Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications by Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Mohsin Kazi, Majdi Khalid, Arnisha Akhter, Mohammad Ali Moni

“…Results The performance analysis demonstrates that among all ML algorithms, random forest surpasses the current works with an accuracy rate of 86% and 98.48% for Dataset 1 and Dataset 2; extreme gradient boosting and decision tree surpass with an accuracy rate of 99.27% and 100% for Dataset 3 and Dataset 4, respectively. …”
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An Invariant Geometric Feature for Inter-Subject Lumbar Curve Alignment to Detect Spondylolisthesis by Podchara Klinwichit, Watcharaphong Yookwan, Athita Onuean, Paramate Horkaew, Sornsupha Limchareon, Jun-Su Jang, Suwanna Rasmequan, Krisana Chinnasarn

“…Factor analysis revealed that the DR feature contributed most significantly, with an average factor loading of 32.1% for the AP view, where the second highest factor loading was 22.4% by ASD, and 42.3% for the LA view, where the second highest factor loading was 24.0% by SDR, across three classifiers—Support Vector Classifier (SVC), Random Forest Classifier (RF), and Gradient Boosting Classifier (GB). …”
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Modeling and Solving Multi-Objective Path Planning Problem for Cooperative Cable-Suspended Load Transportation Considering the Time Variable Risk by Amir Arslan Haghrah, Sehraneh Ghaemi, Mohammad Ali Badamchizadeh

“…To demonstrate the efficiency of the proposed model and algorithm, three case studies inspired by a fire incident in a forest park have been conducted, and the results have been compared with those of seven other well-known algorithms.…”
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Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights by Shahzad Nazir, Muhammad Asif, Shahbaz Ahmad, Hanan Aljuaid, Rimsha Iftikhar, Zubair Nawaz, Yazeed Yasin Ghadi

“…Citations were classified with Support Vector Machine, Multilayer Perceptron, and Random Forest. F-measure, Recall, and Precision were considered to evaluate the results, compared with the state-of-the-art results. …”
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Preventing Escape of Non-Native Species from Aquaculture Facilities in Florida, Part 4: Operational Strategies by Quenton M. Tuckett, Carlos V. Martinez, Jared L. Ritch, Katelyn M. Lawson, Jeffery E. Hill

“…Hill, and published by the School of Forest Resources and Conservation, Program in Fisheries and Aquatic Sciences, August 2016. …”
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Confidence-Aware Ship Classification Using Contour Features in SAR Images by Al Adil Al Hinai, Raffaella Guida

“…Alongside the SVM, a random forest (RF) and a Gaussian process classifier (GPC) were used to examine the effect of entropy derivation from different classifiers while assessing feature robustness. …”
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Evaluating GEMS HCHO Retrievals With TROPOMI Product, Pandora Observations, and GEOS‐Chem Simulations by Weitao Fu, Lei Zhu, Hyeong‐Ahn Kwon, Rokjin J. Park, Gitaek T. Lee, Isabelle DeSmedt, Song Liu, Xicheng Li, Yuyang Chen, Dongchuan Pu, Juan Li, Xiaoxing Zuo, Peng Zhang, Yali Li, Zhuoxian Yan, Xue Zhang, Jiaming Zhang, Xingyi Wu, Huizhong Shen, Jianhuai Ye, Chen Wang, Tzung‐May Fu, Xin Yang

“…Diurnally, we find strong HCHO variations over urban areas but not in forests. During the fire season of mainland Southeast Asia, GEMS HCHO increases in the afternoon, in line with diurnal emission estimates from the Global Fire Emissions Database Version 4 with small fires (GFED4s) and GEOS‐Chem simulations. …”
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An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer by Xue-Feng Gao, Can-Gui Zhang, Kun Huang, Xiao-Lin Zhao, Ying-Qiao Liu, Zi-Kai Wang, Rong-Rong Ren, Geng-Hui Mai, Ke-Ren Yang, Ye Chen

“…The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.Results GC patients with <3 years of survival showed a higher abundance of Aggregatibacter and diminished abundances of Filifactor and Moryella than those who survived ≥3 years. …”
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Towards Circular Economy through Waste to Biomass Energy in Madagascar by Ling Qin, Mengjun Wang, Jinfu Zhu, Yuhu Wei, Xintao Zhou, Zheng He

