Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming by Shyamal Mundada, Pooja Jain

“…The goal of the research is to create a system for evaluating soil organic carbon based on topographic features and soil properties incorporating machine learning algorithms. …”
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Smart driving assistance system for mining operations in foggy environments by Swades Kumar Chaulya, Monika Choudhary, Naresh Kumar, Vikash Kumar, Abhishek Chowdhury

“…The system incorporates image processing algorithms and technologies such as proximity radar and global navigation satellite system to provide multi-stage safety features while driving. …”
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Evaluating anti-VEGF responses in diabetic macular edema: A systematic review with AI-powered treatment insights by S Tamilselvi, M Suchetha, Dhanashree Ratra, Janani Surya, S Preethi, Rajiv Raman

“…According to a review of 50 relevant papers published between 2016 and 2023, the algorithms achieved an average automated sensitivity of 74% (95% CI: 0.55–0.92) in detecting treatment responses.…”
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Two-dimensional spatial orientation relation recognition between image objects by Gong Peiyong, Zheng Kai, Jiang Yi, Zhao Huixuan, Huai Honghao, Guan Ruijie

“…A dedicated fusion module synthesizes features from both branches, generating a structured triple list that documents detected objects, their inter-object spatial orientations, and associated confidence scores. …”
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Identification of sweetpotato virus disease-infected leaves from field images using deep learning by Ziyu Ding, Fanguo Zeng, Haifeng Li, Jianyu Zheng, Junzhi Chen, Biao Chen, Wenshan Zhong, Xuantian Li, Zhangying Wang, Lifei Huang, Xuejun Yue, Xuejun Yue

“…The APF module combines a channel attention mechanism with multi-scale feature fusion to enhance the model’s performance in disease pixel segmentation. …”
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Association between the development of sepsis and the triglyceride-glucose index in acute pancreatitis patients: a retrospective investigation utilizing the MIMIC-IV database by Lin Xu, Xuejing Li, Na Zhang, Chunmei Guo, Pan Wang, Min Gao, Yanhui Zhang, Lixin Zhao

“…Utilizing the formula ln[(triglycerides mg/dl) × (glucose mg/dl)/2], the TyG index was calculated. The Boruta algorithm and Xgboost model were used for feature selection in order to pinpoint the important variables affecting results. …”
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Research review of digital modeling technologies with 3D spatial visualization for pipelines by Xin LIU, Shengwen TU, Yanfang HOU, Dong LIN, Jie SHU, Yu TANG

“…MethodsFrom the perspective of software and algorithm characteristics, this paper classifies visual modeling technologies for pipelines into three categories: 3D modeling software, secondary development based on existing components, and underlying development. …”
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OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers by Mustafa Arikan, James Willoughby, Sevim Ongun, Ferenc Sallo, Andrea Montesel, Hend Ahmed, Ahmed Hagag, Marius Book, Henrik Faatz, Maria Vittoria Cicinelli, Amani A. Fawzi, Dominika Podkowinski, Marketa Cilkova, Diana Morais De Almeida, Moussa Zouache, Ganesham Ramsamy, Watjana Lilaonitkul, Adam M. Dubis

“…This dataset improves upon this with multi-grader and multi-disease labels for training machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection). …”
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Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat by Mateo Gannod, Nicholas Masto, Collins Owusu, Cory Highway, Katherine Brown, Abigail Blake-Bradshaw, Jamie Feddersen, Heath Hagy, Douglas Talbert, Bradley Cohen

“…Advances in multispectral imagery and deep learning algorithms may enable continuous and autonomous detection of these habitat features. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Six machine learning algorithms (random forest, k-nearest neighbor, decision tree, naive Bayes, logistic regression, and support vector machine) were applied to predict state-level opioid mortality risk based on linguistic features derived from Linguistic Inquiry and Word Count. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…The trained RF model was used to model the seasonal trend of cotton’s stem water potential, detecting periods of acute and moderate water stress. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…Subsequently, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms were adopted to screen key ARGs related to DN. …”
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Non-invasive urinary proteomic biomarkers for prognostic assessment in sepsis by Qingbo Zeng, Nianqing Zhang, Junjie Zeng, Lincui Zhong, Qingwei Lin, Longping He, Xiaomin Song, Jingchun Song

“…KEGG and GO analyses were conducted to investigate the possible functions of these proteins. Feature variables were selected from the differentially expressed proteins using the Least Absolute Shrinkage and Selection Operator (LASSO) and the random forest algorithms and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. …”
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Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System by Yaohui Wang, Sheng Guo, Jinliang Zhang, Hongbo Ding, Bo Zhang, Ao Cao, Xiaohu Sun, Guangxin Zhang, Shihe Tian, Yongxu Chen, Jixuan Ma, Guangrong Chen

“…A deep vision-based recognition system, enhanced by an improved YOLOv5 algorithm and multi-feature fusion, was employed for real-time object detection and pose estimation. …”
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Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…An optimization-based feature selection approach has been considered for selecting the optimal Reanalysis node locations used as predictors. …”
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Unraveling genetic predisposition and oxidative stress in vitiligo development and the role of artificial intelligence (AI) in diagnosis and management by Kocić Hristina, Lotti Torello, Jevtović-Stoimenov Tatjana, Wollina Uwe, Valle Yan, Lukić Stevo, Klisić Aleksandra

“…By analysing vast datasets, AI algorithms can identify patterns within complex genetic markers and clinical features, facilitating earlier and more precise detection of vitiligo. …”
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A Robust joint coverless image steganography scheme based on two independent modules by Chang Ren, Bin Wu

“…Moreover, our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication. …”
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Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework by Shaik Khasim Saheb, B. Narayanan, T.V. Narayana Rao

“…The model may also utilize preprocessing techniques which are Gaussian smoothing for handling imbalanced data, such as oversampling or under sampling, as well as feature extraction methods such as Local Binary Pattern to identify the most relevant input features for the prediction task. …”
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Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method by Chia-Hao Shih, Elyssa Charlotte Feuer, Ben Kurzion, Kevin Xu, Hong Xie, Stephen R. Grider, Xin Wang

“…Its efficiency in utilizing a minimal set of questions for prediction highlights its potential for practical application in early PTSD detection and intervention strategies.Conclusion: The CART framework demonstrates a streamlined and efficient method for predicting PTSD onset in trauma survivors. …”
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