IoT-based automated system for water-related disease prediction by Bhushankumar Nemade, Kiran Kishor Maharana, Vikram Kulkarni, Surajit mondal, G S Pradeep Ghantasala, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Classification is performed using Random Forest, XGBoost, and AdaBoost, which have accuracy rates of 99.66%, 99.52%, and 99.64%, respectively. …”
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Surveillance of pathogenic Leptospira among rodents and small mammals in enzootic areas of plague in Pasuruan Indonesia by Siti Amanah Febriani, Kurnia Ritma Dhanti, Kurniawan Kurniawan, Ristiyanto Ristiyanto, Arief Junaedi, Caecilia Hapsari Ceriapuri Sukowati, Farida Dwi Handayani

“…Rattus tanezumi was identified as the Leptospirosis reservoir in settlements habitats with a percentage of 13.2%, Rattus tiomanicus was detected at 28.6% in forest habitats, and Rattus exulans was found at 4.4% in both habitats. …”
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Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction by Emmanuel de-Graft Johnson Owusu-Ansah, Richard Doamekpor, Richard Kodzo Avuglah, Yaa Kyere Adwubi

“…Findings, with the exception of the SMOTE dataset, XGBoost consistently beat the other classifiers across the other datasets in terms of AUC. Random forest, decision tree, and logistic regression all performed well and might be utilized as alternatives to XGBoost classifiers for developing credit scoring models. …”
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Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers by C. Rajathi, P. Rukmani

“…As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. …”
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A Novel Pyroptosis-Based Prognostic Model Correlated with the Parainflammatory Immune Microenvironment of Pancreatic Cancer by Kong-kong Wei, Zhi-xing Du, Jun-ge Deng, Jin-wei Yang, Hao Chen

“…The candidate genes were determined using LASSO Cox regression and random forest analyses. A risk model was developed with the TCGA dataset and validated with the ICGC dataset. …”
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ANALYSIS OF AIR QUALITY IN TELEORMAN BY MEANS OF BIOMONITORS-LICHENS by Cernat POPA

“…In carrying out the study, I ordered the selection of the area, namely Teleorman county - in 5 urban points, the sampling of lichen samples from the Troianul Forest - Teleorman, their transplantation in 5 measurement points on the bark of some trees at a height of 1.50 m and their application with natural resin and the exposure period is 30 days between October 11, 2021 and November 11, 2021. …”
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Evidence of Second Canal between Permanent Mandibular Central and Lateral Incisors in China; a Systematic Review on CBCT Studies by Nyan M. Aung, Kyaw K. Myint

“…The proportion of the second canal with its confidence interval and forest plot for the meta-analyses were calculated. …”
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An assessment of the capacity and responsiveness of a national system to address the threat of invasive species: a systems approach by F. Williams, K. L. Constantine, A. A. Ali, T. W. Karanja, S. Kibet, E. K. Lingeera, G. Muthike, I. Rwomushana, J. Godwin, R. Day

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eNSMBL-PASD: Spearheading early autism spectrum disorder detection through advanced genomic computational frameworks utilizing ensemble learning models by Ayesha Karim, Nashwan Alromema, Sharaf J Malebary, Faisal Binzagr, Amir Ahmed, Yaser Daanial Khan

“…Several ensemble classification methods, including Extreme Gradient Boosting, Random Forest, Light Gradient Boosting Machine, ExtraTrees, and a stacked ensemble of classifiers, were applied to assess the predictive power of the genomic features. …”
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Understanding the Impacts of Recurrent Peat Fires in Padang Island – Riau Province, Indonesia by Ari Susanti, Oka Karyanto, Agus Affianto, Ismail Ismail, Satyawan Pudyatmoko, Trias Aditya, Haerudin Haerudin, Hendra Arditya Nainggolan

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Influence of Vinasse Application in the Structure and Composition of the Bacterial Community of the Soil under Sugarcane Cultivation by Wellington Pine Omori, André Ferreira de Camargo, Karla Cristina Stropa Goulart, Eliana Gertrudes de Macedo Lemos, Jackson Antônio Marcondes de Souza

“…Although the composition and structure of bacterial communities differ significantly in the four environments (Libshuff’s test), forest soils and soil planted with sugarcane without vinasse fertilizer were similar to each other because they share at least 28 OTUs related to Rhizobiales, which are important agents involved in nitrogen fixation. …”
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Biodiversité et viabilité de l'agriculture paysanne dans la Réserve de Biosphère Sierra de Manantlán, Mexique by Enrique J. Jardel Peláez, Sergio H. Graf Montero, Eduardo Santana C., Ricardo Ávila Palafox

“…But peasant farming systems, of which depends the maintenance of agro-biodiversity, have undergone a process of transformation due to : cultural, economic, social and demographic changes in agrarian communities, economic development policies, pressures on the commercial exploitation of natural resources (mineral, forest and phytogenetic resources) and new forms of marginalization. …”
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Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

“…Hence, our findings show impressive accuracy, with the random forest model achieving 99.85% accuracy in classifying autism vs. healthy subjects and 100% accuracy in distinguishing healthy individuals from those with dementia using Support Vector Machines (SVM). …”
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The Developing Progress of Australian Cellular Agriculture in 2023—2024 by TAN Hong-zhuo, FANG Zhong-xiang, YI Cui-ping

“…In this paper,we have explained why developing cellular agriculture is important from 6 aspects: food safety, reduced forest area, preventing biodiversity loss, mitigating climate change, improving public health and promoting animal welfare. …”
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Use of building wastes and red mud as CO2 sorbent and catalyst for the production of hydrogen by Despina Vamvuka, Stavroula Panagiotidou, Agapi Orfanoudaki

“…Selected materials were forest and agricultural wastes as feedstocks, as well as demolition wastes from construction activities and red mud (RM) waste from the aluminum industry as a novel CO2 sorbent and catalyst, respectively. …”
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Searching for the best post-land abandonment management to enhance long-term carbon storage in Mediterranean mountain areas by Melani Cortijos-López, Teodoro Lasanta, Erik Cammeraat, Estela Nadal-Romero

“…A study was conducted in La Rioja (Iberian System, Spain), comparing three post-abandonment management strategies: secondary succession, forest management, and shrub clearing and extensive grazing. …”
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RETRACTED ARTICLE: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks by Shanthalakshmi M, Ponmagal R S

“…The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. …”
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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25; while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. …”
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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…In order to improve the accuracy of our predictions, we combined these models into a stacked ensemble using a Random Forest (RF) meta-model. This resulted in an <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> value of 0.987, MAE of 0.02559, MSE of 0.0013, and RMSE of 0.00624. …”
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Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning by Ying Zhang, Fabao Xu, Zhenzhe Lin, Jiawei Wang, Chao Huang, Min Wei, Weibin Zhai, Jianqiao Li

“…The ensemble algorithm (linear regression + random forest regressor) performed best in VA and VA variance predictions. …”
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