Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine by Yi Guo, Hua Xu, Fei Wang, Jie Xu, Jiang Bian, Robert Lucero, Mattia Prosperi

“…In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. …”
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Application of Phi (<italic>&#x03A6;</italic>), the Golden Ratio, in Computing: A Systematic Review by M. Akhtaruzzaman, Jamal Uddin Tanvin, Amir Akramin Shafie, Fahim Shahryer, Sachitra Halder

“…The review also categorizes findings by their specific applications and contexts, providing valuable insights into <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>&#x2019;s impact on mathematics, cryptography, search algorithms, machine learning, artificial intelligence, photonics, natural sciences, system design, power engineering, robotics, and practical human life. …”
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mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data by Nima Roshandel, Constantin Scholz, Hoang-Long Cao, Milan Amighi, Hamed Firouzipouyaei, Aleksander Burkiewicz, Sebastien Menet, Felipe Ballen-Moreno, Dylan Warawout Sisavath, Emil Imrith, Antonio Paolillo, Jan Genoe, Bram Vanderborght

“…Our dataset serves as a fundamental resource for developing machine learning algorithms to improve the accuracy of pose estimation and gesture recognition using RaDAR data.…”
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Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…Popular species distribution models use statistical and machine learning methods but face limitations with multi‐species predictions at the community level, hindered by scalability and data imbalance sensitivity. …”
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Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees by Dorota Wilk-Kołodziejczyk, Aleksandra Nowotny, Izabela Krzak, Adam Tchórz, Krzysztof Jaśkowiec, Marcin Małysza, Adam Bitka, Mirosław Głowacki, Marzanna Książek, Łukasz Marcjan

“…The available (experimental) data make it possible to unequivocally identify belonging to one of these groups. The use of machine learning methods to recognize the relationships between the physical parameters of particles helps to improve the analysis process. …”
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Prediction of Dynamic Plasmapause Location Using a Neural Network by Deyu Guo, Song Fu, Zheng Xiang, Binbin Ni, Yingjie Guo, Minghang Feng, Jianguang Guo, Zejun Hu, Xudong Gu, Jianan Zhu, Xing Cao, Qi Wang

“…Abstract As a common boundary layer that distinctly separates the regions of high‐density plasmasphere and low‐density plasmatrough, the plasmapause is essential to comprehend the dynamics and variability of the inner magnetosphere. Using the machine learning framework PyTorch and high‐quality Van Allen Probes data set, we develop a neural network model to predict the global dynamic variation of the plasmapause location, along with the identification of 6,537 plasmapause crossing events during the period from 2012 to 2017. …”
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Massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS) by Zhi Weng, Jiangxue Li, Yi Wu, Xuehao Xiu, Fei Wang, Xiaolei Zuo, Ping Song, Chunhai Fan

“…Specifically, even a ~ 1 ×  sequencing depth, with the combination of machine learning, results in an acceptable decoding accuracy of ~80%. …”
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Multilevel Precision-Based Rational Design of Chemical Inhibitors Targeting the Hydrophobic Cleft of Apical Membrane Antigen 1 (AMA1) by Umashankar Vetrivel, Shalini Muralikumar, B Mahalakshmi, K Lily Therese, HN Madhavan, Mohamed Alameen, Indhuja Thirumudi

“…Furthermore, binding free energy calculations of these two compounds also revealed a significant affinity to AMA1. Machine learning approaches also predicted these two compounds to possess more relevant activities. …”
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Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT by Chong Jiang, Chunjun Qian, Qiuhui Jiang, Hang Zhou, Zekun Jiang, Yue Teng, Bing Xu, Xin Li, Chongyang Ding, Rong Tian

“…These features, along with handcrafted radiomics, were utilized to construct a radiomic signature (R-signature) using automatic machine learning in the training and internal validation cohort. …”
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Dynamic Analysis of <i>Spartina alterniflora</i> in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite by Huiying Li, Guoli Cui, Haojie Liu, Qi Wang, Sheng Zhao, Xiao Huang, Rong Zhang, Mingming Jia, Dehua Mao, Hao Yu, Zongming Wang, Zhiyong Lv