“…This paper reviews the following: (1) a variety of available biomass wastes for energy in Madagascar including farming residuals, animal wastes, and forest wastes, as well as urban and industrial organic wastes; (2) advanced technologies, such as gasification, torrefaction, and fermentation, that can convert these wastes to biomass energy in forms with higher energy efficiency such as biogas, biocoal briquette, and ethanol fuel, which can not only help to achieve resource utilization of wastes and circular economy but also ease the energy crisis faced by Madagascar; and (3) Madagascar focused on the development of biomass energy with strategic policies and programs. …”
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Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

“…These top-15 feature pairs were fed into five different ML models (decision tree (DT), neural networks (NN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB)) for predicting the likelihood of diabetes, while also feeding the direct features (without correlated pairing) separately into the same 5[Formula: see text]ML models. …”
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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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Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy by Jing-Mei Yin, Yang Li, Jun-Tang Xue, Guo-Wei Zong, Zhong-Ze Fang, Lang Zou

“…We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. …”
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Volatile Organic Compounds for the Prediction of Lung Cancer by Using Ensembled Machine Learning Model and Feature Selection by Divya Khanna, Arun Kumar, Shahid Ahmad Bhat

“…With 90% accuracy on a validation dataset, the random forest model predicts pleural fluid volatile organic compounds efficiently. …”
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From Policy to Practice: Assessing Carbon Storage in Fujian Province Using Patch-Generating Land Use Simulation and Integrated Valuation of Ecosystem Services and Tradeoffs Models by Qin Nie, Wang Man, Zongmei Li, Xuewen Wu

“…The results show that carbon storage declined from 1995 to 2020, mainly due to the conversion of forests and agricultural land into construction areas. …”
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The effect of ranger patrols on the sighting of large mammals by local herders in Northern Iran by Farid Salmanpour, Zahra Shakoori, Mahan Salmanpour, Mehdi Tizrouyan, Mehdi Kia, Rahman Eshaghi, Saeid Ghomi, Abolfazl Rahbarizadeh, Rahman Naeimaei, Mehdi Ghaderi

“…This study investigates the impact of ranger patrols on large mammal sightings in the Central Alborz Protected Area (CAPA), northern Iran, a crucial wildlife corridor with UNESCO-listed Hyrcanian forests and high-altitude grasslands. CAPA also supports livestock grazing in over 200 villages, making it a key area for studying human-wildlife interactions. …”
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Rockwood Clinical Frailty Scale as a predictor of adverse outcomes among older adults undergoing aortic valve replacement: a protocol for a systematic review by Rose Galvin, Ahmed Gabr, Catherine Peters, Margaret O'Connor, Aoife Leahy, Elaine Shanahan, Tadhg Prendiville, Laura Quinlan, Anastasia Saleh, Ivan Casserly

“…Data will be plotted on forest plots where applicable. The quality of the evidence will be determined using the Grading of Recommendations, Assessment, Development and Evaluation tool.Ethics and dissemination Ethical approval is not required for this study as no primary data will be collected. …”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

“…The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics.ResultsThe analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. …”
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Exploring microbial players for metagenomic profiling of carbon cycling bacteria in sundarban mangrove soils by Basanta Kumar Das, Ayushman Gadnayak, Hirak Jyoti Chakraborty, Smruti Priyambada Pradhan, Subhashree Subhasmita Raut, Sanjoy Kumar Das

“…Abstract The Sundarbans, the world’s largest tidal mangrove forest, acts as a crucial ecosystem for production, conservation, and the cycling of carbon and nitrogen. …”
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Deforestation changes the effectiveness of bed nets for malaria control by Tafesse Kefyalew Estifanos, Brendan Fisher, Taylor H. Ricketts

“…Abstract Land use changes such as deforestation and the degradation of tropical forests can be key drivers of infectious disease, including malaria. …”
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Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions by Zhong Zhuang, Hao Qi, Siyu Huang, Qiqi Wang, Yanan Wan, Huafen Li

“…Results revealed that fertilization, particularly with mineral phosphorus, disrupts soil stability, substantially increases short-term Cd mobility in vulnerable regions. Random Forest analysis identified elevated dissolved organic matter and pH changes as key drivers of Cd remobilization. …”
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