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Journal articles on the topic 'Email'
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Atherton, Helen, Anne-Marie Boylan, Abi Eccles, Joanna Fleming, Clare R. Goyder, and Rebecca L. Morris. "Email Consultations Between Patients and Doctors in Primary Care: Content Analysis." Journal of Medical Internet Research 22, no. 11 (November 9, 2020): e18218. http://dx.doi.org/10.2196/18218.
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Background Increasingly, consultations in health care settings are conducted remotely using a range of communication technologies. Email allows for 2-way text-based communication, occurring asynchronously. Studies have explored the content and nature of email consultations to understand the use, structure, and function of email consultations. Most previous content analyses of email consultations in primary care settings have been conducted in North America, and these have shown that concerns and assumptions about how email consultations work have not been realized. There has not been a UK-based content analysis of email consultations. Objective This study aims to explore and delineate the content of consultations conducted via email in English general practice by conducting a content analysis of email consultations between general practitioners (GPs) and patients. Methods We conducted a content analysis of anonymized email consultations between GPs and patients in 2 general practices in the United Kingdom. We examined the descriptive elements of the correspondence to ascertain when the emails were sent, the number of emails in an email consultation, and the nature of the content. We used a normative approach to analyze the content of the email consultations to explore the use and function of email consultation. Results We obtained 100 email consultations from 85 patients, which totaled 262 individual emails. Most email users were older than 40 years, and over half of the users were male. The email consultations were mostly short and completed in a few days. Emails were mostly sent and received during the day. The emails were mostly clinical in content rather than administrative and covered a wide range of clinical presentations. There were 3 key themes to the use and function of the email consultations: the role of the GP and email consultation, the transactional nature of an email consultation, and the operationalization of an email consultation. Conclusions Most cases where emails are used to have a consultation with a patient in general practice have a shorter consultation, are clinical in nature, and are resolved quickly. GPs approach email consultations using key elements similar to that of the face-to-face consultation; however, using email consultations has the potential to alter the role of the GP, leading them to engage in more administrative tasks than usual. Email consultations were not a replacement for face-to-face consultations.
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Rosário, Albérico Travassos. "E-Mail Marketing." International Journal of Online Marketing 11, no. 4 (October 2021): 63–83. http://dx.doi.org/10.4018/ijom.2021100104.
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Email marketing is a considerable development and includes direct emails, transactional emails, and email newsletters to attract new customers and retain existing ones. This research paper aims to identify and synthesize literature on the effectiveness of email marketing and potential challenges affecting its proper implementation. The research establishes that businesses in the current business environment recognize email marketing's capacity to produce a higher return on investment and generate more sales than traditional marketing channels, such as television. The adoption of permission-based email marketing enables establishing strong relationships between companies and their target audiences, developing emotional, conative, and cognitive responses to the distributed messages. Therefore, salespersons should ensure compliance with legal requirements in email marketing and develop effective strategies of reducing spam emails to avoid negative impressions and increase response rates.
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Riadi, Imam, Sunardi, and Fitriyani Tella Nani. "Analisis Forensik pada Email Menggunakan Metode National Institute of Standards Technology." JISKA (Jurnal Informatika Sunan Kalijaga) 7, no. 2 (May 25, 2022): 83–90. http://dx.doi.org/10.14421/jiska.2022.7.2.83-90.
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Nowadays developments in information technology are growing rapidly, especially in email. Email became one that almost the whole world had. Email is one of the results of developments in information and communication. Email is widely used to exchange information by sending and receiving data, such as document files, pictures, letters, and others. So much for the crimes that often occur in emails. Email crimes that often occur among them are email spoofing. Email spoofing is a forgery that occurs in the header of the email. So, the email is sent as if it were a valid email. Email spoofing is often used in spamming activities. Crimes committed by cybercrime must leave evidence such as IP Address, sender's email, and time of sending the email. This research will do forensics on email spoofing. The research uses the Live Forensics method, where the computer is used in a powered-on state. The research also uses the NIST (National Institute of Standards Technology) research flow. The email that will be analyzed is in the email header section using 3 tools, namely tracer email analyzer, email dossier, and mail header analysis. This analysis will compare and check the accuracy of the email headers using these tools. Emails suspected of email spoofing will be proven using tools. Based on the 'form' received' and 'Message-ID' headers. Based on the results, the tool that meets the value after the analysis is tracer email analysis.
