Development of a zoonotic influenza distribution assessment and ranking system (ZIDAR): Technical application in Nepal to support cross-sectoral risk-based surveillance by Adam Charette-Castonguay, Dipendra Gautam, Binay Shrestha, Hemant Chandra Ojha, Barun Kumar Sharma, Mukul Upadhayaya, Sujan Rana, Roshika Shrestha, Lok Bandu Chaudhary, Bhawana Kandel, Rudra Prasad Marasini, Sharmila Chapagain, Tulsi Ram Gompo, Surendra Karki, Apsara Poudel, Saugat Shrestha, Avinash Sunny Kayastha, Arun Kumar Govindakarnavar, Reuben Samuel, Allison Gocotano, Pushpa Ranjan Wijesinghe, Nilesh Buddha, Edwin Ceniza Salvador, Manish Kakkar, Ricardo J. Soares Magalhães

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Estimation of mangrove carbon stocks using unmanned aerial vehicle over coastal vegetation by S.H. Larekeng, M. Nursaputra, M.F. Mappiasse, S. Ishak, M. Basyuni, E. Sumarga, V.B. Arifanti, A.A. Aznawi, Y.I. Rahmila, M. Yulianti, R. Rahmania, A. Mubaraq, S.G. Salmo III, H. Ali, I. Yenny

“…The survey and subsequent analysis highlighted the wide variation in the density of mangrove forests in the Lantebung mangrove ecosystem. This study demonstrated a strong correlation between the normalized difference vegetation index extracted using unmanned aerial vehicle and mangrove carbon levels obtained from actual field measurements.…”
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Kidney Function and Incident Stroke and Dementia Using an Updated Estimated Glomerular Filtration Rate Equation Without Race: The Multi-Ethnic Study of Atherosclerosis by Samuel R. Moen, Jeffrey R. Misialek, Timothy M. Hughes, Craig W. Johnson, Mark J. Sarnak, Sarah N. Forrester, W.T. Longstreth, Jr., Mercedes R. Carnethon, James S. Pankow, Sanaz Sedaghat

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Feasibility, Usability, and Pilot Efficacy Study of a Software-Enabled, Virtual Pulmonary Rehabilitation with Remote Therapeutic Monitoring by Flynn S, Mosher CL, Cornelison S, Rao E, Metzler KA, Pu W, Davies J, Paladenech C, Doyle D, MacIntyre N, Ohar J

“…Paul Sticht Center on Aging and Rehabilitation, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA; 5Wake Forest University School of Medicine, Winston-Salem, North Carolina, USACorrespondence: Sheryl Flynn, Blue Marble Health, 2400 Lincoln Ave, Altadena, CA, 91001, USA, Tel +1 626 296 6400, Email Sheryl@BlueMarbleHealthCo.ComObjective: Fewer than 3% of adults with Chronic Obstructive Pulmonary Disease (COPD) attend in-person, center-based pulmonary rehabilitation (PR) despite demonstrated health benefits and reduction in mortality. …”
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HESS Opinions: Towards a common vision for the future of hydrological observatories by P. Nasta, G. Blöschl, G. Blöschl, H. R. Bogena, S. Zacharias, R. Baatz, G. De Lannoy, K. H. Jensen, S. Manfreda, L. Pfister, L. Pfister, A. M. Tarquis, I. van Meerveld, M. Voltz, Y. Zeng, W. Kustas, X. Li, H. Vereecken, N. Romano

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Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario by Mitchell T. Bonney, Yuhong He

“…We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. …”
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Exploring Cartographic Differences in Web Map Applications: Evaluating Design, Scale, and Usability by Jakub Zejdlik, Vit Vozenilek

“…For instance, Google Maps does not display forest symbols on its default map, which can reduce clarity, whereas Mapy.cz offers the most comprehensive range of analytical tools. …”
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Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone by Arijit Chowdhury, Tapas Chakravarty, Avik Ghose, Tanushree Banerjee, P. Balamuralidhar

