An Improved Transfer learning Approach for Intrusion Detection

Mathew, Alwyn; Mathew, Jimson; Govind, Mahesh; Mooppan, Asif

doi:10.1016/j.procs.2017.09.132

Cited by 15 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Identifying heritage sites, detecting cracks in concrete [64], and recognizing scholars and well-known individuals from images are significant tasks in image classification. In a research study [65], the authors employed transfer learning to incorporate a preexisting CNN model for heritage data classification. Fabric defect identification can also be addressed using computer vision (CV).…”

Section: Other Potential Applicationsmentioning

confidence: 99%

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Rubaiya Tasnim Sholi,

Sarker,

Md. Salman Sohel

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment.

show abstract

Section: Other Potential Applicationsmentioning

confidence: 99%

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Rubaiya Tasnim Sholi,

Sarker,

Md. Salman Sohel

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Deep learning requires a significant amount of data, but in most cases it is difficult to find enough training data for a specific problem within a certain range. To solve this problem, a solution is proposed, namely to use transfer learning [24].…”

Section: A Transfer Learningmentioning

confidence: 99%

Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN

2020

View full text Add to dashboard Cite

Traffic congestion due to vehicular accidents seriously affects normal travel, and accurate and effective mitigating measures and methods must be studied. To resolve traffic accident compensation problems quickly, a vehicle-damage-detection segmentation algorithm based on transfer learning and an improved mask regional convolutional neural network (Mask RCNN) is proposed in this paper. The experiment first collects car damage pictures for preprocessing and uses Labelme to make data set labels, which are divided into training sets and test sets. The residual network (ResNet) is optimized, and feature extraction is performed in combination with Feature Pyramid Network (FPN). Then, the proportion and threshold of the Anchor in the region proposal network (RPN) are adjusted. The spatial information of the feature map is preserved by bilinear interpolation in ROIAlign, and different weights are introduced in the loss function for different-scale targets. Finally, the results of self-made dedicated dataset training and testing show that the improved Mask RCNN has better Average Precision (AP) value, detection accuracy and masking accuracy, and improves the efficiency of solving traffic accident compensation problems.

show abstract

“…It was proposed in works [8,9] to perform fine-tuning of object detector based on convolutional network VGG-16 using mini-batch stochastic gradient descent. However, this would require a significant volume of training set and a few days of working on the graphics processing unit for successful training.…”

Section: Review Of the Literaturementioning

confidence: 99%

A Model and Training Algorithm of Small-Sized Object Detection System for a Compact Aerial Drone

Москаленко

Moskalenko

Коробов

et al. 2019

Radio Electronics, Computer Science, Control

View full text Add to dashboard Cite

Context. Lightweight model and effective training algorithm of on-board object detection system for a compact drone are developed. The object of research is the process of small object detection on aerial images under computational resource constraint and uncertainty caused by small amount of labeled training data. The subject of the research are the model and training algorithm for detecting small objects on aerial imagery. Objective. Goal of the research is developing efficient model and training algorithm of object detection system for a compact aerial drone under conditions of restricted computing resources and the limited volume of the labeled training set. Methods. The four-stage training algorithm of the object detector is proposed. At the first stage, selecting the type of deep convolutional neural network and the number of low-level layers that is pre-trained on the ImageNet dataset for reusing takes place. The second stage involves unsupervised training of high-level convolutional sparse coding layers using modification of growing neural gas to automatically determine the required number of neurons and provide optimal distributions of the neurons over the data. Its application makes it possible to utilize the unlabeled training datasets for the adaptation of the high-level feature description to the domain application area. At the third stage, there are reduction of output feture map using principan component analysis and building of decision rules. In this case, output feature map is formed by concatenation of feature maps from different level of deep network using upscaling upper maps to uniform shape for each channel. This approach provides more contextual information for efficient recognition of small objects on aerial images. In order to build classifier of output feature map pixels is proposed to use boosted information-extreme learning. Besides that, the extreme learning machine is used to build of regression model for predict bounding box of detected object. Last stage involves fine-tuning of high-level layers of deep network using simulated annealing metaheuristic algorithm in order to approximating the global optimum of complex criterion of training efficiency of detection model. Results. The results obtained on open datasets testify to the suitability of the model and training algorithm for practical usage. The proposed training algorithm utilize 500 unlabeled and 200 labeled training samples to provide 96% correctly detection of objects on the images of the test dataset. Conclusions. Scientific novelty of the paper is a new model and training algorithm for object detection, which enable to achieve high confidence of recognition of small objects on aerial images under computational resource constraint and limited volume of the labeled training set. Practical significance of the paper results consists in the developed model and training algorithm made it possible to reduce requirements for size of labeled training set and computation resources of on-board detection system of aerial drone in tr...

show abstract

An Improved Transfer learning Approach for Intrusion Detection

Cited by 15 publications

References 8 publications

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN

A Model and Training Algorithm of Small-Sized Object Detection System for a Compact Aerial Drone

Contact Info

Product

Resources

About