Characteristics and Rapid Prediction of Seismic Subsidence of Saturated Seabed Foundation with Interbedded Soft Clay–Sand by Liuyuan Zhao, Miaojun Sun, Jianhong Ye, Fuqin Yang, Kunpeng He

“…A total of 4000 sets of seabed foundation models are randomly generated, with layers of saturated soft clay and sand and with a random distribution of layer thickness and burial depth. …”
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Supervised Machine Learning for Real-Time Intrusion Attack Detection in Connected and Autonomous Vehicles: A Security Paradigm Shift by Ahmad Aloqaily, Emad E. Abdallah, Hiba AbuZaid, Alaa E. Abdallah, Malak Al-hassan

“…The selected algorithms encompassed Decision Trees, Random Forests, Naive Bayes, Logistic Regression, XG Boost, LightGBM, and Multi-layer Perceptrons. …”
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APPLICATION OF MACHINE LEARNING ALGORITHMS TO EVALUATE THE UCI DATABASE IN THE CLASSIFICATION OF AUTISM SPECTRUM DISORDERS by Phạm Quang Thuận, Nguyễn Đình Thuân

“…We evaluated the data set with the SVM and Random Forest algorithms and also investigated the Decision Tree, Logistic Regression, K-Nearest-Neighbors, Naïve Bayes, and Multi-Layer Perceptron (MLP) algorithms. …”
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Hybridization of Genetic Algorithm with Neural Networks to Cipher English Texts by Radwan Al-Jawadi, Raid Al-Naima

“…This research aims in the first stage to built a cipher system using hybrid Genetic Algorithm with single layer Neural network to prevent any data attack during the transition process , where the ASCII of the letters are used as inputs to the network and the random numbers are used as outputs to the network , then the weights will be constructed after the network training . …”
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Neural network based image and video coding technologies by Chuanmin JIA, Zhenghui ZHAO, Shanshe WANG, Siwei MA

“…Deep neural networks have achieved tremendous success in artificial intelligence,which makes the broad and in-depth research of neural network resurge in recent years.Recently,the neural network based image and video coding has become one of the front-edge topics.A systematic and comprehensive review of neural network based image and video coding approaches based on network structure and coding modules were provided.The development of neural network based image compression,e.g.multi-layer perceptron,random neural network,convolutional neural network,recurrent neural network and generative adversarial network based image compression methods and neural network based video compression tools were introduced respectively.Moreover,the future trends in neural network based compression were also envisioned and discussed.…”
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Online Semisupervised Learning Approach for Quality Monitoring of Complex Manufacturing Process by Weng Weiwei, Mahardhika Pratama, Andri Ashfahani, Edward Yapp Kien Yee

“…Furthermore, it is equipped by a feature extraction layer in terms of 1D convolutional layer extracting natural features of multivariate time-series data samples of sensors and coping well with the many-to-one label relationship, a common problem of practical quality monitoring. …”
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Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft by Yanhui Guo, Yanpeng Chen, Peibo Li, Xinfu Chi, Yize Sun

“…A multi-objective grasshopper optimization algorithm (MOGOA) with an adaptive curve c(t) and the enhanced Levy fight strategy (CLMOGOA) was proposed to optimize the process parameters of rotary screen coating, setting the thickness and uniformity of the adhesive layer on the structural plates in spacecraft as its optimization objectives. …”
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An improved particle swarm optimization for multilevel thresholding medical image segmentation. by Jiaqi Ma, Jianmin Hu

“…Firstly, according to the fitness value, the particle swarm is divided into three-layer structure. To accommodate the larger search range caused by higher bit depth, the particles in the layer with the worst fitness value are employed random opposition learning strategy. …”
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Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories by Byunghyun Lim, Dongju Kim, Woojin Cho, Jae-Hoi Gu

“…Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…This model integrates a Random Forest (RF), three-layer Deep Neural Networks (DNN), Support Vector Machine Radial (SVM-R), and a Gradient Boosting Model (GBM) meta layer to enhance prediction accuracy. …”
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Hierarchical Resource Management for Mega-LEO Satellite Constellation by Liang Gou, Dongming Bian, Yulei Nie, Gengxin Zhang, Hongwei Zhou, Yulin Shi, Lei Zhang

“…The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. …”
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A Convolution Auto-Encoders Network for Aero-Engine Hot Jet FT-IR Spectrum Feature Extraction and Classification by Shuhan Du, Wei Han, Zhenping Kang, Yurong Liao, Zhaoming Li

“…The encoder network consists of convolutional layers and maximum pooling layers, the decoder network consists of up-sampling layers and deconvolution layers, and the classification network consists of a flattened layer and a dense layer. …”
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Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey by Youbei Lin, Chuang Li, Xiuli Wang, Hongyu Li

“…Using R 4.4.1, seven assessment models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC. …”
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Leveraging BiLSTM-CRF and adversarial training for sentiment analysis in nature-based digital interventions: Enhancing mental well-being through MOOC platforms by Juanjuan Zang

“…This involves incorporating perturbations in the embedding space, generating adversarial samples at the embedding layer and semantic feature fusion layer, and combining these with the original samples for model training. …”
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Anomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints by Mei Wang, Xinyuan Zhu, Guangyue Zhou, Kewen Li, Qingshan Wu, Wankai Fan

“…The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). …”
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Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning by Yi-Hsun Chang, You-Lun Zhang, Cheng-Hao Cheng, Shu-Han Wu, Cheng-Han Li, Su-Yu Liao, Zi-Chun Tseng, Ming-Yi Lin, Chun-Ying Huang

“…Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. …”
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Intelligent Inspection Method for Rebar Installation Quality of Reinforced Concrete Slab Based on Point Cloud Processing and Semantic Segmentation by Ruishi Wang, Jianxiong Zhang, Hongxing Qiu, Jian Sun

“…In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. …”
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Adaptive Impact-Time-Control Cooperative Guidance Law for UAVs Under Time-Varying Velocity Based on Reinforcement Learning by Zhenyu Liu, Gang Lei, Yong Xian, Leliang Ren, Shaopeng Li, Daqiao Zhang

“…Then, in order to improve the applicability and robustness of the agent, environmental uncertainties, including aerodynamic parameter errors, observation noise, and target random maneuvers, are incorporated into the training process. …”
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Sentiment analysis of pilgrims using CNN-LSTM deep learning approach by Aisha Alasmari, Norah Farooqi, Youseef Alotaibi

“…The model is based on four CNN layers for local feature extraction after the One-Hot Encoder, and one LSTM layer to maintain long-term dependencies. …”
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An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things by Cheng Wang, Haiyang Huang, Jianwei Chen, Wei Wei, Tian Wang

“…This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. …”
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