Prediction of methane hydrate equilibrium in saline water solutions based on support vector machine and decision tree techniques

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 5, 2025

The formation of clathrate hydrates offers a powerful approach for separating gaseous substances, desalinating seawater, and energy storage at low temperatures. On the other hand, this phenomenon may lead to practical challenges, including blockage pipelines, in some industries. Consequently, accurately predicting equilibrium conditions hydrate is crucial. This study was undertaken design reliable models capable state methane saline water solutions. A comprehensive collection measured data, consisting 1051 samples, assembled from published sources. prepared databank encompassed temperature (HFTM) presence 26 different machine learning modeling through implementation Decision Tree (DT) Support Vector Machine (SVM) approaches. While both had excellent performance, latter achieved higher accuracy estimating HFTM with mean absolute percentage error (MAPE) 0.26%, standard deviation (SD) 0.78% validation process. Furthermore, more than 90% values predicted by novel fell within [Formula: see text]1% bound. It found that intelligent also favorably describe physical variations operational factors. An examination using William's plot acknowledged truthfulness gathered data suggested estimation techniques. Ultimately, order significance factors governing clarified sensitivity analysis.

Language: Английский

---

Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Heart disease remains a significant health threat due to its high mortality rate and increasing prevalence. Early prediction using basic physical markers from routine exams is crucial for timely diagnosis intervention. However, manual analysis of large datasets can be labor-intensive error-prone. Our goal rapidly reliably anticipate cardiac variety body signs. This research presents unique model heart prediction. We provide system predicting that blends the deep convolutional neural network with feature selection technique based on LinearSVC. integrated method selects subset characteristics are strongly linked disease. feed these features into conventual we constructed. Also improve speed predictor avoid gradient varnishing or explosion, network's hyperparameters were tuned random search algorithm. The proposed was evaluated UCI MIT datasets. number indicators, such as accuracy, recall, precision, F1 score. results demonstrate our attains accuracy rates 98.16%, 98.2%, 95.38%, 97.84% in dataset, an average MCC score 90%. These affirm efficacy reliability predict

Language: Английский

---

Adaptive neural finite-time self-triggered control for nonstrict-feedback nonlinear systems with sensor faults

Robotic Intelligence and Automation, Journal Year: 2025, Volume and Issue: unknown

Published: April 26, 2025

Purpose This paper aims to investigate the problem of adaptive neural finite-time self-triggered tracking control for interconnected large scale nonlinear systems in nonstrict-feedback forms with sensor faults. Design/methodology/approach To begin with, by combining backstepping techniques and networks (NNs), an NN controller is designed compensate Then, command filters are introduced deal complexity explosion design processes. Moreover, reduce unnecessary data transmissions, a strategy presented. Findings Based on strategy, scheme large-scale faults proposed. Originality/value article considers forms. introduction not only effectively avoids arising from repetitive differentiation virtual inputs, but also simplifies process. Besides, this proposes mechanism that calculates next trigger point based current system data, overcoming need continuous monitoring measurement errors event-triggered mechanisms. Furthermore, guarantees stability systems, error converging small neighborhood origin within finite time frame.

Language: Английский

---