Molecular Modeling of Antimalarial Agents by 3D-QSAR Study and Molecular Docking of Two Hybrids 4-Aminoquinoline-1,3,5-triazine and 4-Aminoquinoline-oxalamide Derivatives with the... by Hanine Hadni, Mohamed Mazigh, El’mbarki Charif, Asmae Bouayad, Menana Elhallaoui

“…The QSAR model tested with artificial neural network (ANN) method shows high performance towards its predictability. …”
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Deep-Learning-Based Bughole Detection for Concrete Surface Image by Gang Yao, Fujia Wei, Yang Yang, Yujia Sun

“…A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). …”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

“…In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. …”
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Tribological Behavior and Analysis on Surface Roughness of CNC Milled Dual Heat Treated Al6061 Composites by A. Bovas Herbert Bejaxhin, G.M. Balamurugan, S.M. Sivagami, K. Ramkumar, V. Vijayan, S. Rajkumar

“…The influencing factors are identified by the Taguchi, genetic algorithm (GA), and Artificial Neural Network (ANN) techniques and compared within it. …”
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Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy by Samira Monfared, Neda Abdolvand, Mohammad Taghi Mirtorabi, Saeedeh Rajaee Harandi

“…In both phases, various algorithms such as Naive Bayes, Random Forest, and Generalized linear model (GLM) and Neural network are used to ensure the best results according to the research data. …”
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A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model by Jiaxin Jiang, David Murray, Vladimir Stankovic, Lina Stankovic, Clement Hibert, Stella Pytharouli, Jean-Philippe Malet

“…Leveraging on recent recommendations on embedding humans in the Artificial Intelligence (AI) decision making process, particularly training and validation, we propose a methodology that incorporates data labelling, verification, and re-labelling through a multi-class convolutional neural network (CNN) supported by Explainable Artificial Intelligence (XAI) tools, specifically, Layer-wise Relevance Propagation (LRP). …”
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Expandable Orbit Decay Prediction Using Continual Learning by Junhua He, Hua Wang, Haitao Wang, Xuankun Fang, Chengyi Huo

“…The plasticity of the proposed model is discussed, which originates from the uncertainty of neural network (NN) parameters. The proposed method overcomes the negative effects of uncertain physical parameters and complex space environments. …”
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N2GNet tracks gait performance from subthalamic neural signals in Parkinson’s disease by Jin Woo Choi, Chuyi Cui, Kevin B. Wilkins, Helen M. Bronte-Stewart

“…In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). …”
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A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations by Yiming Lang, Michelle Xin Yi Ng, Kai Xiang Yu, Binghui Chen, Peng Chee Tan, Khang Wei Tan, Weng Hoong Lam, Parthiban Siwayanan, Kek Seong Kim, Thomas Shean Yaw Choong, Joon Yoon Ten, Zhen Hong Ban

“…This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. …”
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Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control by Yufei Ruan, Huang Gao, Dequn Li

“…Once the optimal compensation time is learned, a deep Q-learning algorithm which combined Q-learning with an artificial neural network (ANN) is proposed to learn the optimal compensation quantity. …”
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Rancang Bangun Purwarupa Pemilah Sampah Pintar Berbasis Deep Learning by Kahlil Muchtar, Nyak Twoman Anshari, Chairuman Chairuman, Khalid Alhabibie, Khairul Munadi

“…Deep Learning method applied here is using Convolutional Neural Network (CNN). The algorithm is like human nerves and is one of supervised learning. …”
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Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI) by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional neural network (CNN) dengan transfer learning adalah tiga algorimta klasifikasi populer yang dibandingkan dalam penelitian ini. …”
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Clinical Treatment and Nursing Intervention Study of Clipping Treatment of Cerebral Aneurysm under the Health Model of Data Analysis by Yuyou Huang, Liping Huang

“…The model combines the characteristics of cerebral aneurysms for targeted analysis, and then through the understanding of the clipping treatment of cerebral aneurysms, this paper combines the deep learning in the neural network to train the treatment plan under the data analysis health model. …”
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Geographical patterns of intraspecific genetic diversity reflect the adaptive potential of the coral Pocillopora damicornis species complex. by M Carr, C Kratochwill, T Daly-Engel, T Crombie, R van Woesik

“…A deep-learning, multi-layer neural-network model showed that geographical location played a major role in intraspecific diversity, with mean sea-surface temperature and oceanic regions being the most influential predictor variables differentiating diversity. …”
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A Scalable Blockchain Framework for ELA Assessment by Chunxia Tian

“…We propose a proficiency evaluation model based on a Discrete Hopfield Neural Network (DHNN). Firstly, the hierarchical analysis method is used to construct the evaluation index system of students’ English ability, and then the ability classification indexes are divided into 5 levels.The network achieves the classification of students’ English proficiency through the associative memory of the classification criteria, and the classification results are compared with those of the BPNN model. …”
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Design of an Anti-Windup PID Algorithm for Differential Torque Steering Systems by Dengzhi Peng, Bin Huang, Hao Huang

“…To investigate the differential steering process, the 2 DOF (degree of freedom) dual-track reference models with linear and nonlinear tire models are established, and based on the steering process analysis and yaw rate gain calculation, a BP-NN (backpropagation neural network) model is initiated to maintain the accuracy of the calculated yaw rate gain. …”
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Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction by Neeti Sangwan, Vishal Bhatnagar

“…These latent features train the fused Convolutional Neural Network (CNN) with LSTM to predict the popularity of unseen videos on the trained deep learning network. …”
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Low-latency hierarchical routing of reconfigurable neuromorphic systems by Samalika Perera, Ying Xu, André van Schaik, Runchun Wang

“…A reconfigurable hardware accelerator implementation for spiking neural network (SNN) simulation using field-programmable gate arrays (FPGAs) is promising and attractive research because massive parallelism results in better execution speed. …”
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Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning by Chunjie Li, Pan Liu, Zhenlong Xie, Zhibin Li, Huan Huan

“…Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. …”
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Comparison of numerical model, neural intelligent and GeoStatistical in estimating groundwater table by Maryam Bayatvarkeshi, Rojin Fasihi

“…In this study, MODFLOW numerical code in GMS software, artificial neural network (ANN) and neural – fuzzy (CANFIS) method in NeuroSolution software, wavelet-neural method in MATLAB software and geostatistical method in ArcGIS software were used. …”
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