Two-Layers Particle Swarm Optimizer

Samma, Hussein; Suandi, Shahrel Azmin; Mohamad–Saleh, Junita

doi:10.1109/i2cacis49202.2020.9140204

Cited by 3 publications

(18 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 demonstrates the basic operation of max-pooling operation where it takes the maximum value from the 2 x 2 window. The proposed two-layers optimizer was presented in our previous research as a conference paper [4]. Basically, it consists of two-layers which are global search and local search, as given in Fig.…”

Section: A Vgg-19 Networkmentioning

confidence: 99%

“…6. It is automatically select between local and global search based on generated action by the Qlearning algorithm, as explained in [4]. The main stages of two-layers optimizer are explained as follows.…”

Section: A Vgg-19 Networkmentioning

confidence: 99%

“…As mentioned in [4] that the Q-table will be updated with a reward of +1 when the executed search operations were able to improve search performances; otherwise, a penalty of -1 is given.…”

Section: Figure 7 State Diagram Transition Of Q-learningmentioning

confidence: 99%

“…The complexity measure is represented by the percentage of the remaining VGG-19 filters. It should be noted that our proposed two-layers optimizer was successfully applied for large-scale benchmark problems in a conference paper published recently [4]. However, in this study, it will be used for evolving the pre-trained VGG-19 model to tackle the problem of road damage detection.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Samma¹,

Suandi

Ismail

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

There are numerous pre-trained Convolutional Neural Networks (CNN) introduced in the literature, such as AlexNet, VGG-19, and ResNet. These pre-trained CNN models could be reused and applied to tackle different image recognition problems. Unfortunately, these pre-trained CNN models are complex and have a large number of convolutional filters. To tackle such a complexity challenge, this research aims to evolve a pre-trained VGG-19 using an efficient two-layers optimizer. The proposed optimizer performs filters selection of the last layers of VGG-19 guided by the accuracy of the linear SVM classifier. The proposed approach has three main advantages. Firstly, it adopts a powerful two-layers optimizer that works with a micro swarm population. Secondly, it automatically evolves a lightweight deep model which uses a small number of VGG-19 convolutional filters. Thirdly, It applies the developed model for real-world road damage detection from drone-based images. To evaluate the effectiveness of the proposed approach, a total of 529 images were captured by using a drone-based camera for various road damages. Reported results indicated that the proposed model achieved 96.4% F1-score accuracy with a reduction of VGG-19 filter up to 52%. In addition, the proposed two-layers optimizer was able to outperform several related optimizers such as AOA (

show abstract

Section: A Vgg-19 Networkmentioning

confidence: 99%

Section: A Vgg-19 Networkmentioning

confidence: 99%

“…As mentioned in [4] that the Q-table will be updated with a reward of +1 when the executed search operations were able to improve search performances; otherwise, a penalty of -1 is given.…”

Section: Figure 7 State Diagram Transition Of Q-learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Samma¹,

Suandi

Ismail

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…This optimizer has several advantages, such as (i) it evolved with small population size, (ii) it has one dedicated layer for fine-tuning and one layer of exploration task, and (iii) it incorporated Q-learning to control the switching from exploration to exploitation. It should be noted that the implemented two-layers optimizer was presented in our previous research as a conference paper for the problem of large-scale optimization [24]. The main contribution of this work could be summarized in the following points: • It uses a lightweight, efficient optimizer that evolved with a micro swarm (three particles only).…”

Section: Introductionmentioning

confidence: 99%

Optimized Deep Autoencoder Model for Internet of Things Intruder Detection

Lahasn¹,

Samma

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99% with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

show abstract

Optimized deep learning vision system for human action recognition from drone images

Samma

Sama

2023

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

There are several benefits to constructing a lightweight vision system that is implemented directly on limited hardware devices. Most deep learning-based computer vision systems, such as YOLO (You Only Look Once), use computationally expensive backbone feature extractor networks, such as ResNet and Inception network. To address the issue of network complexity, researchers created SqueezeNet, an alternative compressed and diminutive network. However, SqueezeNet was trained to recognize 1000 unique objects as a broad classification system. This work integrates a two-layer particle swarm optimizer (TLPSO) into YOLO to reduce the contribution of SqueezeNet convolutional filters that have contributed less to human action recognition. In short, this work introduces a lightweight vision system with an optimized SqueezeNet backbone feature extraction network. Secondly, it does so without sacrificing accuracy. This is because that the high-dimensional SqueezeNet convolutional filter selection is supported by the efficient TLPSO algorithm. The proposed vision system has been used to the recognition of human behaviors from drone-mounted camera images. This study focused on two separate motions, namely walking and running. As a consequence, a total of 300 pictures were taken at various places, angles, and weather conditions, with 100 shots capturing running and 200 images capturing walking. The TLPSO technique lowered SqueezeNet’s convolutional filters by 52%, resulting in a sevenfold boost in detection speed. With an F1 score of 94.65% and an inference time of 0.061 milliseconds, the suggested system beat earlier vision systems in terms of human recognition from drone-based photographs. In addition, the performance assessment of TLPSO in comparison to other related optimizers found that TLPSO had a better convergence curve and achieved a higher fitness value. In statistical comparisons, TLPSO surpassed PSO and RLMPSO by a wide margin.

show abstract

Two-Layers Particle Swarm Optimizer

Cited by 3 publications

References 10 publications

Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Optimized Deep Autoencoder Model for Internet of Things Intruder Detection

Optimized deep learning vision system for human action recognition from drone images

Contact Info

Product

Resources

About