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Sarno, Dawn M., Joanna E. Lewis, Corey J. Bohil, Mindy K. Shoss, and Mark B. Neider. "Who are Phishers luring?: A Demographic Analysis of Those Susceptible to Fake Emails." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1735–39. http://dx.doi.org/10.1177/1541931213601915.
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Previous research has identified several populations that are susceptible to inauthentic emails (e.g., spam). However, these studies utilize retrospective, self-report measures to assess email users’ interactions with limited sets of inauthentic emails. In order to fill this gap in the literature, the present study assessed participants’ likelihood to rate a wide variety of emails as spam, authentic, and dangerous. The results highlighted several key findings, 1) there were no gender differences for the email ratings, there were only differences in experience with email, 2) those who do not regularly email and read other electronic documents were more likely to rate emails as spam, possibly indicating an increase in false positives, and 3) the relationship between age and rating an email as spam indicates that younger users may be more susceptible to spam. Overall, the present study identified demographic characteristics that should be considered when training users to detect inauthentic emails.
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Iswanto, Hery, Erni Seniwati, Yuli Astuti, and Dina Maulina. "Comparison of Algorithms on Machine Learning For Spam Email Classification." IJISTECH (International Journal of Information System and Technology) 5, no. 4 (December 30, 2021): 446. http://dx.doi.org/10.30645/ijistech.v5i4.164.
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The rapid development of email use and the convenience provided make email as the most frequently used means of communication. Along with its development, many parties are abusing the use of email as a means of advertising promotion, phishing and sending other unimportant emails. This information is called spam email. One of the efforts in overcoming the problem of spam emails is by filtering techniques based on the content of the email. In the first study related to the classification of spam emails, the Naïve Bayes method is the most commonly used method. Therefore, in this study researchers will add Random Forest and K-Nearest Neighbor (KNN) methods to make comparisons in order to find which methods have better accuracy in classifying spam emails. Based on the results of the trial, the application of Naïve bayes classification algorithm in the classification of spam emails resulted in accuracy of 83.5%, Random Forest 83.5% and KNN 82.75%
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Ali, Sadia. "An Investigation into Academic Email Practices of Arab Female Undergraduate Students and Their Attitudes Towards Correction of Errors in Their Email Messages." Learning and Teaching in Higher Education: Gulf Perspectives 2, no. 2 (December 1, 2005): 2–10. http://dx.doi.org/10.18538/lthe.v2.n2.02.
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Students’ assignments are often much better in style and organisation than the email messages they send to theirteachers. Some teachers, including myself, often ‘covertly’ correct students’ email messages for style, organisation,content, or correctness. While some students appreciate this extra effort from the teachers, others see it as an inhibitingintrusion. However, I have frequently noticed that students who are corrected repeatedly improve in writing emails. Myresearch concerns both the use of academic email writing and the correction of errors in student emails, and concludesthe following: students usually write only formal emails to their teachers; those instructors who correct email errors do notoffer explicit error correction; and if email writing were taught to the students, it would offer variety in the writing genresstudents currently compose
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Ghaleb Abdulkadhim, Ekhlas, and Muqdad Abdulraheem Hayder. "SURVEY OF E-MAIL CLASSIFICATION: REVIEW AND OPEN ISSUES." Iraqi Journal for Computers and Informatics 46, no. 2 (December 3, 2020): 17–23. http://dx.doi.org/10.25195/ijci.v46i2.274.
