Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…Recent research has explored machine learning-based approaches using smartphone accelerometers to classify intoxicated gait patterns. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. …”
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A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

“…Due to the variance of traffic flow patterns between segments, we build a global Distributed Machine Learning Random Forest (DMLRF) regression model to improve the system performance for abnormal traffic flows. …”
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Machine Learning-Based Differential Diagnosis of Parkinson’s Disease Using Kinematic Feature Extraction and Selection by Masahiro Matsumoto, Abu Saleh Musa Miah, Nobuyoshi Asai, Jungpil Shin

“…Initially, 18 kinematic features are extracted, including two newly proposed features: Thumb-to-index vector velocity and acceleration, which provide insights into motor control patterns. In addition, 41 statistical features were extracted here from each kinematic feature, including some new approaches such as Average Absolute Change, Rhythm, Amplitude, Frequency, Standard Deviation of Frequency, and Slope. …”
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Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study by Artur Skoczylas, Natalia Duda-Mróz, Wioletta Koperska, Paweł Stefaniak, Paweł Śliwiński

“…As part of this study, several analytical models (enhanced by machine learning techniques) were developed to identify movement patterns and cooperation among wheeled transport machinery, as well as the entire course of ore logistics within the mining area. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

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Multi-omic and machine learning analysis of mitochondrial RNA modification genes in lung adenocarcinoma for prognostic and therapeutic implications by Xiao Zhang, Jiatao Liu, Yaolin Cao, Wei Wang, Haoran Lin, Yue Yu

“…Integrating multi-omic datasets, we systematically explored the molecular features of MRM-related genes across various cancers and identified distinct expression patterns and prognostic associations. Single-cell analysis further reveals MRM-driven cell-cell interactions and pathway activation, particularly in cycling and epithelial cells. …”
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Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning by Zheng Liu, Xiaohong Wang, Mingzhu Xiu, Rui Luo, Xiaomin Shi, Yizhou Wang, Yusong Ye, Ruiyu Wang, Sha Liu, Muhan Lv, Xiaowei Tang

“…This study integrated bioinformatics analysis and machine learning to identify palmitoylation-related biomarkers for NAFLD. …”
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Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning by Yuanyuan Wang, Yu Ji, Jie Liu, Lianjiang Lv, Zihe Xu, Meimei Yan, Jialu Chen, Zhijun Luo, Xianjun Zeng

“…Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. …”
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Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses by Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

“…Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Understanding environmental factors that influence corn yield is crucial for improving crop management and designing more resilient cropping systems. Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. …”
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High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease: metabolic and clinical predictors identified by machine learning by Markus Gwiggner, Louise Downey, Florina Borca, Martin McDonnell, Richard Felwick, Suranga Dharmasiri, Tilly Mills, Trevor R Smith, J R Fraser Cummings, Mayank Patel, Richard J Harris, Benjamin Zare, Matt Stammers, Hang T T Phan

“…Therefore, CBG monitoring should be included in routine clinical practice. Machine learning methods can identify key risk factors for clinical complications. …”
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Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study by Takahiro Tabuchi, Yusuke Tsugawa, Tadahiro Goto, Itsuki Osawa, Hayami K Koga

“…Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.Conclusion Using machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.…”
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Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors by Heba-Allah Ibrahim El-Azab, R. A. Swief, Noha H. El-Amary, H. K. Temraz

“…Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…MLP-based models also achieved strong results, effectively capturing non-linear patterns in the data. In contrast, ResNet50 exhibited limitations, likely due to overfitting caused by the small dataset. …”
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Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals by Simfukwe C, An SSA, Youn YC

“…The processed PSD data, representing 19 scalp regions, were then input into a Random Forest (RF) machine learning classifier to identify distinctive EEG patterns across the groups. …”
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Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning by Shangkun Li, Haoyu Li, Mingran Qi

“…The key diagnostic biomarkers were determined via a protein-protein interaction (PPI) network combined with two machine learning algorithms. The logistic regression model was subsequently developed based on these key genes. …”
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A Novel Framework for Saraiki Script Recognition Using Advanced Machine Learning Models (YOLOv8 and CNN) by Hafiz Muhammad Raza Ur Rehman, Syed Arfan Haider, Hiba Faisal, Kook-Yeol Yoo, M. Z. Jhandir, Gyu Sang Choi

“…By combining these two domains, machine learning has emerged as a potent instrument in linguistics, improving our capacity to comprehend semantics, analyze verbal patterns, and even simulate human-like replies. …”
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XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy by Wenhan Li, Wenhan Li, Yajing Zhou, Ziyu Luo, Ziyu Luo, Miao Tan, Miao Tan, Rui Yin, Jianhui Li, Jianhui Li

“…PurposeThyroid ultrasound is a primary tool for screening thyroid nodules (TNs), but existing risk stratification systems have limitations. Nowadays, machine learning (ML) offers advanced capabilities to handle high-dimensional data and complex patterns. …”
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