Optimization of non-smooth functions via differentiable surrogates. by Shikun Chen, Zebin Huang, Wenlong Zheng

“…These models are commonly used to predict outputs based on a combination of fixed parameters and adjustable variables. …”
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Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS by Ahmed A. Mostfa, Nawfal A. Zakar, Rasha Raad Al-Mola, Abdel-Nasser Sharkawy

“…To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. …”
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RRMSE-enhanced weighted voting regressor for improved ensemble regression. by Shikun Chen, Wenlong Zheng

“…By using an RRMSE-based weighting function, our method gives more importance to models that demonstrate higher accuracy, thereby enhancing the overall prediction quality. We tested the RRMSE Voting Regressor on six popular regression datasets and compared its performance with several state-of-the-art ensemble regression algorithms. …”
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矿用自卸车盘式制动器热固耦合研究 by 解淑英

“…In order to further study the dump truck disc brake thermal-structural coupling characteristics,by analyzing the major impact parameter of brake pressure,brake initial speed,plate/sheet friction coefficient and the equivalent moment of inertia on the disc brake performance and using orthogonal experiment principle,the GA-BP network training sample is formed,a GA-BP network model with traditional genetic algorithm BP neural network combined with the introduction of disc brakes thermal-structural coupling finite element predictive model is proposed.The study results show that for the temperature and stress time history results,the overall trend of the training results and finite element simulation results are basically the same,but in the individual condition curve,there are some differences in local curves.Aiming at the specific conditions,the main characteristics of the temperature-time history is basiclly predicted by the GA-BP Network,the data extreme values of stress time history prediction effect and other major information are consistent with the finite element results.The training network has higher approximation performance and better prediction performance,the maximum error of prediction and simulation is only 8%,the computational accuracy is higher.…”
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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data by Kai Wang, Yulong Li, Fei Liu, Xiaoli Luan, Xinglong Wang, Jingwen Zhou

“…Conclusions The experimental results and case studies illustrate the considerable performance of GRLGRN in predicting gene interactions and provide interpretability for the prediction tasks, such as identifying hub genes in the network and uncovering implicit links.…”
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BETASCAN: probable beta-amyloids identified by pairwise probabilistic analysis. by Allen W Bryan, Matthew Menke, Lenore J Cowen, Susan L Lindquist, Bonnie Berger

“…BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator) in beta-structure prediction and amyloid propensity prediction. …”
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Extending the forecasting horizon of daily new COVID-19 cases using non-pharmaceutical measures and the effective reproduction number (Rt): A deep learning-based framework by Tuga Mauritsius

“…The inclusion of additional variables was found to diminish the predictive accuracy of DL algorithms.…”
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Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities by Mahdieh Shokrollahi Barough, Mohammad Darzi, Masoud Yunesian, Danesh Amini Panah, Yekta Ghane, Sam Mottahedan, Sohrab Sakinehpour, Tahereh Kowsarirad, Zahra Hosseini-Farjam, Mohammad Reza Amirzargar, Samaneh Dehghani, Fahimeh Shahriyary, Mohammad Mahdi Kabiri, Marzieh Nojomi, Neda Saraygord-Afshari, Seyedeh Ghazal Mostofi, Zeynab Yassin, Nazanin Mojtabavi

“…Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores. …”
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Analysing learning behaviour: A data-driven approach to improve time management and active listening skills in students by Vinayak Hegde, Vishrutha M, Pallavi M. Shanthappa, Rekha Bhat, Nisha Raveendran, Roshin C

“…Active listening is an indispensable skill in both educational and interpersonal contexts. Methodologically, the study began with comprehensive data collection through a survey, data preprocessing tasks and feature selection, followed by training and evaluating predictive models using various ML algorithms. …”
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Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada by Dania Tamayo-Vera, Kai Liu, Antonio Bolufé-Röhler, Xiuquan Wang

“…Under the high-emission SSP5-8.5 scenario, our models predict a potential potato yield reduction of up to 70%. …”
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Machine learning in dentistry and oral surgery: charting the course with bibliometric insights by Shuangwei Liu, Yuquan Hao, Shijie Zhu, Liyao Wan, Zhe Yi, Zhichang Zhang

“…Analysis of the co-cited references revealed clusters related to disease diagnosis and risk prediction, treatment planning, clinical decision support systems, and dental education. …”
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Unsupervised learning analysis on the proteomes of Zika virus by Edgar E. Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

“…Among the four dimensionality reduction (DR) algorithms, the performance was better for UMAP. …”
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Dynamic forecasting module for chronic graft-versus-host disease progression based on a disease-associated subpopulation of B cells: a multicenter prospective studyResearch in cont... by Yuanchen Ma, Jieying Chen, Zhiping Fan, Jiahao Shi, Gang Li, Xiaobo Li, Tao Wang, Na Xu, Jialing Liu, Zhishan Li, Heshe Li, Xiaoran Zhang, Dongjun Lin, Wu Song, Qifa Liu, Weijun Huang, Xiaoyong Chen, Andy Peng Xiang

“…Consequently, identifying appropriate immune cell subsets or molecules as prognostic or predictive biomarkers for cGVHD is essential. Methods: Building on the pivotal role of B-cell homeostasis in cGVHD progression, we integrated spectral flow cytometry with advanced machine learning algorithms to systematically analyze the relationship between B cells and cGVHD progression. …”
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Role,application and challenges of IoT in smart EV charging management:a review by Tripti Kunj, Kirti Pal

“…Additionally, the paper emphasizes the importance of adaptive algorithms and machine learning models for predictive maintenance and efficient resource allocation. …”
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BREATHE: A GeoAI-Powered Air Quality Monitoring and Forecasting System for Urban Sustainability by S. D. Adduri, T. Ali

“…Traditional air quality monitoring systems lack the predictive capabilities needed for proactive intervention and sustainable urban planning. …”
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Discovering emergent connections in quantum physics research via dynamic word embeddings by Felix Frohnert, Xuemei Gu, Mario Krenn, Evert van Nieuwenburg

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Radiomics in pediatric brain tumors: from images to insights by Pranjal Rai, Sabha Ahmed, Abhishek Mahajan

“…By integrating radiomics with machine learning algorithms, studies have demonstrated strong performance in classifying tumor types such as medulloblastoma, ependymoma, and gliomas, and predicting molecular subgroups and mutations such as H3K27M and BRAF. …”
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DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies. by Shaun Mahony, Philip E Auron, Panayiotis V Benos

“…As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. …”
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Digital solutions for risk management in sustainable development conditions of business ecosystems by Oleksii HNIEZDOVSKYI, Danylo DOMASHENKO, Svitlana DOMASHENKO, Denys MOROZOV, Serhii SHYLO

“…A prediction system that uses various machine learning algorithms was developed and tested. …”
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Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis by Alikhan Anuarbekov, Jiří Kléma

“…By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy. …”
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