Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma by Miaodong Wang, Qin He, Zeshan Chen, Yijue Qin

“…We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. …”
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Machine Learning-Based Mobile Device Resale Price Estimator: A Predictive Analytics Approach by Umm-e-Laila Mehdi, Khalid Mahboob, Samar Raza Talpur, Khaliq Ahmed, Mohammad Khurram

Subjects: “…Machine Learning…”
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A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

Subjects: “…machine learning…”
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Estimation of PM10 and PM2.5 Using Backscatter Coefficient of Ceilometer and Machine Learning by Bu-Yo Kim, Joo Wan Cha, Yong Hee Lee

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A Collection of Machine Learning Models for Improved Airport Operations Amidst Adverse Weather Conditions by Ramon Dalmau, Jonathan Attia

Subjects: “…Machine Learning…”
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Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning by Ji Hao, Wenbin Jiao, Xinpo Xie, Dula Man, Shengwei Huang

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Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach by Siti Aminah, Gianinna Ardaneswari, Mufarrido Husnah, Ghani Deori, Handi Bagus Prasetyo

“…This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. …”
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Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning by Xue-Jian Zhang, Fang-Fang Lin, Ya-Qing Wen, Kun-Ping Guan

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Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway by Mohib Ullah, Haijun Qiu, Wenchao Huangfu, Dongdong Yang, Yingdong Wei, Bingzhe Tang

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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…The present inquiry advocates for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. …”
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An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution by Sen Yang, Yanxiong Wang, Yanfeng Jiang, Tian Qiang

“…Furthermore, the measurement data are processed using machine learning. Specifically, the WBC measurement data are subjected to a further linear fitting. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…Given the nonlinear characteristics of supplementary cement material concrete (SCMC) mixtures, researchers are increasingly turning to machine learning methods. This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

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Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms by Jie Zhu, Jianmei Zhou, Chunhui Tao, Guomei Xia, Bingyan Liu, Xiaowei Zheng, Xu Li, Zhenhua Zhang

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A Novel Approach Based on Machine Learning, Blockchain, and Decision Process for Securing Smart Grid by Nabil Tazi Chibi, Omar Ait Oualhaj, Wassim Fassi Fihri, Hassan El Ghazi

“…This paper presents a new approach that integrates Machine Learning (ML), Blockchain Technology (BT), and Markov Decision Process (MDP) to improve the security of SG networks while ensuring accurate storage of events reported by various network devices through BT. …”
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Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning by Sresakoolchai Jessada, Cheputeh Ni-Asri

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An efficient smart phone application for wheat crop diseases detection using advanced machine learning. by Awais Amir Niaz, Rehan Ashraf, Toqeer Mahmood, C M Nadeem Faisal, Muhammad Mobeen Abid

“…By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices.…”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). …”
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Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data by Swarnima Kollampallath Radhakrishnan, Dipanwita Nath, Dominic Russ, Dominic Russ, Dominic Russ, Laura Bravo Merodio, Laura Bravo Merodio, Laura Bravo Merodio, Priyani Lad, Folakemi Kola Daisi, Animesh Acharjee, Animesh Acharjee, Animesh Acharjee

“…Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. …”
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