"Survey on Spam Filtering Techniques"
written by Saadat Nazirova,
published by Communications and Network, Vol.3 No.3, 2011
has been cited by the following article(s):
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[7] Machine Learning Approach to Predict Student Academic Performance
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[8] Spam Detection in Online Social Networks Using Feed Forward Neural Network
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[10] Email Classification Using Artificial Neural Network
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[11] Implementing an Agent-based Multi-Natural Language Anti-Spam Model
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[12] Spam Filtration using Boyer Moore Algorithm and Naïve Method
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[13] A Comparative Analysis of Various Spam Classifications
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[14] A Comparative Analysis of Various Spam
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[15] A Content-Based Phishing Email Detection Method
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[16] Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
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[17] Email Classification Using Machine Learning Algorithms
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[18] A New Machine Learning based Approach for Text Spam Filtering Technique
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[19] Machine Learning Approach to Predict and Improve Student Academic Performance
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[20] An Optimized Approach to Improve the Quality of Education
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[21] Technical Study of Spam Filtering Process
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[22] PROCEDIMENTO PARA PLANEJAMENTODO EMPREGO DAS FOR?AS ARMADAS BRASILEIRAS EM APOIO A LOGíSTICA HUMANITáRIA NAGEST?O DE DESASTRES
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[23] New approaches for content-based analysis towards Online Social Network spam detection
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[24] A New SMS Spam Detection Method Using Both Content-Based and Non Content-Based Features
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[25] CONTENT FILTERING USING ARITIFICIAL INTELLIGENCE
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[26] Does sentiment analysis help in bayesian spam filtering?
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[27] E-Mail Spam Detection Using SVM and RBF
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[28] A Survey Paper on Spam Mail Detection Using RFD
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[29] Spam Mail Detection Using Relevance Feature Discovery
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[30] Incremental learning for large-scale stream data and its application to cybersecurity
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[31] Spam Detection Techniques: A Review
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[32] An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing
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[33] Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques
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[34] SPAM-NSGA-II-NVBYS: AN EFFICIENT HYBRID APPROACH FOR E-MAIL SPAM FILTERING
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[35] The challenges faced by management science research scholars in different stages of research: A Study for Maharashtra (INDIA)
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[36] UMA PROPOSTA PARA SISTEMA DE GERÊNCIA DE PAVIMENTOSAPLICADA A AEROPORTOS MILITARES
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[37] A Case Study of User-Level Spam Filtering.
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[38] SECUMAIL [Secure Email System]
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[39] Inured to Obscenity but Sensitive to Pornography: Aren’t Our Definitions Blurred?
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[40] Shrihari Ahire Vishakha Panjabi Rahul Jagtap Department of Computer Engineering Department of Computer Engineering Department of Computer Engineering
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[41] A Case Study of User-Level Spam Filtering
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[42] Identifying spam e-mail messages using an intelligence algorithm
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[43] Effective Spam Detection Method for Email
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[44] A Review on Different Spam Detection Approaches
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[45] Spam Filtering using K mean Clustering with Local Feature Selection Classifier
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[46] SURVEY PAPER ON INTELLIGENT SYSTEM FOR TEXT AND IMAGE SPAM FILTERING
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[47] Can We CAN the Email Spam
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[48] A Behavioral Spam Detection System
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[49] Baeza-Yates and Navarro approximate string matching for spam filtering
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[50] An Off - Line Character Recognition System for Marathi Handwritten S cript, a Review and Study
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[51] MINISTÉRIO DA DEFESAEXÉRCITO BRASILEIRODEPARTAMENTO DE CIÊNCIA E TECNOLOGIAINSTITUTO MILITAR DE ENGENHARIACURSO DE MESTRADO EM ENGENHARIA MECÂNICA
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[52] UMA METODOLOGIA PARA APOIO AO DESENVOLVIMENTO SEMI-AUTOMATICO DE SISTEMAS ´ MULTI-AGENTES
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[53] Approximate String Matching for Spam Filtering
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[54] MINISTÉRIO DA DEFESA EXÉRCITO BRASILEIRO DEPARTAMENTO DE CIÊNCIA E TECNOLOGIA INSTITUTO MILITAR DE ENGENHARIA CURSO DE …