Statistical Feature Extraction for Classification of Image Spam Using Artificial Neural Networks

Soranamageswari, M.; Meena, C.

doi:10.1109/icmlc.2010.72

Cited by 36 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the First layer it is applied Bayesian classifier and SVM classifier in the remaining layers. In paper [48] it is offered statistical feature extraction for classification of image-based spam using artificial neural networks. They consider statistical image feature histogram and mean value of block of image for image classification.…”

Section: Classification Of Spam Filtering Methodsmentioning

confidence: 99%

Survey on Spam Filtering Techniques

Nazirova¹

2011

View full text Add to dashboard Cite

In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.

show abstract

Section: Classification Of Spam Filtering Methodsmentioning

confidence: 99%

Survey on Spam Filtering Techniques

Nazirova¹

2011

View full text Add to dashboard Cite

show abstract

“…In [9,10] fuzzy clustering procedure is used. In this paper the author analyzed the fuzzy clustering usage and mining of textual content for spam filtering.…”

Section: Fuzzy Clustering Methodsmentioning

confidence: 99%

“…In [8,9], for the problem of clustering and classification, Bayesian approach is applied. The classification is based on assumptions like subject, population, sampling scheme and latent variable…”

Section: Bayesian Classificationmentioning

confidence: 99%

Automated Spam Filtering through Data Mining Approach

Mallampati¹,

Madhur²,

Abhinay³

et al. 2017

SIJST

View full text Add to dashboard Cite

Spam messages can be referred as those mails which come into act in the absence of a standard agreement among the senders and receivers for receiving e-mail solicitation. Usually these messages are sent in bulk quantities. For preventing the spam delivery, an automatic system based spam filter tool is employed. The objectives of spam filters and spam are contradicted diametrically. A spam filter can be termed effective if it recognizes spam. On the other hand, it is ineffective when it escapes the filters. It is the need of the hour that these bulk unsolicited e-mails be effectively filtered. Increasing volume of these mails emphasizes on the requirement and design of dependable anti-spam filters. One of the techniques which is used widely to filter these spam e-mails is the machine learning technique. They possess in built algorithms which filters spam e-mails at commendable rates. In this project we present a method, to access classifier security against their attacks profoundly concentrating on the content of the message. The dependence on a predefined set of keywords is reduced. The paper also focuses on related works which apply machine learning techniques using naïve Bayes classification for e-mail message classification.

show abstract

“…Soranamageswari et al [25] presented experimental method for image spam classification by using Artificial Neural Network. It is an effective method in image classification for finding and solving feature extraction problems.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Data Mining Algorithms for Image Classification

Thamilselvana¹,

Sathiaseelan²

2015

IJEME

View full text Add to dashboard Cite

Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It's also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques.

show abstract

Statistical Feature Extraction for Classification of Image Spam Using Artificial Neural Networks

Cited by 36 publications

References 14 publications

Survey on Spam Filtering Techniques

Survey on Spam Filtering Techniques

Automated Spam Filtering through Data Mining Approach

A Comparative Study of Data Mining Algorithms for Image Classification

Contact Info

Product

Resources

About