi Jiang

“…Physical actions and learning engagement have been the primary recognition targets; (2) behavioral categorizations have been defined in various ways and lack connections to instructional content and events; (3) existing studies have focused on college students, especially in a natural classical classroom; (4) deep learning was the main recognition method, and the YOLO series was applicable for multiple behavioral purposes; (5) moreover, we identified challenges in experimental design, recognition methods, practical applications, and pedagogical research in computer vision. …”
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Emotional recognition of EEG signals utilizing residual structure fusion in bi-directional LSTM by Yue Xu, Yunyuan Gao, Zhengnan Zhang, Shunlan Du

“…Additionally, ablation experiments were conducted in this study to affirm the effectiveness of each deep learning component within MRBiL. The experimental results demonstrated that the novel neural network model proposed in this paper had outperformed currently available methods in emotion recognition tasks.…”
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Automated quantification of Enchytraeus crypticus juveniles in different soil types using RootPainter by Bart G. van Hall, Cornelis A.M. van Gestel

“…The manual counting of juveniles in enchytraeid soil toxicity tests is time-consuming, labour-intensive, repetitive, prone to subjectivity, but can potentially be automated through deep learning methods using convolutional neural networks. …”
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Spontaneous curvature in two-dimensional van der Waals heterostructures by Yuxiang Gao, Fenglin Deng, Ri He, Zhicheng Zhong

“…In this paper, we predict that 2D interfaces undergo “spontaneous curvature”, which means when two flat 2D layers approach each other, they inevitably experience out-of-plane curvature. Based on deep-learning-assisted large-scale molecular dynamics simulations, we observe significant out-of-plane displacements up to 3.8 Å in graphene/BN bilayers induced by curvature, producing a stable hexagonal moiré pattern, which agrees well with experimentally observations. …”
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PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology by David P. G. Thomas, Carlos M. Garcia Fernandez, Reza Haydarlou, K. Anton Feenstra

“…Abstract Protein interactions are crucial for understanding biological functions and disease mechanisms, but predicting these remains a complex task in computational biology. Increasingly, Deep Learning models are having success in interface prediction. …”
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Transferability analysis of adversarial attacks on gender classification to face recognition: Fixed and variable attack perturbation by Zohra Rezgui, Amina Bassit, Raymond Veldhuis

“…Abstract Most deep learning‐based image classification models are vulnerable to adversarial attacks that introduce imperceptible changes to the input images for the purpose of model misclassification. …”
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Efficacy of Moxibustion in the Treatment of Parkinson’s Disease Based on Meta-Analysis under Intelligent Medical Treatment by Qianqian Niu, Weijie Xu

“…This article puts forward the related conceptual knowledge of smart medicine and meta-analysis and moxibustion treatment and proposes a deep learning method based on smart medicine to analyze the effects of moxibustion treatment on patients. …”
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Interpretable Detection of Malicious Behavior in Windows Portable Executables Using Multi-Head 2D Transformers by Sohail Khan, Mohammad Nauman

“…We report our findings and show how causality can be established between malicious code and actual classification by a deep learning model, thus opening up this black-box problem for deeper analysis.…”
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Kidney Segmentation with LinkNetB7 by Cihan Akyel

“…In studies of kidney segmentation with artificial intelligence, 3d deep learning models are used in the literature. These methods require more training time than 2d models. …”
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