Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

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Why does the Media Disregard Internal Fact-Checking? The Fact-Checking Logic Determination in the Case of Ukrainian Media by Artem Zakharchenko, Anna Nagorna, Oleksandr Chekmyshev

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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

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Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes by Fang Wang, Yukang Mao, Jinyu Sun, Jiaming Yang, Li Xiao, Qingxia Huang, Chenchen Wei, Zhongshan Gou, Kerui Zhang

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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

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Policies and programmes to improve preconception nutrition in South Asia by Avishek Hazra, Tashi Choedon, Monica Shrivastav, Raj Kumar Verma, Cheshta Gulati, Dhammica Rowel, Abner Daniel, Preetu Mishra, Naveen Paudyal, Naureen Arshad, Muhammad Salman, Wisal Khan, Khadija Khalif Osman Warfa, Muhammad Amin, Ahmadwali Aminee, Ireen Akhter Chowdhury, Kinley Dorji, Indrani Chakma, Aishath Shahula Ahmed, Hari Prasad Pokhrel, Danielle Schoenaker, Jane Hirst, Ranadip Chowdhury, Naomi M. Saville, Faith Miller, Zivai Murira, Vani Sethi

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Bioethical aspects of ensuring the well-being of pigs under intensive farming technologies by N. Yu. Krempa, O. V. Kozenko, B. V. Gutyj, I. V. Dvyliuk, N. V. Magrelo, H. V. Klym, T. V. Martyshuk, U. M. Vus, A. O. Vysotskyi

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Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila. by Jean-Baptiste Masson, François Laurent, Albert Cardona, Chloé Barré, Nicolas Skatchkovsky, Marta Zlatic, Tihana Jovanic

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Comprehensive evaluation and scenario simulation for determining the optimal conservation priority of ecological services in Danjiangkou Reservoir Area, China by Pengcheng Zhao, Lihui Wang, Yuhong Huang, Yifan Zhao, Qichi Yang, Jinliang Huang, Yun Du, Feng Ling

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Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study by Yongsheng Zhang, Hongyu Zhang, Dawei Wang, Na Li, Haoyue Lv, Guang Zhang

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OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial by Xulong Qiu, Xulong Qiu, Jidi Chen, Jidi Chen, Muting Wang, Kaixin Zheng, Ruixiong Li

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Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer by Ye Niu, Hao-Bo Jia, Xue-Meng Li, Wen-Juan Huang, Ping-Ping Liu, Le Liu, Zeng-Yao Liu, Qiu-Jun Wang, Yuan-Zhou Li, Shi-Di Miao, Rui-Tao Wang, Ze-Xun Duan

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Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices by Maurizio Morisio, Emanuela Noris, Chiara Pagliarani, Stefano Pavone, Amedeo Moine, José Doumet, Luca Ardito

“…For each quadrant, nine different vegetation indices (VIs) were computed, and in parallel, each tree quadrant was tagged as “healthy/unhealthy” by visual inspection. Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…The primary endpoint was the occurrence of HF within 3 years after operation. For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. …”
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Construction of a machine learning-based prediction model for mitral annular calcification by LI Runqian, TAN Yanyi, GE Tiantian, QI Lei, BAI Song, TONG Jiayi

“…The subjects were randomly divided into a training set (350 cases) and a test set (150 cases) at a 7∶3 ratio. Nine machine learning algorithms, including logistic regression, relaxed support vector machines (RSVM) , decision tree, elastic net, multilayer perceptron, K-nearest neighbors, random forest, extreme gradient boosting (XGBoost) , and light gradient boosting machine (LightGBM) , were used to build prediction models for MAC. …”
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Rationale and feasibility of a rapid integral biomarker program that informs immune-oncology clinical trials: the ADVISE trial by Akshita Gupta, Riyue Bao, F Stephen Hodi, Jaclyn Neely, Jason J Luke, Ashish Massey, Santanu Dutta, Janis Taube, George Lee, Katherine Bever, Peter Szabo, David Yao, Rachel Tam, Tim Reilly

“…Tumor tissue blocks of melanoma, non-small cell lung cancer, renal cell carcinoma, urothelial carcinoma, squamous cell carcinoma of the head and neck, and gastroesophageal junction/gastric cancer were stained by IHC to assess expression of CD8, colony-stimulating factor 1 receptor, glucocorticoid-induced tumor necrosis factor receptor (GITR), indoleamine 2,3-dioxygenase 1, lymphocyte-activation gene 3, NKp46, forkhead box P3, and PD-L1. …”
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RETRACTED ARTICLE: Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks by K. Krishna Kishore, Arun Sekar Rajasekaran, Ismail Keshta, Haewon Byeon, Mohammed Wasim Bhatt, Azeem Irshad, Isaac Ofori

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Developing a machine learning model with enhanced performance for predicting COVID‐19 from patients presenting to the emergency room with acute respiratory symptoms by Maha Mesfer Alghamdi, Naael H. Alazwary, Waleed A. Alsowayan, Mohmmed Algamdi, Ahmed F. Alohali, Mustafa A. Yasawy, Abeer M. Alghamdi, Abdullah M. Alassaf, Mohammed R. Alshehri, Hussein A. Aljaziri, Nujoud H. Almoqati, Shatha S. Alghamdi, Norah A. Bin Magbel, Tareq A. AlMazeedi, Nashaat K. Neyazi, Mona M. Alghamdi, Mohammed N. Alazwary

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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Results The RF model incorporated RDW-SD, MCV, PDW, CD3+CD8+, APTT, P-LCR, Ca, MPV, CD4+/CD8+ ratio, and AST. …”
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