Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models by Getahun Mulugeta, Temesgen Zewotir, Awoke Seyoum Tegegne, Mahteme Bekele Muleta, Leja Hamza Juhar

“…Various models were evaluated, including Standard and penalized Cox models, Random Survival Forest, and Stochastic Gradient Boosting. Prognostic predictors were selected based on statistical significance and variable importance. …”
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Spatial heterogeneity in deployment and upscaling of wind power in Swedish municipalities by Yodefia Rahmad, Fredrik Hedenus, Jessica Jewell, Vadim Vinichenko

“…Municipalities with the highest large-scale deployment typically have extensive forest cover, low population density and wind speeds within a lower median range relative to the national median. …”
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Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia by Kang Junmei, Yang Fengshuo, Wang Jun, Liu Yang, Fang Dengmao, Jiang Chengcheng

“…The results show that (1) during 2000–2022, forest in Cambodia covered a wide range, showing a landscape pattern mainly distributed in the east and west. (2) The degree of fragmentation of impervious landscape pattern increased gradually from 2000 to 2022, indicating that it was seriously affected by human activities and natural factors, and with the increase in elevation and slope, the area of various types converted to impervious decreased. (3) During 2000–2022, high habitat quality areas were concentrated in Tonle SAP Lake in the east, southwest, and central part of Cambodia, while low habitat quality areas were concentrated in the central part. (4) Natural factors, socio-economic factors, policies, and regulations all have an impact on the change in land use pattern and habitat quality in Cambodia.…”
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Remote sensing applications for managing a large-scale restoration dynamics project throughout the City of Gold Coast conservation estate by Jean-Marc Hero, Luke Shoo, Michael Duncan, Darren Roche, Jesse Kenny, Howard Taylor, Tim Robson, Sanjeev Kumar Srivastava, Josh Veitch-Michaelis

“…The City of Gold Coast established a program to restore native forests throughout the conservation estate (∼13,789 ha) utilizing constructed (CNR), assisted (ANR), and unassisted natural regeneration (UNR). …”
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High-quality DNA extraction for Desmodium gangeticum: A medicinal shrub of Shivalik Himalayas by Shiwani Singh, Maneesh S. Bhandari, Neeraj Dwivedi

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Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…The SBFS technique generated all possible combinations of protein groups from the 146 proteins, which were then trained and tested using five machine learning models: Decision Tree, Random Forest, Extremely Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting. …”
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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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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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Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction by Zhibo Wang, Xin Chen, Xi Tan, Lingfeng Yang, Kartik Kannapur, Justin L. Vincent, Garin N. Kessler, Boshu Ru, Mei Yang

“…For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. …”
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The Climate Change Impact on Agriculture Productivity in Karama Sector, Nyagatare District, Rwanda. by Odile, Twagiramariya

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Decade-Long Analysis: Unravelling the Spatio-Temporal Dynamics of PM10 Concentrations in Malaysian Borneo by Salwa Naidin, Justin Sentian, Farrah Anis Fazliatul Adnan, Franky Herman, Siti Rahayu Mohd Hashim

“…In order to improve air quality in Malaysian Borneo, it is necessary to take a multifaceted approach encompassing source emissions reduction, inter-country collaboration, region-wide strategies for land and forest management improvement, and reinforced cooperation on pollution monitoring, reporting and reduction efforts.…”
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Enhanced genome editing with a Streptococcus equinus Cas9 by Jingtong Liu, Yao Wang, Jingjing Wei, Shengzhou Wang, Miaomiao Li, Zheyong Huang, Sufang Zhang, Huihui Liu, Jinhai Huang, Yongming Wang

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Pengaruh Ciri Temporal, Spasial, dan Frekuensi pada Klasifikasi Motor Imagery by Afin Muhammad Nurtsani, Muhammad Adib Syamlan, Agung Wahyu Setiawan

“…Keywords: motor imagery, temporal, spatial, frequency, random forest …”
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Distribution and Main Influencing Factors of Net Ecosystem Carbon Exchange in Typical Vegetation Ecosystems of Southern China by Yike Wang, Xia Liu, Weijia Lan, Shuxian Yin, Liya Fan, Boru Mai, Xuejiao Deng

“…This study investigated the NEE characteristics of typical evergreen coniferous forest ecosystems (ECFEs), tree-and-crop mixed ecosystems (TCMEs), and coastal crop ecosystems (CCEs) in southern China. …”
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Climate variability, population growth, and globalization impacting food security in Pakistan by Sohail Abbas, Ahtisham Haider, Shazia Kousar, Heli Lu, Siqi Lu, Fang Liu, Huan Li, Changhong Miao, Wanfu Feng, Muhammad Irfan Ahamad, Muhammad Sajid Mehmood, Rana Muhammad Zulqarnain

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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

“…Four machine learning algorithms, known as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and naive Bayes (NB), were used to develop models for predicting the risk of lower extremity DVT occurrence in GC patients. …”
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Prevalence of prolonged transitional neonatal hypoglycemia and associated factors in Ethiopia: A systematic review and meta-analysis. by Solomon Demis Kebede, Amare Kassaw, Tigabu Munye Aytenew, Kindu Agmas, Demewoz Kefale

“…Heterogeneity among the studies was assessed using a forest plot, I2 statistics, and Egger's test. Data extraction was conducted from May 20 to May 27, 2023, for studies published since 2020. …”
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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
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The emerging role of blood-based biomarkers in early detection of colorectal cancer: A systematic review by Faris Shweikeh, Yuhao Zeng, Abdur Rahman Jabir, Erica Whittenberger, Saurav P. Kadatane, Yuting Huang, Mohamad Mouchli, Dani Ran Castillo

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Resource availability and competition shape pollinator trophic specialization in longleaf pine savannas by Pablo Moreno-García, Johanna E. Freeman, Benjamin Baiser, Joshua W. Campbell, Daijiang Li

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