World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level


    FEBRUARY 2023 Issue has been successfully launched on 1 February 2023.

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    Its Our pleasure to inform you that, WJERT February 2023 Issue has been Published, Kindly check it on

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level




Prakash Mani Badal*


Messages have become an important component of our daily communication in today’s society, and the number of messages received by a user has increased significantly. There has been a large increase in the amount of spam messages in tandem with the rise in message volume. Spam communications are unsolicited mass messages that are sent to a large number of people. As a result, we get a lot of spam. These spam messages frequently obscure essential messages and may contain dangerous links that lead to harmful websites, jeopardising users’ security. The bulk of spam classifiers are created using a single machine learning method, which may not be sufficient for correctly identifying a message as spam or ham. This thesis adds to the development of a Hybrid Spam classifier through the use of ensemble machine learning. In our research, a total of seven machine learning algorithms were shortlisted. These were then utilised to create 7 basic classifiers using a single method, 35 level 3 hybrid models using three algorithms, 21 level 5 hybrid models using five algorithms, and one level 7 hybrid model using seven algorithms. To narrow down the best hybrid models, a total of 64 models were trained and assessed.

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