Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition by Shiyuan Liu, Xiao Yu, Xu Qian, Fei Dong

“…In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. …”
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Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis by Francesca Frau, Paul Loustalot, Margaux Törnqvist, Nina Temam, Jean Cupe, Martin Montmerle, Franck Augé

“…Since biomedical knowledge graphs (BKGs) are limited to the integration of prior knowledge data and do not integrate real-world data (RWD) that would allow for the incorporation of patient level information, we propose a first step towards using RWD, BKGs and graph machine learning (ML) to enable a fully integrated precision medicine strategy. …”
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Remote sensing forest health assessment – a comprehensive literature review on a European level by Drechsel Johannes, Forkel Matthias

“…We conclude that the availability of new satellite systems and advances in artificial intelligence and machine learning should be translated into FHA practice according to ICP standards.…”
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Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making by Oreofeoluwa A. Akintan, Kifle G. Gebremedhin, Daniel Dooyum Uyeh

“…Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. …”
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A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. by Yuqi Cheng, Dawei Wu, Ying Wu, Youcai Guo, Xinze Cui, Pengquan Zhang, Jie Gao, Yanming Fu, Xin Wang

“…We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. …”
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An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors by Bin Wang, Enhui Wang, Zikun Zhu, Yangyang Sun, Yaodong Tao, Wei Wang

“…We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. …”
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Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes by Cillian Thompson, Oscar Higgins

“…Furthermore, in diabetic disease modelling, artificial neural networks have demonstrated outstanding accuracy compared to alternative methods such as machine learning, regression, artificial neural networks, and decision trees.…”
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Self-Organizing Mapping Neural Network Implementation Based on 3-D NAND Flash for Competitive Learning by Anyi Zhu, Lei Jin, Wen Zhou, Tianchun Ye, Zongliang Huo

“…Self-organizing Map (SOM) neural network is a prominent algorithm in unsupervised machine learning, which is widely used for data clustering, high-dimensional visualization, and feature extraction. …”
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Analysis of Noise Pollution during Dussehra Festival in Bhubaneswar Smart City in India: A Study Using Machine Intelligence Models by Sourav Kumar Bhoi, Chittaranjan Mallick, Chitta Ranjan Mohanty, Ranjan Soumya Nayak

“…In this work, the noise pollution level of Bhubaneswar smart city during Dussehra 2020 is predicted using different supervised machine learning (ML) prediction models. The input parameters considered for this work are area or zones of Bhubaneswar city, time at which sound level recorded, equivalent continuous sound level (Leq in dBA), and noise level (high/low compared to the standard value). …”
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The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength by Rami Al-Qasimi, Firas Al-Hajri

“…To address these limitations, this study introduces innovative machine learning techniques, employing the Support Vector Regression (SVR) model to accurately predict USS. …”
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A data-driven state identification method for intelligent control of the joint station export system by Guangli Xu, Yifu Wang, Zhihao Zhou, Yifeng Lu, Liangxue Cai

“…Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy. …”
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On the enhancement of estimator efficiency of population variance through stratification, transformation, and formulation with application to COVID-19 data by Hameed Ali, Zafar Mahmood, T.H. AlAbdulaal

“…This work introduces a novel data-driven machine learning algorithm aiming stratification problem, formally based on subjective approach. …”
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Transcriptomics profiling of Parkinson’s disease progression subtypes reveals distinctive patterns of gene expression by Carlo Fabrizio, Andrea Termine, Carlo Caltagirone

“…Methods Whole blood RNA-Sequencing data underwent differential gene expression analysis, followed by multiple pathway analyses. A Machine Learning (ML) classifier, namely XGBoost, was trained using data from multiple modalities, including gene expression values. …”
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Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms by Ali Alhchaimi

“…Objectives: This study assesses machine learning (ML) algorithms' ability to detect fraud in cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and XGBoost (XGB). …”
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STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…This paper investigates the difficulties encountered in human activity recognition (HAR), precisely differentiating between various activities by extracting spatial and temporal features from sequential data. Traditional machine learning approaches necessitate manual feature extraction, hindering their effectiveness. …”
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Side information-driven image coding for hybrid machine–human vision by Zhongpeng Zhang, Ying Liu, Wen-Hsiao Peng

“…Abstract With the development of machine learning, advanced photography and image transmission systems, images are being processed more and more by machines, so image coding for machines (ICM) came into being. …”
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Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities by Sarat Kumar Chettri, Rup Kumar Deka, Manob Jyoti Saikia

“…It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. …”
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Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM by Hang Wang, Min-jun Peng, Yong-kuo Liu, Shi-wen Liu, Ren-yi Xu, Hanan Saeed

“…Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.…”
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Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study by C. Banerjee, K. Nguyen, C. Fookes, G. Hancock, T. Coulthard

“…Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. …”
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Detection and identification technology of rotor unmanned aerial vehicles in 5G scene by Fengtong Xu, Tao Hong, Jingcheng Zhao, Tao Yang

“…In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. …”
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