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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A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems by Zhu Huang, Tao Wang, Wei Liu, Luis Valencia-Cabrera, Mario J. Pérez-Jiménez, Pengpeng Li

“…Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. …”
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Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study by Klaudia Lenart

“…Results: While the accuracy of the statistical methods was dependent on the precise prediction of the percentage of the anomalies that would occur in the data, the machine learning algorithms’ recall was significantly lower when the change in the marginal distribution of the value parameters was smaller. …”
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Sex estimation with ensemble learning: an analysis using anthropometric measurements of piriform aperture by Muhammed Emin Parlak, Yasin Etli, Murat Beyhan, Kubilay Kanat, Hüseyin Alper Kızıloğlu

“…Results Sex prediction results were obtained with a maximum accuracy of 76.5% with discriminant analysis, 84.2% with machine learning algorithms, and 85.7% with the ensemble learning method. …”
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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values by Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye

“…Five machine learning algorithms and ten preoperative or intraoperative variables were used to develop a predictive model for SIRS. …”
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APPLICATION OF LINEAR AND NONLINEAR EXTRAPOLATIONS IN ANALYZING THE TRENDS OF CHANGES IN FINANCIAL STABILITY by EVGENY Negashev

“…The article researches the possibilities of applying the methods of linear and nonlinear extrapolation in the predictive analysis of a company’s financial stability based on the study of its trends. …”
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Role of artificial intelligence in medical radiology and imaging by Mohd Arfat, T. K. Nisha, Sapna Sahu, Mohd Rashid

“…AI is crucial in analyzing large medical datasets, including imaging and clinical data, through advanced algorithms and ML techniques. In veterinary medicine, AI is key to addressing complex challenges in host–pathogen interactions, precision medicine, and predictive epidemiology. …”
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Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

“…Bayesian causal discovery provides a principled framework for modeling observational data, generating posterior distributions that best represent the underlying structure. While recent algorithms offer efficient and accurate structure learning, integrating experiment design can further enhance predictive performance. …”
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Assessment of Urban Water Supply System Based on Query Optimization Strategy by Shibao Lu, Yizi Shang, Wei Li, Zhimin Wang

“…The results show that the validity and convenience of all reduction algorithms involve the whole query process. …”
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Improved YOLOv8n based helmet wearing inspection method by Xinying Chen, Zhisheng Jiao, Yuefan Liu

“…Experimental results on the expanded SHWD dataset demonstrate a 46.63% reduction in model volume, a 44.19% decrease in parameter count, a 54.88% reduction in computational load, and an improvement in mean Average Precision (mAP) to 93.8% compared to the original YOLOv8 algorithm. …”
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An Information Granulated Based SVM Approach for Anomaly Detection of Main Transformers in Nuclear Power Plants by Wenmin Yu, Ren Yu, Cheng Li

“…This study introduces a framework for main transformer predictive-based maintenance management. A condition prediction method based on the online support vector machine (SVM) regression model is proposed, with the input data being preprocessed using the information granulation method, and the parameters of the model are optimized using the particle swarm optimization (PSO) algorithm. …”
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Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement. by José L Safanelli, Tomislav Hengl, Leandro L Parente, Robert Minarik, Dellena E Bloom, Katherine Todd-Brown, Asa Gholizadeh, Wanderson de Sousa Mendes, Jonathan Sanderman

“…From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). …”
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Key Technologies in Intelligent O&M System Construction for Urban Rail Transit Communication by WANG Xianlei

“…The multi-source heterogeneous data fusion and analysis technologies are applied to communication system fault diagnosis and prediction. An equipment health index algorithm model and a big data architecture are proposed, and a tracking and evaluation platform encompassing ′cloud-data-intelligence′ is built. …”
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NLP-Based Fusion Approach to Robust Image Captioning by Riccardo Ricci, Farid Melgani, Jose Marcato Junior, Wesley Nunes Goncalves

“…The disjunction between the two phases of prediction and selection/generation provides high flexibility for inserting different captioning algorithms, each with its peculiarities and strengths. …”
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Service quality evaluation of integrated health and social care for older Chinese adults in residential settings based on factor analysis and machine learning by Zhihan Liu, Caini Ouyang, Nian Gu, Jiaheng Zhang, Xiaojiao He, Qiuping Feng, Chunguyu Chang

“…Comparative indices such as Mean Squared Error, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and predictive performance metrics were employed to assess the models. …”
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Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning by Christopher P. Cheng, Ryan Sicard, Dragan Vujovic, Vikram Vasan, Chris Choi, David K. Lerner, Alfred‐Marc Iloreta

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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms. …”
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Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases by Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Tianming Yao, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani

“…This approach has become feasible with the advent of machine learning, which can uncover hidden patterns in human microbiome data and enable disease prediction. Accordingly, the aim of our study was to test the hypothesis that machine learning algorithms can distinguish stool microbiota patterns—and their responses to fiber—across diseases with previously reported overlapping dysbiotic microbiota profiles. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.…”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. …”
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