Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“… This study develops a hierarchical machine learning framework to address the challenges of multi-crop yield prediction in semi-arid regions, focusing on sparse climate data, model interpretability, and heterogeneous climate-crop interactions. …”
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Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis by Gaddam Advitha, Allada Nagasai Varaprasad, Koti Vennela Khushi, Pullabhotla Vijay, Sukanta Nayak

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Model Analisis Aktivitas Tutor Dalam Learning Management System Berdasarkan Data Log Menggunakan K-Means Dan Deteksi Outlier by Agusriandi Agusriandi, Elihami Elihami, Irman Syarif, Ita Sarmita Samad

“…Abstract The Tutors’ learning in LMS stores data in logs which can be used as knowledge to find out about tutor performance. …”
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Application of flexible sensor multimodal data fusion system based on artificial synapse and machine learning in athletic injury prevention and health monitoring by XiaoLan Gai

“…Abstract This research proposes a intelligent system of prevention of athletic injuries and monitoring of health with flexible sensors, artificial synapses, and machine learning. The primary goal is to achieve real-time monitoring of athletes' health and injury prevention through the collection and analysis of sport data. …”
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A Machine Learning Model for Predicting Intensive Care Unit Admission in Inpatients with COVID-19 Using Clinical Data and Laboratory Biomarkers by Alfonso Heriberto Hernández-Monsalves, Pablo Letelier, Camilo Morales, Eduardo Rojas, Mauricio Alejandro Saez, Nicolás Coña, Javiera Díaz, Andrés San Martín, Paola Garcés, Jesús Espinal-Enriquez, Neftalí Guzmán

“…Sociodemographic and clinical data as well as laboratory biomarker results were obtained from medical records and the clinical laboratory information system. …”
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Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments by Faridoon Khan, Amena Urooj, Saud Ahmed Khan, Abdelaziz Alsubie, Zahra Almaspoor, Sara Muhammadullah

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FLPneXAINet: Federated deep learning and explainable AI for improved pneumonia prediction utilizing GAN-augmented chest X-ray data. by Shuvo Biswas, Rafid Mostafiz, Mohammad Shorif Uddin, Muhammad Shahin Uddin

“…Additionally, the need to protect patient privacy complicates the sharing of sensitive clinical data. This study introduces FLPneXAINet, an effective framework that combines federated learning (FL) with deep learning (DL) and explainable AI (XAI) to securely and accurately predict pneumonia using chest X-ray (CXR) images. …”
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Prediction of persistent type II endoleak after endovascular aortic repair using machine learning based on preoperative clinical data and radiomic by Jinqing Mo, Qi Liu, Kangjie Wang, Lin Huang, Chen Yao

“…Feature selection was performed before machine learning model training. Six common machine learning algorithms were used to predict persistent T2ELs based on preoperative features. …”
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Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study by Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, Patrícia H.C. Rondó

“…Methods We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort, a population-based longitudinal study. …”
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Enhancing malignant transformation predictions in oral potentially malignant disorders: A novel machine learning framework using real-world data by Jing Wen Li, Meng Jing Zhang, Ya Fang Zhou, John Adeoye, Jing Ya Jane Pu, Peter Thomson, Colman Patrick McGrath, Dian Zhang, Li Wu Zheng

“…Summary: This study addresses the challenge of accurately predicting malignant transformation risk in patients with oral potentially malignant disorders (OPMDs). Using data from 1,094 patients across three institutions (2004–2023), the researchers compared traditional statistical methods, including a Cox proportional hazards (Cox-PH) nomogram, with machine learning (ML) algorithms. …”
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Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang, Jeong-Jin Yu

“…Afterward, unsupervised learning techniques were employed to explore the data’s inherent structure. …”
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Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…By categorizing outlets into 12 datasets based on monitoring parameters, 17 machine learning models were evaluated to identify emission patterns and detect potential data anomalies. …”
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Models for analysis of water suitability by L. Makarchuk, T. Likhouzova

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Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning by Steve Agius, Vincent Cassar, Caroline Magri, Wasiq Khan, Dhiya Al-Jumeily Obe, Godwin Caruana, Luke Topham

“…Conclusions This research demonstrates the efficacy of machine learning models in predicting key ED outcomes, highlighting their potential to transform emergency care through data-driven insights.…”
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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

“…Abstract Artificial Intelligence is playing a crucial role in healthcare by enhancing decision‐making and data analysis, particularly during the COVID‐19 pandemic. …”
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BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation by Lei Zhang, Changchun Li, Xifang Wu, Hengmao Xiang, Yinghua Jiao, Huabin Chai

“…With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.MethodsSolar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. …”
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Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm by LIN Kaifeng, LI Yiming, ZHANG Bo, YANG Changyu, ZHU Zeting, YANG Zhenda

“…Aiming at the problem that after the distribution automation coverage, the massive signal data in the zone I system is not effectively used, and the switchgear in the distribution network ring network cabinet has frequent transient flashover, grounding and other conditions before failure, which cannot be found in time and lead to tripping, this paper proposes a defect perception model of distribution network based on big data analysis and waveform matching algorithm. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…These algorithms exhibit various strengths and limitations, which require further analysis and comparison. Therefore, this paper reviews the literature on deep learning-based spatiotemporal fusion methods, analyzes and compares existing deep learning-based fusion algorithms, summarizes current challenges in this field, and proposes possible directions for future studies.…”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…To address these limitations, this study developed an NDVI time-series prediction optimization model, LSKRX, which integrates multiple machine learning algorithms with local geographic and climatic data. …”
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Deep learning-based triple-tracer brain PET scanning in a single session: A simulation study using clinical data by Yiyi Hu, Amirhossein Sanaat, Gregory Mathoux, Pirazzo Andrade Teixeira Eliluane, Valentina Garibotto, Habib Zaidi, for the Alzheimer’s Disease Neuroimaging Initiative

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