Additive Manufacturing of Composite Materials and Functionally Graded Structures Using Archerfish Hunting Technique

Lubrication Science, Journal Year: 2024, Volume and Issue: 36(8), P. 595 - 609

Published: Aug. 7, 2024

ABSTRACT This paper proposes an optimisation method for fabricating composite materials and functionally graded structures. Using the proposed method, 3D printing of copper (Cu)–polyethylene (PE) composite, Al 2 O 3 –ZrO ceramic CuO foams are utilised. work aims to advance capabilities additive manufacturing by leveraging nature‐inspired approaches create complex, tailored structures with enhanced performance across various industries. The major objective is reduce feed rate increase airflow temperature heat transfer process. technique in advanced preparation conditions, Cu–PE composites unreliable Cu substances fabricated. PE binder particle melting as well forming thick means soft surfaces. AHO approach, common distributions can be efficiently optimised. By then, model implemented on MATLAB platform, its execution calculated using current procedures. displays superior outcomes all existing methods like wild horse optimiser, swarm heap‐based optimiser. shows a throughput 57 mm . 32, 27 45 results show that has higher compared methods.

Language: Английский

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DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: 37(17)

Published: Aug. 8, 2024

Summary Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option various healthcare applications. In this manuscript, Novel Health Monitoring Approach WBSNs (DIWGAN‐WBSN) proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it responsibility of WBSN nodes to transfer sensed data sink node. The Volcano Eruption (VEA) applied select optimum cluster heads in WBSN. results from VEA are fed target node; consists DIWGAN classify records portray patient's status. Generally, does not adopt any optimization methods measuring ideal parameters guaranteeing accurate risk assessment. So proposed WSOA considered enhance DIWGAN. method activated MATLAB; its efficacy estimated under performance metrics, like precision, specificity, accuracy, energy utilization. approach attains 23.9%, 21.34%, 51.09% higher accuracy; 21.45%, 13.94%, 20.6% precision; 31.32%, 29.61%, 11.03% specificity; 20.9%, 19.87%, 24.6% lower utilization HD classification using Cleveland database than existing back propagation neural network‐based detection monitoring, random forest algorithm–based WBSN, ensemble deep learning feature fusion methods, respectively.

Language: Английский

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Optimizing solar photovoltaic and biomass integration for electric vehicle charging stations in metropolitan cities: A hybrid approach

Optimal Control Applications and Methods, Journal Year: 2024, Volume and Issue: 45(6), P. 2874 - 2896

Published: Aug. 8, 2024

Abstract This paper proposes a hybrid strategy for designing and optimizing solar photovoltaic (PV) biomass‐based electric vehicle charging station (EVCS) in metropolitan cities. The proposed is the joint execution of dung beetle optimizer (DBO) Finite Basis Physics‐Informed Neural Networks Technique. It hence called DBO‐FBPINNs approach. aims are to minimize initial cost operating cost, net present levelized energy. design phase involves energy storage systems, integration PV panels, biomass generators warranty reliable continuous power supply EV infrastructure. Feasibility analysis encompasses various technical, economic, environmental aspects. converter's control signal optimized via DBO method. FBPINNs model used forecast optimal parameters converter. By then, method implemented MATLAB platform evaluated their performance with strategy's like deep neural network (DNN), fuzzy (FNN), recurrent (RNN). When compared other current technologies, exhibits low $1.2.

Language: Английский

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Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things

Smart Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 15

Published: Aug. 12, 2024

Nowadays, the network intrusion and cyberattack have emerged as two main issues with Internet of Things (IoT) applications. The existing methods for preventing detecting intrusions are limited in many ways, making it impossible to accurately identify any kind attack occurring within traffic. A number machine learning-based that attains poor performance multiple class categorization accuracy provided by researchers. This research presents Data-Driven Intrusion Detection System utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) overcome these issues. input data is taken from TON_IoT Dataset. balancing training dataset enhanced using Class decomposition synthetic minority oversampling method (CDSMOTE). Then, pre-processed Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) noise removal enhancement. preprocessed output given into feature extraction extract features Dual-Tree Biquaternion Wavelet Transform (DTBWT). extracted fed (BRBPNN) which detects Ransomware, Password attack, Scanning, Denial Service (DoS), Distributed (DDoS), Data injection, Backdoor, Cross-Site Scripting (XSS), Man-In-The-Middle (MITM). In general, BRBPNN does not show optimization adaption determine optimal parameter appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) proposed this manuscript improve classifier precisely. DIDS-BRBPNN-BBWOA-IoT implemented Python. approach examined metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, ROC. SAPVAEGAN-LCC-IR 18.44%, 26% ,and 29% greater accuracy; 26.55%, 24.12%, 27.22% recall compared MIDS-MIoT, AID-SDN-IoT, IID-LW-IoT techniques.

Language: Английский

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Cardio vascular disease prediction by deep learning based on IOMT: review

Smart Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 11

Published: Aug. 19, 2024

The global burden of disease caused by cardiovascular diseases (CVDs) is increasing despite technical advancements in healthcare because a dramatic rise the developing nations that are experiencing rapid health transitions. World Health Organization (WHO) estimates 17.9 million deaths worldwide 2021 and connected to CVDs, or 32% all deaths. Since ancient times, people have experimented with methods extend their lives. proposed technology still long way for attaining aim lessening mortality rates. Early detection proactive management CVD risk factors crucial reducing these diseases. In recent years, researchers been exploring potential deep learning predicting depending upon data collected from IoMT devices. Deep (DL) used prediction popular this domain. Several DL techniques implemented accomplish efficient prediction-based CVD. There several steps employing model. IoT sensors process large amounts patient-related biomedical data, enabling doctors closely monitor patients make choices real-time. An outline IoT, sensors, provided after discussion cardiac its existing treatments. A complete analysis current pertinent deep-learning heart reviewed. result shows performance metrics comparison different approaches. This review undertaken pulling 44 papers published between years 2020 2023, provides thorough statistical analysis. Finally, survey will be beneficial researchers.

Language: Английский

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