The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches by Md. Idris Ali, Md. Atikur Rahaman, Mohammed Julfikar Ali, Md. Ferdausur Rahman

“…To validate robustness, the Kernel Regularized Least Squares (KRLS) machine learning approach is employed, confirming the consistency of results. …”
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Machine learning–based automated image processing for quality management in industrial Internet of Things by Nematullo Rahmatov, Anand Paul, Faisal Saeed, Won-Hwa Hong, HyunCheol Seo, Jeonghong Kim

“…In this article, we propose a highly efficient model to automate central processing unit system production lines in an industry such that images of the production lines are scanned and any abnormalities in their assembly are pointed out by the model and information about this is transferred to the system administrator via a cyber-physical cloud system network. A machine learning–based approach is used for proper classification. …”
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Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques by Zhang Chenwei

“…With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a thorough analysis of the major machine learning techniques utilized in contemporary spam filtering. …”
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Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning by Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam

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Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning by Meiyue Shen, Yine Zhang, Runqing Zhan, Tingwei Du, Peixuan Shen, Xiaochuan Lu, Shengnan Liu, Rongrong Guo, Xiaoli Shen

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Context aware machine learning techniques for brain tumor classification and detection – A review by Usman Amjad, Asif Raza, Muhammad Fahad, Doaa Farid, Adnan Akhunzada, Muhammad Abubakar, Hira Beenish

“…Background: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. …”
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A machine learning based variable selection algorithm for binary classification of perinatal mortality by Maryam Sadiq, Ramla Shah

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Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches by Pablo Antúnez, Christian Wehenkel, Erickson Basave-Villalobos, Celi Gloria Calixto-Valencia, César Valenzuela-Encinas, Faustino Ruiz-Aquino, David Sarmiento-Bustos

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A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries by Anitha Palakshappa, Sumana Maradithaya, Charunayana V

“…The objective of this study is to investigate whether the machine learning algorithms can be used to predict the delivery dates of the products based on the trained data. …”
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Detection and diagnosis of unknown threats in power equipment using machine learning and Spark technology by Li Di, Cen Chen, Zhuo Lv, Mingyan Li, Nuannuan Li, Hao Chang

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A machine learning based algorithm accurately stages liver disease by quantification of arteries by Zhengxin Li, Xin Sun, Zhimin Zhao, Qiang Yang, Yayun Ren, Xiao Teng, Dean C. S. Tai, Ian R. Wanless, Jörn M. Schattenberg, Chenghai Liu

Subjects: “…Machine learning…”
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Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning by Zeynep Kucukakcali, Ipek Balikci Cicek

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Integrating pharmacogenetics, multi‐omics and machine learning in the novel therapeutic view of pulmonary hypertension by Chenxi Li, Jipeng Xue, Dongchen Zhou, Xiaomeng Qi, Ting Chen, Zhoubin Li

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Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training by Abdullah Al Siam, Md Maruf Hassan, Md Atikur Rahaman, Masuk Abdullah

“…The framework comprises four key components: (1) converting DICOM files to GIF format with encryption, (2) decrypting EGIF files for processing, (3) enabling confidentiality-preserving machine learning training using EGIF data, and (4) facilitating physician diagnosis and report generation based on trained machine learning models. …”
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A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity by Victoria Shevchenko, R. Austin Benn, Robert Scholz, Wei Wei, Carla Pallavicini, Ulysse Klatzmann, Francesco Alberti, Theodore D. Satterthwaite, Demian Wassermann, Pierre-Louis Bazin, Daniel S. Margulies

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Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning by Zheng-Yong Zhang, Jian-Sheng Su, Huan-Ming Xiong

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A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation by Reem Tluli, Ahmed Badawy, Saeed Salem, Mahmoud Barhamgi, Amr Mohamed

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Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions by Bilal Ahmad, Wang Jian, Zain Anwar Ali

“…This study highlights the developing research about the application of machine learning and data mining in Internet security. …”
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Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT by Rongqun Peng, Xiuhua Fu, Tian Ding

“…We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. …”
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Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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