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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Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants by Xiangjun Qi, Shujing Wang, Caishan Fang, Jie Jia, Lizhu Lin, Tianhui Yuan

Subjects: “…Machine learning…”
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Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source by Parisa Sadeghli Dizaji, Hamidreza Habibiyan

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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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Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms by Huu Nam Nguyen, Quoc Thanh Tran, Canh Tung Ngo, Duc Dam Nguyen, Van Quan Tran

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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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Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers by Karissa Chan, Corinne Fischer, Pejman Jabehdar Maralani, Sandra E. Black, Alan R. Moody, April Khademi

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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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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

Subjects: “…Machine learning…”
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Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology by Anand E. Rajesh, Abraham Olvera-Barrios, Alasdair N. Warwick, Yue Wu, Kelsey V. Stuart, Mahantesh I. Biradar, Chuin Ying Ung, Anthony P. Khawaja, Robert Luben, Paul J. Foster, Charles R. Cleland, William U. Makupa, Alastair K. Denniston, Matthew J. Burton, Andrew Bastawrous, Pearse A. Keane, Mark A. Chia, Angus W. Turner, Cecilia S. Lee, Adnan Tufail, Aaron Y. Lee, Catherine Egan, UK Biobank Eye and Vision Consortium

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Machine Learning Models for Predicting the Compressive Strength of Concrete with Shredded PET Bottles and M-Sand as Fine Aggregate by Altamashuddinkhan Nadimalla, Siti Aliyyah Masjuki, Abdullah Gubbi, Anjum Khan, Imran Mokashi

Subjects: “…Machine learning…”
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R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster by Morimasa Suzuna, Iwasaki Masako, Suehara Taikan, Tanaka Junichi, Saito Masahiko, Nagahara Hajime, Nakashima Yuta, Takemura Noriko, Nakano Takashi

“…We have developed an energy calibration method using machine learning for the ILC electromagnetic (EM) calorimeter (ECAL), a sampling calorimeter consisting of Silicon-Tungsten layers. …”
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