Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning by Zheng-Meng Zhai, Mohammadamin Moradi, Ying-Cheng Lai

“…Further justification for using the LSTM is provided by our comparing its performance with that of alternative machine-learning architectures such as feedforward neural networks and random forest. …”
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Machine learning method based on enhanced CT to predict perineural invasion of rectal cancer by Jiacheng Shen, Long Jin, Shengnan Yin

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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…The main objective of this paper is to assess the vulnerability of reinforced concrete (RC) educational buildings in Dhaka city to seismic activity by utilizing machine learning (ML) algorithms. There are three main stages in traditional seismic vulnerability assessment: rapid visual assessment (RVA), preliminary engineering assessment (PEA), and detailed engineering assessment (DEA). …”
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Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, W. Kent Tobiska

Subjects: “…machine learning…”
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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

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Estimation of Anthocyanins in Apple Leaves Based on Ground Hyperspectral Imaging and Machine Learning Models by Yu Zhang, Mi Zou, Yanjun Li, Qingrui Chang, Xing Chen, Zhiyong Dai, Weihao Yuan

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Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era by Lathan Liou, Elizabeth Mostofsky, Laura Lehman, Soziema Salia, Francisco J. Barrera, Ying Wei, Amal Cheema, Anuradha Lala, Andrew Beam, Murray A. Mittleman

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A machine learning based variable selection algorithm for binary classification of perinatal mortality. by Maryam Sadiq, Ramla Shah

“…The identification of significant predictors with higher model performance is the key objective in classification domain. A machine learning-based variable selection technique termed as CARS-Logistic model is proposed by coupling competitive adaptive re-weighted sampling(CARS) and logistic regression for binary classification. …”
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Prediction of end-point phosphorus content of molten steel in BOF with machine learning models by Kang Y., Ren M.-M., Zhao J.-X., Yang L.-B., Zhang Z.-K., Wang Z., Cao G.

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A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things by Chen Dong, Jinghui Chen, Wenzhong Guo, Jian Zou

“…In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. …”
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A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel by Ngoc-Long Tran, Trong-Ha Nguyen, Van-Tien Phan, Duy-Duan Nguyen

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Design of an Early Prediction Model for Parkinson’s Disease Using Machine Learning by K. Velu, N. Jaisankar

“…In literature, diverse Machine Learning models are employed for Parkinson’s disease diagnosis utilizing voice analysis. …”
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Web application using machine learning to predict cardiovascular disease and hypertension in mine workers by Sohrab Effati, Alireza Kamarzardi-Torghabe, Fatemeh Azizi-Froutaghe, Iman Atighi, Somayeh Ghiasi-Hafez

“…Abstract This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. …”
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Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms by Zhichao Wang, Long Cheng, Guanghui Li, Huiyan Cheng

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Open Issues in Statistical Forecasting of Solar Proton Events: A Machine Learning Perspective by Mirko Stumpo, Simone Benella, Monica Laurenza, Tommaso Alberti, Giuseppe Consolini, Maria Federica Marcucci

Subjects: “…Machine learning…”
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Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches by Samina Amin, Muhammad Irfan Uddin, Duaa H. alSaeed, Atif Khan, Muhammad Adnan

“…This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). …”
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PRAF2 as a novel biomarker for breast cancer with machine learning and experimentation validation by Zheng Wang, Zilin Bi, Hongguang Bo, Junyi Xu, Rui Sha, Zhaocai Yin, Changsheng Yu, Yufa Xu, Xiaomeng Shi, Wenbo Song, Bin Chen, Yabing Wang, Qian Zhang, Jianping Chen

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Fully automated segmentation and classification of renal tumors on CT scans via machine learning by Jang Hee Han, Byung Woo Kim, Taek Min Kim, Ji Yeon Ko, Seung Jae Choi, Minho Kang, Sang Youn Kim, Jeong Yeon Cho, Ja Hyeon Ku, Cheol Kwak, Young-Gon Kim, Chang Wook Jeong

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Optimal Scheduling Model of WDM/OTN Network Transmission Line Based on Machine Learning by Jianhua Zhao, Jingquan Li, Huicong Fan, Wenxiao Li, Jingna Zhang, Xiaoyuan Dai

“…In order to solve the problem that the influencing factors are difficult to parameterize in the design and development of WDM/OTN backbone network routing planning tools, the author proposes an optimal scheduling model for WDM/OTN network transmission lines based on machine learning. Using the machine learning classification algorithm as a tool, the weight coefficients of each constraint factor are extracted from the historical design decisions, and the routing parameter model is constructed, so as to realize the intelligent routing selection, through actual simulation analysis and engineering verification. …”
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Research on telecom industry customer churn prediction based on explainable machine learning models by WANG Shengjie, ZHANG Qinghong

Subjects: “…machine learning…”
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