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Email is an economical facet of communication, the importance of which is increasing in spite of access to other approaches, such as electronic messaging, social networks, and phone applications. The business arena depends largely on the use of email, which urges the proper management of emails due to disruptive factors such as spams, phishing emails, and multi-folder categorization. The present study aimed to review the studies regarding emails, which were published during 2016-2020, based on the problem description analysis in terms of datasets, applications areas, classification techniques, and feature sets. In addition, other areas involving email classifications were identified and comprehensively reviewed. The results indicated four email application areas, while the open issues and research directions of email classifications were implicated for further investigation.
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Lappin, James, Tom Jackson, Graham Matthews, and Ejovwoke Onojeharho. "The defensible deletion of government email." Records Management Journal 29, no. 1/2 (March 11, 2019): 42–56. http://dx.doi.org/10.1108/rmj-09-2018-0036.
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PurposeTwo rival approaches to email have emerged from information governance thought: the defensible deletion approach, in which emails are routinely deleted from email accounts after a set period of time; and the Capstone approach, in which the email accounts of important government officials are selected for permanent preservation. This paper aims to assess the extent to which the defensible deletion approach, when used in conjunction with efforts to move important emails into corporate records systems, will meet the needs of originating government departments and of wider society.Design/methodology/approachThe paper forms the first stage of a realist evaluation of policy towards UK government email.FindingsThe explanation advanced in this paper predicts that the routine deletion of email from email accounts will work for government departments even where business email is inconsistently or haphazardly captured into records systems, provided officials have access to their own emails for a long enough period to satisfy their individual operational requirements. However the routine deletion of email from email accounts will work for wider society only if and when business email is consistently captured into other systems.Originality/valueThe paper looks at the policy of The National Archives (TNA) towards UK government email and maps it against the approaches present in records management and information governance thought. It argues that TNA’s policy is best characterised as a defensible deletion approach. The paper proposes a realist explanation as to how defensible deletion policies towards email work in a government context.
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Lim, Hajin, Dan Cosley, and Susan R. Fussell. "Understanding Cross-lingual Pragmatic Misunderstandings in Email Communication." Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (March 30, 2022): 1–32. http://dx.doi.org/10.1145/3512976.
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Communication tools such as email facilitate communication and collaboration between speakers of different languages, who use two primary strategies-English as a common language and machine translation (MT) tools-to help them overcome language barriers. However, each of these communication strategies creates its own challenges for cross-lingual communication. In this paper, we compare how people's interpretations of an email sender's social intention, and their evaluation of the email and the senders, differ when using a common language versus MT in email communication. We conducted an online experiment in which monolingual native English speakers read and rated request emails written by native English speakers, emails written by bilingual Chinese speakers in English, and emails written in Chinese then machine-translated into English. We found that participants interpreted the social intentions of the email sender less accurately for machine-translated emails than for emails written by non-native speakers in English. Participants also rated the senders and emails less positively overall for machine-translated emails compared to emails written by non-native speakers in English. Based on these findings, we suggest design possibilities that could better aid multilingual communication.
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Kachole, Abhinav, Aniket Nagpure, Atharva Wagh, and Prof T. H. Patil. "Email Platform: Spam Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 1782–89. http://dx.doi.org/10.22214/ijraset.2024.60175.
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Abstract: In practically every industry today, from business to education, emails are used. Ham and spam are the two subcategories of emails. Email spam, often known as junk email or unwelcome email, is a kind of email that can be used to hurt any user by sapping their time and computing resources and stealing important data. Spam emailvolume is rising quickly day by day. Today’s email and IoT service providers face huge and massive challenges with spam identification and filtration. Email filteringis one of the most important and well-known methods among all the methods createdfor identifying and preventing spam. SVM, decision trees, and other machine learning and deep learning approaches have all been applied to this problem. Together with the explosive growth in internet users, email spam has increased substantially in recent years. Individuals are using them for illegal and dishonest purposes, such as fraud, phishing, and distributing malicious links through unsolicited email that can harm our systems and attempt to access your systems. By quickly constructing phone-y/fake profiles and email accounts, spammers prey on those who are ignorant of these scams. They use a real name in their spam emails. As a result, it’s critical to identify spam emails that include fraud. This project will accomplish this by utilizing machine learning methods, and this article will examine the machine learning algorithms, put them to use on our data sets, and select the approach that can detect emailspam with the maximum degree of precision and accuracy.
