Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings by Özge Öztürk Hacar, Müslüm Hacar, Fatih Gülgen, Luca Pappalardo

“…The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. …”
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Influence of seeding rates on the quantum yield of photosynthesis for some field crops by K. S. Panchenko, N. V. Ovcharova, L. V. Sokolova, M. M. Silantyeva

“…The work was carried out in the steppe and forest-steppe zone of Altai Territory. The scheme of the experiment was conducive to studying the quantum yield of photosynthesis as a method for assessing various seeding rates in the fields of sunflower grown for seeds (‘Pioneer LE 10’) and maize grown for silage (‘Clifton’). …”
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Assessing the Effects of Land Use Practices on Land Cover in Bubare Sub-County Rubanda District. by Kembabazi, Naome

“…This study examined the impact of land use practices on land cover in Bubare sub-county, Rubanda district, addressing three main objectives: to identify and characterize land use practices in Bubare, to assess how farming systems influence forest cover, and to evaluate the effectiveness of land use policies in managing local land resources. …”
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Molecular epidemiology of anaplasmosis in small ruminants along a human-livestock-wildlife interface in Uganda by Keneth Iceland, Kasozi, Simon Peter, Musinguzi

“…Small ruminants located at the forest edge (<0.3 km) showed higher A. ovis prevalence than those found inland with infections present in the midland regions associated with increased agricultural activity. …”
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Molecular epidemiology of anaplasmosis in small ruminants along a human-livestock-wildlife interface in Uganda by Keneth Iceland, Kasozi, Susan Christina, Welburn

“…Small ruminants located at the forest edge (<0.3 km) showed higher A. ovis prevalence than those found inland with infections present in the midland regions associated with increased agricultural activity. …”
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Relapse-free survival is progressively shortened in a subset of Black patients with immune-mediated TTP treated in the rituximab era by Ayotola Fatola, Michael D. Evans, Jenna Brown, Elizabeth Davis, Andrew Johnson, Ana G. Antun, Andrew M. Farland, Ryan Woods, Ara Metjian, Yara A. Park, Gustaaf de Ridder, Briana Gibson, Raj S. Kasthuri, Darla K. Liles, Susan Eubanks, Frank Akwaa, Todd Clover, Lisa Baumann Kreuziger, J. Evan Sadler, Meera Sridharan, Ronald S. Go, Keith R. McCrae, Harsh Vardhan Upreti, Ming Y. Lim, Nicole K. Kocher, Radhika Gangaraju, X. Long Zheng, Jay S. Raval, Camila Masias, Spero R. Cataland, Marshall Mazepa, Shruti Chaturvedi

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Phasic and periodic change of drought under greenhouse effect by Yang Li, Zhicheng Zheng, Yaochen Qin, Haifeng Tian, Zhixiang Xie, Peijun Rong

“…In the future, the project of afforestation and returning farmland to forest and grassland in this region can increase the planting proportion of water-loving tree species to obtain better ecological benefits. …”
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Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer by Jing Wang, Zhe Li, Wei Wang, Md Tauhidul Islam, Xiaoyan Wang, Zhen Han, Zihan Li, Guoxin Li, Yuming Jiang, Taojun Zhang, Wenjun Xiong, Zepang Sun, Lequan Yu, Zhicheng Zhang, Xianqi Yang, Shengtian Sang, Alyssa A Guo

“…An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. …”
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Study on the effects of land use transformation on habitat quality and its driving mechanisms: a case study of the Qin-Mang River Basin by Jiwei Zhao, Luyao Wang, Dong Jia, Yaowen Wang

“…To improve HQ, it is recommended to strictly enforce ecological protection red lines, control the expansion of built-up areas, improve ecological compensation mechanisms, and promote ecological restoration measures such as returning farmland to forest and grassland.…”
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The impact of dietary fiber on colorectal cancer patients based on machine learning by Xinwei Ji, Lixin Wang, Pengbo Luan, Jingru Liang, Weicai Cheng

“…Additionally, four machine learning models—Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM)—were developed based on nutritional and clinical indicators.ResultsIn the observation group, levels of procalcitonin (PCT), beta-endorphin (β-EP), C-reactive protein (CRP), interleukin-1 (IL-1), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) were significantly lower compared to the control group (p &lt; 0.01). …”
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Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…Our results show that the Random Forest algorithm achieved the highest accuracy with 98 %.This research shows how machine learning techniques embedded in learning analytics could expand the functionalities of VLEs toward greater personalization and effectiveness, with every student receiving the best educational experience that suits their learning styles.…”
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Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects by Ajinkya Zalkikar, Bimal Nepal, Mani Venkata Rakesh Mutyala, Anika Varshney, Lianne Dsouza, Hazlina Husin, Om Prakash Yadav

“…Machine learning models including support vector machines, linear discriminant analysis, random forest bagging, and Artificial Neural Networks have been meticulously crafted to forecast the safety status of pipelines, considering variables such as the pipe dimensions, material characteristics, fluid velocity, and flow rate. …”
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Potential Application of Green Composites for Cross Arm Component in Transmission Tower: A Brief Review by M. R. M. Asyraf, M. R. Ishak, S. M. Sapuan, N. Yidris, R. A. Ilyas, M. Rafidah, M. R. Razman

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Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais by Fernanda Oliveira de Sousa, Victor Andre Ariza Flores, Christhian Santana Cunha, Sandra Oda, Hostilio Xavier Ratton Neto

“…The models evaluated included linear regression, random forest, decision tree, and support vector machines. …”
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EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism by Jayoti Bansal, Gaurav Gangwar, Mohammad Aljaidi, Ali Alkoradees, Gagandeep Singh

“…Results: AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model’s performance. …”
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Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis by Ziyuan Jiang, Qinkai Han, Xueping Xu

“…Subsequently, four classical machine learning models, including the support vector machine (SVM), decision tree (DT), random forest (RF), and AdaBoost, are used for fault classifications based on the features extracted via principal component analysis (PCA). …”
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Using the alternative model of personality disorders for DSM-5 traits to identify personality types, and the relationship with disordered eating, depression, anxiety and stress by Tanya Gilmartin, Caroline Gurvich, Joanna F. Dipnall, Gemma Sharp

“…A systematic four-step process using hierarchical, k-means, and random forest cluster analyses were used to identify the best fitting cluster solution in the data. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…We compared Extreme Gradient Boosting, Support Vector Regression (SVR) and Random Forest algorithms to assess community structure based on FP raw data. …”
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Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach by Tian Lei, Jia Peng, Xingliang Liu, Qin Luo

“…To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. …”
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Sierra Espuña (Librillos, 2023) by Miguel Ángel González Espinosa

“…It presents a green mantle composed of a pine forest as a result of the repopulation undertaken by Ricardo Codorníu more than a century ago, with species such as Aleppo pine, maritime pine, black pine, laricio pine and other pine varieties. …”
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