Application of Rotating Machinery Fault Diagnosis Based on Deep Learning by Wei Cui, Guoying Meng, Aiming Wang, Xinge Zhang, Jun Ding

“…After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. …”
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Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions by Silviu-Ionut BABTAN

“…This article examines several AI methods – Regression Models, Decision Trees, Random Forests, Artificial Neural Networks, and XGBoost – and explores their applications for improving property valuation accuracy and efficiency, with implications for other professions involved, e.g. audit. …”
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The application of expert system to exploration of the robot motion by Olegas Ramašauskas, Artūras Bielskis

“…The coherency using XPS principles with artificial neural networking methods also discussed. The software implementation in Visual LISP environment examples and knowledge base files are presented. …”
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Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning by Zhang Jiaming

“…The research focuses on leveraging Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods, and Transfer Learning to enhance medical diagnostics. …”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…The comparison involves three machine learning models - Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Networks (CNN) - to assess their efficacy in the FL context. …”
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Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning by Zhang Wenxi

“…AI techniques, including Support Vector Machines (SVM), decision trees, random forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), show great potential in improving the accuracy and personalization of rehabilitation assessment. …”
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Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends by Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti, Laurent Despoisse, Jens Nieke

“…Deep learning architectures, such as convolutional neural networks (CNNs), autoencoders, deep belief networks, generative adverserial networks (GANs), and recurrent neural networks are examined for their suitability in processing hyperspectral data. …”
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HSSCIoT: An Optimal Framework Based on Internet of Things-Cloud Computing for Healthcare Services Selection in Smart Hospitals by Oanh Nguyen

“…The combination of Recurrent Neural Networks (RNN) means Long Term Short Memory (LSTM) and new kinds of Convolutional Neural Networks (CNN) means Atrous Spatial Pyramid Pooling (ASPP) deep learning methods considered for HSSCIoT.…”
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Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions by Katarzyna Pentoś, Krzysztof Pieczarka, Krzysztof Lejman

“…To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. …”
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Prefrontal Abilities by D. F. Benson

“…While the prefrontal cortex does not appear to contain the neural networks that carry out cognitive activities, the management of these high level manipulations, so uniquely characteristic of the human, appears dependent upon the prefrontal cortex.…”
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Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks by Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

“…The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. …”
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Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning by Dongbao Jia, Weixiang Xu, Dengzhi Liu, Zhongxun Xu, Zhaoman Zhong, Xinxin Ban

“…Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. …”
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Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems by Zachary Kral, Walter Horn, James Steck

“…A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. …”
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Channel state information–based multi-level fingerprinting for indoor localization with deep learning by Tao Li, Hai Wang, Yuan Shao, Qiang Niu

“…In the offline training phase, deep neural networks are used to train the optimal weights. …”
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On Stability of Nonautonomous Perturbed Semilinear Fractional Differential Systems of Order α∈(1,2) by Mohammed M. Matar, Esmail S. Abu Skhail

“…Two examples related to fractional-order Hopfield neural networks with constant external inputs and a marine protected area model are introduced to illustrate the applicability of stability results.…”
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Dynamic Analysis of Nonlinear Impulsive Neutral Nonautonomous Differential Equations with Delays by Jinxian Li

“…A class of neural networks described by nonlinear impulsive neutral nonautonomous differential equations with delays is considered. …”
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Evolutionary Computation and Its Applications in Neural and Fuzzy Systems by Biaobiao Zhang, Yue Wu, Jiabin Lu, K.-L. Du

“…Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. …”
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Complex Risk Statistics with Scenario Analysis by Fei Sun, Yichuan Dong

“…Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representations for such risk.…”
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Forecasting Models for Time and Cost Performance Predicting of Infrastructural Projects by Aldhamad Saja Hadi Raheem, Maya Rana, Alazawy Suha Falih Mahdi, Alzwainy Faiq M.

“…The efficacy of residential property investment projects will be assessed through the implementation of models that incorporate Artificial Neural Networks and Multiple Linear Regression. Historical information of thirteen boundaries for twenty finished Private Property Venture Tasks were separated from the records of the Directorate of Lodging, then four models were created by utilizing Multiple Linear Regression strategy and Artificial Neural Networks method. …”
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