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Eze, Chibuike Samuel, and Lior Shamir. "Analysis and Prevention of AI-Based Phishing Email Attacks." Electronics 13, no. 10 (May 9, 2024): 1839. http://dx.doi.org/10.3390/electronics13101839.
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Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.
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Algryani, Ali, and Khalid Salim Al Jardani. "Email Literacy in Higher Education Institutions: A Case Study on Student-Instructor Email Communication at Dhofar University in Oman." World Journal of English Language 13, no. 6 (July 11, 2023): 549. http://dx.doi.org/10.5430/wjel.v13n6p549.
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Email is one of the means via which students in higher education institutions (HEIs) liaise with instructors to enquire about course materials, assignments, upcoming assessments and seek advice on personal or academic matters. The current study is an attempt to investigate student-instructor email communication focusing on the problematic aspects in students’ emails that affect the process of communication. The study is based on the analysis of one-hundred email messages composed and sent by undergraduate students to their instructors at Dhofar University in Oman. It is concluded that student email messages are often characterized by lack email etiquette rules, linguistic inaccuracies and traits of texting and instant messaging mediums. Several student messages, for instance, lack proper email layout and contain grammar, spelling and punctuation mistakes, indicating that reviewing emails before sending them to teachers does not take place. In addition, informal features such as use of informal vocabulary, excessive use of punctuation marks, non-standard spelling, emojis and emoticons, which reflect unawareness of formality, professionalism and university setting etiquette, are noticeable in students’ email messages. Therefore, pedagogical intervention with respect to acquisition of skills required for writing and perceiving emails is recommended. Education and guidance on the conventions governing email communication can help students communicate more effectively and professionally via email in HEIs, which will promote not only decent practices but also future employability opportunities.
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Bouchareb, Nasser, and Ismail Morad. "Analyzing The Impact of Ai-Generated Email Marketing Content on Email Deliverability in Spam Folder Placement." HOLISTICA – Journal of Business and Public Administration 15, no. 1 (June 1, 2024): 96–106. http://dx.doi.org/10.2478/hjbpa-2024-0006.
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Abstract This study investigated the impact of AI-generated email content on email marketing deliverability, specifically its placement in spam folders. A controlled experiment was conducted with 450 participants who received AI-generated emails sent from different domains using plain text content and clear subject lines. The emails were analyzed for placement in inboxes or spam folders based on participant responses. The results revealed no significant impact of AI-generated content on email deliverability, regardless of the sender's domain or the recipient's email provider. All emails consistently reached primary inboxes, suggesting that the applied precautions (plain text, clear subject lines, and avoidance of suspicious elements) mitigated any potential spam triggers. This study shows AI-generated email content can be deliverable and personalized, challenging concerns about spam placement. Marketers can use AI to craft engaging emails that land in inboxes, not spam folders. This finding also aligns with SEO trends, where AI content isn't automatically penalized.
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Smith, C. N. C., S. D. Quan, D. Morra, P. G. Rossos, H. Khatibi, V. Lo, H. Wong, and R. C. Wu. "Understanding interprofessional communication: a content analysis of email communications between doctors and nurses." Applied Clinical Informatics 03, no. 01 (2012): 38–51. http://dx.doi.org/10.4338/aci-2011-11-ra-0067.
