From Herbarium to Landscape: New Records and Mapping Rare and Threatened Species of Brazilian Atlantic Rainforest by Otávio Miranda Verly, Luiz Claudio Medeiros Cabral‐da‐Silva, Marcos Sobral, Klisman Oliveira, Laura Beatriz Assis Teixeira, Maria Paula Miranda Xavier Rufino, Aline Ferreira deMendonça, Kesleyane Pereira Camilo, Carlos Moreira Miquelino Eleto Torres

“…Atlantic Rainforest of Minas Gerais state, Brazilian southeastern. We used multi‐level forest inventory data from 137 plots across nine Semideciduous Seasonal Forest fragments, sampled 1–9 times over 30 years. …”
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A multi-level, multi-scale comparison of LiDAR- and LANDSAT-based habitat selection models of Mexican spotted owls in a post-fire landscape by Ho Yi Wan, Michael A. Lommler, Samuel A. Cushman, Jamie S. Sanderlin, Joseph L. Ganey, Andrew J. Sánchez Meador, Paul Beier

“…Both models had low out-of-bag (OOB) error rates (0.037 for LANDSAT and 0.050 for LiDAR), indicating high classification reliability. …”
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A Comparative Analysis of Machine Learning Models in News Categorization by Mohammad Hossein Zolfagharnasab, Siavash Damari

“…Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline. …”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. …”
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Assessing Environmental Policy Impact through the Ecological Footprint: The Case of Türkiye by Sacit Sarı

“…This study investigates the long-term effectiveness of environmental policies in Türkiye by examining the stochastic properties of the ecological footprint (EF) and its six subcomponents, carbon footprint, cropland footprint, grazing land footprint, forest products footprint, fishing grounds footprint, and built-up land footprint over the period 1961–2022. …”
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Movie Box Office Prediction Based on IFOA-GRNN by Wei Lu, Xiaoqiao Zhang, Xinchen Zhan

“…By using the actual box office data and performing virtual simulation calculations, the root means square error of the IFOA-GRNN model predicting the movie box office is 0.3412, and the classification accuracy is about 90%. …”
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Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST by Abdullah Hulusi Kökçam, Gültekin Çağıl, Şevval Toprak

“…According to the calculated error metrics, LSTM and RFR algorithms gave better results than CNN with an MSE value less than 0.02. …”
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DISPUTES IN THE APPLICATION OF EMPLOYMENT COPYRIGHT LAW RELATING TO THE PRINCIPLE OF STRICT LIABILITY IN ENVIRONMENTAL CLUSTERS by Mashudi Mashudi, Abdul Basid

“…In this instance, it is evident in the environmental cluster of Article 22 Number 33 of the Job Creation Law, where the phrase "without the need to prove elements of error" is omitted, leading to ambiguity in its meaning. …”
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Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets by Alysha van Duynhoven, Suzana Dragićević

“…The RF-SSW, NN-SSW, and XGB-SSW models forecasted more realistic changes across multiple timesteps with fewer errors than baseline configurations. The presented methodology provides a step toward establishing spatialized cost-sensitive learning strategies and extending classical ML models to multitemporal LC datasets.…”
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Experimental-Numerical Study of Indexation of Scenic Road Vertical Alignment in China by Ronghua Wang, Xingliang Liu, Zhe Yuan

“…According to verification results, relative errors of climbing velocity vary from 0.0104 to 0.0205, showing the dynamic model’s accuracy and further proving the practicality of MLS and LSL values obtained. …”
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Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset by Ali Suliman AlSalehy, Mike Bailey

“…Linear interpolation excelled for short gaps (R² up to 0.97), and PCHIP and Akima minimized errors in sequential gaps (R² up to 0.95, lowest MSE). …”
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Integrating system dynamics and machine learning for environmental impact analysis of building materials in the demolition process by Babatunde Oluwaseun Ajayi, Mayowa Emmanuel Bamisaye, Hugo Miguel Magalhães Macedo, Issara Sereewatthanawut

“…The RF model shows strong predictive accuracy, with R-squared values of 0.9776 to 0.9990 and mean squared errors between 0.0003 and 0.0356. Among the combinations, OEIC4—comprising a demolition robot and hydraulic splitter—proves to be the most environmentally sustainable, producing impacts up to four times lower than the others. …”
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Problems of modelling carbon biogeochemical cycle in agricultural landscapes by O.E. Sukhoveeva

“…The latter ones fall into carbon cycle models (agroecosystem, phytocenosis, and greenhouse gases (СО2 and СН4) emissions) and carbon-nitrogen cycle models (various ecosystems, forest, and microbiological). The following difficulties arising when mathematical methods are used for description of the carbon cycle were discussed: multiple methods of calculation; high requirements to input data; limited availability of input information; necessity to consider climate change; errors in description of the functional dependence of CO2 emission on temperature. …”
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Patient Safety Culture Among Healthcare Settings in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis by Natnael Atnafu Gebeyehu, Kirubel Tegegne, Biruk Admass, Nathan Shewangashaw, Yibeltal Atalay, Dagne Sewuyew, Awoke Gebremariam, Kelemu Abebe Gelaw

“…To evaluate publication bias, methods such as Egger's regression tests, rank tests, and forest plots were utilized. The I2 statistic was used to assess heterogeneity, followed by an overall estimated analysis. …”
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Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction by İrem Üstek, Miguel Arana‐Catania, Alexander Farr, Ivan Petrunin

“…The second approach used a deep autoencoder to reconstruct the input data and use reconstruction errors to identify anomalies. Long Short‐Term Memory autoencoders and fully connected (FC) autoencoders were employed in this part, both in an unsupervised way learning only from nominal data. …”
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CIA-UNet: An Attention-Enhanced Multi-Scale U-Net for Single Tree Crown Segmentation by Jiapeng Duan, Chuanzhao Tian, Wansi Liu, Lixiang Cao, Teng Feng, Xiaomin Tian

“…Accurate segmentation of single tree crowns enables precise measurement of tree height, DBH, and stock volume, facilitating effective assessment of forest growth and productivity. However, single tree crown segmentation currently faces several challenges, including insufficient segmentation accuracy, mis-segmentation and crown adhesion. …”
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Use of modified snares to estimate bobcat abundance by Heather K. Stricker, Jerrold L. Belant, Dean E. Beyer Jr., Jeanette Kanefsky, Kim T. Scribner, Dwayne R. Etter, Jean Fierke

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Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion by Jiayi Ge, Xiaoming Zhang, Juan Li, Huijuan Wang, Dawei Xu, Xiaojun Jiang

“…Cross validation and independent testing show that AadRF reduces the root mean square error for albedo and diameter predictions by 64.0 percentage points and 70.2 percentage points, respectively, compared to the traditional method, with corresponding mean absolute percentage errors of 28.9% and 17.5%. …”
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Accuracy fluctuations of ICESat-2 height measurements in time series by Xu Wang, Xinlian Liang, Weishu Gong, Pasi Häkli, Yunsheng Wang

“…To bridge the knowledge gap, this study analyzes 59 months of ATL08 version 006 data in Finland to assess terrain and surface height accuracy, with a focus on temporal fluctuations across six major land cover types. A random forest (RF) model is employed to quantify the relative importance of error factors affecting height accuracy. …”
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Time Series Modelling of the Caspian Kutum (Rutilus frisii) Catch Using SARIMA Model by Fateh Moezzi, Hadi Poorbagher, Soheil Eagderi, Jahangir Feghhi

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