Using Support Vector Machine For Classification And Feature Extraction Of Spam In Email
DOI:
https://doi.org/10.59615/ijie.2.2.26DOR:
https://dorl.net/dor/20.1001.1.27831906.2022.2.2.3.4Keywords:
Email, Spam, SVM, Classification, Feature ExtractionAbstract
We provide an overview of recent and successful content-based e-mail spam filtering algorithms in this article. Our main focus is on spam filters based on machine learning and variants influenced by them. We report on significant ideas, methodologies, key endeavors, and the field's current state-of-the-art. The initial interpretation of previous work demonstrates the fundamentals of spam filtering and feature engineering in e-mail. We finish by looking at approaches, procedures, and evaluation standards, as well as exploring intriguing offshoots of recent breakthroughs and proposing directions of future research.
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• Bhowmik, C., Ghantasala, G. P., & AnuRadha, R. (2021). A Comparison of Various Data Mining Algorithms to Distinguish Mammogram Calcification Using Computer-Aided Testing Tools. In Proceedings of the Second International Conference on Information Management and Machine Intelligence (pp. 537-546). Springer, Singapore.
• CADe, M. (2020). CADx for Identifying Microcalcification Using Support Vector Machine. Journal of Communication Engineering & Systems, 10(2), 9-16p.
• Chandana, P., Ghantasala, G. P., Jeny, J. R. V., Sekaran, K., Deepika, N., Nam, Y., & Kadry, S. (2020). An effective identification of crop diseases using faster region based convolutional neural network and expert systems. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6531-6540.
• Gadde, S. S., Anand, D., Sasidhar Babu, N., Pujitha, B. V., Sai Reethi, M., & Pradeep Ghantasala, G. S. (2022). Performance Prediction of Students Using Machine Learning Algorithms. In Applications of Computational Methods in Manufacturing and Product Design (pp. 405-411). Springer, Singapore.
• Ghantasala, G. P., & Kumari, N. V. (2021a). Identification of Normal and Abnormal Mammographic Images Using Deep Neural Network. Asian Journal For Convergence In Technology (AJCT), 7(1), 71-74.
• Ghantasala, G. P., & Kumari, N. V. (2021b). Breast Cancer Treatment Using Automated Robot Support Technology For Mri Breast Biopsy. INTERNATIONAL JOURNAL OF EDUCATION, SOCIAL SCIENCES AND LINGUISTICS, 1(2), 235-242.
• Ghantasala, G. P., Kallam, S., Kumari, N. V., & Patan, R. (2020a, March). Texture Recognization and Image Smoothing for Microcalcification and Mass Detection in Abnormal Region. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.
• Ghantasala, G. P., Kumari, N. V., & Patan, R. (2021a). Cancer prediction and diagnosis hinged on HCML in IOMT environment. In Machine Learning and the Internet of Medical Things in Healthcare (pp. 179-207). Academic Press.
• Ghantasala, G. P., Reddy, A. R., & Arvindhan, M. (2021c). Prediction of Coronavirus (COVID-19) Disease Health Monitoring with Clinical Support System and Its Objectives. In Machine Learning and Analytics in Healthcare Systems (pp. 237-260). CRC Press.
• Ghantasala, G. P., Reddy, A., Peyyala, S., & Rao, D. N. (2021b). Breast Cancer Prediction In Virtue Of Big Data Analytics. INTERNATIONAL JOURNAL OF EDUCATION, SOCIAL SCIENCES AND LINGUISTICS, 1(1), 130-136.
• Ghantasala, G. P., Tanuja, B., Teja, G. S., & Abhilash, A. S. (2020b). Feature Extraction and Evaluation of Colon Cancer using PCA, LDA and Gene Expression. Forest, 10(98), 99.
• Kishore, D. R., Syeda, N., Suneetha, D., Kumari, C. S., & Ghantasala, G. P. (2021). Multi Scale Image Fusion through Laplacian Pyramid and Deep Learning on Thermal Images. Annals of the Romanian Society for Cell Biology, 3728-3734.
• Krishna, N. M., Sekaran, K., Vamsi, A. V. N., Ghantasala, G. P., Chandana, P., Kadry, S. & Damaševičius, R. (2019). An efficient mixture model approach in brain-machine interface systems for extracting the psychological status of mentally impaired persons using EEG signals. IEEE Access, 7, 77905-77914.
• Kumari, N. V., & Ghantasala, G. P. (2020). Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region. Journal of Operating Systems Development & Trends, 7(1), 26-33.
• Mandal, K., Ghantasala, G. P., Khan, F., Sathiyaraj, R., & Balamurugan, B. (2020). Futurity of Translation Algorithms for Neural Machine Translation (NMT) and Its Vision. In Natural Language Processing in Artificial Intelligence (pp. 53-95). Apple Academic Press.
• Patan, R., Ghantasala, G. P., Sekaran, R., Gupta, D., & Ramachandran, M. (2020). Smart healthcare and quality of service in IoT using grey filter convolutional based cyber physical system. Sustainable Cities and Society, 59, 102141.
• Pradeep Ghantasala, G. S., Nageswara Rao, D., & Patan, R. (2022). Recognition of Dubious Tissue by Using Supervised Machine Learning Strategy. In Applications of Computational Methods in Manufacturing and Product Design (pp. 395-404). Springer, Singapore.
• Reddy, A. R., Ghantasala, G. S., Patan, R., Manikandan, R., & Kallam, S. (2021). Smart Assistance of Elderly Individuals in Emergency Situations at Home. In Internet of Medical Things (pp. 95-115). Springer, Cham.
• Reddy, A., Gude, V., Mamatha, K., & Rao, D. N. (2022). Smart Waste Management Systems by Using Automated Machine Learning Techniques. Journal of Artificial Intelligence, Machine Learning and Neural Network (JAIMLNN) ISSN: 2799-1172, 2(04), 36-45.
• Sreehari, E., & Ghantasala, P. G. (2019). Climate Changes Prediction Using Simple Linear Regression. Journal of Computational and Theoretical Nanoscience, 16(2), 655-658.
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Copyright (c) 2022 Anuradha Reddy, M. Uma Maheswari, A. Viswanathan, G. Vikram
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