Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model by David Rehkopf, Alice Furumoto-Dawson, Anthony Kiszewski, Tamara Awerbuch-Friedlander

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A Novel Ferroptosis-Related Gene Signature for Prognosis Prediction in Ewing Sarcoma by Runhan Zhao, Zefang Li, Yanran Huang, Chuang Xiong, Chao Zhang, Hao Liang, Jingtao Xu, Xiaoji Luo

“…Based on the train cohort, AURKA, RGS4, and RIPK1 were identified as key genes through the univariate Cox regression analysis, the random survival forest algorithm, and the multivariate Cox regression analysis and utilized to establish a prognostic FRG signature. …”
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Analysis of Noise Pollution during Dussehra Festival in Bhubaneswar Smart City in India: A Study Using Machine Intelligence Models by Sourav Kumar Bhoi, Chittaranjan Mallick, Chitta Ranjan Mohanty, Ranjan Soumya Nayak

“…The supervised ML models taken in this work are Decision Tree (DT), Neural Network (NN), k-Nearest Neighbor (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). The predictions of the models are evaluated using Orange 3.26 data analytics tool. …”
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Optimal life-cycle adaptation of coastal infrastructure under climate change by Ashmita Bhattacharya, Konstantinos G. Papakonstantinou, Gordon P. Warn, Lauren McPhillips, Melissa M. Bilec, Chris E. Forest, Rahaf Hasan, Digant Chavda

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Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches by Alaa Khadidos, Adil Khadidos, Olfat M. Mirza, Tawfiq Hasanin, Wegayehu Enbeyle, Abdulsattar Abdullah Hamad

“…Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. …”
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NADPH Oxidase versus Mitochondria-Derived ROS in Glucose-Induced Apoptosis of Pericytes in Early Diabetic Retinopathy by Nik M. Mustapha, Joanna M. Tarr, Eva M. Kohner, Rakesh Chibber

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Clonal and seasonal genetic variation of major oil components of Salvia fruticosa Mill. by Fatma Uysal, Muharrem Golukcu, Murat Alan, Esra Alim, Kenan Turgut

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Uso do solo e Dinâmica dos Nutrientes nas Águas do Reservatório da Hidrelétrica de Manso no Estado de Mato Grosso, Brasil Central by Jeater Waldemar Maciel Correa Santos, Simoni Maria Loverde Oliveira, William Pietro de Souza

“…As a result it was found that in only 7 months the water lamina of reservoir had already covered more than 60% of the previewed area for filling leaving submerged almost the all original vegetation existing in the area, it was 62%covered with forests and savannas and 19% of grasses for grazing cattle. …”
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An efficient smart phone application for wheat crop diseases detection using advanced machine learning. by Awais Amir Niaz, Rehan Ashraf, Toqeer Mahmood, C M Nadeem Faisal, Muhammad Mobeen Abid

“…The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). …”
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Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. by Weihao Wang, Wei Zhu, Janos Hajagos, Laura Fochtmann, Farrukh M Koraishy

“…We conducted a retrospective cohort study on patients hospitalized at Stony Brook University Hospital in 2020 who were followed for 36 months post discharge. Random Forest (RF) identified the top ten features associated with fast eGFR decline. …”
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Tackling local environmental challenges with livelihoods strategies: insights from small-scale farmers in the vulnerable region of Ghana by Bright Osei Boateng, Henry Mensah, Eric Kwame Simpeh, Patrick Opoku, Lilian Amponsaa Boateng

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The Use of Critical Levels for Determining Plant Response to Ozone in Europe and in North America by Robert C. Musselman, Allen S. Lefohn

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Empirical modeling potential transfer of land cover change pa city with neural network algorithms by fatemeh mohammadyary, hamidreza pourkhabbaz, hossin aghdar, morteza Tavakoly

“…According to the horizontal tabulation results of the 2028 map, it can be stated that from the total area of the area 28336.22 hectares of land were unchanged and 33223.78 hectares of land use change. Also Rangeland and forest degradation during this time period can be a danger to urban planners and natural resources.   …”
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Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets by Durr e Nayab, Rehan Ullah Khan, Ali Mustafa Qamar

“…We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. …”
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Analysing the trend of land changes and urban development of Shushtar by using remote sensing data by milad khayat, Atefeh Bosak, zahra hejazizadeh, ebrahim afifi

“…To accomplish this objective, two datasets were utilized: urban land use maps (including educational spaces, healthcare facilities, residential areas, etc.) and Landsat satellite imagery for key land uses such as rivers, barren lands, and forests, spanning three time periods: 1991, 2004, and 2014. …”
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Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning by Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees

“…Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. …”
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Determination of the Properties of Medium-Density Fiberboards Produced Using Urea-Formaldehyde Resins Modified with Boron Compounds by Recai Arslan, Ebru Karaçay, Derya Maraşlıoğlu, Bilge Aslan Tanrıverdi, Ebru Fırat, Ayhan Tozluoğlu, Halil İbrahim Uğraş, Zeki Candan

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Spatial-temporal Evolution of the Normalized Difference Vegetation Index (NDVI) in the Huaihe River Basin from 1999 to 2018 by GAO Zheng, LIU Saiyan, QIN Xuan, XU Liuxin

“…Vegetation is one of the main participants in the global carbon cycle and plays an important role in land surface water transport and energy transmission.Based on the normalized difference vegetation index (NDVI) of the Huaihe River Basin (HRB) from 1999 to 2018,the spatial-temporal evolution law of NDVI was studied on three time scales (month,quarter,and year) through the Mann-Kendall trend test and empirical orthogonal function (EOF) decomposition.The following findings are obtained.① Affected by crop maturity and different landforms,the multi-year average values of monthly NDVI in the HRB present an M-shaped distribution as a whole,and there are some differences in the mean value and variation range of NDVI in different sub-basins.② In the downstream area with more farmlands,the NDVI shows an insignificant downward trend in summer and winter,while in other three sub-basins of the river,the NDVI follows an upward trend in all seasons,especially in spring and autumn.③ The annual NDVI of the basin exhibits a significant upward trend,which is the same as the global greening trend.This also indicates that returning cropland back to forests and other soil and water conservation measures have achieved remarkable results in vegetation restoration and ecological protection in the basin for the past 20 years.④ The spatial distribution of NDVI modes in the HRB shows simultaneous increases or decreases overall,with a great difference existing between the western and eastern areas.Vegetation coverage gradually decreases from inland to coastal.These research results can provide a reference basis for understanding the vegetation restoration characteristics and protecting the ecological environment in the HRB.…”
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FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. by Nicholas T Gigliotti, Justin Lee, Emily H Mang, Giancarlo R Zambrano, Mitra L Taheri

“…Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. …”
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Hybrid MCDM-FMEA Model for Process Optimization: A Case Study in Furniture Manufacturing by Kristina Klarić, Ivana Perić, Karla Vukman, Fran Papić, Miljenko Klarić, Petra Grošelj

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