An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance by Gunapriya Balan, Singaravelan Arumugam, Suresh Muthusamy, Hitesh Panchal, Hossam Kotb, Mohit Bajaj, Sherif S. M. Ghoneim, null Kitmo

“…As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. …”
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EEG-RegNet: Regressive Emotion Recognition in Continuous VAD Space Using EEG Signals by Hyo Jin Jon, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, Eun Yi Kim

“…In this paper, we introduce EEG-RegNet, a novel deep neural network tailored for precise emotional score prediction across the continuous valence–arousal–dominance (VAD) space. …”
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A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation by Chun Zhang, Yinjie Zhao, Guangyu Wu, Han Wu, Hongli Ding, Jian Yu, Ruoqing Wan

“…Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. …”
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Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. …”
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The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm by Rongxin Tang, Yuhao Tao, Jiahao Li, Zhou Chen, Xiaohua Deng, Haimeng Li

“…In order to predict the variations of energetic electron fluxes for different energy channels, we proposed a new ensemble machine leaning model for differential electron flux from 30 keV to 4 MeV in the Earth's radiation belts based on the RBSP‐A observation data from March 2013 to December 2017. The deep neural network (DNN), the convolutional neural network (CNN), the combination of CNN and DNN (CNN&DNN), the linear regression (LR), and the light gradient boosting machine (LightGBM) are among the machine learning models chosen. …”
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Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence by I.V. Belkin, A.A. Abramenko, V.D. Bezuglyi, D.A. Yudin

“…It includes matching a noisy depth image and visible point cloud based on the modified Nelder-Mead optimization method. Deep neural network for image semantic segmentation is used to eliminate dynamic obstacles. …”
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A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure by Wenmao Wu, Zhizhou Yu, Jieyue He

“…SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of discriminations. …”
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Constant force grinding controller for robots based on SAC optimal parameter finding algorithm by Chosei Rei, Qichao Wang, Linlin Chen, Xinhua Yan, Peng Zhang, Liwei Fu, Chong Wang, Xinghui Liu

“…An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. …”
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Comparative analysis of neural network models performance on low-power devices for a real-time object detection task by A. Zagitov, E. Chebotareva, A. Toschev, E. Magid

“…The paper presents results of benchmarks on popular deep neural network models, which are often used for this task. …”
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SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors by Zhizhan Fu, Fazhi Feng, Xingguang He, Tongtong Li, Xiansong Li, Jituome Ziluo, Zixing Huang, Jinlin Ye

“…Timely and accurate identification of ICC histological grade is critical for guiding clinical diagnosis and treatment planning.MethodWe proposed a dual-branch deep neural network (SiameseNet) based on multiple-instance learning and cross-attention mechanisms to address tumor heterogeneity in ICC histological grade prediction. …”
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Indoor Positioning System in Learning Approach Experiments by Dodo Zaenal Abidin, Siti Nurmaini, Erwin, Errissya Rasywir, Yovi Pratama

“…The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. …”
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Fusion of MHSA and Boruta for key feature selection in power system transient angle stability by WANG Man, ZHOU Xiaoyu, CHEN Fan, LAI Yening, ZHU Ying

“…A transient power angle stability key feature selection method that seamlessly integrates multi-head self-attention (MHSA) and the Boruta algorithm. A deep neural network (DNN) with an MHSA model is initially constructed to execute transient stability assessments directly on the input grid features. …”
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Assessment of Rear-End Collision Risk Based on a Deep Reinforcement Learning Technique: A Break Reaction Assessment Approach by Muhammad Sameer Sheikh, Yinqiao Peng

“…Firstly, we introduce the deep neural network (DNN) to learn the movements of LAV. …”
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BIPE: A Bi-Layer Predictive Ensemble Framework for Forest Fire Susceptibility Mapping in Germany by Ling Hu, Volker Hochschild, Harald Neidhardt, Michael Schultz, Pegah Khosravani, Hadi Shokati

“…Our results confirm that BIPE outperforms traditional high-performance models like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Convolutional Neural Network (CNN), showcasing its practical effectiveness and reliability on the data of nonlinear, high-dimensional, and complex interactions. …”
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When Remote Sensing Meets Foundation Model: A Survey and Beyond by Chunlei Huo, Keming Chen, Shuaihao Zhang, Zeyu Wang, Heyu Yan, Jing Shen, Yuyang Hong, Geqi Qi, Hongmei Fang, Zihan Wang

“…Most deep-learning-based vision tasks rely heavily on crowd-labeled data, and a deep neural network (DNN) is usually impacted by the laborious and time-consuming labeling paradigm. …”
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MSGU-Net: a lightweight multi-scale ghost U-Net for image segmentation by Hua Cheng, Yang Zhang, Huangxin Xu, Huangxin Xu, Dingliang Li, Zejian Zhong, Yinchuan Zhao, Zhuo Yan

“…Moreover, MSGU-Net can serve as a lightweight deep neural network suitable for deployment across a range of intelligent devices and mobile platforms, offering considerable potential for widespread adoption.…”
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Machine and Deep Learning Models for Stress Detection Using Multimodal Physiological Data by Eman Abdelfattah, Shreehar Joshi, Shreekar Tiwari

“…Seven traditional machine learning algorithms – Logistic Regression, Gaussian Naïve Bayes Classifier, AdaBoost Classifier, XGB Classifier, Decision Trees Classifier, Extra Trees Classifier, and Random Forest Classifier and three widely used deep learning algorithms – Deep Neural Network, Convolutional Neural Network and Recurrent Neural Network were trained and tested on the dataset on two phases to predict the state of different subject at any given time. …”
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Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection by Urszula Jachymczyk, Paweł Knap, Krzysztof Lalik

“…The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. …”
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Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models by Zhe Yang, Mengjie Qu, Yun Pan, Ruohong Huan

“…In this paper, we evaluate three traditional machine learning methods and five deep neural network architectures under the same metrics on three popular HAR datasets: mHealth, PAMAP2, and UCIDSADS. …”
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