Hotspot Spatial Patterns Using SNNP-VIIRS for Fire Potential Monitoring by Rosalina Kumalawati, Astinana Yuliarti, Syamani D. Ali, Karnanto Hendra Murliawan, Rijanta Rijanta, Ari Susanti, Erlis Saputra

“…The province of East Kalimantan is officially designated as the State Capital because the area has the least risk of disaster, even though it cannot be separated from disasters such as forest and land fires. This study aims to determine the spatial pattern of hotspots using SNPP-VIIRS for monitoring potential fires. …”
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Preventing Escape of Non-Native Species from Aquaculture Facilities in Florida, Part 4: Operational Strategies by Quenton M. Tuckett, Carlos V. Martinez, Jared L. Ritch, Katelyn M. Lawson, Jeffery E. Hill

“…Hill, and published by the School of Forest Resources and Conservation, Program in Fisheries and Aquatic Sciences, August 2016. …”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. …”
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EPIDEMIOLOGICAL MONITORING OF AVIAN INFLUENZA IN THE REPUBLIC OF CRIMEA by D. V. Gadzevich, S. I. Danilchenko, S. G. Yerofeyev, M. A. Pasunkina, A. B. Grinchenko, V. N. Irza, M. S. Volkov, A. V. Varkentin

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The Role of Mangrove Vegetation in Supporting Birdlife Diversity in Coastal Habitats, Study Case Benoa, Bali by Christy Shanie Avissa Nathanya, Saptarini Dian

“…This study analyzes the relationship between mangrove characteristics and the species composition of birds in coastal Benoa areas to explore the role of mangroves in Ngurah Rain Forest Park in supporting bird diversity. Point counts and line transects were utilized to obtain data to observe bird communities, and plot sampling was used to evaluate the characteristics of the mangrove vegetation, including species composition, tree height, and density. …”
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Complete Whole-Genome Sequence of Streptomyces sp. MUM 178J, a Potential Anti-Vibrio Agent by Ke-Yan Loo, Loh Teng-Hern Tan, Kah-Ooi Chua, Priyia Pusparajah, Kok-Gan Chan, Learn-Han Lee, Jodi Woan-Fei Law, Vengadesh Letchumanan

“…MUM 178J was isolated from a mangrove forest in Malaysia. This isolate was found to harbor anti-Vibrio properties as its crude extract inhibited the growth of multidrug-resistant Vibrio parahaemolyticus. …”
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Effect of Growth Rate on Physical and Mechanical Properties of 4-year-old Acacia mangium Wood from Wonogiri, Central Java by Fanny Hidayati, Ramadhani Ayu Purnama, Harry Praptoyo, Sri Sunarti

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Analysis of Germination Curves of Cinchona officinalis L. (Rubiaceae) Using Sigmoidal Mathematical Models by Lenin Quiñones-Huatangari, Annick Estefany Huaccha-Castillo, Franklin Hitler Fernandez-Zarate, Eli Morales-Rojas, Jenny Del Milagro Marrufo-Jiménez, Leslie Lizbeth Mejía-Córdova

“…Seed germination is the fundamental phenomenon that determines the successful growth and development of each plant species, even more so in Cinchona officinalis, which is a forest species that stands out for its medicinal importance. …”
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Association between frailty and hospital-related adverse events in older hospitalised patients: a systematic literature review protocol by Jay Banerjee, Brad Manktelow, Abdullah Alshibani, Faris Alotaibi

“…If feasible, a meta-analysis will be conducted using the R statistical programme, and results will be visually presented using a forest plot. If there is high heterogeneity and a meta-analysis is not feasible, a narrative synthesis and analysis guided by Cochrane criteria will be conducted, and results will be presented in appropriate tables and figures.Ethics and dissemination No ethical approval will be obtained for this review since it will use secondary published data. …”
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A problem-agnostic approach to feature selection and analysis using SHAP by John T. Hancock, Taghi M. Khoshgoftaar, Qianxin Liang

