Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values by Ahmed A. Khalil, Zaiming Liu, Ahmed Fathalla, Ahmed Ali, Ahmad Salah

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Machine learning-driven insights into ctDNA for oral cancer: Applications, models, and future prospects by Dheeraj Kumar, Saraswati Patel

“…We highlight the integration of advanced machine learning (ML) models—Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—in ctDNA detection and analysis. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

Subjects: “…supervised machine learning…”
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Data Augmentation and Machine Learning algorithms for multi-class imbalanced morphometrics data of stingless bees by Daisy Salifu, Lorna Chepkemoi, Eric Ali Ibrahim, Kiatoko Nkoba, Henri E.Z. Tonnang

“…These techniques are applied in combination with machine learning (ML) algorithms; specifically Random Forest (RF), and Support Vector Machine (SVM), to assess the models’ predictive performance to infer stingless bee samples identities. …”
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Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters by C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson, Jacob P. Tavenner, Edward M. Kober, Garritt J. Tucker, Gus L. W. Hart, Eric R. Homer

“…Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. …”
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A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks by Wei Kuang Lai, Mei-Tso Lin, Yu-Hsuan Yang

“…In MARS, road information is maintained in roadside units with the help of machine learning. We use machine learning to predict the moves of vehicles and then choose some suitable routing paths with better transmission capacity to transmit packets. …”
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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

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Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain by Xiaoang Zhang, Yuping Liao, Daying Zhang, Weichen Liu, Zhijian Wang, Yaxin Jin, Shushu Chen, Jianmei Wei

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Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation by Dong Q, Wu J, Zhang H, Chen X, Xu X, Chen J, Shi C, Luo L, Zhang D

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Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials by Cosimo Ricci, Agata Gadaleta, Annamaria Gerardino, Angelo Didonna, Giuseppe Ferrara, Francesca Romana Bertani

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NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING by Reehana SHAIK, Ibrahim SIDDIQUE

“…This module extracts the specific features and the proposed novel Obstructive Erasing Module remove the remaining artifacts and then the extracted features are fed into the Multi-Uni-Net for the fusion of the two modalities and the fused image is classified using EXplainable Machine Learning (EX-ML). From this classification the performance metrics like Accuracy, Precision, Recall, F1-Score and AUC can be determined. …”
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Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning by Umar Farooq Shafi, Waheed Anwar, Imran Sarwar Bajwa, Hina Sattar, Iqra Yaqoob, Aqsa Mahmood, Shabana Ramzan

“…In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. …”
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From density functional theory to machine learning predictive models for electrical properties of spinel oxides by Yuval Elbaz, Maytal Caspary Toroker

“…To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. …”
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Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia by Abu Bakar Nurul Asyiqin, Wan Mohd Jaafar Wan Shafrina, Omar Hamdan, Muhammad Nor Siti Mariam, Muhmad Kamarulzaman Aisyah Marliza, Kemarau Ricky Anak

“…This study investigates machine learning algorithms for modelling aboveground biomass (AGB) in mangrove forests across Peninsular Malaysia. …”
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Applying the machine learning methods to determine the linear optics parameters in the ThomX collector ring by D. Klekots, O. Bezshyyko, L. Golinka-Bezshyyko, V. Kubytskyi, I. Chaikovska

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Efficient and accurate machine learning models for volume of fluid (VOF) simulation on uniform Cartesian mesh by Mostafa A. Rushdi, Shigeo Yoshida, Changhong Hu, Tarek N. Dief, Abdulrahman E. Salem, Mohamed M. Kamra

Subjects: “…Machine learning…”
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A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images by Shuyun Liu, Haifeng Zhao, Zihao Yuan, Liping Xiao, Chengcheng Shen, Xue Wan, Xuhai Tang, Lu Zhang

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Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms by Rivindu Weerasekera, Mohan Sridharan, Prakash Ranjitkar

“…Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. …”
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A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections by Jesse Torres, Lulu Zhao, Philip K. Chan, Ming Zhang

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Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation by Madhu Yadav, Pushpendra Kumar Verma, Sumaiya Ansari

Subjects: “…machine learning…”
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