Constrained Fuzzy Predictive Control Using Particle Swarm Optimization by Oussama Ait Sahed, Kamel Kara, Mohamed Laid Hadjili

“…A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. …”
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Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients by Yong Si, Armin Abdollahi, Negin Ashrafi, Greg Placencia, Elham Pishgar, Kamiar Alaei, Maryam Pishgar

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Study on the gas outflow pattern and outflow prediction model of the return mining face under complex geological conditions by Yi Sun, Lulin Zheng, Hong Lan, Zhaoxing Yu, Jin Wang, Bo Li, Feng Yang, Fangbo Wen

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PREDICTING TRAVEL-TIME RELIABILITY IN ROAD NETWORKS: A FITRNET-BASED APPROACH – A CASE STUDY OF ENGLAND by Navid KHORSHIDI, Shahriar AFANDIZADEH ZARGARI, Hamid MIRZAHOSSEIN, Hanif HEIDARI, Travis WALLER

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A data driven predictive viscosity model for the microemulsion phase by Akash Talapatra, Bahareh Nojabaei, Pooya Khodaparast

“…This study develops a computational, data-driven model to accurately estimate and predict peak phase viscosity in microemulsion systems at dynamic environments. …”
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A Robust Conformal Framework for IoT-Based Predictive Maintenance by Alberto Moccardi, Claudia Conte, Rajib Chandra Ghosh, Francesco Moscato

“…This study, set within the vast and varied research field of industrial Internet of Things (IoT) systems, proposes a methodology to address uncertainty quantification (UQ) issues in predictive maintenance (PdM) practices. At its core, this paper leverages the commercial modular aero-propulsion system simulation (CMAPSS) dataset to evaluate different artificial intelligence (AI) prognostic algorithms for remaining useful life (RUL) forecasting while supporting the estimation of a robust confidence interval (CI). …”
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Predictive estimations of health systems resilience using machine learning by Alessandro Jatobá, Paula de Castro-Nunes, Paloma Palmieri, Omara Machado Araujo de Oliveira, Patricia Passos Simões, Valéria da Silva Fonseca, Paulo Victor Rodrigues de Carvalho

“…This research highlights the potential of ML in predictive modeling to inform strategic health decision-making, targeting interventions and more effective resource allocation. …”
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Link quality prediction based on random forest by Linlan LIU, Shengrong GAO, Jian SHU

“…Link quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based on unsupervised clustering was employed to divide the link quality level.Zero-phase component analysis (ZCA) whitening was applied to remove the correlation between samples.The mean and variance of signal to noise ratio,link quality indicator,and received signal strength indicator were taken as the estimation parameters of link quality,and a link quality estimation model was constructed by using a random forest classification algorithm.The random forest regression algorithm was used to build a link quality prediction model,which predicted the link quality level at the next moment.In different scenarios,comparing with exponentially weighted moving average,triangle metric,support vector regression and linear regression prediction models,the proposed prediction model has higher prediction accuracy.…”
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Deep learning for predicting the occurrence of tipping points by Chengzuo Zhuge, Jiawei Li, Wei Chen

“…Here, we address this challenge by developing a deep learning algorithm for predicting the occurrence of tipping points in untrained systems, by exploiting information about normal forms. …”
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Slipping Trend Prediction Based on Improved Informer by Jingchun Huang, Sheng He, Haoxiang Feng, Yongjiang Yu

“…The transformer-based Informer algorithm performs well in time series prediction and analysis. …”
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Modify possibilities of the secondary structures prediction method by Alvydas Špokas, Albertas Timinskas

“… It was analyzed dependence of the average accuracy of secondary protein structure prediction on various GOR algorithm modifications. In essence new modification has expanded informational parameter set by taking into account secondary structure of neighboring amino acid. …”
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Outcome prediction of the measles vaccination in healthcare employees by A. A. Ereshchenko, O. A. Gusyakova, N. B. Migacheva, F. N. Gilmiyarova, A. V. Lyamin

“…These models allowed to develop algorithm for predicting failures of the measles vaccination in healthcare workers that can be used for detection of persons at risk for non-forming specific humoral immunity. …”
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Explainable machine learning to predict the cost of capital by Niklas Bussmann, Paolo Giudici, Paolo Giudici, Alessandra Tanda, Alessandra Tanda, Ellen Pei-Yi Yu

“…Our findings pave the way for future investigations on the impact of ESG and country factors in predicting the cost of capital.…”
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Prediction of amphipathic helix-membrane interactions with Rosetta. by Alican Gulsevin, Jens Meiler

“…The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. …”
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Application of federated learning in predicting breast cancer by Chai Jiarui

“…The prediction and diagnosis of breast cancer relies on multimodal data, such as imaging, genetic information, and patient lifestyle habits. …”
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Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication by Cristian Daniel Marineci, Andrei Valeanu, Cornel Chiriță, Simona Negreș, Claudiu Stoicescu, Valentin Chioncel

“…This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. …”
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The Prediction of the Compaction Curves and Energy of Bituminous Mixtures by Filippo Giammaria Praticò, Giusi Perri

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Comparing prediction efficiency in the BTW and Manna sandpiles by Denis Sapozhnikov, Alexander Shapoval, Mikhail Shnirman

“…The existence of the inactivity allows for the prediction of these events in advance. In this work, we explore the predictability of the Bak–Tang–Wiesenfeld (BTW) and Manna models on the square lattice as a function of the lattice length. …”
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Multimodal deep learning for allergenic proteins prediction by Lezheng Yu, Yuxin Luo, Shiqi Wu, Siyi Chen, Li Xue, Runyu Jing, Jiesi Luo

“…Results Here, we present Multimodal-AlgPro, a unified framework based on a multimodal deep learning algorithm designed to predict allergens by integrating multiple dimensions, including physicochemical properties, amino acid sequences, and evolutionary information. …”
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Prediction of Drifter Trajectory Using Evolutionary Computation by Yong-Wook Nam, Yong-Hyuk Kim

“…In contrast to existing numerical models that use the Lagrangian method, we used an optimization algorithm to predict the trajectory. As the evaluation measure, a method that gives a better score as the Mean Absolute Error (MAE) when the difference between the predicted position in time and the actual position is lower and the Normalized Cumulative Lagrangian Separation (NCLS), which is widely used as a trajectory evaluation method of drifters, were used. …”
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