Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis by M Darshan Teja, G Mokesh Rayalu

“…Model performance was assessed using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). …”
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PSLDV-Hop: a robust localization algorithm for WSN using PSO and refinement process by Bhupinder Kaur, Deepak Prashar, Arfat Ahmad Khan, Seifedine Kadry, Jungeun Kim

“…By utilizing an improved iterative evolution algorithm, the PSLDV-Hop algorithm reduces localization errors by achieving a higher degree of accuracy in node localization. …”
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Refractive Profile and Angle of Deviation in Patients with Congenital Esotropia and Congenital Exotropia by Masoud Khorrami-Nezhad, Mohamad Reza Akbari, Ali Hassan Khaleel, Razieh Bahreini, Froozan Narooie-Noori, Babak Masoomian

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Visualización multi-escala de aciertos y errores de un mapa de usos de suelo: El caso de la cuenca del lago de Cuitzeo, Michoacán, México by Stephane Couturier, Valdemar Coria, Yannick Deniau, Francisco Javier Osorno Covarrubias

“…Presentamos aquí la visualización de aciertos y errores, con criterios de lógica difusa, del mapa de usos de suelo de alta taxonomía de la cuenca del lago de Cuitzeo extraído de la cartografía a escala 1:250,000 del Inventario Forestal Nacional del año 2000 en México. …”
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Nanocellulose Addition to Paper and the ‘Cai Lun Principle’ – Maybe Not Such a Good Idea After All by Martin A. Hubbe

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The reduction of the standard of census route length using double lamination of sample by V. M. Glushkov

“…Census data were recorded and processed by two methods: traditional - winter route census (WRC) with grouping of sample by category of land (forest, field, swamp), and the new one with grouping of segments of the route according to the level of linear density (trace / 1 km of route), separately for each stratum. …”
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USING OF REMOTE SENSING IN NATURAL RESOURCE OF FOREST MANAGEMENT AT ZAWITA FOREST REGION

“…The result showed that we obtained six land cover types (dense forests, open forests, pastures, agricultural lands, soil and rocky lands). …”
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Forest attribute maps: a support for small area estimation of forest disturbances by Ankit Sagar, Cédric Vega, Olivier Bouriaud, Christian Piedallu, Jean-Pierre Renaud

“…Abstract Key Message Forest attribute maps generated using national forest inventory and remote sensing data can be used to quantify forest attributes (i.e., growing stock volume, basal area) affected by disturbance events (like bark-beetle induced damage) through small area estimation techniques. …”
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Empirical Study on Myopia Identification Using CNN Hereditary Model for Resource Constrained Ophthalmology by Aqila Nazifa, Manisha Shivaram Joshi, Soumya Ramani

“…During capturing, the photos are processed using recent image processing techniques to identify any irregularities or asymmetries that may indicate refractive errors. By comparing our method to other current models, we hope to illustrate the advantage of our Hereditary model, which combines a random forest and a convolutional neural network, in accurately diagnosing and classifying refractive errors. …”
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The ecological forecast limit revisited: Potential, absolute and relative system predictability by Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington, William J. Castillo, Florian Pappenberger, Carsten F. Dormann

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The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive by A. Viana-Soto, C. Senf

“…<p>Forests in Europe are undergoing complex changes that require a comprehensive monitoring of disturbance occurrence. …”
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Hybrid regression method to predict forest variables from Earth observation data in boreal forests by Eelis Halme, Matti Mõttus

“…This study introduces a hybrid regression method, integrating the forest reflectance and transmittance model FRT with a random forest regressor. …”
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Optimizing collection methods for noninvasive genetic sampling of neotropical felids by Claudia Wultsch, Lisette P. Waits, Eric M. Hallerman, Marcella J. Kelly

“…Additionally, we tested fecal samples collected from 4 different locations on the scat (top, side, bottom, inside) at 2 different tropical forest types (tropical broadleaf and tropical pine forests). …”
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ForestAlign: Automatic forest structure-based alignment for multi-view TLS and ALS point clouds by Juan Castorena, L. Turin Dickman, Adam J. Killebrew, James R. Gattiker, Rod Linn, E. Louise Loudermilk

“…Access to highly detailed models of heterogeneous forests, spanning from the near surface to above the tree canopy at varying scales, is increasingly in demand. …”
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Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques by Isteaq Kabir Sifat, Tajin Ahmed Jisa, Jyoti Shree Roy, Nourin Sultana, Farhana Hasan, Md Parvez Mosharaf, Md. Kaderi Kibria

“…ABSTRACT Background and Aims Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. …”
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize by Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun, Xiuliang Jin

“…Key scientific findings demonstrate: (1) SEBAL outperformed FAO-56 in daily ET estimation (R² = 0.76 vs. 0.71, RMSE = 1.15 vs. 1.31 mm/d). (2) The machine learning yield model exhibited robust predictive capability (R² = 0.77, RMSE = 0.98 t/ha), successfully capturing yield variability across treatments. (3) Error propagation analysis validated framework reliability (CWP RMSE = 0.67 kg/m³), effectively differentiating CWP performance among management practices. …”
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Hydrological and hydrodynamic coupling simulation under composite underlying surfaces in urban polder areas by Cheng Chen, Binquan Li, Yang Xiao, Huihui Li, Taotao Zhang, Dong Xu, Huanghao Yu

“…A BP neural network (BPNN) was employed for error correction to reduce model uncertainty in forecasting. …”
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Leveraging Open-Source Tools to Analyse Ground-Based Forest LiDAR Data in South Australian Forests by Spencer O’Keeffe, Bruce H. Thomas, Jim O’Hehir, Jan Rombouts, Michelle Balasso, Andrew Cunningham

“…Results showed that stratified tool selection, optimized for each forest development stage, achieved high accuracy for inventory, achieving stem detection rates up to 99.1% and errors as low as 0.94 m for height and 1.18 cm for diameter at breast height (DBH) in specific cases. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Finally, five kinds of machine learning methods, random forest, extreme gradient boosting, light gradient boosting machine, linear support vector classifier and support vector machine, were used to establish classification prediction models, and error-correcting output codes were used to optimize each model. …”
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Comparative Analysis of Ultra-Wideband and Mobile Laser Scanning Systems for Mapping Forest Trees under A Forest Canopy by Z. Liu, H. Kaartinen, T. Hakala, H. Hyyti, A. Kukko, A. Kukko, J. Hyyppa, J. Hyyppa, R. Chen

“…To our best knowledge, this is the first study to compare UWB and MLS for mapping forest trees in the literature. The experimental results show that the proposed method can accurately measure tree stem locations under the forest canopy with a root-mean-square-error (RMSE) of 14.44 cm and a mean-absolute-error (MAE) of 12.39 cm, providing accuracy comparable to that of the three tested MLSs. …”
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