A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants by Lijie Guo, Jianxin Kang

“…Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural network (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study. …”
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Refinement Method of Evaluation and Ranking of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Optimal Weight Model by Wei Guo, Lihua Liu, Yao Yao, Tong Shen

“…This paper proposes a sorting refinement method based on the optimal weight model and uses the BP neural network to determine the optimal weight. Weight is a scoring mechanism for comprehensive ranking, that is, a scoring system. …”
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Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning by Won Joong Kim, Gunho Jung, Sun-Yong Choi

“…In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. …”
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The process of budgeting the strategy of industrial complex transformation in the context of digitalisation by A. G. Boev

“…The author determines the recommended amount and horizon of the strategy budget planning and also presents a 6-stage algorithm of methodical reception on the use of neural network modeling tools to optimize the budget of the strategy of changes in the industrial complex. …”
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Semantic-Based Classification of Long Texts on Higher Education in China by Chun Li, Yanying Fei

“…To solve these problems, this paper improves the convolutional neural network (CNN) into the HE-CNN classification model for HE texts. …”
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Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique by Saud Altaf, Muhammad Waseem Soomro, Mirza Sajid Mehmood

“…This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. …”
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Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…Gated recurrent unit (GRU) neural network and autoregressive moving average (ARMA) models are utilized to predict trend and noise components, respectively. …”
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The analysis of dance teaching system in deep residual network fusing gated recurrent unit based on artificial intelligence by Mengying Li

“…According to the experimental results, this model’s F1 score is 85.34%, and its maximum accuracy on the NTU-RGBD60 datasets is more than 5% greater than that of the current 3D Convolutional Neural Network (3D-CNN) baseline algorithm. In addition, the model shows high efficiency and resource utilization in test time, training time and CPU occupancy. …”
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Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm by Sherril Sophie Maria Vincent

“…Further, the Hierarchical Deep Belief Network Convolutional Neural Network (HDBNCNN) algorithm has analysed the accumulated data in detecting the anomalies revealing the sinkhole activity centred on learning routing patterns. …”
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Deep Learning for Plastic Waste Classification System by Janusz Bobulski, Mariusz Kubanek

“…One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. …”
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Pharmacovigilance study of the association between progestogen and depression based on the FDA adverse event reporting System (FAERS) by Hui Gao, Xiaohan Zhai, Yan Hu, Hang Wu

“…The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS) were used for Bayesian analysis and disproportionation analysis. …”
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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…They are trained in machine learning and deep neural network learning to detect attack patterns. There are important parameters for setting up a machine learning network, and choosing the right value for these parameters has a great impact on system accuracy. …”
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A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network by Yulian Li, Yang Su

“…Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction. …”
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Real-Time Multi-Task Deep Learning Model for Polyp Detection, Characterization, and Size Estimation by Phanukorn Sunthornwetchapong, Kasichon Hombubpha, Kasenee Tiankanon, Satimai Aniwan, Pasit Jakkrawankul, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr

“…In this work, we present a modified convolutional neural network (CNN) based deep learning (DL) model to perform these tasks in real-time, utilizing existing object detection models: YOLOv5 and YOLOv8. …”
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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

“…A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.…”
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Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network by Mutasem Sh. Alkhasawneh, Umi Kalthum Ngah, Lea Tien Tay, Nor Ashidi Mat Isa, Mohammad Subhi Al-batah

“…The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.…”
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Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes by Ehsan Homayouny, Rasoul Mahdavifar Khayati, Seyed Massood Nabavi, Vania Karami

“…Therefore, a Bayesian classifier based on the adaptive mixture method was used to segment all lesions, and an artificial neural network (ANN) employed a multi‐layer Perceptron as a subtype classifier. …”
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Motor Cortical Networks for Skilled Movements Have Dynamic Properties That Are Related to Accurate Reaching by David F. Putrino, Zhe Chen, Soumya Ghosh, Emery N. Brown

“…Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. …”
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Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning by Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández

“…We conducted an in-depth exploration of the use of different machine learning (ML) for regression algorithms, including Linear, Ridge, LASSO, Bayesian Ridge, Decision Tree, and a variety of Deep Neural Network (DNN) architectures, to estimate the slope of the high-frequency feature (HFF), a prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). …”
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Improving Linearity and Symmetry of Synaptic Update Characteristics and Retentivity of Synaptic States of the Domain-Wall Device Through Addition of Edge Notches by Raman Hissariya, Debanjan Bhowmik

“…Compute-in-memory (CIM) crossbar arrays of non-volatile memory (NVM) synapse devices have been considered very attractive for fast and energy-efficient implementation of various neural network (NN) algorithms. High retention time of the synaptic states and high linearity and symmetry of the synaptic weight update characteristics (long-term potentiation (LTP) and long-term depression (LTD)) are major requirements for the NVM synapses in order to obtain high classification accuracy upon implementation of the NN algorithms on the corresponding crossbar arrays. …”
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