Ms. Marya Jehad Profile

Marya Jehad

at Univ of Sharjah

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 12 June 2023 Poster + Paper

Spam SMS detection based on long short-term memory and recurrent neural network

Marya Jehad, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12538, 125381Y (2023) https://doi.org/10.1117/12.2674147

KEYWORDS: Neural networks, Machine learning, Performance modeling, Deep learning, Data modeling, Support vector machines, Feature extraction

Read Abstract +

Short Messaging Service (SMS) becomes a more easy, affordable way to communicate and increasingly replace phone calls. Spam is any kind of unwanted, random unsolicited message that gets sent without any authorization from the receiver. hackers use spam SMS to get their important information. Effective spam detection is an essential tool for assisting users in determining whether an SMS is a spam or not. Different machine learning methods such as Deep Learning techniques have attempted to distinguish between spam and ham SMS texts. This paper proposes the Spam-Ham Classification method using Recurrent Neural Networks (RNN) and Long Short-Term Memories (LSTM). The proposed model utilizes Keras and TensorFlow to detect Spam SMS. The dataset used is SpamSMSCollection from the UCI machine learning repository. The dataset contains a set of 5574 SMS messages. The dataset is preprocessed using tokenization, Lemmatization, padding, and stopword removal. The overall accuracy of the proposed model is 98%. The performance of the proposed method is compared with different machine learning algorithms such as Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-layer Perceptron (MLP).

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years