Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types by Mahrukh Zakir, Alishbah Saddiqa, Mawara Sheikh, Lalarukh Zakir, Fatima Sami, Faisal Sardar Ahmad, Sadaf Abdul Rauf, Iqra Ali, Zahid Muneer, Wadi B. Alonazi, Abdul Rauf Siddiqi

Get full text

A model for predicting academic performance on standardised tests for lagging regions based on machine learning and Shapley additive explanations by Mario Suaza-Medina, Rita Peñabaena-Niebles, Maria Jubiz-Diaz

Get full text

Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

“…This study proposes a novel Shapley additive explanations (SHAP)-driven machine learning framework for multi-energy supply station revenue forecasting. …”
Get full text

Hybrid mechanism‐data‐driven iron loss modelling for permanent magnet synchronous motors considering multiphysics coupling effects by Lin Liu, Wenliang Yin, Youguang Guo

Get full text

A Comprehensive Review and Analysis of Blockchain-Based Security Solutions for Cloud Computing Ecosystems by Noor Ghazi M. Jameel, Zryan Najat Rashid, Abdulkadir Şengür

Get full text

Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics by Di Luo, Linguo Xie, Jingdong Zhang, Chunyu Liu

“…Gene expression data from the Gene Expression Omnibus (GEO) microarray database were integrated with machine learning techniques to identify key genes involved in both osteoporosis and kidney stones. …”
Get full text

Urban Recreational Space Heat-Emotion Distribution and Matching Pattern Based on Machine Learning: A Case Study of Guangzhou City by Wang Fuyuan, Zhang Zhiyu, Xie Yuanjing, Yang Xinyi, Sun Miao

“…Using Guangzhou as a case study, this study harnesses big data sourced from Ma Feng Wo and Ctrip. Based on machine learning techniques, it discerns recreational emotions and synergizes Geographic Information System (GIS) spatial analysis with heat-emotion matching analysis. …”
Get full text

Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach by Gagandeep Sharma, Tejbir Kaur, Sanjay Kumar Mangal, Amit Kohli

Get full text

Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models by Jingmei Yang, Xinglong Ju, Feng Liu, Onur Asan, Timothy Church, Jeff Smith

“…The models were developed and validated using checkup data from 451,425 working population collected by the healthcare providers. …”
Get full text

Surrounding Rock Squeezing Classification in Underground Engineering Using a Hybrid Paradigm of Generative Artificial Intelligence and Deep Ensemble Learning by Shouye Cheng, Xin Yin, Feng Gao, Yucong Pan

“…Finally, a comparative analysis with traditional machine learning techniques is conducted and the superiority of this paradigm is further verified. …”
Get full text

Burst Localisation in Water Pressurised Pipelines Combining Numerical Data Generation and ANN Transient Signal Processing by Andrea Menapace, Maurizio Tavelli, Daniele Dalla Torre, Maurizio Righetti

Get full text

Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data by Mandy M. J. Wittens, Diana M. Sima, Arne Brys, Hanne Struyfs, Ellis Niemantsverdriet, Ellen De Roeck, Christine Bastin, Florence Benoit, Bruno Bergmans, Jean-Christophe Bier, Peter Paul de Deyn, Olivier Deryck, Bernard Hanseeuw, Adrian Ivanoiu, Gaëtane Picard, Eric Salmon, Kurt Segers, Anne Sieben, Evert Thiery, Jos Tournoy, Anne-Marie van Binst, Jan Versijpt, Dirk Smeets, Maria Bjerke, Maura Bellio, Neil P. Oxtoby, Daniel C. Alexander, Annemie Ribbens, Sebastiaan Engelborghs

Get full text

Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

Get full text

Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. …”
Get full text

An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization by Chenhao Li, Jiyin Zhang, Weilin Chen, Xiaogang Ma

Get full text

Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In the evolving cyber threat landscape, one of the most visible and pernicious challenges is malware activity detection and analysis. Traditional detection and analysis methods face threats of data high-dimensionality, lack of strength against adversarial attacks, and non-efficient use of unlabeled data samples. …”
Get full text

Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model by Hongyang Zhang, Ju Jing, Jason T. L. Wang, Haimin Wang, Yasser Abduallah, Yan Xu, Khalid A. Alobaid, Hameedullah Farooki, Vasyl Yurchyshyn

Get full text

Implementing interventions that include mentoring components with young people with foster care experience: Lessons learned from the field by Amy M. Salazar, Sara S. Spiers, Deborah G. Fabiyi, Jenna Thompson

Get full text

Deep neural networks excel in COVID-19 disease severity prediction—a meta-regression analysis by Márton Rakovics, Fanni Adél Meznerics, Péter Fehérvári, Tamás Kói, Dezső Csupor, András Bánvölgyi, Gabriella Anna Rapszky, Marie Anne Engh, Péter Hegyi, Andrea Harnos

“…Neural Network-based tools have the highest performance with a pooled AUC of 0.893 (0.748–1.000), 0.752 (0.614–0.853) sensitivity, 0.914 (0.849–0.952) specificity, using clinical, laboratory, and imaging data. The relevant confounders of performance are the geographic region of patients, the rate of severe cases, and the use of C-Reactive Protein as input data. 88% of studies have a high risk of bias, mostly because of deficiencies in the data analysis. …”
Get full text

Machine learning algorithms of riverbed change and environments of the Lower Apalachicola River by Ali R. Alruzuq, Joann Mossa

“…Using a comparative analysis of two machine learning regression models to determine the long-term riverbed change, we employed the Random Forest (RF) regression model and the Extreme Gradient Boosting regression model (XGBoost). …”
Get full text