“…The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, <i>S. alterniflora</i> expanded rapidly, increasing from 4055.06 hm² to 6105.50 hm², primarily in tidal flats and water bodies. …”
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Smart Driving Hardware Augmentation by Flexible Piezoresistive Sensor Matrices with Grafted‐on Anticreep Composites by Kaifeng Chen, Hua Yang, Ang Wang, Linsen Tang, Xin Zha, Ndeutala Selma Iita, Hong Zhang, Zhuoxuan Li, Xinyu Wang, Wei Yang, Shaoxing Qu, Zongrong Wang

“…The recognition of sitting postures is achieved by two 12 × 12 matrices facilitated by machine learning, which prompts the potential for the augmentation of smart driving.…”
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The Accuracy of the NSQIP Universal Surgical Risk Calculator Compared to Operation-Specific Calculators by Mark E. Cohen, PhD, Yaoming Liu, PhD, Bruce L. Hall, MD, PhD, MBA, FACS, Clifford Y. Ko, MD, MS, MSHS, FACS

“…For the N-RC, a cohort of 5,020,713 NSQIP patient records were randomly divided into 80% for machine learning algorithm training and 20% for validation. …”
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A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories by Ahmed M. Abd El-Haleem, Mohab Mohammed Eid, Mahmoud M. Elmesalawy, Hadeer A. Hassan Hosny

“…A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. …”
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Challenges and Technology Trends in Implementing a Human Resource Management System: A Systematic Literature Review by Rahma Destriani, Raihansyah Yoga Adhitama, Dana Indra Sensuse, Deden Sumirat Hidayat, Erisva Hakiki Purwaningsih

“…Exciting technology trends offer promise for next-generation HRMS solutions, including artificial intelligence (AI), machine learning, predictive analytics, and mobile accessibility. …”
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Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots by Fei Tan, Xiuwen Gao, Hao Cang, Nianyi Wu, Ruoyu Di, Jingkun Yan, Chengkai Li, Pan Gao, Xin Lv

“…This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. …”
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Fetal-BET: Brain Extraction Tool for Fetal MRI by Razieh Faghihpirayesh, Davood Karimi, Deniz Erdogmus, Ali Gholipour

“…Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. …”
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Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease by María C. García, Sebastián A. Cuesta, José R. Mora, Jose L. Paz, Yovani Marrero-Ponce, Frank Alexis, Edgar A. Márquez

“…This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. An ensemble modeling approach was employed to screen the DrugBank database, aiming to repurpose approved medications as potential treatments for Parkinson’s disease (PD). …”
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Estimating rare disease prevalence and costs in the USA: a cohort study approach using the Healthcare Cost Institute claims data by Keith A Crandall, Christine M Cutillo, Ainslie Tisdale, Mahdi Baghbanzadeh, Reva L Stidd, Manpreet S Khural, Laurie J Hartman, Jeff Greenberg, Kevin B Zhang, Ali Rahnavard

“…Building capabilities to use machine learning to accelerate the diagnosis of RDs would vastly improve with changes to healthcare data, such as standardising data input, linking databases, addressing privacy issues and assigning ICD-10 codes for all RDs, resulting in more robust data for RD analytics.…”
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Coordinated conformational changes in P450 decarboxylases enable hydrocarbons production from renewable feedstocks by Wesley Cardoso Generoso, Alana Helen Santana Alvarenga, Isabelle Taira Simões, Renan Yuji Miyamoto, Ricardo Rodrigues de Melo, Ederson Paulo Xavier Guilherme, Fernanda Mandelli, Clelton Aparecido Santos, Rafaela Prata, Camila Ramos dos Santos, Felippe Mariano Colombari, Mariana Abrahão Bueno Morais, Rodrigo Pimentel Fernandes, Gabriela Felix Persinoti, Mario Tyago Murakami, Leticia Maria Zanphorlin

“…Combining X-ray crystallography, molecular dynamics simulations, and machine learning, we have identified intricate molecular rearrangements within the active site that enable the Cβ atom of the substrate to approach the heme iron, thereby promoting oleate decarboxylation. …”
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Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servi... by Emily R. Edwards, Joseph C. Geraci, Sarah M. Gildea, Claire Houtsma, Jacob A. Holdcraft, Chris J. Kennedy, Andrew J. King, Alex Luedtke, Brian P. Marx, James A. Naifeh, Nancy A. Sampson, Murray B. Stein, Robert J. Ursano, Ronald C. Kessler

“…As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. …”
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