Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models by Yumei Shi, Yumei Shi, Xini Wang, Shaokang Chen, Yanhui Zhao, Yan Wang, Xihui Sheng, Xiaolong Qi, Lei Zhou, Yu Feng, Jianfeng Liu, Chuduan Wang, Kai Xing

Subjects: “…machine learning…”
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Prediction of Acute Kidney Injury for Critically Ill Cardiogenic Shock Patients with Machine Learning Algorithms by Zhang X, Xiong Y, Liu H, Liu Q, Chen S

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A multi-source threat intelligence confidence value evaluation method based on machine learning by Hansheng LIU, Hongyu TANG, Mingxia BO, Jianfeng NIU, Tianbo LI, Lingxiao LI

“…During the collection process of multi-source threat intelligence,it is very hard for the intelligence center to make a scientific decision to massive intelligence because the data value density is low,the intelligence repeatabil-ity is high,and the ineffective time is very short,etc.Based on those problems,a new multi-source threat intelligence confidence value evaluation method was put forward based on machine learning.First of all,according to the STIX intelligence standard format,a multi-source intelligence data standardization process was designed.Secondly,ac-cording to the characteristic of data,14 characteristics were extracted from four dimensions of publishing time,source,intelligence content and blacklist matching degree to be the basis of determining the intelligence reliability.After getting the feature encoding,an intelligence confidence value evaluation model was designed based on deep neural network algorithm and Softmax classifier.Backward propagation algorithm was also used to minimize recon-struction error.Last but not least,according to the 2 000 open source marked sample data,k-ford cross-validation method was used to evaluate the model and get an average of 91.37% macro-P rate and 84.89% macro-R rate.It was a good reference for multi-source threat intelligence confidence evaluation.…”
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Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning by Zhanjie Liu, Yixuan Huo, Qionghai Chen, Siqi Zhan, Qian Li, Qingsong Zhao, Lihong Cui, Jun Liu

“…A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes. …”
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Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients by Cao Shaoshan, Chen Niannian, Ma Ying

“…This study aims to develop a machine learning-based nomogram model for predicting the early detection of endometrial lesions in patients. …”
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Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves by Hiroharu Natsume, Shogo Okamoto

“…In this study, we used reservoir computing, a machine learning technique well-suited for time-series data, to predict temporal changes in liking based on the temporal evolution of dominant sensations. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

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RETRACTED: Machine learning insights into log files with ML.NET for real-time farm monitoring by Nuriev Marat, Zaripova Rimma, Filimonova Tamara, Kuznetsov Maxim

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Retracted: An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms by Adsorption Science and Technology

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Machine Learning-Based Relative Performance Analysis of Monocrystalline and Polycrystalline Grid-Tied PV Systems by Asfand Yar, Muhammad Yousaf Arshad, Faran Asghar, Waseem Amjad, Furqan Asghar, Muhammad Imtiaz Hussain, Gwi Hyun Lee, Faisal Mahmood

“…In this research study, the design and performance evaluation of grid-tied photovoltaic systems has been carried out through experimentation, HelioScope simulation, and black-box machine learning methods for data-driven artificial intelligence system performance assessment and validation. …”
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Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions by Shuxian Yu, Haiyang Jiang, Jing Xia, Jie Gu, Mengting Chen, Yan Wang, Xiaohong Zhao, Zehua Liao, Puhua Zeng, Tian Xie, Xinbing Sui

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An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia by Junying Wei, Heshan Cao, Mingling Peng, Yinzhou Zhang, Sibei Li, Wuhua Ma, Yuhui Li

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Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review by V. Ramesh and P. Kumaresan

Subjects: “…crop yield prediction, machine learning, deep learning, artificial neural networks, optimization algorithms…”
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Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques by Waleed Bin Tahir, Shah Khalid, Sulaiman Almutairi, Mohammed Abohashrh, Sufyan Ali Memon, Jawad Khan

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Harnessing Machine Learning for Diabetes Prediction: Optimizing Classifiers to Tackle Canada's Growing Health Challenge by Mark Gerald Anastacio, Alam Md Twariqul

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The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia by Ilija Doknić, Mirjana Mitrović, Zoran Bukumirić, Marijana Virijević, Nikola Pantić, Nikica Sabljić, Darko Antić, Živko Bojović

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Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators by Shenqiong Jiang, Xiangju Cheng, Baoshan Shi, Dantong Zhu, Jun Xie, Zhihong Zhou

Subjects: “…Machine learning model…”
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Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning by Dorijan Radočaj, Mateo Gašparović, Mladen Jurišić

“…The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (<i>Glycine max</i> L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. …”
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Analysis of stress distribution of CFRP bonded joints: A study of numerical and machine learning approach by Shah Mohammad Azam Rishad, Md Ashraful Islam, Md Shahidul Islam

Subjects: “…Machine learning…”
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Machine learning algorithms to predict depression in older adults in China: a cross-sectional study by Yan Li Qing Song, Lin Chen, Haoqiang Liu, Yue Liu

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