“…We observe that Random Forest classifier offers the best results. These results have great implications for various stakeholders since the proposed method can identify a driver based on his/her naturalistic driving style which is quantified in terms of statistical parameters extracted from only GPS data.…”
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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms by Zehra Koyuncu, Ömer Ekmekcioğlu

“…It is worth mentioning that this is the first time this approach has been used for flood hazard mapping studies in Turkey. Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. …”
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Machine Learning-Driven Scattering Efficiency Prediction in Passive Daytime Radiative Cooling by Changmin Shi, Jiayu Zheng, Ying Wang, Chenjie Gan, Liwen Zhang, Brian W. Sheldon

“…As a result, the random forest model demonstrated superior prediction performance with minimal error, effectively capturing complex, non-linear interactions between material features. …”
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Recruitment in Swarm-Founding Wasps: Polybia occidentalis Does not Actively Scent-Mark Carbohydrate Food Sources by Benjamin J. Taylor, Erik V. Nordheim, Teresa I. Schueller, Robert L. Jeanne

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A Review on Natural Fiber Reinforced Polymer Composite and Its Applications by Layth Mohammed, M. N. M. Ansari, Grace Pua, Mohammad Jawaid, M. Saiful Islam

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Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital by Mohammad Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh, Yara Alkayed, Yasmin Al Trabsheh, Bahaaldeen Alshraideh

“…Various artificial intelligence techniques, namely, random forest, SVM, decision tree, naive Bayes, and K-nearest neighbours (KNN) were explored with particle swarm optimization (PSO) for feature selection. …”
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Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory by Manuela Ortega Monsalve, Mario Cerón-Muñoz, Luis Galeano-Vasco, Marisol Medina-Sierra

“…Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination (R2), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. …”
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Predicting and Investigating the Permeability Coefficient of Soil with Aided Single Machine Learning Algorithm by Van Quan Tran

“…To acquire this purpose, five single ML algorithms including K-nearest neighbors (KNN), support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), and gradient boosting (GB) are used to build ML models for predicting the permeability coefficient of soils. …”
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Prediction of inhibitory peptides against E.coli with desired MIC value by Nisha Bajiya, Nishant Kumar, Gajendra P. S. Raghava

“…Feature selection techniques, particularly mRMR, were utilized to refine our model inputs. Our Random Forest regressor built using default parameters achieved a correlation coefficient (R) of 0.78, R2 of 0.59, and RMSE of 0.53 on the validation set. …”
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Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease by Arshiya S. Ansari, Malik Jawarneh, Mahyudin Ritonga, Pragti Jamwal, Mohammad Sajid Mohammadi, Ravi Kishore Veluri, Virendra Kumar, Mohd Asif Shah

“…Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.…”
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Application of Big Data Technology in the Impact of Tourism E-Commerce on Tourism Planning by Heqing Zhang, Tingting Guo, Xiaobo Su

“…This paper proposes a research strategy on the impact of tourism e-commerce on customized tourism in the EBD, including related theoretical research methods, random forest algorithms, support vector machine classification algorithms, and Bayesian estimation algorithms, which are used to customize tourism e-commerce in the EBD, and research experiment on the impact of tourism. …”
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Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data by Mihail Senyuk, Svetlana Beryozkina, Inga Zicmane, Murodbek Safaraliev, Viktor Klassen, Firuz Kamalov

“…To enhance the speed and accuracy of disturbance classification, we employ a range of ensemble machine learning techniques, including Random forest, AdaBoost, Extreme gradient boosting (XGBoost), and LightGBM. …”
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An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning by S. M. Taslim Uddin Raju, Amlan Sarker, Apurba Das, Md. Milon Islam, Mabrook S. Al-Rakhami, Atif M. Al-Amri, Tasniah Mohiuddin, Fahad R. Albogamy

“…This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. …”
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