An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms by Anwer Mustafa Hilal, Maha M. Althobaiti, Taiseer Abdalla Elfadil Eisa, Rana Alabdan, Manar Ahmed Hamza, Abdelwahed Motwakel, Mesfer Al Duhayyim, Noha Negm

“…The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. …”
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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…In this work, we explore the application of machine learning algorithms to suggest suitable parameter values. We propose a methodology to use k-nearest neighbours and artificial neural network algorithms to predict suitable parameter values based on instance features. …”
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Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms by Li Yang, Shixin He, Li Tang, Xiao Qin, Yan Zheng

“…Recent studies have extensively highlighted the strong associations between psoriasis and various inflammatory markers, which are considered novel predictive tools for evaluating systemic inflammation.MethodsCross-sectional data from the NHANES were analyzed in this study. …”
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Optimizing Crop Yield Prediction: An In-Depth Analysis of Outlier Detection Algorithms on Davangere Region by C. S. Anu, C. R. Nirmala, A. Bhowmik, A. Johnson Santhosh

“…Crop yield prediction is a critical aspect of agricultural planning and resource allocation, with outlier detection algorithms playing a vital role in refining the accuracy of predictive models. …”
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Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species by Irene T. Liao, Karen E. Sears, Lena C. Hileman, Lachezar A. Nikolov

“…While the diploid + higher ploidy set had a lower proportion of orthogroups with identical compositions, the average degree of similarity between the orthogroups was not different from the diploid set. Discussion Three algorithms—OrthoFinder, SonicParanoid, and Broccoli—are helpful for initial orthology predictions. …”
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Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms by Benjamin Mukanya Ntumba, Jean Paul Ngbolua Koto-Te-Nyiwa, Blaise Bikandu Kapesa, Nathanael Kasoro Mulenda

“…Based on the RS126 dataset, we compared our hybrid model with individual approaches, revealing that our model achieves an accuracy of 80% and a Q3 score of 86%, outperforming each of the algorithms separately. These results validate the effectiveness of combining models for protein secondary structure prediction (PSSP). …”
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Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms by Yonghan Luo, Wenrui Ding, Xiaotao Yang, Houxi Bai, Feng Jiao, Yan Guo, Ting Zhang, Xiu Zou, Yanchun Wang

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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. …”
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Risk Factors and Prediction Model for Postoperative Pneumonia Following Hip Arthroplasty in Older Adults by Xiang B, Zhang J, Deng C, Yang H, Qian L, Zhang W

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Large Linguistic Corpus Reduction with SCP Algorithms by Nelly Barbot, Olivier Boëffard, Jonathan Chevelu, Arnaud Delhay

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DEVELOPING PREDICTIVE MODELS OF INTERNET SERVICE STRATEGIES by Maxim Yu. Khramov

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Predictive channel scheduling algorithm between macro base station and micro base station group by Yinghai XIE, Ruohe YAO, Bin WU

“…A novel predictive channel scheduling algorithm was proposed for non-real-time traffic transmission between macro-base stations and micro-base stations in 5G ultra-cellular networks.First,based on the stochastic stationary process characteristics of wireless channels between stationary communication agents,a discrete channel state probability space was established for the scheduling process from the perspective of classical probability theory,and the event domain was segmented.Then,the efficient scheduling of multi-user,multi-non-real-time services was realized by probability numerical calculation of each event domain.The theoretical analysis and simulation results show that the algorithm has low computational complexity.Compared with other classical scheduling algorithms,the new algorithm can optimize traffic transmission in a longer time dimension,approximate the maximum signal-to-noise ratio algorithm in throughput performance,and increase system throughput by about 14% under heavy load.At the same time,the new algorithm is accurate.Quantitative computation achieves a self-adaption match between the expected traffic rate and the actual scheduling rate.…”
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Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors by Michael Reich, Njira Lugogo, Laurie D Snyder, Megan L Neely, Guilherme Safioti, Randall Brown, Michael DePietro, Roy Pleasants, Thomas Li, Lena Granovsky

“…This analysis aimed to determine if a machine learning algorithm capable of predicting impending exacerbations could be developed using data from an integrated digital inhaler.Patients and methods A 12-week, open-label clinical study enrolled patients (≥40 years old) with COPD to use ProAir Digihaler, a digital dry powder inhaler with integrated sensors, to deliver their reliever medication (albuterol, 90 µg/dose; 1–2 inhalations every 4 hours, as needed). …”
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Safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm for robotic system by Wang Helin

Subjects: “…nonlinear optimal predictive control…”
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Advanced predictive disease modeling in biomedical IoT using the temporal adaptive neural evolutionary algorithm by Chandragandhi S, Arvind C, Srihari K

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Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights by Priya Metri, Swetta Kukreja

“…Among these, Random Forest and SVM emerged as the most commonly used algorithms, featured in 35 % and 27 % of studies respectively. …”
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Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare by Okan Bulut, Bin Tan, Elisabetta Mazzullo, Ali Syed

“…Originally developed as an effective feature selection method in healthcare predictive analytics, Recursive Feature Elimination (RFE) has gained increasing popularity in Educational Data Mining (EDM) due to its ability to handle high-dimensional data and support interpretable modeling. …”
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Predictors of Occupational Adaptation in Individuals With Parkinson’s Disease by Jafar Haj Ghani, Ghorban Taghizadeh, Akram Azad, Seyed Amir Hasan Habibi

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Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

“…This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. …”
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AVS3 intra frame prediction parallel algorithm based on minimum CU cost by ZHANG Quan, WANG Shun, LIU Yangyi, DUAN Chang, PENG Bo

“…To address the time-consuming issue of audio video coding standard 3(AVS3) intra frame prediction, an intra frame prediction parallel algorithm based on the cost of the minimum coding unit (CU) was proposed. …”
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