A complete, multi-layered quranic treebank dataset with hybrid syntactic annotations for classical arabic processingMendeley Data by Wadee A. Nashir, Abdulqader M. Mohsen, Asma A. Al-Shargabi, Mohamed K. Nour, Badriyya B. Al-onazi

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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. …”
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Intelligent Prediction and Numerical Simulation of Landslide Prediction in Open-Pit Mines Based on Multi-Source Data Fusion and Machine Learning by Li Qing, Linfeng Xu, Juehao Huang, Xiaodong Fu, Jian Chen

“…A GIS is then applied to analyze the slope, curvature, and slope direction. Multi-source data fusion is employed to link spatial coordinates and create a dataset for further analysis. …”
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A Hybrid Machine Learning-Based Framework for Data Injection Attack Detection in Smart Grids Using PCA and Stacked Autoencoders by Shahid Tufail, Hasan Iqbal, Mohd Tariq, Arif I. Sarwat

“…Cyberattacks, especially data injection attacks, are becoming more common as smart grids are increasingly interconnected. …”
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Alloys innovation through machine learning: a statistical literature review by Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi

“…Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. …”
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SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning by Anjali Shinde, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari, Waheb A. Jabbar

“…To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. …”
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Machine learning models integrating dietary data predict all-cause mortality in U.S. NAFLD patients: an NHANES-based study by Pinchu Chen, Yao Li, Chenfenglin Yang, Qifan Zhang

“…Conclusions This study integrates dietary data into machine learning models, demonstrating the potential for predicting all-cause mortality in NAFLD patients. …”
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Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data by Parth Naik, Rupsa Chakraborty, Sam Thiele, Richard Gloaguen

“…A majority of hyperspectral super-resolution methods aim to enhance the spatial resolution of hyperspectral imaging data (HSI) by integrating high-resolution multispectral imaging data (MSI), leveraging rich spectral information for various geospatial applications. …”
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Interpolation Theory and Artificial Intelligence: A Roadmap for Satellite Data Augmentation by Isabella Mereu, Mariarosaria Natale, Michele Piconi, Alessio Troiani, Vincenzo Suriani, Domenico Daniele Bloisi, Paolo Burghignoli, Danilo Costarelli, Alessandro Veneri, Davide Comite

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Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests by Lucia Čahojová, Ivan Jarolímek, Barbora Klímová, Michal Kollár, Michaela Michalková, Karol Mikula, Aneta A. Ožvat, Denisa Slabejová, Mária Šibíková

“…Landscape changes caused by climate change require new methods for forest research, analysis, mapping, and monitoring. This study aims to combine ground-based and remote sensing data utilising deep learning techniques to map protected forest habitats and communities within the Natura 2000 network. …”
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Assessing particulate matter (PM2.5) concentrations and variability across Maharashtra using satellite data and machine learning techniques by Ganesh Machhindra Kunjir, Suvarna Tikle, Sandipan Das, Masud Karim, Sujit Kumar Roy, Uday Chatterjee

“…In this context, the present study aims to predict PM2.5 concentrations across Maharashtra, India, for the year 2023, employing machine learning models to improve spatial and temporal air quality assessments. …”
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Robust two stages federated learning for sensor based human activity recognition with label noise by Haifeng Sun, Junping Yao, Xiaojun Li, Yanfei Liu, Hongyang Gu

“…Abstract Federated learning is widely used for collaborative training of human activity recognition models across multiple devices with limited local data. …”
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Approaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction. by André G Coimbra, Cleiane G Oliveira, Matheus P Libório, Hasheem Mannan, Laercio I Santos, Elisa Fusco, Marcos F S V D'Angelo

“…This study conducts a comparative analysis of techniques for handling imbalanced data and evaluates their effectiveness in combination with a set of classification algorithms, specifically focusing on stroke prediction. …”
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Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches by Xinmin Jin, Lei Li, Xiaoyan Hu, Pengfei Bi, Song Zhang, Qian Wang, Zhongwei Xiao, Hua Yang, Tongtong Liu, Lifang Feng, Jinhuan Wang

“…Methods: Data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) were used, involving 2390 participants with complete data on urinary metal elements, diabetes, and sarcopenia. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…By leveraging healthy images to learn the normal skeletal distribution, the approach reduces the dependency on labeled fracture data and effectively addresses the challenges posed by limited pediatric datasets. …”
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A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective by Dung Nguyen Tien, Huong Thi Thu Bui, Tram Hoang Thi Ngoc, Thuy Thi Pham, Dac Trung Nguyen, Huyen Nguyen Thi Thu, Thi Thu Hang Vu, Thi Lan Anh Luong, Lan Thu Hoang, Ho Cam Tu, Nina Körber, Tanja Bauer, Lam Khanh Ho

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Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy by Polina Lemenkova

“…To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. …”
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A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island by Pavlos Fylaktos, George P. Petropoulos, Ioannis Lemesios

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scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links by Gefei Wang, Jia Zhao, Yingxin Lin, Tianyu Liu, Yize Zhao, Hongyu Zhao

“…As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. …”
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Battery management in IoT hybrid grid system using deep learning algorithms based on crowd sensing and micro climatic data by Srinivasan Rajamani, Arulmozhiyal Ramasamy

“…IPWS has crowd sensing for microclimatic conditions data acquisition system. Microclimatic Data is used for tuning zero export converters and Battery Management System (BMS) through IPWS. …”
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