Using Machine Learning and Nationwide Population-Based Data to Unravel Predictors of Treated Depression in Farmers by Pascal Petit, Vincent Bonneterre, Nicolas Vuillerme

“…While ML algorithms have shown high accuracy in identifying depression predictors in mental health research, no study has yet applied ML in farmers. We aimed to identify key predictors of depression among the entire French farmer workforce across professional categories, activities, and sexes using ML (XGBoost). …”
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Sustained Repigmentation in Vitiligo and Leukodermas Using Melanocyte–Keratinocyte Transplantation: 7 Years of Data by Nuntawisuttiwong N, Yothachai P, Paringkarn T, Chaiyabutr C, Wongpraparut C, Silpa-archa N

“…However, there is limited data on the long-term outcomes of the MKTP, especially in Thai patients.Objective: To assess the long-term efficacy and safety of the MKTP in patients with vitiligo and other leukodermas.Methods: This retrospective observational study analyzed data from 23 patients who underwent the MKTP for vitiligo and other leukodermas at the Siriraj MKTP Clinic, Thailand, and had a follow-up period exceeding 12 months. …”
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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data by Byeongcheol Kang, Kyungbook Lee

“…This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. …”
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Flood Risk Forecasting: An Innovative Approach with Machine Learning and Markov Chains Using LIDAR Data by Luigi Bibbò, Giuliana Bilotta, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. …”
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Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach by Sarfaraz Natha, Fareed Ahmed Jokhio, Muhammad Shafique, Naeem Ahmed, Danish Munir Arain

“…Furthermore, we introduced a benchmark real-world Road Outlier Dataset (ROD) containing roads, streets, and highways videos and images that presented different road anomalies performed by vehicles and humans. Experimentation using different pre-trained CNN models i.e., VGG19, InceptionV3, ResNet50, and DenseNet201 with a data augmentation approach has been performed on the ROD dataset. …”
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Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data by Xiaoli Cai, Yunfei Bao, Qiaolin Huang, Zhong Li, Zhilong Yan, Bicen Li

“…As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. …”
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N-dimensional Lomb Scargle Periodogram analysis of traveling ionospheric disturbances using ionosonde data by Joe Hughes, Ian Collett, Anastasia Newheart, Ryan Kelly, Walter Junk Wilson, Ken Obenberger, Russell Landry, Jonah Colman, Joe Malins

“…To our knowledge, all prior use of the LSP in space science has been one-dimensional. …”
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Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data by Andrea Angarita-Rodríguez, Andrea Angarita-Rodríguez, Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Nicolás Mendoza-Mejía, Janneth González, Jason Papin, Jason Papin, Jason Papin, Andrés Felipe Aristizábal, Andrés Pinzón

“…IntroductionThe availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. …”
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Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data by Alireza Farnoush, Zahra Sedighi-Maman, Behnam Rasoolian, Jonathan J. Heath, Banafsheh Fallah

“…While traditional methods to discover ADRs are very costly and limited, it is prudent to predict ADRs through non-invasive methods such as machine learning based on existing data. Although various studies exist regarding ADR prediction using non-clinical data, a process that leverages both demographic and non-clinical data for ADR prediction is missing. …”
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P-Wave Arrival-Time Tomography of the Middle East Using ISC-EHB and Waveform Data by Ebru Bozdağ, Susini Desilva, Guust Nolet, Ridvan Orsvuran, Rengin Gok, Yahya M. Tarabulsi, Ahmed Hosny, Khalid Yousef, Abdullah Mousa

“…We use data from the ISC-EHB bulletin and onset-time readings of first-arrival P waves from waveforms recorded in the Arabian Peninsula. …”
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Probabilistic approach to monitoring vegetation water stress using solar-induced chlorophyll fluorescence data by Muhammad Abrar Faiz, Qiumei Wang, Shehakk Muneer, Yongqiang Zhang, Faisal Baig, Farah Naz

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IoT enabled smart water metering using multi sensor data and machine learning techniques by Mallikarjun Jamadarkhani, Mallikarjun Jamadarkhani, Rohit Raphael, Sri Hari Prasath Ramprasad, Harish Babu, Sridharakumar Narasimhan, Sridharakumar Narasimhan, Sridharakumar Narasimhan

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Cancer Cell Classification From Peripheral Blood Smear Data Using the YOLOv8 Architecture by Joao C. S. Nunes, Jose E. B. De S. Linhares, Miguel Angel Orellana Postigo, Daniel Guzman del Rio, Angilberto Muniz Ferreira Sobrinho, Israel Gondres Torne

“…The accurate classification of cancer cells in the peripheral blood is essential for the diagnosis of leukemia and has traditionally been carried out by analyzing laboratory images. …”
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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

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Bus driver deceleration behavior modeling at intersections using multi-source on-board sensor data by Yancheng Ling, Zhenliang Ma, Yuchen Song, Qi Zhang, Xiaoxiong Weng, Xiaolei Ma

“…This paper develops a multiple linear regression model to analyze the factors influencing bus driver deceleration (a proxy of safe driving state) at intersections using data from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation, and the driver profile data. …”
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An Approach for Multi-Source Land Use and Land Cover Data Fusion Considering Spatial Correlations by Jing Yang, Yiheng Jiang, Qirui Song, Zheng Wang, Yang Hu, Kaiqiang Li, Yizhong Sun

“…Although existing research has explored land use type recognition from remote sensing imagery, interpretation algorithms, and other perspectives, significant spatial discrepancies exist between these data products. …”
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Esophageal cancer trends in the US from 1992 to 2019 with projections to 2044 using SEER data by Zhaomin Xie, Jiajia Lin, Zihuan Li, Hexing Sun, Kaiyuan Huang, Danping Lin, Yingsheng Xiao, Congzhu Li, De Zeng

“…We analyzed esophageal cancer data from the Surveillance, Epidemiology, and End Results-12 cancer registry program, using Joinpoint regression for trend analysis and a decomposition method to attribute changes to population growth, population aging, and epidemiological changes. …”
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Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia by Teguh P. Sidiq, Irwan Gumilar, Hasanuddin Z. Abidin, Irwan Meilano, Ayu Purwarianti, Rahayu Lestari

“…This study utilized Sentinel-1 Synthetic Aperture Radar (SAR) data from 2017 to 2023, analyzed using Small Baseline Subset (SBAS) interferometry, to map LS across Java Island. …”
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Data augmented lung cancer prediction framework using the nested case control NLST cohort by Yifan Jiang, Yifan Jiang, Venkata S. K. Manem, Venkata S. K. Manem, Venkata S. K. Manem

“…PurposeIn the context of lung cancer screening, the scarcity of well-labeled medical images poses a significant challenge to implement supervised learning-based deep learning methods. While data augmentation is an effective technique for countering the difficulties caused by insufficient data, it has not been fully explored in the context of lung cancer screening. …”
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Restoration of missing ocean color data in high-latitude oceans using neural network model by Hao Li, Xianqiang He, Yan Bai, Difeng Wang, Teng Li, Fang Gong

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