Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation by Amirreza Kandiri, Ramin Ghiasi, Maria Nogal, Rui Teixeira

“…In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. …”
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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

“…This article presents a reproducible machine learning methodology for the early prediction of Alzheimer’s disease (AD) using clinical and behavioural data. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…Data on 5750 patients were selected, of which atherosclerotic plaques were detected in 385 people. …”
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Finding Possible Precursors for the 2015 Cotopaxi Volcano Eruption Using Unsupervised Machine Learning Techniques by Juan C. Anzieta, Hugo D. Ortiz, Gabriela L. Arias, Mario C. Ruiz

“…In this work we use records from a broadband seismic station located at less than 4 km from the vent that encompass data from April to December of 2015, to detect and study low-frequency seismic events. …”
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Machine Learning Techniques in Chronic Kidney Diseases: A Comparative Study of Classification Model Performance by Nguyen Dong Phuong, Nguyen Trung Tuyen, Vu Thi Thai Linh, Nghi N Nguyen, Thanh Q Nguyen

“…The data were collected from the UCL machine learning database, with missing values filled using the “mean/mode” and the “random sampling method.” …”
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A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation by Xianghu Jia, Weiwen Luo, Jiaqi Li, Jieqi Xing, Hongjie Sun, Shunyao Wu, Xiaoquan Su

“…Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. …”
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In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing by André Jaquemod, Marijana Palalić, Kamil Güzel, Hans-Christian Möhring

“…Machine learning algorithms were used to process and interpret the collected data. …”
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The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach by Abed Mutemi, Fernando Bacao

“…Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. …”
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SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces by M. P. Michalak, C. Gerhards, P. Menzel

“…<p>The study presents a novel approach for fault detection on subsurface geological homoclinal interfaces (slopes) using a supervised learning algorithm and careful input variable (feature) selection. …”
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Improving Fall Classification Accuracy of Multi-Input Models Using Three-Axis Accelerometer and Heart Rate Variability Data by Seunghui Kim, Jae Eun Ko, Seungbin Baek, Daechang Kim, Sungmin Kim

“…Patterns were identified from the various movement characteristics of acceleration sensor data using a deep learning model consisting of CNN-LSTM, and heart rate variability (HRV) data were analyzed using a wide learning model to provide additional weight values for fall classification. …”
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The potential of machine learning in classifying relapse and non-relapse in children with clubfoot based on movement patterns by Lianne Grin, Sieglinde Bogaert, Saskia Wijnands, Arnold Besselaar, Marieke van der Steen, Jesse Davis, Benedicte Vanwanseele

“…Machine learning algorithms have the capacity to analyse such complex nonlinear relationships, offering the potential to train a model that assesses whether a child has relapsed clubfoot based on their movement pattern. …”
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Machine Learning Prediction of Left Ventricular Assist Device Thrombosis from Acoustic Harmonic Power by Kent D. Carlson, Dan Dragomir-Daescu, Barry A. Boilson

“…Researchers have focused on investigating the acoustic signatures of LVADs to enable earlier detection and treatment of this condition. This study explored the use of machine learning algorithms to predict thrombosis from harmonic power values determined from the acoustic signatures of a cohort of HVAD patients (<i>n</i> = 11). …”
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Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes by Jiaying Ge, Siqi Sun, Jiangping Zeng, Yujie Jing, Huihui Ma, Chunhua Qian, Ran Cui, Shen Qu, Hui Sheng

“…Method Data from NHANES 2011–2018 (n = 2,176) were analyzed. …”
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Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning by Hasballah Zakaria, Elsa Sari Hayunah Nurdiniyah, Astri Maria Kurniawati, Dziban Naufal, Nana Sutisna

“…This work also details the method covering data set preparation, algorithm and design parameters exploration for pre-processing ECG signals, features extraction and selection, and hyper parameter tuning for employed machine learning methods. …”
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Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion by Li Guo, Qin Gao, Mengyi Zhang, Panting Cheng, Peng He, Lujun Li, Dong Ding, Changcheng Liu, Francis Collins Muga, Masroor Kamal, Jiangtao Qi

“…Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. …”
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Highly Variable Quasar Candidates Selected from 4XMM-DR13 with Machine Learning by Heng Wang, Yanli Ai, Yanxia Zhang, Yuming Fu, Wenfeng Wen, Liming Dou, Xue-Bing Wu, Xiangru Li, Zhiying Huo

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Automated Body Condition Scoring in Dairy Cows Using 2D Imaging and Deep Learning by Reagan Lewis, Teun Kostermans, Jan Wilhelm Brovold, Talha Laique, Marko Ocepek

“…Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. …”
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Machine learning for improved size estimation of complex marine particles from noisy holographic images by Zonghua Liu, Zonghua Liu, Marika Takeuchi, Yéssica Contreras, Thangavel Thevar, Alex Nimmo-Smith, John Watson, Sarah L. C. Giering

“…Our results show that the proposed strategy of training the model on synthetic and real holographic data improves the model’s performance on edge detection in holographic images. …”
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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. …”
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A Biologically Inspired Cost-Efficient Zero-Trust Security Approach for Attacker Detection and Classification in Inter-Satellite Communication Networks by Sridhar Varadala, Hao Xu

“…MRFCO-ZTS improves the accuracy of attacker detection while maintaining secure data exchange between authenticated satellites. …”
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