Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. by Tian Xia, Lanju Zhou, Khalil Ahmad

“…The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. …”
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Geospatial Forecasting of Electric Energy in Distribution Systems Using Segmentation and Machine Learning with Convolutional Methods by Héctor Chávez, Yuri Molina

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Machine Learning-Based Prediction of Ecosystem-Scale CO₂ Flux Measurements by Jeffrey Uyekawa, John Leland, Darby Bergl, Yujie Liu, Andrew D. Richardson, Benjamin Lucas

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Prediction and Analysis of PDC Bit Wear in Conglomerate Layer with Machine Learning and Finite-Element Method by Li-qiang Wang, Ming-ji Shao, Wei Zhang, Zhi-peng Xiao, Shuo Yang, Ming-he Yang

“…The numerical simulation results of the wear loss of the PDC bit cutting teeth, such as the caster angle, temperature, linear velocity, and bit pressure, as well as previous experimental research results, were combined into a training dataset. Then, machine learning methods for equal-probability gene expression programming (EP-GEP) were used. …”
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Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods by Ahmad Almadhor, K. Mallikarjuna, R. Rahul, G. Chandra Shekara, Rishu Bhatia, Wesam Shishah, V. Mohanavel, S. Suresh Kumar, Sojan Palukaran Thimothy

“…Therefore, the article’s goals are to estimate Mashhad, Iran’s electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. …”
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Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis by Jiarui Wang, Shiyan Jiang, Jiaxin Shi, Jing Wang, Shengrong Du, Zufang Huang

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Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

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Development of a novel physics-informed machine learning model for advanced thermochemical waste conversion by Surika van Wyk

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Robust fault detection and classification in power transmission lines via ensemble machine learning models by Tahir Anwar, Chaoxu Mu, Muhammad Zain Yousaf, Wajid Khan, Saqib Khalid, Ahmad O. Hourani, Ievgen Zaitsev

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Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization by Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li

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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. by Hilman Nordin, Bushroa Abdul Razak, Norrima Mokhtar, Mohd Fadzil Jamaludin, Adeel Mehmood

“…Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). …”
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Forecasting power generation of wind turbine with real-time data using machine learning algorithms by Asiye Bilgili, Kerem Gül

“…Accurate forecasting of wind energy generation is crucial for managing its intermittent nature and ensuring effective integration into the electricity grid. We employed machine learning techniques to predict wind power generation by utilizing historical weather data in conjunction with corresponding wind power generation data. …”
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Development of robust machine learning models for predicting flexural strengths of fiber-reinforced polymeric composites by Abdulhammed K. Hamzat, Umar T. Salman, Md Shafinur Murad, Ozkan Altay, Ersin Bahceci, Eylem Asmatulu, Mete Bakir, Ramazan Asmatulu

Subjects: “…Machine learning…”
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Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning by Y. Qiu, X. Li, X. Li, W. Chai, Y. Liu, M. Song, X. Tian, Q. Zou, W. Lou, W. Zhang, J. Li, Y. Zhang

“…In this study, we integrated a machine learning model, an observation-based model, and a positive matrix factorization model based on 4 years of continuous observation data from a typical urban site. …”
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Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion by Xin Kang Li, Li Jun Tang, Ze Ying Li, Dian Qiu, Zhuo Ling Yang, Xiao Yi Zhang, Xiang-Zhi Zhang, Jing Jing Guo, Bao Qiong Li

“…Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. …”
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Analysis of sensitive urban form indicators of flood susceptible prediction based on machine learning models by M. Srivanit, S. Pattanasri, N. Phichetkunbodee, S. Manokeaw, S. Sitthikankun, D. Rinchumphu

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Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning by Ying Zhang, Fabao Xu, Zhenzhe Lin, Jiawei Wang, Chao Huang, Min Wei, Weibin Zhai, Jianqiao Li

“…The VA predicted by machine learning was compared with the ground truth. Results. …”
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Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms by Dong Duan, Xiaojing Feng, Ruizhe Zhang, Xiaoyu Chen, Hongzhi Zhang

“…Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. …”
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Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma by Zi-Qi Pan, Shu-Jun Zhang, Xiang-Lian Wang, Yu-Xin Jiao, Jian-Jian Qiu

“…The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. …”
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Retracted: Feasibility on the Integrated Teaching Method of Machine Learning Algorithm inside and outside Physical Education Class by International Transactions on Electrical Energy Systems

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