Cite this DOI

Afshan Anjum, A. A. (2023). Suspicious Account Detection Using Machine Learning Techniques. *Journal of Science & Technology*, *8*(7), 124–132. https://doi.org/10.46243/jst.2023.v8.i06.pp124-132

@article{afshananjum2023suspicious,
  author    = {Afshan Anjum, Afshan Anjum},
  title     = {{Suspicious Account Detection Using Machine Learning Techniques}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {aug},
  volume    = {8},
  number    = {7},
  pages     = {124--132},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i06.pp124-132},
  url       = {https://doi.org/10.46243/jst.2023.v8.i06.pp124-132},
  language  = {en},
  abstract  = {In the current generation, social networking sites have become an integral part of life for most people. On social networking sites such as Facebook, Instagram, and Twitter, thousands of people create their profiles daily, interacting with each based on the classification for detecting Suspicious accounts on social networks. Here the traditionally way has been used for different classification methods in this paper. The implementation of machine learning and natural language processor (NLP) techniques are done to enhance the accuracy of others regardless of location and time. Our goal is to understand who encourages threats in social networking profiles. To determine which social network profiles are genuine and which ones are Suspicious profiles, The support vector machine (SVM) and Naves bays algorithm technique can also be applied to achieve this strategy.}
}

TY  - JOUR
TI  - Suspicious Account Detection Using Machine Learning Techniques
AU  - Afshan Anjum, Afshan Anjum
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/08/07/
VL  - 8
IS  - 7
SP  - 124
EP  - 132
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - In the current generation, social networking sites have become an integral part of life for most people. On social networking sites such as Facebook, Instagram, and Twitter, thousands of people create their profiles daily, interacting with each based on the classification for detecting Suspicious accounts on social networks. Here the traditionally way has been used for different classification methods in this paper. The implementation of machine learning and natural language processor (NLP) techniques are done to enhance the accuracy of others regardless of location and time. Our goal is to understand who encourages threats in social networking profiles. To determine which social network profiles are genuine and which ones are Suspicious profiles, The support vector machine (SVM) and Naves bays algorithm technique can also be applied to achieve this strategy.
DO  - 10.46243/jst.2023.v8.i06.pp124-132
UR  - https://doi.org/10.46243/jst.2023.v8.i06.pp124-132
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i06.pp124-132",
    "title": "Suspicious Account Detection Using Machine Learning Techniques",
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    "language": "en",
    "abstract": "In the current generation, social networking sites have become an integral part of life for most people. On social networking sites such as Facebook, Instagram, and Twitter, thousands of people create their profiles daily, interacting with each based on the classification for detecting Suspicious accounts on social networks. Here the traditionally way has been used for different classification methods in this paper. The implementation of machine learning and natural language processor (NLP) techniques are done to enhance the accuracy of others regardless of location and time. Our goal is to understand who encourages threats in social networking profiles. To determine which social network profiles are genuine and which ones are Suspicious profiles, The support vector machine (SVM) and Naves bays algorithm technique can also be applied to achieve this strategy.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i06.pp124-132 gives all four in one JSON answer.