“…We explore feature selection for data reduction with Isolation Forest and SHAP for this case. When data of one class is available, a one-class classifier, such as Gaussian Mixture Model (GMM) can be used in combination with SHAP for determining feature importance, and for feature selection. …”
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Effects of Income and Price on Household’s Charcoal Consumption in Three Cities of Tanzania by G. Z. Nyamoga, H. K. Sjølie, G. Latta, Y. M. Ngaga, R. Malimbwi, B. Solberg

“…This charcoal is supplied from natural forests, mainly Miombo woodlands, and the high charcoal consumption is a main trigger for deforestation, forest degradation, and climate gas emissions. …”
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An Optimal Routing Framework for an Integrated Urban Power–Gas–Traffic Network by Mohammad Jadidbonab, Hussein Abdeltawab, Yasser Abdel-Rady I. Mohamed

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Etude de cas : la bipédie des chimpanzés de la communauté de Sebitoli, Ouganda by Lise Pernel, Brigitte Senut, Dominique Gommery, John Paul Okimat, Edward Asalu, Sabrina Krief

“…Ten instances were related to vigilant postures at the edge of their forest habitat where there are cornfields guarded by farmers and 18 show bipedal behaviours associated with foraging for honey using stick-like tools. …”
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C-SHAP: A Hybrid Method for Fast and Efficient Interpretability by Golshid Ranjbaran, Diego Reforgiato Recupero, Chanchal K. Roy, Kevin A. Schneider

“…C-SHAP excels across various datasets and machine learning methods, matching SHAP’s accuracy in selected features while maintaining an accuracy of 0.73 for Random Forest with substantially faster performance. Notably, in the Diabetes dataset collected by the National Institute of Diabetes and Digestive and Kidney Diseases, C-SHAP reduces the execution time from nearly 2000 s to just 0.21 s, underscoring its potential for scalable, efficient interpretability in time-sensitive applications. …”
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A Prediction Model Optimization Critiques through Centroid Clustering by Reducing the Sample Size, Integrating Statistical and Machine Learning Techniques for Wheat Productivity by Muhammad Islam, Farrukh Shehzad

“…This research is taken as follows: firstly three more effective numerical optimized datasets are generated (D1, D2, and D3) from D1 by taking the centroid points of features which decrease the sample size; secondly MLM is integrated with the traditional statistical models (TSMs) for multiple linear regression (MLR), and thirdly decision tree regression (DTR) and random forest regression (RFR) are deployed to get the optimized models able to predict the wheat productivity well with 75% datasets to train and 25% to test the model using the evaluation metrics (R2, RMSE), information criterion (AIC) with weights (AICW), evidence ration (E.R), and decompositions of prediction error. …”
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Performance Analysis of Diabetes Detection Using Machine Learning Classifiers by Hung Huynh, Liu Hui, Ngoc Han Nguyen, Ruixuan Qiao

“…Results have shown that Stochastic Gradient Descent (function), Logistic Regression (function), JRip (rules) and Random Forests (trees) are among the top performing classifiers. …”
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Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. …”
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CLIMATE CHANGE AND LAND-BASED CONFLICT: THE IMPACT ON FOOD SECURITY IN NIGERIA by OGUIKE MIRIAM ADAEZE UJU

“…The aftermath of climate change is prominent on agricultural production such as livestock, crop, forest, fishery, nutrition, health and the general livelihood of the people. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). Federated Averaging (FedAvg) aggregates data from local clients and then creates a global model; fedavg-dwa provides dynamic weight averaging, which enhances each client’s performance based on their data quality. …”
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Predictive analysis of ratings of perceived exertion in elite Gaelic football by Dermot Sheridan, Aidan J. Brady, Dongyun Nie, Mark Roantree

“…Data were analysed using decision tree, random forest (RF), and bootstrap aggregation (BS) models. …”
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