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SummaryBackground: Clinical communication is recognized as a major source of errors in hospitals. The lack of documentation of communication, especially among verbal interactions, often creates hindrances and impedes improvement efforts. By providing smartphones to residents and encouraging nurses to communicate with residents by email shifted much of the communication to emails which permitted analysis of content.Objective: Description on the interprofessional email communication between doctors and nurses occurring on the general internal medicine wards at two academic hospitals.Design: A prospective analysis of email communications between doctors and nurses.Setting: 34 out of the 67 residents who were on the general medicine clinical teaching units consented to allow analysis of their emails over a 6 month period.Main measures: Statistical tabulations were performed on the volume and frequency of communications as well the response time of messages. Two physicians coded the content of randomly selected emails for urgency, emotion, language, type of interaction, and subject content.Key results: A total of 13,717 emails were available for analysis. Among the emails from nurses, 39.1% were requests for a call back, 18.9% were requests for a response by email and the remaining 42.0% indicated no response was required from physicians. For the messages requesting a response by email, only 50% received an email response. Email responses had a median response time of 2.3 minutes. Content analysis revealed that messages were predominantly non-urgent. The two most frequent purposes for communications were to convey information (91%) and to request action by the physician (36%).Conclusions: A smartphone-based email system facilitated the description and content analysis of a large amount of email communication between physicians and nurses. Our findings provide a picture of the communication between physicians, nurses and other healthcare professionals. This work may help inform the further development of information and communications technology that can improve clinical communication.
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Ahmed, Naeem, Rashid Amin, Hamza Aldabbas, Deepika Koundal, Bader Alouffi, and Tariq Shah. "Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges." Security and Communication Networks 2022 (February 3, 2022): 1–19. http://dx.doi.org/10.1155/2022/1862888.
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Nowaday, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.
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Zhang, Xi (Alan), V. Kumar, and Koray Cosguner. "Dynamically Managing a Profitable Email Marketing Program." Journal of Marketing Research 54, no. 6 (December 2017): 851–66. http://dx.doi.org/10.1509/jmr.16.0210.
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Although email marketing is highly profitable and widely used by marketers, it has received limited attention in the marketing literature. Extant research has focused on either customers’ email responses or the “average” effect of emails on purchases. In this article, the authors use data from a U.S. home improvement retailer to study customers’ email open and purchase behaviors by using a unified hidden Markov and copula framework. Contrary to conventional wisdom, the authors find that email-active customers are not necessarily active in purchases, and vice versa. Furthermore, the number of emails sent by the retailer has a nonlinear effect on both the retailer's short- and long-term profitability. Through a counterfactual study, the authors provide a decision support system to guide retailers in making optimal email contact decisions. This study shows that sending the right number of emails is vital for long-term profitability. For example, sending four (ten) emails instead of the optimal number of seven emails can cause the retailer to lose 32% (16%) of its lifetime profit per customer.
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Nimase, Ketan, Siddhesh Thakur, and Sahil Gaonkar. "AI Based E-Mail Scraper and Sending Tool." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3749–55. http://dx.doi.org/10.22214/ijraset.2023.50679.
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Abstract: Our research ‘AI BASED E-MAIL SCRAPER AND SENDING TOOL’ is basically a fast, affordable and easy-to-find marketing and communication solution. Using email cangreatly help businesses as it provides an efficient and effective way to advertise variety of electronic information. Email extractor is a type of software used to extract email address fromonline bases which generate a huge list of email addresses. Even though these extractors can assist multiple genuine purposes such as marketing campaigns, unfortunately they are mostly used to direct spamming and phishing emails. Email filter is a tool to extract emails based on specified criteria. It split up all types of emails such as Gmail, Yahoo Mail in various text files according to their name automatically. Email validator is a toolto check the validation of email existence these means it checks that given mail is originally exist. It checks the username of email in all the mail facility for the existence. Bulk mail sender tool is work on distribution of a lot of mail at once. You drop anemail list to director to send the letter to the user. You can sendmail to a multiple user at once. There is no restriction for this tosend the mails.
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Hira, Swati, Samir Ajani, Pratibha Kokardikar, and Shubhangi Neware. "IPV4 and IPV6 based hybrid approach for spam and virus detection." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 147. http://dx.doi.org/10.14419/ijet.v7i2.4.13026.
