Assessment of a decentralization model in improving treatment and care of visceral leishmaniasis in Turkana County, Kenya: A mixed method study. by Jane Mbui, Mariam Macharia, Dawn Maranga, Collins Okoyo

“…<h4>Background</h4>Visceral Leishmaniasis (VL) is a vector-borne disease caused by the protozoa Leishmania and transmitted by sandflies. …”
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Scalable and Efficient Multi-Class Brain Tumor Classification with a Compact Hybrid Deep Learning Model for Real-Time Applications by Sohaib R. Awad, Amar I. Daood, Akram A. Dawood

“…A newly engineered compact CNN model linked with an SVM classifier brought decreased model dimensions while keeping excellent accuracy rates. …”
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Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms by Shangmei Cao, Shaozhe Yang, Bolin Chen, Xixia Chen, Xiuhong Fu, Shuifu Tang

“…Using the decision tree, random forest, support vector machine, and extreme gradient boosting (Xgboost) to establish a differential diagnosis model for PMN and non-PMN. …”
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Machine Learning-Based Virtual Screening and Molecular Modeling Reveal Potential Natural Inhibitors for Non-Small Cell Lung Cancer by Zafer Saad Al Shehri, Faez Falah Alshehri

“…Several machine-learning techniques were used in our work, including k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). …”
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Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data by Fangqiao Wei, Zailong Wu, Guanghui Li, Xiangyu Sun, Xiangru Shi, Lei Tan, Tianxiang Ai, Long Qu, Shuguo Zheng

“…Conclusion The current work provided reliable diagnostic models for early childhood caries, and established a robust computational framework for AI-driven microbiome analysis. …”
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Development and Validation of a Neonatal Hypothermia Prediction Model for In-Hospital Transport Using Machine Learning Algorithms: A Single-Center Retrospective Study by Zhang W, Gu X, Gu C, Yao L, Zhang Y, Wang K

“…Six machine learning algorithms—Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naive Bayes (NB)—were used to develop predictive models. …”
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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…Four machine learning models were developed: random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN). …”
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Modeling, Control, and Simulation of Battery Storage Photovoltaic-Wave Energy Hybrid Renewable Power Generation Systems for Island Electrification in Malaysia by Nahidul Hoque Samrat, Norhafizan Bin Ahmad, Imtiaz Ahmed Choudhury, Zahari Bin Taha

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Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique by Hasnain Iftikhar, Moiz Qureshi, Paulo Canas Rodrigues, Muhammad Usman Iftikhar, Javier Linkolk Lopez-Gonzales, Hasnain Iftikhar

“…On the other hand, the short-run random part is modeled and forecasted using four benchmark time series (linear and non-linear AR, ARMA, ESM) and four machine learning (Neural network autoregressive, Random forest, support vector regression with polynomial and radial basal functions) models. …”
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A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers by Mehtab Ur Rahman, Cem Direkoglu

“…We conducted a comparative analysis against related works, including the Mel frequency cepstral coefficient (MFCC), MFCC-glottal features, and features extracted using the wav2vec and HuBERT models with SVM classifier. …”
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A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making. by Alexander C Keyel, Morgan E Gorris, Ilia Rochlin, Johnny A Uelmen, Luis F Chaves, Gabriel L Hamer, Imelda K Moise, Marta Shocket, A Marm Kilpatrick, Nicholas B DeFelice, Justin K Davis, Eliza Little, Patrick Irwin, Andrew J Tyre, Kelly Helm Smith, Chris L Fredregill, Oliver Elison Timm, Karen M Holcomb, Michael C Wimberly, Matthew J Ward, Christopher M Barker, Charlotte G Rhodes, Rebecca L Smith

“…In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. …”
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PAPR Reduction of OTSM with Random and Orthogonal SLM Phase Sequences and its Recovery in the Presence of EPA, EVA and ETU Channel Models by M. I. Al-Rayif

