Recognition model for major depressive disorder in Arabic user-generated content by Esraa M. Rabie, Atef F. Hashem, Fahad Kamal Alsheref

“…Results In binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” …”
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A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). …”
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Atmospheric Methane Condition over the South Sumatera Peatland during the COVID-19 Pandemic by Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim

“…Thus, the restrictions during lockdown, which reduced anthropogenic activities, such as land use conversion and biomass burning, and related events, such as peatland and forest fires, significantly influenced the level of atmospheric CH4 above the peatlands in Indonesia.…”
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MetaRange.jl: A Dynamic and Metabolic Species Range Model for Plant Species by Jana Blechschmidt, Juliano Sarmento Cabral

“…Our results show that climate change reduces habitat suitability overall, but some regions like the Franconian Forest and the Alps see increased suitability and abundance, confirming their role as refugia. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…In pursuit of these objectives, the CNN GRU model was rigorously tested and compared against three additional models: CNN with bidirectional long short-term memory (BiLSTM), extreme gradient boosting (XGBoost), and random forest (RF). Key performance metrics—namely, mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²)—were employed to assess the efficacy of each model. …”
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AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis by Hocheol Lee, Myung-Bae Park, Young-Joo Won

“…Machine learning algorithms, including random forest, gradient boosting model, light gradient boosting model, extreme gradient boosting model, and k-nearest neighbors, were employed for analysis. …”
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Evolution of Cerebral Atrophy in a Patient with Super Refractory Status Epilepticus Treated with Barbiturate Coma by Christopher R. Newey, Pravin George, Premkumar Nattanmai, Christine Ahrens, Stephen Hantus, Aarti Sarwal

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Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania by Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar

“…A variety of supervised machine-learning algorithms such as RBF Kernel in SVM, Linear Kernel in SVM, Polynomial Kernel in SVM, Random Forest, Logistic regression classifier, Decision tree, Gradient Boosting, K-Nearest Neighbours Classifier, Naïve Bayes Classifier, Artificial Neuron Network and Ensemble Learning Model were implemented to predict multidimensional poverty status for each dataset. …”
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Structure and Composition of Mangrove Vegetation on Kelasa Island: Dominance of Rhizophora apiculata and Its Implications for Coastal Ecosystem Sustainability by Akhrianti Irma, Oka Arizona Mohammad, Harapan Putera Batubara Geothani

“…The mangrove community is largely dominated by R. apiculata, indicating a trend towards monospecific dominance with robust regeneration. The forest spans approximately 2.57 ha on the island’s eastern coastline, characterized by sandy coral fronts and muddy-rocky substrates. …”
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Diversity and abundance of butterflies in urban areas of Bankura district, Bankura, West Bengal, India by Avisek Patra, Anirban Patra, Biplab Mandal, Anupam Ghosh

“…Abstract Objective During the present study, the biodiversity profile of butterflies was assessed on the Bankura Christian College campus, a large area with a huge amount of trees, bushes, rain forest, and undisturbed areas within the urban areas of Bankura district of West Bengal, India. …”
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Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms by Mingjin Liu, Ruijie Gao, Wei Fu

“…The accuracy of classification and early warning results of the random forest algorithm is relatively high, and the detection rate of the decision tree model is relatively high, but the cost is also the highest. …”
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Bursting Phenomenon and Chaos Phase Control in Plant Dynamics by Makenne Yemeli Lola, Kengne Romanic, Pelap Francois Beceau

“…It also helps to understand the impact of wind on the plant dynamics, in particular, and in forest in general, which is very crucial for understanding several plant communities. …”
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A review of the role of nature-based solutions in mitigating food insecurity in Africa by Solomon Asamoah, Henry Mensah, Eric Kwame Simpeh, Eric Oduro-Ofori, Sandra Serwaa Boateng, Sarah Boateng, Listowel Koda Frimpong, Priscilla Okyere

“…The study identified agroforestry, permaculture practices, urban agriculture and rooftop gardens, wetland restoration, cover cropping and crop rotation, aquaponics, eco-friendly pest control, and community-based forest management as components of nature-based solutions that can help address food insecurity. …”
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Breast Cancer Prediction: A Fusion of Genetic Algorithm, Chemical Reaction Optimization, and Machine Learning Techniques by Md. Rafiqul Islam, Md. Shahidul Islam, Saikat Majumder

“…In our study, we employed two metaheuristic algorithms, namely, genetic algorithm (GA) and chemical reaction optimization (CRO) with machine learning techniques, including support vector machine (SVM), decision tree, random forest, and XGBoost. GA and CRO are used to optimize the feature selection process. …”
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Comparative Microstructural Evaluation of Wood in Three Dominant Ziziphus Species of Desert Ecosystem (Cholistan), Pakistan by Muhammad Bilal, Zaheer-ud-din Khan, Sohaib Muhammad, Uzma Hanif, Khalid Hussain, Muhammad Tayyab, Andleeb Anwar Sardar, Hassan Nawaz, Muhammad Jawad Tariq Khan, null Mahrukh, Aneela Rasool, Summiya Faisal, Muhammad Zahid

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Effect of PVA-co-MMA Copolymer on the Physical, Mechanical, and Thermal Properties of Tropical Wood Materials by Md. Saiful Islam, Sinin Hamdan, Mansor B. Ahmad, Mahbub Hasan, Azman Hassan, M. K. Mohamad Haafiz, M. Jawaid

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Ensemble Deep Learning Technique for Detecting MRI Brain Tumor by Rasool Fakhir Jader, Shahab Wahhab Kareem, Hoshang Qasim Awla

“…These results significantly surpass the accuracy of other methods such as Naive Bayes, decision tree classifier, random forest, and DNN models.…”
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Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey by Mohammed Majeed Hameed, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abd Kareem Khalaf, Wajdi Jaber Baniya, Ahmad Sharafati, Mohammed Abdulhakim AlSaadi

“…For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regression MLR, random forest RF, and extreme learning machine ELM. Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. …”
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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms by Mana Alyami, Irfan Ullah, Furqan Ahmad, Hisham Alabduljabbar

“…Conventional ML techniques like random forest (RF) and decision tree (DT) were also employed for comparison. …”
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Breeding properties and cytological characteristics of interspecific hybrids between the Ussuri plum and bullace by D. S. Garapov, O. V. Mochalova

“…The research was conducted in 2004–2023 in the forest-steppe zone of the Ob river region in Altai. …”
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