An Efficient Worm Detection System Using Multi Feature Analysis and Classification Techniques

2019 ◽  
pp. 1054-1064
Author(s):  
B Leelavathi ◽  
Rajesh M Babu
Keyword(s):  
Detection System ◽  
Feature Analysis ◽  
Worm Detection ◽  
Classification Techniques

scholarly journals A Worm Detection System Based on Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 205444-205454
Author(s):  
Hanxun Zhou ◽  
Yeshuai Hu ◽  
Xinlin Yang ◽  
Hong Pan ◽  
Wei Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Worm Detection

Shape feature analysis using artificial neural networks for improvements of hybrid lung nodule detection system

1993 ◽  
Author(s):  
Y. S. P. Chiou ◽  
Fleming Y. M. Lure ◽  
Panos A. Ligomenides ◽  
Matthew T. Freedman ◽  
Steven L. Fritz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Detection System ◽  
Lung Nodule ◽  
Feature Analysis ◽  
Shape Feature ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Artificial Neural

Detection System on Weak Electrical Signal in Plants

2013 ◽  
Vol 427-429 ◽  
pp. 2037-2040 ◽  
Author(s):  
Hai Wu ◽  
Li Guo Tian ◽  
Shan Hu ◽  
Zhi Liang Chen ◽  
Meng Li
Keyword(s):  
Detection System ◽  
Electrical Signal ◽  
Feature Analysis ◽  
Electric Signal ◽  
System Construction ◽  
Experiment System ◽  
Growth Status ◽  
Experimental Platform ◽  
Analysis Process ◽  
Characteristics Analysis

To achieve the accurate extraction and characteristics analysis of electrical signal in plants, the system was developed for detecting and extracting weak electrical signal in plants. As experimental platform for the extraction and detection of plants electric signal, training experiment system on the growth of plants, had done the experiments of extraction and feature analysis on plants electric signal by it, described in detail the extraction and analysis process of plants electrical signal, and also discussed the methods of anti-interference process for the system. The system construction is feasible, it has positive significance for real-time monitoring the environment factors changes and the plants growth status.

Design and Implementation of a Multi-gigabit Intrusion and Virus/Worm Detection System

2006 ◽  
Author(s):  
Seok-min Kang ◽  
Il-seop Song ◽  
Youngseok Lee ◽  
Taeck-geun Kwon
Keyword(s):  
Detection System ◽  
Worm Detection ◽  
Design And Implementation

A distributed host-based worm detection system

2006 ◽  
Author(s):  
Senthilkumar G. Cheetancheri ◽  
John Mark Agosta ◽  
Denver H. Dash ◽  
Karl N. Levitt ◽  
Jeff Rowe ◽  
...  
Keyword(s):  
Detection System ◽  
Worm Detection

scholarly journals Hook Worm Detection and its Classification Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 1795-1798
Keyword(s):  
Capsule Endoscopy ◽  
Complex Structure ◽  
Wireless Capsule Endoscopy ◽  
Neural Systems ◽  
Worm Detection ◽  
Classification Techniques ◽  
The One ◽  
Hook Worm ◽  
Wireless Capsule ◽  
Highlight Extraction

Wireless Capsule endoscopy (WCE) has transformed into a by and large used demonstrative strategy to look at some fiery infections and disarranges. Customized and completely robotized hookworm recognition and characterization models are testing task because of low nature of pictures, nearness of incidental issues, complex structure of gastrointestinal and various appearances to the extent shading and surface. There are a few endeavours were made to thoroughly research the robotized hookworm discovery from WCE pictures. A definite review is taken for identifying Hookworm in Endoscopy picture and its partner pre and post preparing specialized application. A profound report on AI system and highlight extraction approaches were examined. The different advances engaged with Hookworm location utilizing neural systems alongside their sorts were additionally talked about. The significant highlights which can be utilized for extricating the one of a kind highlights were considered.

scholarly journals Intrusion detection system based on bagging with support vector machine

2021 ◽  
Vol 24 (2) ◽  
pp. 1100
Author(s):  
Ali Khalid Hilool ◽  
Soukaena H. Hashem ◽  
Shatha H. Jafer
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Feature Selection Method ◽  
Support Vector ◽  
Security Risk ◽  
Chi Square ◽  
Worm Detection ◽  
Computer Worms ◽  
Detection Algorithms ◽  
Data Mining Algorithms

<p>Due to their rapid spread, computer worms perform harmful tasks in networks, posing a security risk; however, existing worm detection algorithms continue to struggle to achieve good performance and the reasons for that are: First, a large amount of irrelevant data affects classification accuracy. Second, individual classifiers do not detect all types of worms effectively. Third, many systems are based on outdated data, making them unsuitable for new worm species. The goal of the study is to use data mining algorithms to detect worms in the network because they have a high ability to detect new types accurately. The proposal is based on the UNSW NB15 dataset and uses a support vector machine to train and test the ensemble bagging algorithm. To detect various types of worms efficiently, the contribution suggests combining correlation and Chi2 feature selection method called Chi2-Corr to select relevant features and using support vector machine (SVM) in the bagging algorithm. The system achieved accuracy reaching 0.998 with Chi2-Corr, and 0.989, 0.992 with correlation and chi-square separately.</p>

scholarly journals Segmentation and Detection of Brain Tumor by using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 3226-3235
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Detection System ◽  
Research Area ◽  
Support Vector ◽  
Subsequent Stage ◽  
Gray Level ◽  
K Nearest Neighbors ◽  
Classification Techniques ◽  
Occurrence Matrix

The segmentation and detection of brain pathologies in medical images is an indispensible step. This helps the radiologist to diagnose a variety of brain deformity and helps in the set up for a suitable treatment. Magnetic Resonance Imaging (MRI) plays a significant character in the research area of neuroscience. The proposed work is a study and probing of different classification techniques used for automated detection and segmentation of brain tumor from MRI in the field of machine learning. This paper try to present the feature extraction from raw MRI and fed the same to four classifier named as, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). This mechanism was done in various stages for Computer Aided Detection System. In the preliminary stage the pre-processing and post-processing of MR image enhancement is done. This was done as the processed image is more likely suitable for the analysis. Then the k-means clustering is used to sectioning the MRI by applied mean gray level method. In the subsequent stage, statistical feature analysis were done, the features were computed using Haralick’s equation for feature based on the Gray Level Co-occurrence Matrix. Feature chosen dependent on tumor region, location, periphery, and color from the sectioned image is then classified by applying the classification techniques. In the third stage SVM, DT, ANN, and KNN classifiers were used for diagnoses. The performances of the classifiers are tested and evaluated successfully.

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