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The proposed email setup consist of multiple mail servers distributed at two levels to achieve desirable email access performance. It is a well-established fact that 99% of the emails received over internet are either spam or contained viruses and such emails can be dropped at the first entry point of the Network. Thus, the first level in the proposed architecture has been taken as an email gateway which is equipped with antivirus and anti-spam software. Spam assassion is an open source freeware software for filtering of the spam emails. Perl based spamassassion id to CPU and Memory hungry for heavily loaded server thus this new arrangement would then overcome the Problem of slow Email access for users by detaching spamassassion from email repository servers. The second level of the servers with email repositories for users all the email servers used in this implementation would be Linux X86 servers. Virtualization technique presents a software interface to virtual machines that is similar but not identical to that of the underlying hardware. First level is implemented in Virtual machine. So as to provide scalability, portability, migration and vendor independent.
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Konuk, Sümeyye. "E-mail Literacy in Higher Education Academic Settings." International Journal of Education and Literacy Studies 9, no. 3 (August 1, 2021): 29. http://dx.doi.org/10.7575/aiac.ijels.v.9n.3p.29.
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The research purpose was to identify (1) the problems encountered by academic and administrative staff in emails received from students, (2) positive and negative qualities of the authentic emails of higher education students, (3) functional explanations of the academic email, (4) the problems encountered by students in emails received from academic and administrative staff, and (5) higher education students’ email writing awareness. An exploratory sequential mixed design was used. The study group consisted of 15 staff and 1064 higher education students. The qualitative data were collected from staff interviews and 80 authentic emails of students. And a survey was prepared based on qualitative data and then quantitative data were collected. The problems encountered by staff are style, carelessness, articulacy problem, spelling and punctuation problem, email incivility. The negative qualities of authentic emails are as follows: not using institutional username, formal language, paragraph structure in the email body, salutation, closing statement, contact information; username without name and surname, blank subject line, spelling and punctuation problems, sloppy wording, lack of self-introduction. Non-descriptive, late, and short answers, not getting answers, sloppy answers, emails with negative feelings disturbed students. Students’ awareness of writing academic emails displayed a more positive picture than the emails they wrote. Items in which students’ awareness is weak are as follows: trying to reflect their feelings to email, using punctuation marks to convey the feeling, writing email for long and complex matter, using paragraph structure, adding contact details, CC - BCC. Research results were discussed with relevant literature.
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Balamurugan, S. Appavu Alias, G. Athiappan, M. Muthu Pandian, and R. Rajaram. "Classification Methods in the Detection of New Suspicious Emails." Journal of Information & Knowledge Management 07, no. 03 (September 2008): 209–17. http://dx.doi.org/10.1142/s0219649208002044.
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Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.
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Ben Amor, Imen Aribi. "Email as a mode of communication among Tunisian postgraduate students." International Journal of Learning and Teaching 9, no. 3 (September 5, 2017): 388–409. http://dx.doi.org/10.18844/ijlt.v9i3.2381.
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Abstract
Emails have great potential in facilitating academic communication. However, writing effective academic emails depends on many factors including students’ knowledge, experiences and perceptions about emails. According to Brown and Levinson (1987), people use certain politeness strategies to enhance face between themselves and their interlocutors. Yet, the underlying processes of email communication may be poorly understood, especially as far as politeness is concerned. This study seeks to examine the perceptions, practices and attitudes of 38 postgraduateTunisian students towards email communication through a questionnaire. The aim is to reveal whether the sociolinguistic and pragmatic dynamics of writing emails in English are adequately understood by Tunisian postgraduate students. The questionnaire comprises four parts which include email use, participants’ attitudes towards emails, email practices and social factors. Each section includes closed and open-ended questions. The findings show that the informants have a positive attitude towards the use of emails in their academic life. However, their perceptions concerning politeness and their comment on the effect of social factors show a relative variability. A possible avenue for pedagogical intervention with regard to the instruction of pragmatics in general and politeness routines in particular in email communication is presented.
Keywords: Emails, politeness, pragmatic competence, social factors.
