Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS by Ruizhi Zhang, Dayong Zhang, Bo Shu, Yang Chen

“…This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. …”
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Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction by Mohammed Majeed Hameed, Mohamed Khalid Alomar, Siti Fatin Mohd Razali, Ali Salem

“…The current study aims to enhance the prediction accuracy of Cd for rectangular sharp-crested side weirs by addressing the limitation of the output layer of the Extreme learning machine (ELM). The output layer of ELM depends mainly on the linear system which limits its generalization capabilities. …”
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A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning by Keyue Yan, Chi-Fai Lam, Simon Fong, João Alexandre Lobo Marques, Richard Charles Millham, Sabah Mohammed

“…In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. …”
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Machine learning for classifying affective valence from fMRI: a systematic review and meta-analysis by Charith Chitraranjan, Ruwan Dayananda, Dakshitha Suriyaaratchie, Nuwan Abeynayake, Svetlana Shinkareva

“…In this work, we systematically review studies published up to October 2023 that have applied machine learning as a multi-variate pattern analysis approach to classify valence from fMRI trials of healthy adults. …”
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Prediction of groundwater level and potential zone identification in Keonjhar, Odisha based on machine learning and GIS techniques by B. Ritushree, Shubhshree Panda, Abinash Sahoo, Sandeep Samantaray, Deba P Satapathy

“…Population growth, change in climate, changing land use pattern, and increase in mining activities causes over exploitation of groundwater in Keonjhar district to fulfill the freshwater demand. …”
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Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering by Usama Ahmed, Mohammad Nazir, Amna Sarwar, Tariq Ali, El-Hadi M. Aggoune, Tariq Shahzad, Muhammad Adnan Khan

“…Deep learning models LSTM and ANN rapidly find long-term and complex pattern in network data. It is extremely effective when dealing with complex intrusions since it is characterised by high precision, accuracy and recall.Based on our study, SVM and Random Forest are considered promising solutions for real-world IDS applications because of their versatility and explainability. …”
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Machine Learning-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems by Lei Gong, Yanhui Chen

“…The PM-C-LSTM model combines CNN for recognizing spatial patterns and LSTM networks for analyzing sequential data in a way that doesn't affect the accuracy of WT-PM. …”
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Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods by Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi

“…Hub genes were selected by machine learning methods to establish a diagnostic model. …”
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Analysis of Combined Strength Training with Small-Sided Games in Football Education Using Machine Learning Methods by Huseyin Guneralp, Hasan Ulas Yavuz, Boran Sekeroglu, Musa Oytun, Cevdet Tinazci

“…Eighteen physical measurements of the players were obtained using sensitive devices before and after they were completed. Four tree-based machine learning models, decision tree, random forest, gradient boosting, and extreme gradient boosting, were applied to solve the complex pattern of training strategies using the measurements. …”
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The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms by Hongxia Yan, Yixin Tan, Fan Qiao, Zhuotong Zeng, Yaqian Shi, Xueqin Zhang, Lu Li, Ting Zeng, Yi Zhan, Ruixuan You, Xinglan He, Rong Xiao, Xiangning Qiu

“…This study aims to develop and validate two machine learning models to predict the therapeutic effect of HMME-PDT for PWS. …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…Objective To build a supervised machine learning-based classifier, which can accurately predict whether Tai Chi practitioners may experience knee pain after years of exercise.Design A prospective approach was used. …”
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Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients by Eva Paddenberg-Schubert, Kareem Midlej, Sebastian Krohn, Agnes Schröder, Obaida Awadi, Samir Masarwa, Iqbal M. Lone, Osayd Zohud, Christian Kirschneck, Nezar Watted, Peter Proff, Fuad A. Iraqi

“…The primary outcome of this prospective cross-sectional study was developing a machine learning model for classifying patients as skeletal class I and III. …”
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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. …”
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Machine Learning-Based Identification of Phonological Biomarkers for Speech Sound Disorders in Saudi Arabic-Speaking Children by Deema F. Turki, Ahmad F. Turki

“…SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. …”
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Machine Learning-Driven Prediction of Offshore Vessel Detention: The Role of Neural Networks in Port State Control by Zlatko Boko, Tatjana Stanivuk, Nenad Radanović, Ivica Skoko

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Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
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Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics by Syeda Faiza Nasim, Umm-e-Kulsoom, Syeda Alishba Fatima, Salka Naushad

“…It is vital for event planners to comprehend local climate dynamics since this variability causes unpredictable weather patterns, such as monsoon rains, intense heat waves, and droughts. …”
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Advancing Cybersecurity Through Machine Learning: A Scientometric Analysis of Global Research Trends and Influential Contributions by Kamran Razzaq, Mahmood Shah

“…This scientometric study aims to comprehensively analyse the study patterns and key contributions at the nexus of cybersecurity and machine learning. …”
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Improving the Efficiency of Screen Type Potato Sorting Machines by a Modification of the Sieve Drive Movement Algorithm by A. G. Ivanov, M. N. Erokhin, S. P. Kazantsev, P. V. Dorodov, I. I. Khuzyakhmetov, I. T. Khakimov

“…(Research purpose) To upgrade the sizing machine drive by modifying the sieve movement patterns for improving its operation efficiency. …”
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