Research Article
Enhanced MNB Method for SPAM E-mail/SMS Text Detection Using TF-IDF Vectorizer
Anoushka Dasgupta,
Shideh Yavary Mehr*
Issue:
Volume 9, Issue 1, March 2024
Pages:
1-8
Received:
8 March 2024
Accepted:
3 April 2024
Published:
28 April 2024
Abstract: Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS and email detection using a dataset comprising 5574 messages from reputable sources. The collection includes contributions from the National University of Singapore SMS Corpus, Grumble text Website, Caroline Tag’s PhD Theses, and SMS Spam Corpus v.0.1 Big. With a meticulous approach encompassing data cleaning, balancing, preprocessing, and exploratory data analysis, the research employs the TF-IDF (Term Frequency and Inverse Document Frequency) vectorizer to enhance the model’s ability to capture the importance of individual words. This foundational work sets the stage for evaluating various machine learning models, including Support Vector Machine, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors, XGBoost, Gradient Boost Classifier, Bagging Classifier, and Extra Tree Classifier. Notably, the Multinomial Naïve Bayes model emerges as a standout performer with 100% accuracy and 97% precision in phishing detection. The research introduces an intuitive user interface, facilitating real-time interactivity for model assessment and offering valuable insights for cybersecurity applications. The study contributes to the advancement of robust cybersecurity systems, emphasizing precision and accuracy in spam SMS and email text detection.
Abstract: Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS an...
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Research Article
A Hybrid Intelligent Noise Pollution Prediction Model Based on ANFIS and Nature-Inspired Algorithms
Peter Ekow Baffoe*
,
Cynthia Borkai Boye
Issue:
Volume 9, Issue 1, March 2024
Pages:
9-21
Received:
11 April 2024
Accepted:
30 April 2024
Published:
17 May 2024
DOI:
10.11648/j.ajmcm.20240901.12
Downloads:
Views:
Abstract: In developing countries, researches in the areas of epidemiology, urban planning and environmental issues, it is extremely difficult to predict urban noise level in the neighborhoods. The majority of the noise-predicting algorithms in use today have limitations when it comes to prediction of noise level changes during intra-urban development and hence, the resulting noise pollution. Two hybrid noise prediction models, including ANFIS and PSO; and ANFIS and GA, were developed for Tarkwa Nsuaem Municipality and their performances were evaluated by applying statistical indicators. These hybrids were created to supplement and improve ANFIS's shortcomings based on their respective strengths and capabilities. To compare the performances of the models, statistical indicators were used; ANFIS-PSO performed better than the ANFIS-GA. The indications show the disparities, with the RMSE of ANFIS-PSO being 0.8789 and that of ANFIS-GA being 1.0529. Moreover, the Standard Deviation and Mean Square Error of ANFIS-PSO are 0.8898 and 0.7725 respectively, then those of ANFIS-GA are 1.0660 and 1.1086 respectively. A map showing the distribution of the predicted noise levels was produced from the outcome of the ANFIS-PSO model. Comparing the predicted noise levels to the EPA standards, it was observed that there is a danger which means people living in that area with noise levels above 65 dB are at high risk of health effects.
Abstract: In developing countries, researches in the areas of epidemiology, urban planning and environmental issues, it is extremely difficult to predict urban noise level in the neighborhoods. The majority of the noise-predicting algorithms in use today have limitations when it comes to prediction of noise level changes during intra-urban development and he...
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