Junk Mail Content Detection Using Logistic Regression Algorithm

Tao Hai; Shaoyi Li; Ezinne C. Maxwell-Chigozie; Chidera Eze; Zunhai Gao; Celestine Iwendi; Zakaria Boulouard

doi:10.1007/978-3-031-37164-6_21

Junk Mail Content Detection Using Logistic Regression Algorithm

Tao Hai
, Shaoyi Li
, Ezinne C. Maxwell-Chigozie
, Chidera Eze
, Zunhai Gao
, Celestine Iwendi
, Zakaria Boulouard

Qiannan Normal College for Nationalities
Nanchang Institute of Science and Technology
University of Bolton
University of Hassan II Casablanca

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In contemporary times, things have moved away from traditional method to sophisticated way of communication via social media. One of the common ways information is disseminated amongst people is in the use of emails. Emails are very effective, easy and less costly to use by the sender but invariably costly to the recipient. This is due to the effect unwarranted messages which are thrown in tons are being received daily. This paper focus is on developing an effective junk mail content detector to effectively detect the content of messages and properly classify them thereby eliminate spurious emails. Logistic regression and Random Forest algorithms were employed and the result showed thar our model Logistics regression proves a superior performance.

Original language	English
Title of host publication	Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions
Editors	Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	299-308
Number of pages	10
ISBN (Print)	9783031371639
DOIs	https://doi.org/10.1007/978-3-031-37164-6_21
State	Published - 2023
Externally published	Yes
Event	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 - Bolton, United Kingdom Duration: 24 Feb 2023 → 25 Feb 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	735 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Country/Territory	United Kingdom
City	Bolton
Period	24/02/23 → 25/02/23

Keywords

Junk mail content decision
Logistic regression
Machine learning
Random forest

Access to Document

10.1007/978-3-031-37164-6_21

Cite this

Hai, T., Li, S., Maxwell-Chigozie, E. C., Eze, C., Gao, Z., Iwendi, C., & Boulouard, Z. (2023). Junk Mail Content Detection Using Logistic Regression Algorithm. In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions (pp. 299-308). (Lecture Notes in Networks and Systems; Vol. 735 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37164-6_21

Hai, Tao ; Li, Shaoyi ; Maxwell-Chigozie, Ezinne C. et al. / Junk Mail Content Detection Using Logistic Regression Algorithm. Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. editor / Celestine Iwendi ; Zakaria Boulouard ; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. pp. 299-308 (Lecture Notes in Networks and Systems).

@inproceedings{8f628f1d5a754b30b344338ace01108f,

title = "Junk Mail Content Detection Using Logistic Regression Algorithm",

abstract = "In contemporary times, things have moved away from traditional method to sophisticated way of communication via social media. One of the common ways information is disseminated amongst people is in the use of emails. Emails are very effective, easy and less costly to use by the sender but invariably costly to the recipient. This is due to the effect unwarranted messages which are thrown in tons are being received daily. This paper focus is on developing an effective junk mail content detector to effectively detect the content of messages and properly classify them thereby eliminate spurious emails. Logistic regression and Random Forest algorithms were employed and the result showed thar our model Logistics regression proves a superior performance.",

keywords = "Junk mail content decision, Logistic regression, Machine learning, Random forest",

author = "Tao Hai and Shaoyi Li and Maxwell-Chigozie, \{Ezinne C.\} and Chidera Eze and Zunhai Gao and Celestine Iwendi and Zakaria Boulouard",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 ; Conference date: 24-02-2023 Through 25-02-2023",

year = "2023",

doi = "10.1007/978-3-031-37164-6\_21",

language = "English",

isbn = "9783031371639",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "299--308",

editor = "Celestine Iwendi and Zakaria Boulouard and Natalia Kryvinska",

booktitle = "Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions",

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}

Hai, T, Li, S, Maxwell-Chigozie, EC, Eze, C, Gao, Z, Iwendi, C & Boulouard, Z 2023, Junk Mail Content Detection Using Logistic Regression Algorithm. in C Iwendi, Z Boulouard & N Kryvinska (eds), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Lecture Notes in Networks and Systems, vol. 735 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 299-308, International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023, Bolton, United Kingdom, 24/02/23. https://doi.org/10.1007/978-3-031-37164-6_21

Junk Mail Content Detection Using Logistic Regression Algorithm. / Hai, Tao; Li, Shaoyi; Maxwell-Chigozie, Ezinne C. et al.
Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. ed. / Celestine Iwendi; Zakaria Boulouard; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. p. 299-308 (Lecture Notes in Networks and Systems; Vol. 735 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Junk Mail Content Detection Using Logistic Regression Algorithm

AU - Hai, Tao

AU - Li, Shaoyi

AU - Maxwell-Chigozie, Ezinne C.

AU - Eze, Chidera

AU - Gao, Zunhai

AU - Iwendi, Celestine

AU - Boulouard, Zakaria

PY - 2023

Y1 - 2023

N2 - In contemporary times, things have moved away from traditional method to sophisticated way of communication via social media. One of the common ways information is disseminated amongst people is in the use of emails. Emails are very effective, easy and less costly to use by the sender but invariably costly to the recipient. This is due to the effect unwarranted messages which are thrown in tons are being received daily. This paper focus is on developing an effective junk mail content detector to effectively detect the content of messages and properly classify them thereby eliminate spurious emails. Logistic regression and Random Forest algorithms were employed and the result showed thar our model Logistics regression proves a superior performance.

AB - In contemporary times, things have moved away from traditional method to sophisticated way of communication via social media. One of the common ways information is disseminated amongst people is in the use of emails. Emails are very effective, easy and less costly to use by the sender but invariably costly to the recipient. This is due to the effect unwarranted messages which are thrown in tons are being received daily. This paper focus is on developing an effective junk mail content detector to effectively detect the content of messages and properly classify them thereby eliminate spurious emails. Logistic regression and Random Forest algorithms were employed and the result showed thar our model Logistics regression proves a superior performance.

KW - Junk mail content decision

KW - Logistic regression

KW - Machine learning

KW - Random forest

UR - https://www.scopus.com/pages/publications/85174484445

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DO - 10.1007/978-3-031-37164-6_21

M3 - Conference contribution

AN - SCOPUS:85174484445

SN - 9783031371639

T3 - Lecture Notes in Networks and Systems

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EP - 308

BT - Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions

A2 - Iwendi, Celestine

A2 - Boulouard, Zakaria

A2 - Kryvinska, Natalia

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

Y2 - 24 February 2023 through 25 February 2023

ER -

Hai T, Li S, Maxwell-Chigozie EC, Eze C, Gao Z, Iwendi C et al. Junk Mail Content Detection Using Logistic Regression Algorithm. In Iwendi C, Boulouard Z, Kryvinska N, editors, Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Springer Science and Business Media Deutschland GmbH. 2023. p. 299-308. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-37164-6_21

Junk Mail Content Detection Using Logistic Regression Algorithm

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Keywords

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