[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119890-en":3,"doc-seo-119890-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119890,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",6,"Technology","Enhancing Email Communication Security through Hierarchical Machine Learning Models - Advanced Spam Detection System","Digital communication’s rapid expansion has made spam emails a widespread threat that undermines both efficiency and security of communication channels. The work surveys recent advances in spam text detection and classification for social media, covering machine learning, deep learning, and NLP-based text approaches. It also addresses challenges in spam identification, control mechanisms, and commonly used datasets. Building on this, the study designs an advanced system using labeled and unlabeled data and methods such as tokenization, stemming, lemmatization, Tf-idf, and ML models.","Enhancing Email Communication Security through Hierarchical  \nMachine Learning Models  \nNeha Mangesh Dandvekar  \nComputer Science & Engineering, Shri Sant Gadge Baba College of Engineering and Technology, Bhusawal, INDIA  \nCorresponding Author: [dandvekarneha@gmail.com](dandvekarneha@gmail.com)  \nReceived: 13-11-2023 Revised: 28-11-2023 Accepted: 13-12-2023  \nABSTRACT  \nWith the exponential growth of digital communication, the menace of spam emails has become a pervasive issue, threatening the efficiency and security of communication channels. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developmentsin spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection. This paper presents a comprehensive approach to designing and implementing an advanced spam detection system that leverages the power of machine learning and Natural Language processing(NLP)techniques.  \nOur focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labelled and unlabelled data and proposed Techniques such as Tokenization Stemming, Lemmatization, Tf-idf Vectorization and ML Algorithm such as Naïve Bayes, Random Forest, KNN, Support Vector Machine.  \nKeywords-- SMS, Spam, Machine Learning, NLP, Tokenization, Stemming, Lemmatization, Tf-idf Vectorization, Naïve Bayes, Random Forest, KNN, Support Vector Machine  \nI. INTRODUCTION  \nEmail communication is an integral part of modern communication systems, playing a crucial role in both personal and professional spheres. However, the proliferation of email spam poses significant challenges to users, organizations, and email service providers. This research focuses on the development and implementation of an advanced email spam detection system using stateof-the-art machine learning techniques  \nThe proposed system leverages a diverse set of features extracted from email content, headers, and sender information. Machine Learning Algorithm & Natural Language Processing (NLP) algorithms are employed to analyse the textual content of emails, while metadata features contribute to a holistic understanding of the email context. The model is trained on a large  \ndataset comprising both spam and non-spam emails to ensure robust generalization.  \nThe primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.  \nII. LITERATURE REVIEW  \nThe systematic literature review provides the answers to specific research questions, whereas the general survey paper gives a broad idea about the domain. The key objective of this research is to identify the best available feature extraction techniques, and present different existing models for spam review detection and available parameters to analyse these models. The process of SLR helps to determine different studies available in the domain of spam review detection and answer different research questions. The distinct phases of the systematic literature review are shown in Figure 1. Preceding the study, the researchers discuss how different steps are performed in each phase of SLR.  \nFigure 1: Necessity ofthe SLR  \nIt is necessary to collect the best evidence from the existing literature. The SLR process provides the best techniques to collect and analyse evidence from primary studies. It also addresses the importance of the different methods of each research question. Following a search string is done to confirm that there exists no similar lit","cbCaiuFMltBiX7NQ","https://ap.wps.com/l/cbCaiuFMltBiX7NQ","pdf",416315,1,3,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n# Methodology","[{\"question\":\"What problem does the document address?\",\"answer\":\"The document addresses the threat of spam emails and related deceptive text reviews to communication security and efficiency.\"},{\"question\":\"Which techniques and models are proposed for detection?\",\"answer\":\"It uses labeled and unlabeled data and applies tokenization, stemming, lemmatization, Tf-idf vectorization, along with Naïve Bayes, Random Forest, KNN, and Support Vector Machine.\"},{\"question\":\"What does the literature review section aim to do?\",\"answer\":\"It performs a systematic literature review to identify suitable feature extraction techniques, existing models and parameters for spam/review detection, and to answer research questions by evaluating prior studies.\"}]","Enhancing Email Communication Security through Hierarchical Machine Learning Models - 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