An Improved Particle Swarm Optimization Algorithm for Data Classification

Bangyal, Waqas Haider; Nisar, Kashif; Soomro, Tariq Rahim; Ibrahim, Ag Asri Ag; Mallah, Ghulam Ali; Hassan, Nafees Ul; Rehman, Najeeb Ur

doi:10.3390/app13010283

Cited by 16 publications

(5 citation statements)

References 54 publications

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“…The authors of Ref. 94 proposed an enhanced PSO algorithm that uses pseudo-random sequences and opposing rank inertia weights instead of random distributions for initialization to improve convergence speed and population diversity. The authors also introduced a new initialization population approach that uses a quasi-random sequence to initialize the swarm and generates an opposing swarm using the opposition-based method.…”

Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

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Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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show abstract

“…This holistic approach could significantly contribute to the system's ability to handle different environmental conditions and improve generalization across various scenarios. Additionally, the realization of real-time decisionmaking systems for traffic control and optimization holds great potential, necessitating research efforts to design and implement algorithms [30] capable of providing instantaneous responses to dynamic traffic conditions. By addressing these research directions, future studies can contribute to the ongoing evolution of intelligent traffic monitoring systems, ensuring their robustness, adaptability, and efficiency in the everchanging landscape of smart transportation.…”

Section: Discussionmentioning

confidence: 99%

“…2 shows the schematic of a license plate recognition system. Recently, deep learning-based approaches [16], particularly those employing You Only Look Once (YOLO) architecture [17], have gained attention due to their superior performance in object detection tasks [18], [19,30]. These methods leverage large-scale datasets and powerful deep neural network architectures to achieve high accuracy as well as realtime processing capabilities [20].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in computer vision, machine learning, and data analytics, this study addresses the pressing need for advancements in car license plate recognition within these systems. Employing an innovative approach based on the YOLOv5 architecture in deep learning, the study focuses on refining the accuracy of license plate recognition. A bespoke dataset is meticulously curated to facilitate a comprehensive evaluation of the proposed methodology, with extensive experiments conducted and metrics such as precision, recall, and F1-score employed for assessment. The outcomes underscore the efficacy of the approach in significantly enhancing the precision and accuracy of license plate recognition using performance evaluation of the proposed method. This tailored dataset ensures a rigorous evaluation, affirming the practical viability of the proposed approach in realworld scenarios. The study not only showcases the successful application of deep learning and YOLOv5 in achieving accurate license plate detection and recognition but also contributes to the broader discourse on advancing intelligent traffic monitoring for more robust and efficient smart transportation systems.

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“…The proposed study's decision tree algorithm performed best for the considered task. Similarly, authors in [22] proposed an improved particle swarm optimization method for data classification. Their proposed method has been tested to optimize the weight of the feed-forward neural network for fifteen datasets.…”

Section: Related Workmentioning

confidence: 99%

SmokerViT: A Transformer-Based Method for Smoker Recognition

Khan,

Hassan

et al. 2023

CMC

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Smoking has an economic and environmental impact on society due to the toxic substances it emits. Convolutional Neural Networks (CNNs) need help describing low-level features and can miss important information. Moreover, accurate smoker detection is vital with minimum false alarms. To answer the issue, the researchers of this paper have turned to a self-attention mechanism inspired by the ViT, which has displayed state-of-the-art performance in the classification task. To effectively enforce the smoking prohibition in non-smoking locations, this work presents a Vision Transformer-inspired model called SmokerViT for detecting smokers. Moreover, this research utilizes a locally curated dataset of 1120 images evenly distributed among the two classes (Smoking and NotSmoking). Further, this research performs augmentations on the smoker detection dataset to have many images with various representations to overcome the dataset size limitation. Unlike convolutional operations used in most existing works, the proposed SmokerViT model employs a self-attention mechanism in the Transformer block, making it suitable for the smoker classification problem. Besides, this work integrates the multi-layer perceptron head block in the SmokerViT model, which contains dense layers with rectified linear activation and linear kernel regularizer with L2 for the recognition task. This work presents an exhaustive analysis to prove the efficiency of the proposed SmokerViT model. The performance of the proposed SmokerViT performance is evaluated and compared with the existing methods, where it achieves an overall classification accuracy of 97.77%, with 98.21% recall and 97.35% precision, outperforming the state-of-the-art deep learning models, including convolutional neural networks (CNNs) and other vision transformer-based models.

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An Improved Particle Swarm Optimization Algorithm for Data Classification

Cited by 16 publications

References 54 publications

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

A Deep Learning-based Framework for Vehicle License Plate Detection

SmokerViT: A Transformer-Based Method for Smoker Recognition

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