Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai by Hengyuan Liu, Guibin Lu, Yangjun Wang, Nikola Kasabov

“…Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models.…”
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A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees by Joaquín Abellán, Javier G. Castellano, Carlos J. Mantas

“…As a benchmark point, the known Random Forest (RF) classification method is also used. It will be shown that the bagging ensemble using pruned credal trees outperforms the successful bagging C4.5 and RF when data sets with medium-to-high noise level are classified.…”
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Antioxidant and cytotoxic potentials of Streptomyces gilvigriseus MUSC 26T isolated from mangrove soil in Malaysia by Hooi-Leng Ser, Wai-Fong Yin, Kok-Gan Chan, Tahir Mehmood Khan, Bey-Hing Goh, Learn-Han Lee

“…The Gram-positive, filamentous streptomycete, Streptomyces gilvigriseus MUSC 26T was firstly isolated from mangrove forest at Tanjung Lumpur, Malaysia. After 7-day fermentation, the supernatant of MUSC 26T was collected and subjected to chemical extraction. …”
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Flight Delay Classification Prediction Based on Stacking Algorithm by Jia Yi, Honghai Zhang, Hao Liu, Gang Zhong, Guiyi Li

“…There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. …”
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Impact of extruded feed ingredient on meat quality of broiler chickens: A meta analysis by Risyahadi Sazli Tutur, Retnani Yuli, Sukria Heri Ahmad, Sumiati, Jayanegara Anuraga, Sukarman Sukarman

“…Data from seven articles with publication time between 2013 and 2024 were calculated using OpenMEE software to determine the hedges-D of the meta-analysis and interpreted using forest plot images. The results showed that extruded feed did not negatively influence meat quality parameters, such as UFA, SFA, TBARS, Omega-3, pH, drip loss, freshness, and yellowness (P>0.05). …”
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Ephedra alte (Joint Pine): An Invasive, Problematic Weedy Species in Forestry and Fruit Tree Orchards in Jordan by Jamal R. Qasem

“…Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. …”
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A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats by Dheyaaldin Alsalman

“…In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. …”
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Rural Acoustic Landscape Analysis Based on Segmentation and Extraction of Spectral Image Feature Information by Huijun Xiao, Tangsen Huang, Ensong Jiang

“…Through the detailed analysis of the insect and bird calls of the forest community near the village of Guilin, Guangxi, finally, the satisfaction and attention of the rural villagers to the acoustic landscape are investigated. …”
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Machine Learning-Based Network Detection Research for SDNs by Lai Jiayue

“…Employing a tactical blend of established and cutting-edge machine learning algorithms, including Random Forest, Logistic Regression, and Decision Tree, alongside the advanced XGBoost and LightGBM models, this study conducted an exhaustive investigation to pinpoint the most efficacious methods for swiftly and precisely identifying DoS threats. …”
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Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia by Miao Sun, Tan Qin, Yuanxiao Kuang, Jianchang Lv

“…It extracts vector footprints of buildings from aerial imagery through image segmentation, establishes a feature engineering model comprising 11 distinct indicators, and introduces a Random Forest model to enhance the analysis. By mining the implicit spatial design requirements present in geographical information, this methodology facilitates the classification of industrial buildings from hundreds of thousands of buildings. …”
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Analysis of Emotional Stress of Teachers in Japanese Teaching Process Based on EEG Signal Analysis by Jie Dong

“…Finally, this paper uses the feature selection algorithm of tree model and the random forest model classifier to establish the recognition model of teachers’ emotional stress discharge in Japanese teaching process based on EEG signals and achieves the effect of more accurate recognition of teachers’ emotional stress discharge in Japanese teaching process. …”
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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods by Pragya Kashyap, Kalbhavi Vadhi Raj, Jyoti Sharma, Naveen Dutt, Pankaj Yadav

“…Next, benchmarking was performed across six different supervised-classification algorithms viz. logistic-regression, naïve-bayes, random-forest, extreme-gradient-boost (XGBoost), k-nearest neighbor, and deep neural network. …”
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Methane trapping in permafrost soils: a biogeochemical dataset across Alaskan boreal-Arctic gradient by Jinhyun Kim, Yongwon Kim, Sungjin Nam, Ji Young Jung, You Jin Kim, Jeong Ho Hwang, Mincheol Kim

“…This study presents an integrated biogeochemical and microbial dataset from ~1.8 m deep soil cores collected across a 970 km latitudinal gradient in Alaskan permafrost regions, spanning boreal forest and Arctic tundra biomes. This dataset includes vertical profiles of trapped greenhouse gases, their stable isotope signatures, soil physicochemical properties, and the composition and abundance of key methanogenic and methanotrophic genes. …”
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Expanding horizon of invasive alien plants under the interacting effects of global climate change: Multifaceted impacts and management prospects by Roger Bruce Syngkli, Prabhat Kumar Rai, Lalnuntluanga

“…Moreover, IAP-climate change impacted the forestry/agroforestry systems by restricting abiotic resources and influencing forest regeneration, litter stock, and nutrient cycling. …”
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Time series forecasting of bed occupancy in mental health facilities in India using machine learning by G. Avinash, Hariom Pachori, Avinash Sharma, SukhDev Mishra

“…This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. …”
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Application of an Improved TF-IDF Method in Literary Text Classification by Lin Xiang

“…Using the improved TF-IDF method suggested in this research with the random forest (RF) classifier, the experimental results show that the classifier has a good classification impact, which can meet the actual work needs, based on comparative experiments on feature dimension selection, feature selection algorithm, feature weight algorithm, and classifier. …”
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Time-Series Load Online Prediction of Wind Turbine Based on Adaptive Multisource Operational Data Fusion by Ruojin Wang, Xiaodong Wang, Deyi Fu, Bin Yang, Yingming Liu

“…Aiming at the problem that the stochastic change of wind turbine generator (WTG) working conditions and the complex nonlinear relationship between load and operation data make it difficult to predict the short-term load online, this paper proposes an adaptive multi-information source data fusion online prediction method for WTG load. Random forest (RF) and WaveNet time series (WTS) are established as subinformation source models, and the influence of input features and historical data on load prediction is considered from horizontal and vertical dimensions. …”
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Machine learning approaches to Landsat change detection analysis by Galen Richardson, Anders Knudby, Morgan A. Crowley, Michael Sawada, Wenjun Chen

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In vitro shoot induction from petiole explants of tembesu (Fagraea fragrans Roxb) using 6-benzylaminopurine by Mayta Novaliza Isda, Riche Afrilla

“…Unfortunately, the tembesu population continues to decline due to excessive logging, forest fires, and a lack of cultivation efforts. Planting through seeds takes a long time, making in vitro propagation an attractive alternative. …”
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Regional Climate Variability Responses to Future Land Surface Forcing in the Brazilian Amazon by Tao Zhang, Jinyan Zhan, Feng Wu, Jiao Luo, Juan Huang

“…This paper aimed to model the potential climatological variability caused by future forest vulnerability in the Brazilian Amazon over the 21th century. …”
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