Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis by Masoumeh Vali, Hossein Motahari Nezhad, Levente Kovacs, Amir H Gandomi

“…We conducted a systematic review and random-effect meta-analysis summarizing predictive model development and validation studies using machine learning in diverse samples to predict PTSD. Model performances were pooled using the area under the curve (AUC) with a 95% confidence interval (CI). …”
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Systematic review of machine learning applications using nonoptical motion tracking in surgery by Teona Z. Carciumaru, Cadey M. Tang, Mohsen Farsi, Wichor M. Bramer, Jenny Dankelman, Chirag Raman, Clemens M. F. Dirven, Maryam Gholinejad, Dalibor Vasilic

“…Abstract This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. …”
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Skillful prediction of Indian Ocean Dipole index using machine learning models by J.V. Ratnam, Swadhin K. Behera, Masami Nonaka, Kalpesh R. Patil

“…In this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). …”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

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Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms by Mingjin Liu, Ruijie Gao, Wei Fu

“…On the basis of traditional credit risk control, this paper proposes the demand and direction of a new credit risk control strategy based on machine learning and relying on big data. First, on the basis of introducing the basic algorithmic principles of machine learning, we give reasons for choosing machine learning models and build a machine learning-based Internet consumer finance credit risk control strategy model to provide theoretical support for the empirical analysis later. …”
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Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications by Sivasubramanian Palanisamy, Nadir Ayrilmis, Kumar Sureshkumar, Carlo Santulli, Tabrej Khan, Harri Junaedi, Tamer Ali Sebaey

Subjects: “…machine learning…”
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A Study on Elements of Machine Vision and Machine Learning in Industrial Vision Systems by Jarosław PANASIUK (jaroslaw.panasiuk@wat.edu.pl), Adrianna ADAMUS (adrianna.adamus@student.wat.edu.pl)

“…The article presents an analysis of issues regarding the current state and prospects of using machine learning elements in industrial applications of vision systems. …”
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Development of a Neuroevolution Machine Learning Potential of Al-Cu-Li Alloys by Fei Chen, Han Wang, Yanan Jiang, Lihua Zhan, Youliang Yang

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Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

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A recurrence model for non-puerperal mastitis patients based on machine learning by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

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Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method by Zhuosheng Xu, Meng Ma, Zikai Zhou, Xintong Xie, Haoxiang Xie, Bolong Jiang, Zhongshuai Zhang

“…To improve the accuracy efficiency, the machine learning (ML) method was introduced to predict the tunnel vibration responses. …”
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Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 by A. S. Rajesh, M. S. Prabhuswamy, M. Rudra Naik

“…Thus, this work provides the best machine learning approach to the prognosis of the roughness in dry turning of Inconel 625, which would save a lot of time and unnecessary wastage of the work material.…”
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Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm by Jinqing Zhang, Pengchao Zhang, Bin Xu

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DC serial arc fault recognition in aircraft using machine learning techniques by Raul Carreira Rufato, Thierry Ditchi, Cyril Van de Steen, Thierry Lebey, Yacine Oussar

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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

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Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms by Reem A. Alzahrani, Malak Aljabri, Rami A. Mustafa Mohammad

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Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels by Hamdi Chaouk, Emil Obeid, Jalal Halwani, Jack Arayro, Rabih Mezher, Omar Mouhtady, Eddie Gazo-Hanna, Semaan Amine, Khaled Younes

“…This study explores the application of machine learning techniques, specifically principal component analysis (PCA), to analyze the influence of silica content on the physical and chemical properties of aerogels. …”
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Machine Learning- and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction by Sandeep Gupta, Angelina Geetha, K. Sakthidasan Sankaran, Abu Sarwar Zamani, Mahyudin Ritonga, Roop Raj, Samrat Ray, Hussien Sobahi Mohammed

“…In this study, a machine learning framework for agricultural yield prediction is presented. …”
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Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds by Joarder Jafor Sadique, Imtiaz Nasim, Ahmed S. Ibrahim

“…We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. …”
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Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes by Mohammad Rashidmayvan, Amin Mansoori, Elahe Derakhshan-Nezhad, Davoud Tanbakuchi, Fatemeh Sangin, Maryam Mohammadi-Bajgiran, Malihehsadat Abedsaeidi, Sara Ghazizadeh, MohammadReza Mohammad Taghizadeh Sarabi, Ali Rezaee, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…We aimed to investigate the relationship between intake of macro/micronutrients and the incidence of type 2 diabetes (T2D) using logistic regression (LR) and a decision tree (DT) algorithm for machine learning. Method Our research explores supervised machine learning models to identify T2D patients using the Mashhad Cohort Study dataset. …”
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