“…Consequently, the PAPR (without explicit side information) of the original phase vectors is reduced and restored in the presence of a nonlinear power amplifier, and three different multipath fading channel models: Extended pedestrian A (EPA), Extended vehicular A (EVA) and Extended typical urban (ETU), with different user speed, namely 150 and 500 km/h. …”
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Construction of Q&A methods based on knowledge graphs and large language models-improving the accuracy of landscape pest and disease Q&A by Zhixin Gu, Ting Long, Shuairan Wang, Xiaowei Shang, Weizheng Shen, Xiaoli Wei, Kaihong Xu

“…By vectorizing the knowledge and using similarity matching, the most relevant data is retrieved, combined with the question to form prompts, and input into the language model to generate natural language answers. …”
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Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation by Vlatko Nikolovski, Dimitar Trajanov, Ivan Chorbev

“…The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. …”
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Approximate Solution of the Fractional Order Sterile Insect Technology Model via the Laplace–Adomian Decomposition Method for the Spread of Zika Virus Disease by William Atokolo, Remigius Okeke Aja, Stephen Ekwueme Aniaku, Ifeanyi Sunday Onah, Godwin C. E. Mbah

“…The fractional order sterile insect technology (SIT) model to reduce the spread of Zika virus disease is considered in this present work. …”
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Fractional-Order System Identification: Efficient Reduced-Order Modeling with Particle Swarm Optimization and AI-Based Algorithms for Edge Computing Applications by Ignacio Fidalgo Astorquia, Nerea Gómez-Larrakoetxea, Juan J. Gude, Iker Pastor

“…This work proposes a hybrid framework that combines Particle Swarm Optimization (PSO) with various artificial intelligence (AI) techniques to estimate reduced-order models of fractional systems. …”
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Study on the risk identification of abnormal gas outbursts based on the mechanism of biological immunity by Jufeng Zhang, Shiliang Shi, Lizhi Zhang

“…The results show: (1) The adaptive recognition algorithm based on T-B cell principles can adaptively recognize the characteristic vectors of abnormal gas outbursts by adaptive adjustment of detectors and cloning and mutation of learning vectors, achieving the recognition and memorization of known or unknown feature vectors under dynamically changing environmental conditions. (2) The adaptive recognition algorithm for abnormal gas outbursts based on T-B cell principles and the type recognition algorithm for abnormal gas outbursts based on the Dynamic Time Warping algorithm, combined with the characteristics of biological immune systems, construct a risk identification model for abnormal gas outbursts based on the biological immune mechanism, which has the characteristics of adaptability, learning, and memorization. (3) Taking a abnormal gas outburst event in a certain 9111 working face of a mine in Huaibei as an example, the model was verified by inputting the characteristic vectors of abnormal gas outbursts and the output of the risk identification of abnormal gas outbursts based on the biological immune mechanism. …”
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The Development of Enterprise Competencies in the Context of Digitalization by Serhiienko Olena A., Ippolitova Inna Yaroslavovna

“…During the research, the works of domestic scientists were analyzed, which highlighted current issues of ensuring enterprise competencies and the impact of digitalization on the functioning of enterprises. …”
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To bounce or not to bounce in generalized Proca theory and beyond by Lara Bohnenblust, Serena Giardino, Lavinia Heisenberg, Nadine Nussbaumer

“…Abstract It is notoriously difficult to construct a stable non-singular bouncing cosmology that avoids all possible instabilities throughout the entire evolution of the universe. In this work, we explore whether a non-singular bounce driven by a specific class of modifications of General Relativity, the vector-tensor generalized Proca theories, can be constructed without encountering any pathologies in linear perturbation theory. …”
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Estimation and prediction on the economic burden of schistosomiasis in 25 endemic countries by Xian-Fa Chen, Qin Li, Robert Bergquist, Jin-Xin Zheng, Su-Ying Guo, Qiu-Feng Lan, Zheng-Ze He, Li-Juan Zhang, Chun-Li Cao, Jing Xu, Xiao-Nong Zhou

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