Dual Passive-Aggressive Stacking k-Nearest Neighbors for Class-Incremental Multi-Label Stream Classification by Hann Hsen Tan, Chu Kiong Loo, Chaw Seng Woo

“…MetakNN includes label-specific fitness for stored instances and a fitness rejuvenating mechanism based on relative performance to the base predictions for enhanced synergy. Our experiment compares DPAkNN against state-of-the-art kNN-based MLSC algorithms on 30 multi-label benchmark datasets. …”
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A Machine Learning Approach to Analyze Manpower Sleep Disorder by Reza Amiri

“…Moreover, a combination of machine learning and metaheuristic algorithms such as eXtreme Gradient Boosting and particle swarm optimization are used to make an accurate predictive model. …”
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Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid by Josue N. Otshwe, Bin Li, Songsong Chen, Feixiang Gong, Bing Qi, Ngouokoua J. Chabrol

“…System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. …”
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Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models by Yunye Shi, Diego Mauricio Yepes Maya, Electo Silva Lora, Albert Ratner

“…These algorithms outperform traditional models by accurately handling complex, high-dimensional data more efficiently and cost-effectively. …”
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Urban heat island classification through alternative normalized difference vegetation index by N. Chanpichaigosol, C. Chaichana, D. Rinchumphu

“…Future studies could expand to other urban areas, incorporate additional variables, and refine predictive algorithms for broader applications. This study will serve as a foundation for the development of future real-time monitoring tools that will enable proactive and sustainable solutions to UHI problems.…”
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Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances by Mehmet Eren Ahsen

“…Ensemble learning, particularly unsupervised ensemble approaches, emerges as a compelling solution by integrating predictions from multiple algorithms to leverage their strengths and mitigate weaknesses. …”
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Housing Value Forecasting Based on Machine Learning Methods by Jingyi Mu, Fang Wu, Aihua Zhang

“…In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. And these algorithms are compared according to the predicted results. …”
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A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks by Hatice Büşra Lüleci, Dilara Uzuner, Müberra Fatma Cesur, Atılay İlgün, Elif Düz, Ecehan Abdik, Regan Odongo, Tunahan Çakır

“…The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. …”
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Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods by Panagiota-Kyriaki Revelou, Efstathia Tsakali, Anthimia Batrinou, Irini F. Strati

“…Conclusively, applying ML algorithms allows real-time monitoring and predictive analytics and can significantly reduce the risks associated with ASF consumption.…”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. …”
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Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury by Aref Andishgar, Maziyar Rismani, Sina Bazmi, Zahra Mohammadi, Sedighe Hooshmandi, Behnam Kian, Amin Niakan, Reza Taheri, Hosseinali Khalili, Roohallah Alizadehsani

“…Nineteen variables were assessed using ML algorithms to predict outcomes. Data preparation addressed missing values and balancing methods corrected imbalances. …”
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Proximal remote sensing of dissolved organic matter in aqua-culture ponds via multi-temporal spectral correction by Wenxu Lv, Yancang Wang, Huiqiong Cao, Peng Cheng, Xiaohe Gu, Zhuoran Ma, Mengjie Li, Ruiyin Tang, Qichao Zhao, Xuqing Li, Lan Zhang, Shuaifei Liu

“…Compared with the original spectrum, an average R2 improvement of 30.67%, along with reductions of 17% in RMSE and 11.67% in MAE. Among the three algorithms, the Random Forest model yielded the best performance, with an R2 of 0.82, RMSE of 3.1 mg/L, and MAE of 2.37 mg/L on the test set. …”
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METHODOLOGY OF DETERMINING FORECASTING CONTROLLERS OF DISTRIBUTED GENERATION PLANTS by Yu. N. Bulatov, A. V. Kryukov, N. V. Huan

“…Therefore, the predictive algorithms built on the basis of the model laws of regulation may prove to be very promising for real systems of technological process control, especially in the need to accelerate the commissioning of objects, such as distributed generation (DG) plants, working on the basis of synchronous generators with automatic excitation and speed controls (AEC and ASC). …”
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Active Learning for Medical Article Classification with Bag of Words and Bag of Concepts Embeddings by Radosław Pytlak, Paweł Cichosz, Bartłomiej Fajdek, Bogdan Jastrzębski

“…The proper choice of text representation for such algorithms may have a significant impact on their predictive performance. …”
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Application of Digital Twin Technology in Information and Measurement Systems by V. A. Baronova, N. V. Romantsova

“…This information was further used to develop a mathematical and algorithmic support for predicting the state of technological equipment. …”
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Predictors of Acute Myocardial Infarction: A Machine Learning Analysis After a 7-Year Follow-Up by Marco Casciaro, Pierpaolo Di Micco, Alessandro Tonacci, Marco Vatrano, Vincenzo Russo, Carmine Siniscalchi, Sebastiano Gangemi, Egidio Imbalzano

“…We found that the potential of machine learning to predict life-threatening events is significant. <b>Conclusions:</b> Machine learning algorithms can be used to create models to identify patients at risk for acute myocardial infarction. …”
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A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints by Tianli Ma, Rong Zhang, Song Gao, Hong Li, Yang Zhang

“…By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. …”
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P. I. E. N. O.&#x2013;Petrol-Filling Itinerary Estimation aNd Optimization by Marco Savarese, Antonio de Blasi, Carmine Zaccagnino, Carlo Augusto Grazia

“…Different domains are stressed to reach the goal: microcontroller and OEM to retrieve the fuel level from the car, national authorities to retrieve the daily fuel price, AI models to predict the price trend for the next days, and algorithms to compute the best fuel station and the best time to fill. …”
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Machine learning approaches for grain seed quality assessment: a comparative study of maize seed samples in Malawi by Wisdom Richard Mgomezulu, Moses M. N. Chitete, Beston B. Maonga, Mthakati A. R. Phiri

“…Abstract The study assessed machine and deep learning algorithms’ ability to predict and classify the quality of maize grain seed for increased agricultural output. …”
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Assessing the potential of a Bayesian ranking as an alternative to consensus meetings for decision making in research funding: A case study of Marie Skłodowska-Curie actions. by Rachel Heyard, David G Pina, Ivan Buljan, Ana Marušić

“…While we set out to understand if algorithmic approaches, with the aim of summarising individual evaluation scores, could replace consensus meetings, we concluded that currently individual scores assigned prior to the consensus meetings are not useful to predict the final funding outcomes of the proposals. …”
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