Particle swarm optimisation based AdaBoost for object detection

Mohemmed, Ammar; Johnston, Mark; Zhang, Mengjie

doi:10.1007/s00500-010-0615-x

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The traditional object detection methods, such as the algorithm of fast multi-feature pedestrian detection based on the histogram of an oriented gradient (HOG) using discrete wavelet transform proposed by Gwang-Soo Hong [25], and distinctive image features from scale-invariant key points proposed by Lowe D. [26], etc. It is necessary to manually design features according to different experimental scenarios, and then input the extracted object features into classifiers such as Support Vector Machines (SVM) [27] and Adaboost [28,29] for recognition. The feature extraction process of the traditional object detection algorithms is more complicated.…”

Section: Methodsmentioning

confidence: 99%

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

et al. 2021

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Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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Section: Methodsmentioning

confidence: 99%

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

et al. 2021

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“…The EC method known as particle swarm optimization (PSO), which is based on swarm intelligence, is relatively new. PSO is computationally less costly and has a faster convergence rate when compared to other EC algorithms like genetic programming (GP) and genetic algorithms (GAs) [29][30][31][32][33]. Eleven features were selected with this feature selection out of 16 parameters.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Chadaga,

Sampathila

et al. 2023

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Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment.

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“…Implementation of PSO is less complex and it has only few parameters that are needed to be adjust, so it has been widely used in many fields, including Artificial Intelligence, Pattern Recognition and Computer Engineering, etc [11][12][13]. However, one drawback of the traditional PSO is that still having some typical issues as convergence to local optimum and slow convergence rate [14].…”

Section: Introductionmentioning

confidence: 98%

A hybrid PSO-GSA strategy for high-dimensional optimization and microarray data clustering

Sun

Peng

2014

2014 IEEE International Conference on Information and Automation (ICIA)

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High-dimensional data analysis and its great chances of overfitting result in great challenges for constructing efficient models in practical applications. To overcome these problems swarm intelligence algorithms can be utilized. However, the balance between global and local search throughout the course of a run is critical to the success of an intelligence optimization algorithm. Moreover, almost all the available algorithms are still having issues like premature convergence to local optimum and slow convergence rate, especially in high-dimensional space. As motivated above, a new hybrid optimization algorithm integrating particle swarm optimization(PSO) with gravitational search algorithm(GSA) is presented (denoted as PSOGSA). Based on the analysis of the compensatory advantages of the PSO and the GSA, in this paper, we integrate the ability of exploitation in PSO with the ability of exploration in GSA to update velocity equations. To update position equations a mobility factor is used which is guided by diversity of population to improve the final accuracy and the convergence speed of the PSOGSA. We also apply proposed algorithm to the cluster analysis of microarray data. Experiments are conducted on six benchmark test functions, four artificial data sets and three microarray data sets, and the results demonstrate that the proposed algorithm possess better robustness.

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Particle swarm optimisation based AdaBoost for object detection

Cited by 5 publications

References 30 publications

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

A hybrid PSO-GSA strategy for high-dimensional optimization and microarray data clustering

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