%0 Journal Article %1 shirpurkar2024spyder %A Shirpurkar, Anuja Kathwate | Dnyaneshwari Chafle | Sharda Moharle | Mansi Singh | Prof. Rina %D 2024 %J INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT %K Email Image Learning, Machine Natural Spam Text classification, detection, filtering, language processing %N 5 %P 999-1007 %T Spam Spyder Spam Detection using MI and AI %U https://www.ijtsrd.com/computer-science/other/70490/spam-spyder-spam-detection-using-mi-and-ai/anuja-kathwate %V 8 %X Spam emails and messages are a major problem for both users and organizations in the digital era. With the help of machine learning techniques and the Spyder Integrated Development Environment IDE , this project seeks to create a reliable spam detection system. Classifying messages as eitherspam or ham non spam with high accuracy is the main goal. The project starts with gathering and preparing a dataset comprising tagged spam and non spam message instances. Text normalization, tokenization, and feature extraction are important preprocessing tasks. Text input is transformed into numerical features appropriate for machine learning models using methods like word embeddings and Term Frequency Inverse Document Frequency. Using sophisticated data filtering algorithms and web crawling techniques, Spam Spyder is a system that finds and analyzes spam content on the internet. The proliferation of uninvited and destructive messages, popularly known as spam, has become a serious concern for online platforms, businesses, and users due to the exponential growth of digital communication and user generated content. In order to combat this, Spam Spyder automates the process of identifying spam on websites, social media networks, and other online platforms. To detect spammy content, the system combines machine learning, natural language processing NLP , and pattern recognition. Through website crawling and scanning for specified spam traits such dubious links, misleading wording, or excessive keyword repetition , Spam Spyder is able to identify and classify. Now a days communication plays a major role in every thing be it professional or personal. Email communication service is being used extensively because of its free use services, low cost operations, accessibility, and popularity. This security flaw is being exploited by some businesses and ill motivated persons for advertising, phishing, malicious purposes, and finally fraud. This produces a kind of email category called SPAM. Spam refers to any email that contains an advertisement, unrelated and frequent emails. These emails are increasing day by day in numbers. Studies show that around 55 percent of all emails are some kind of spam. A lot of effort is being put into this by service providers. Moreover, the spam detection of service provider scan ever be aggressive with classification because it may cause potential information loss to in case of a misclassification. Anuja Kathwate | Dnyaneshwari Chafle | Sharda Moharle | Mansi Singh | Prof. Rina Shirpurkar "Spam Spyder (Spam Detection using MI & AI)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5 , October 2024, URL: https://www.ijtsrd.com/papers/ijtsrd70490.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/70490/spam-spyder-spam-detection-using-mi-and-ai/anuja-kathwate

@article{shirpurkar2024spyder, abstract = {Spam emails and messages are a major problem for both users and organizations in the digital era. With the help of machine learning techniques and the Spyder Integrated Development Environment IDE , this project seeks to create a reliable spam detection system. Classifying messages as eitherspam or ham non spam with high accuracy is the main goal. The project starts with gathering and preparing a dataset comprising tagged spam and non spam message instances. Text normalization, tokenization, and feature extraction are important preprocessing tasks. Text input is transformed into numerical features appropriate for machine learning models using methods like word embeddings and Term Frequency Inverse Document Frequency. Using sophisticated data filtering algorithms and web crawling techniques, Spam Spyder is a system that finds and analyzes spam content on the internet. The proliferation of uninvited and destructive messages, popularly known as spam, has become a serious concern for online platforms, businesses, and users due to the exponential growth of digital communication and user generated content. In order to combat this, Spam Spyder automates the process of identifying spam on websites, social media networks, and other online platforms. To detect spammy content, the system combines machine learning, natural language processing NLP , and pattern recognition. Through website crawling and scanning for specified spam traits such dubious links, misleading wording, or excessive keyword repetition , Spam Spyder is able to identify and classify. Now a days communication plays a major role in every thing be it professional or personal. Email communication service is being used extensively because of its free use services, low cost operations, accessibility, and popularity. This security flaw is being exploited by some businesses and ill motivated persons for advertising, phishing, malicious purposes, and finally fraud. This produces a kind of email category called SPAM. Spam refers to any email that contains an advertisement, unrelated and frequent emails. These emails are increasing day by day in numbers. Studies show that around 55 percent of all emails are some kind of spam. A lot of effort is being put into this by service providers. Moreover, the spam detection of service provider scan ever be aggressive with classification because it may cause potential information loss to in case of a misclassification. Anuja Kathwate | Dnyaneshwari Chafle | Sharda Moharle | Mansi Singh | Prof. Rina Shirpurkar "Spam Spyder (Spam Detection using MI & AI)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5 , October 2024, URL: https://www.ijtsrd.com/papers/ijtsrd70490.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/70490/spam-spyder-spam-detection-using-mi-and-ai/anuja-kathwate }, added-at = {2025-01-03T06:19:21.000+0100}, author = {Shirpurkar, Anuja Kathwate | Dnyaneshwari Chafle | Sharda Moharle | Mansi Singh | Prof. Rina}, biburl = {https://www.bibsonomy.org/bibtex/2df7254e40f29f301e0064d55882fd22f/ijtsrd}, interhash = {425430f71b3e9f6c5cdb2c54e34d744b}, intrahash = {df7254e40f29f301e0064d55882fd22f}, issn = {2456-6470}, journal = {INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT}, keywords = {Email Image Learning, Machine Natural Spam Text classification, detection, filtering, language processing}, language = {english}, month = oct, number = 5, pages = {999-1007}, refid = {37761804}, timestamp = {2025-01-03T06:19:21.000+0100}, title = {Spam Spyder Spam Detection using MI and AI }, url = {https://www.ijtsrd.com/computer-science/other/70490/spam-spyder-spam-detection-using-mi-and-ai/anuja-kathwate}, volume = 8, year = 2024 }