Predicting breast cancer recurrence using deep learning by Deepa Kumari, Mutyala Venkata Sai Subhash Naidu, Subhrakanta Panda, Jabez Christopher

“…Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). This hybridization leads to the creation of 16 robust models that enhance interpretability, facilitate generalization, and effectively manage challenges related to small datasets, class imbalance, and data preprocessing. …”
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Sulfur Hexafluoride (SF6) versus Perfluoropropane (C3F8) in the Intraoperative Management of Macular Holes: A Systematic Review and Meta-Analysis by Idan Hecht, Michael Mimouni, Eytan Z. Blumenthal, Yoreh Barak

“…Publications up to October 2018 that focused on macular hole surgery in terms of primary closure, complications, and clinical outcomes were included. Forest plots were created using a weighted summary of proportion meta-analysis. …”
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Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

“…We assessed the performance of six widely used empirical models (Hargreaves Samani, modified Hargreaves Samani, Romanenko, Schendel, Priestley-Taylor, and Makkink) and four ML models (random forest, extreme gradient boosting, deep neural network, and long short-term memory) to estimate ET0 with limited climatic variables in Nepal. …”
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A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection by Wasswa Shafik, Ali Tufail, Chandratilak Liyanage De Silva, Rosyzie Anna Awg Haji Mohd Apong

“…To assess the presented IX-CNN model performance, different classifiers, namely, support vector machine (SVM), decision tree (DT) and random forest (RF), were used. The experiments used six datasets, including PlantVillage, Turkey Disease, Plant Doc, Rice Disease, RoCole, and NLB datasets. …”
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The Use of Novel Alginate Capsules in a Monitoring System for <i>Drosophila suzukii</i> in a Cherry Orchard in the Region of La Araucanía, Chile by Marcelo Lizama, Fernando Manuel Alves-Santos, Luis Manuel Navas-Gracia, Daniel Martínez-Cisterna, Cristian Medina, Ramón Rebolledo, Manuel Chacón-Fuentes, Leonardo Bardehle

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A Systematic Review and Network Meta-Analysis of Biomedical Mg Alloy and Surface Coatings in Orthopedic Application by XinYue Lu, HongXin Cai, Yu Ru Li, Xinru Zheng, Jiahao Yun, Wenhui Li, XiaoYu Geng, Jae-Sung Kwon, Heng Bo Jiang

“…Network structure and forest plots were created, and ranking probabilities were estimated. …”
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Risk factors for African swine fever spread in wild boar in the Russian Federation by O. I. Zakharova, A. A. Blokhin, O. A. Burova, I. V. Yashin, F. I. Korennoy

“…Other significant factors were the length of roads, the presence of forest cover and outbreaks in domestic pigs. However, on the whole, for all the infected Subjects, the regression model demonstrated the failure of the wild boar population density factor to explain the observed ASF outbreak distribution, and this may be indicative of the existence of other epizootic drivers of the disease spread in the wild. …”
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Multidimensional patterns of bird diversity and its driving forces in the Yangtze River Basin of China by Wei Liu, Tong Mu, Sijia Yuan, Jianfeng Yi, Dandan Yu, Jiaqi Li, Fangzhou Ma, Yaqiong Wan, Jing Chen, Riquan Zhang, David S. Wilcove, Haigen Xu

“…Here, we constructed an optimized living planet index (LPIO) by combining Partial Least Squares Structural Equation Modeling and Random Forest Modeling. Using data from a monitoring network of 536 sites, we observed an increasing trend in terrestrial bird diversity and functional complexity across the entire watershed from 2011 to 2020. …”
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A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols by Nicolas Francos, Daniela Heller-Pearlshtien, José A. M. Demattê, Bas Van Wesemael, Robert Milewski, Sabine Chabrillat, Nikolaos Tziolas, Adrian Sanz Diaz, María Julia Yagüe Ballester, Asa Gholizadeh, Eyal Ben-Dor

“…A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. …”
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Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework by Mingyue Xue, Yinxia Su, Chen Li, Shuxia Wang, Hua Yao

“…Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. …”
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DATA MINING ALGORITHMS FOR PREDICTION OF STUDENT TEACHERS’ PERFORMANCE IN ICT: A SYSTEMATIC LITERATURE REVIEW by Juma Habibu Shindo, Mohamedi Mohamedi Mjahidi, Mohamed Dewa Waziri

