相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。A weighted feature enhanced Hidden Markov Model for spam SMS filtering
Tian Xia et al.
NEUROCOMPUTING (2021)
A feature selection approach for spam detection in social networks using gravitational force-based heuristic algorithm
Poria Pirozmand et al.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)
Opinion spam detection framework using hybrid classification scheme
Muhammad Zubair Asghar et al.
SOFT COMPUTING (2020)
Applicability of machine learning in spam and phishing email filtering: review and approaches
Tushaar Gangavarapu et al.
ARTIFICIAL INTELLIGENCE REVIEW (2020)
A semantic-based classification approach for an enhanced spam detection
Nadjate Saidani et al.
COMPUTERS & SECURITY (2020)
Deep learning to filter SMS Spam
Pradeep Kumar Roy et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2020)
Spam filtering using a logistic regression model trained by an artificial bee colony algorithm
Bilge Kagan Dedeturk et al.
APPLIED SOFT COMPUTING (2020)
A Discrete Hidden Markov Model for SMS Spam Detection
Tian Xia et al.
APPLIED SCIENCES-BASEL (2020)
Detection of spam and threads identification in E-mail spam corpus using content based text analytics method
U. Murugavel et al.
MATERIALS TODAY-PROCEEDINGS (2020)
SMSAD: a framework for spam message and spam account detection
Kayode Sakariyah Adewole et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2019)
Improved email spam detection model based on support vector machines
Sunday Olusanya Olatunji
NEURAL COMPUTING & APPLICATIONS (2019)
A new semantic-based feature selection method for spam filtering
Jose R. Mendez et al.
APPLIED SOFT COMPUTING (2019)
Unsupervised feature learning for spam email filtering
Melvin Diale et al.
COMPUTERS & ELECTRICAL ENGINEERING (2019)
A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation
Kapil Juneja
APPLIED SOFT COMPUTING (2019)
An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks
Hossam Faris et al.
INFORMATION FUSION (2019)
A review of soft techniques for SMS spam classification: Methods, approaches and applications
Olusola Abayomi-Alli et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)
An effective feature selection method for web spam detection
Faeze Asdaghi et al.
KNOWLEDGE-BASED SYSTEMS (2019)
SMS Spam Message Detection using Term Frequency-Inverse Document Frequency and Random Forest Algorithm
Nilam Nur Amir Sjarif et al.
FIFTH INFORMATION SYSTEMS INTERNATIONAL CONFERENCE (2019)
Machine learning for email spam filtering: review, approaches and open research problems
Emmanuel Gbenga Dada et al.
HELIYON (2019)
Hybrid Water Cycle Optimization Algorithm With Simulated Annealing for Spam E-mail Detection
Ghada Al-Rawashdeh et al.
IEEE ACCESS (2019)
Visual and textual features based email spam classification using S-Cuckoo search and hybrid kernel support vector machine
T. Kumaresan et al.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2019)
Relief-based feature selection: Introduction and review
Ryan J. Urbanowicz et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2018)
Ham and Spam E-Mails Classification Using Machine Learning Techniques
M. Bassiouni et al.
JOURNAL OF APPLIED SECURITY RESEARCH (2018)
SMS spam filtering and thread identification using bi-level text classification and clustering techniques
Naresh Kumar Nagwani et al.
JOURNAL OF INFORMATION SCIENCE (2017)