Detection of mite infested saffron plants using aerial imaging and machine learning classifier by Hossein Sahabi, Jalal Baradaran-Motie

“… Aim of study: To evaluate and develop a machine learning code that uses aerial images in visible and near infrared (NIR) spectra to detect mite-infested Saffron (Crocus sativus L.) plants through processing the spectral indices to classify healthy and diseased plants. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

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Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning by Zhang Jiaming

“…This paper explores the use of machine learning (ML) in medicine, emphasizing how important it is to enhance patient outcomes and diagnostic precision. …”
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ML-NIC: accelerating machine learning inference using smart network interface cards by Raghav Kapoor, David C. Anastasiu, Sean Choi

Subjects: “…machine learning…”
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Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate by Saeed H. Moghtaderi, Alias Jedi, Ahmad Kamal Ariffin, Prakash Thamburaja

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SequenceCraft: machine learning-based resource for exploratory analysis of RNA-cleaving deoxyribozymes by M. Eremeyeva, Y. Din, N. Shirokii, N. Serov

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A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE by Wu L, Chen L, Zhang L, Liu Y, Ouyang D, Wu W, Lei Y, Han P, Zhao H, Zheng C

“…Therefore, this study aimed to establish and validate a machine learning-based explainable prediction model of prognosis in patients with HCC and T2DM undergoing transarterial chemoembolization (TACE).Patients and Methods: The prediction model was developed using data from the derivation cohort comprising patients from three medical centers, followed by external validation utilizing patient data extracted from another center. …”
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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

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Reply to: comment on “Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning” by Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu, Xiangheng Xiao

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Mediation Analysis of Diabetes and Heart Diseases Influenced by Obesity Using Machine Learning Classifiers by Ajay Verma, Manisha Jain

“…Methods: The present study established a relationship between diabetes, heart disease and body mass index using mediation analysis and machine learning classifiers. R software with Hayes Macro Process and Python was used as a statistical tool to conclude the study. …”
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images by Anne M. Davis, Asahi Tomitaka

Subjects: “…machine learning…”
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Adulteration detection in cactus seed oil: Integrating analytical chemistry and machine learning approaches by Said El Harkaoui, Cristina Ortiz Cruz, Aaron Roggenland, Micha Schneider, Sascha Rohn, Stephan Drusch, Bertrand Matthäus

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High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning by Shaheer Mohiuddin Khalil, Shahzaib Ali, Vu Dat Nguyen, Dae‐Hyun Cho, Doyoung Byun

“…Moreover, a predictive model for droplet deposition, exhibiting an accuracy of 87%, is developed utilizing supervised machine learning (ML). This approach significantly improves the accuracy and repeatability of droplet deposition. …”
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Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…This study investigates the lightning-induced vulnerability in Bangladesh using Geographic Information Systems (GIS) and Machine Learning (ML) techniques, addressing the limited research in this area. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

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Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy by Nan Zheng, Yongchao Cai, Zehua Zhang, Huimin Zhou, Yu Deng, Shuang Du, Mai Tu, Wei Fang, Xiaole Xia

“…The universality of the iCASE strategy is validated by four sorts of enzymes with different structures and catalytic types. This machine learning-based iCASE strategy provides guidance for future research on the fitness evolution of enzymes.…”
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Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning by Wen Qian, Jing Huang, Shi-tai Guo, Bo-wen Duan, Wei-yu Xie, Jian Liu, Chao-yang Zhang

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SHASI-ML: a machine learning-based approach for immunogenicity prediction in Salmonella vaccine development by Ottavia Spiga, Ottavia Spiga, Ottavia Spiga, Anna Visibelli, Francesco Pettini, Bianca Roncaglia, Annalisa Santucci, Annalisa Santucci, Annalisa Santucci

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A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance by Na Xiao, Yonghui Li, Peiyan Sun, Peihua Zhu, Hongyan Wang, Yin Wu, Mingyu Bai, Ansheng Li, Wuyi Ming

“…Using a dual-perspective analysis approach, it highlights the unique advantages of machine learning in data processing, predictive modeling, and optimization. …”
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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

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