“…They are Naive Bayes, K-Nearest Neighbour, Support Vector Machine, Random Forest, and Decision Tree. The findings of this study would assist the government, college tutors, and student teachers in making better decisions to improve ICT performance for pre-service and in-service teachers. …”
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The efficacy of chelated micronutrient fertilisers in tomato cultivation by Volodymyr Sievidov, Oksana Serhienko, Ivan Lebedynskyi, Igor Sievidov

“…The research, conducted during 2018-2021 in film greenhouses at the experimental site of the State Biotechnological University, located in the south eastern part of the Left-Bank Forest-Steppe of Ukraine, examined the F1 indeterminate tomato hybrids Berberana (early maturity) and Bostina (mid-early maturity). …”
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Resistance of old winter bread wheat landraces to tan spot by N. V. Mironenko, N. M. Kovalenko, O. A. Baranova, O. P. Mitrofanova

“…Vavilov: “steppe winter bread wheat (Banatka wheats)”, “North European forest awnless bread wheats (Sandomirka wheats)”, and “Caucasian mountain winter bread wheat”.Conclusion. …”
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Association of Septic Shock with Mortality in Hospitalized COVID-19 Patients in Wuhan, China by Shaoqiu Chen, Zitong Gao, Ling Hu, Yi Zuo, Yuanyuan Fu, Meilin Wei, Emory Zitello, Gang Huang, Youping Deng

“…A prediction model was established using random forest. Results. The mortality of septic shock and nonshock patients with COVID-19 was 96.7% (29/30) and 3.8% (7/182). …”
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Integrating river transport processes and seasonal dynamics to assess watershed nitrogen export risk by Jian Peng, Tianhan Lan, Jiabin Wang, Pei Xia, Huining Zheng

“…Additionally, the monthly variations within the year were further explored, together with the identification of the priority area of returning cropland to forest land. The results showed that in 2021, the total nitrogen load outside the Dongting Lake was 11.34 × 108 kg·year−1, with the sub-basins retaining a total of 9.02 × 108 kg·year−1, accounting for 79.57 % of the total nitrogen load. …”
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Trees, seeds and seedlings analyses in the process of obtaining a quality planting material for black locust (Robinia pseudoacacia L.) by Andrea M. ROMAN, Irina M. MORAR, Alina M. TRUTA, Catalina DAN, Adriana F. SESTRAS, Liviu HOLONEC, Florin IORAS, Radu E. SESTRAS

“…In Romania, black locust has established itself as a forest tree appreciated for multiple uses. The objective of the hereby study was to identify a quality planting material at black locust using seeds from trees with superior traits from five stands geographically close, located in North-western of Romania. …”
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Advanced characterization of deforestation frontiers in Nigeria utilizing deep learning and Bayesian approaches with sentinel-1 SAR imagery by Isiaka Lukman Alage, Yumin Tan, Ahmed Wasiu Akande, Agus Suprijanto

“…Addressing these challenges, we propose an innovative approach that integrates spatial and temporal features for biannual deforestation mapping using synthetic aperture radar data in hotspots, study site 1 (Akure) and study site 2 (Okomu) forest reserve (First and second halves of 2020, 2021, 2022, and 2023). …”
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Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models by Matheus Dalmolin, Karolayne S. Azevedo, Luísa C. de Souza, Caroline B. de Farias, Martina Lichtenfels, Marcelo A. C. Fernandes

“…Gene expression data from RNA-seq, extracted from The Cancer Genome Atlas (TCGA), were used to train ML models, including decision trees (DTs), random forest (RF), and XGBoost (XGB), which achieved accuracies of 98.69%, 99.82%, and 99.37%, respectively. …”
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Spatiotemporal Relationship Between Carbon Metabolism and Ecosystem Service Value in the Rural Production–Living–Ecological Space of Northeast China’s Black Soil Region: A Case Stu... by Yajie Shang, Yuanyuan Chen, Yalin Zhai, Lei Wang

“…The flow of ecological value from forest ecological space to cropland production space represents the main loss pathway. (4) A significant negative correlation exists between carbon metabolism density and ESV, with areas of high correlation predominantly centered around cropland production space. …”
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Assessment of trade-off balance of maize stover use for bioenergy and soil erosion mitigation in Western Kenya by Keiji Jindo, Keiji Jindo, Golaleh Ghaffari, Manisha Lamichhane, Asher Lazarus, Yoshito Sawada, Hans Langeveld

“…IntroductionKakamega Forest, Kenya's last tropical rainforest, faces threats from escalating demands for firewood, charcoal, and agricultural expansion driven by population growth. …”
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