Electric Submersible Pump Fault Diagnosis Based on Laplacian Eigenmaps and Weighted Extreme Learning Machine by XU Zekun, FU Jun, GAO Xiaoyong, ZHANG Yu, LI Qiang, TAN Chaodong

“…In response to the serious imbalance in the data collected by ESP, firstly, a fault diagnosis model is established using a weighted extreme learning machine; Then, to solve the problems of insufficient algorithm learning, high computational costs caused by weighted strategies, and poor performance in applying to high-dimensional feature spaces, the Laplacian eigenmaps method is introduced to further optimize the model; finally, the effectiveness of the proposed method was validated on the TE chemical process dataset, and the practicality of the algorithm was experimentally validated on the real-time fault dataset of electric submersible pump. …”
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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Ayman Nabil

“…Also, hyperparameter tuning and cross-validation have been applied with machine learning to optimize models and enhance accuracy. …”
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Predicting drug-target interactions using machine learning with improved data balancing and feature engineering by Md. Alamin Talukder, Mohsin Kazi, Ammar Alazab

“…To address data imbalance, Generative Adversarial Networks (GANs) are employed to create synthetic data for the minority class, effectively reducing false negatives and improving the sensitivity of the predictive model. The Random Forest Classifier (RFC) is utilized to make precise DTI predictions, optimized for handling high-dimensional data. …”
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Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering by Thommas K. S. Flores, Morsinaldo Medeiros, Marianne Silva, Daniel G. Costa, Ivanovitch Silva

“…TinyML enables the deployment of Machine Learning (ML) models on resource-constrained devices, addressing a growing need for efficient, low-power AI solutions. …”
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Thermodynamic and Heat Transfer Evaluation of Pocket Drying Section in the Multi-Cylinder Dryers of Paper Machine by Sh. Ghodbanan, R. Alizadeh, S. Shafiei

“…In this study a developed model has been used to evaluate the paper drying process and examine the pocket dryer conditions of a multi-cylinder fluting paper machine. …”
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Optimization design of unloading mechanism for intelligent cleaning robots based on Adams by LIN Xinqiang

“…Secondly, based on Adams software, a parameterized virtual prototype model of the unloading mechanism was established to perform kinematic and dynamic simulations of the unloading mechanism. …”
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Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations by Chen YT, Chen GJ, Lin YS

“…This study employed multiple machine learning models to perform risk prediction and result exploration for first-trimester Down syndrome in East Asian populations, aiming to identify an optimal risk prediction model that will enhance future predictions of Down syndrome risk and improve the efficiency of the screening process.Patients and Methods: This study collected data from the Down syndrome screening database at Taipei Chang Gung Memorial Hospital from May 1, 2018, to February 29, 2024. …”
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Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms by Jean‐Michel Roué, Amir Avnit, Behnood Gholami, Wassim M. Haddad, Kanwaljeet J. S. Anand

“…An optimal model, having the highest F‐1 score, was obtained and evaluated on the independent testing dataset. …”
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Heat treatment control technology of high-strength steel gears based on support vector machine by Yanzhong Wang, Libin Zhang, Yulu Su, Hai Liu, HaiLong Yang, Yanyan Chen

“…In this study, with the help of machine learning, a support vector machine prediction model of gear tissue distribution is constructed based on heat treatment parameters, and the radial basis functions kernel function is selected as the kernel function of the support vector machine to improve the accuracy of model prediction by optimizing the kernel parameters. …”
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Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System by Xiongwei Zhang, Hager Saleh, Eman M. G. Younis, Radhya Sahal, Abdelmgeid A. Ali

“…The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. …”
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Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques by Anjali Jaglan, Rati Ram Singh

“…In this study, Response Surface Methodology (RSM) and machine learning models were used to predict the mechanical properties of recycled aggregate concrete (RAC) containing ground granulated blast furnace slag (GGBS) and polypropylene fibers (PPFs). …”
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Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification by Lei Wang, Lei Wang, Xueying Jin, Yuchen Wu, Runing Qiu, Jianfang Wang

“…Ferroptosis-immune subtypes were identified through consensus clustering and immune profiling, while prognostic models were constructed via LASSO/stepwise Cox regression and machine learning optimization.ResultsThree ferroptosis-immune subtypes exhibiting distinct survival outcomes and immune phenotypes were identified. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. …”
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Micro-electrical Discharge Machining of Micro-holes Based on Integrated Orthogonal Experiments and CNN Methods by Yuandong MO, Yazhi WANG, Shuqi HUANG, Jiajun ZHONG

“…The findings contribute to the theoretical understanding of micro-EDM processes while also providing practical insights for optimizing machining parameters in real-world applications. …”
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Temperature Sensor Fault Detection in Chiller Based on One-class Support Vector Machine Algorithm by Mao Qianjun, Liang Zhiyuan, Liu Donghua, Hu Yunpeng, Li Guannan, Fang Xi

“…The optimized model parameters were obtained by the 10-fold cross validation method. …”
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Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis by Heung-Shik Lee

“…This study addresses this gap by conducting dynamic simulations of standardized rollover tests to evaluate the static stability factor (SSF) and by developing a machine-learning-based model for predicting rollover risk. …”
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Tool wear monitoring method based on AMIDBOAB and imbalanced data optimization by Guan-Hua Xu, Hong-Yu Wu, Bo Tang, Jun-Long Xu

“…To address the issues of data imbalance and low monitoring accuracy in various tool wear stages of CNC machine tools, an AMIDBOAB tool wear monitoring method based on imbalanced data optimization is proposed. …”
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Optimizing Wheat Yields: AI and IoT Techniques for Continuous Agricultural Monitoring by Nandy Manish, Afzal Md

“…This data is analyzed using AI algorithms developed predictive models and actionable insights. The model helps farmers in optimizing the resources such as irrigation, fertilization, pest control and harvesting. …”
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Optimization of Beamforming and Transmit Power Using DQN and Comparison With Traditional Techniques by Parmida Geranmayeh, Eckhard Grass

“…As Artificial Intelligence (AI) and Machine Learning (ML) technologies continue to evolve, their integration into 5G and 6G networks has become critical for improving performance and efficiency. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…This paper presents an advanced decomposition-integration framework which seamlessly integrates econometric models with machine learning techniques to enhance carbon price forecasting. …”
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