Particle swarm optimization performance improvement using deep learning techniques

Pawan, Y. V. R. Naga; Prakash, Kolla Bhanu; Chowdhury, Subrata; Hu, Yu‐Chen

doi:10.1007/s11042-022-12966-1

Cited by 61 publications

(7 citation statements)

References 26 publications

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“…27,28 The larger the value of the inertia weight, the longer the iterative steps of a particle in the updating process and the stronger the global search capability of the algorithm are; conversely, the smaller the value of the inertia weight, the stronger the local search capability of the algorithm becomes. 29 Therefore, to avoid the algorithm falling into a local optimum while simultaneously improving its operational efficiency, this study introduces the sinusoidal adaptive weights defined as follows:where ω max and ω min are the maximum and minimum values of the inertia weights, respectively, and f min , f avg and f are the minimum value of the current all-particle fitness value, the average fitness and the current particle fitness, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Dwsa-pso Algorithmmentioning

confidence: 99%

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An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers

Hu,

Zhou,

et al. 2023

Analyst

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Section: Methodsmentioning

confidence: 99%

Section: Dwsa-pso Algorithmmentioning

confidence: 99%

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers

Hu,

Zhou,

et al. 2023

Analyst

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“…Within each picture, the model predicts the IDT with a blue line, whereas a green asterisk shows the experimentally measured IDT. The prediction accuracy increased by 25.34% when the neuron configuration was changed from [25,26] to [8,27]. This change also increased the average correlation coefficient of the BP-MRPSO model from 0.9745 to 0.9896.…”

Section: Comparison Of Bp-based and Bp-mrpso Neural Networkmentioning

confidence: 99%

Study on Hydrocarbon Fuel Ignition Characterization Based on Optimized BP Neural Network

Chen,

Wei,

et al. 2024

Energies

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The investigation of the ignition delay of hydrocarbon fuel is highly valuable for enhancing combustion efficiency, optimizing fuel thermal efficiency, and mitigating pollutant emissions. This paper has developed a BP-MRPSO neural network model for studying hydrocarbon fuel ignition and clarified the novelty of this model compared to the traditional BP and ANN models from the literature. The model integrates the particle swarm optimization (PSO) algorithm with MapReduce-based parallel processing technology. This integration improves the prediction accuracy and processing efficiency of the model. Compared to the traditional BP model, the BP-MRPSO model can increase the average correlation coefficient, from 0.9745 to 0.9896. The R2 value for predicting fire characteristics using this model can exceed 90%. Meanwhile, when the two hidden layers of both the BP and BP-MRPSO models consist of 9 and 8 neurons, respectively, the accuracy of the BP-MRPSO model is increased by 38.89% compared to the BP model. This proved that the new BP-MRPSO model has the capacity to handle large datasets while achieving great precision and efficiency. The findings could provide a new perspective for examining the properties of fuel ignition, which is expected to contribute to the development and assessment of aviation fuel ignition characteristics in the future.

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“…Given a training dataset 𝐷𝐷 = ��𝑥𝑥 (1) , 𝐶𝐶 (1) �, �𝑥𝑥 (2) , 𝐶𝐶 (2) �, … , �𝑥𝑥 (𝑚𝑚) , 𝐶𝐶 (𝑚𝑚) �� of 𝑚𝑚 labelled network activities, train a NB classifier to approximate the likelihoods 𝑃𝑃(𝑥𝑥 𝑖𝑖 | 𝐶𝐶) for each feature 𝑥𝑥 𝑖𝑖 and prior probabilities 𝑃𝑃(𝐶𝐶) to effectively classify new unseen loT network activities.…”

Section: Problem Statementmentioning

confidence: 99%

“…While the expansive growth of IoT presents numerous opportunities, it simultaneously contains plenty of challenges, particularly concerning data security [2]. With billions of devices interconnected, each transmitting and receiving data, the vulnerability to cyber threats has significantly heightened.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

Juarez Vargas,

Ghanimi,

et al. 2024

JMC

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The Internet of Things (IoT) has proliferated, transitioning from modest home automation to encompass sectors like healthcare, agriculture, transportation, and manufacturing. This evolution is characterized by devices' ability to autonomously gather, disseminate, and analyze data, leading to improved real-time decision-making, predictive insights, and customized user experiences. The ubiquity of IoT, while promising, introduces significant data security concerns. The vast number of interlinked devices and diverse and often insufficient security features make them vulnerable to cyber threats, emphasizing the need for robust security mechanisms. Intrusion Detection Systems (IDS) have traditionally acted as vital guards against such threats; however, with the ever-increasing data in the IoT, traditional IDS models, such as Naive Bayes, face processing speed and accuracy challenges. This paper introduces a novel model, "FE+NB," which merges advanced Feature Extraction (FE) with the Naive Bayes (NB) classifier. Central to this model is the "Temporal-Structural Synthesis" technique tailored for IoT traffic data, focusing on data compression, temporal and structural analyses, and Feature Selection (FS) using mutual information. Consequently, the model enhances efficiency and accuracy in Intrusion Detection (ID) in complex IoT networks.

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Particle swarm optimization performance improvement using deep learning techniques

Cited by 61 publications

References 26 publications

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers

Study on Hydrocarbon Fuel Ignition Characterization Based on Optimized BP Neural Network

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

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