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Armstrong, Melissa J. "Improving email strategies to target stress and productivity in clinical practice." Neurology: Clinical Practice 7, no. 6 (October 24, 2017): 512–17. http://dx.doi.org/10.1212/cpj.0000000000000395.
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AbstractPhysician burnout is gaining increased attention in medicine and neurology and often relates to hours worked and insufficient time. One component of this is administrative burden, which relates to regulatory requirements and electronic health record tasks but may also involve increased time spent processing emails. Research in academic medical centers demonstrates that physicians face increasing inbox sizes related to mass distribution emails from various sources on top of emails required for patient care, research, and teaching. This commentary highlights the contribution of administrative tasks to physician burnout, research to date on email in medical contexts, and corporate strategies for reducing email burden that are applicable to neurology clinical practice. Increased productivity and decreased stress can be achieved by limiting the amount one accesses email, managing inbox size, and utilizing good email etiquette. Department and practice physician leaders have roles in decreasing email volume and modeling good practice.
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Nivedha, M. A., and S. Raja. "Detection of Email Spam using Natural Language Processing Based Random Forest Approach." International Journal of Computer Science and Mobile Computing 11, no. 2 (February 28, 2022): 7–22. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.002.
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An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanism through feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.
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Takhmiri, Hamoon, and Dr Ali Haroonabadi. "Identifying Valid Email Spam Emails Using Decision Tree." International Journal of Computer Applications Technology and Research 5, no. 2 (January 31, 2016): 61–65. http://dx.doi.org/10.7753/ijcatr0502.1004.
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Anandpara, Rahul. "Secured Mail Transformation System Using Machine Learnin." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1880–86. http://dx.doi.org/10.22214/ijraset.2021.36764.
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Today, Email Spam has become a major problem, with Rapid increament of internet users, Email spams is also increasing. People are using email spam for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can damage the system and can also seek in into your system. Spammer creates a fake profile and email account which is easier for them. These spammers target those peoples who are not aware about frauds. So there is a need to identify the fraud in terms of spam emails. In this paper we will identify the spam by using machine learning algorithms.
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Dr., Jayamurugan. "AI Based Spam Detection Using Logistic Regression Algorithm." International Research Journal of Computer Science 10, no. 06 (July 31, 2023): 307–10. http://dx.doi.org/10.26562/irjcs.2023.v1006.06.
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Nowadays, a big part of people caused by unsolicited bulk email messages commonly referred to as Spam. Email has now become one of the best ways for advertisements due to which spam emails are generated. Spam emails are rely on available email or messages sent by the stranger. The possibility that anybody can leave an email or a message provides a golden opportunity for spammers to write spam message about our different interests. Spam fills inbox with number of ridiculous emails. Degrades our internet speed to a great extent .Steals useful information like our details on our contact list. Identifying these spammers and also the spam content can be a hot topic of research and laborious tasks. Email spam is an operation to send messages in bulk by mail .Since the expense of the spam is borne mostly by the recipient, it is effectively postage due advertising. Spam email is a kind of commercial advertising which is economically viable because email could be a very cost effective medium for sender .With this proposed model the specified message can be stated as spam or not using Bayes’ theorem and Naive Bayes’ Classifier and Also IP addresses of the sender are often detected.
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Rastenis, Justinas, Simona Ramanauskaitė, Ivan Suzdalev, Kornelija Tunaitytė, Justinas Janulevičius, and Antanas Čenys. "Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation." Electronics 10, no. 6 (March 12, 2021): 668. http://dx.doi.org/10.3390/electronics10060668.
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Spamming and phishing are two types of emailing that are annoying and unwanted, differing by the potential threat and impact to the user. Automated classification of these categories can increase the users’ awareness as well as to be used for incident investigation prioritization or automated fact gathering. However, currently there are no scientific papers focusing on email classification concerning these two categories of spam and phishing emails. Therefore this paper presents a solution, based on email message body text automated classification into spam and phishing emails. We apply the proposed solution for email classification, written in three languages: English, Russian, and Lithuanian. As most public email datasets almost exclusively collect English emails, we investigate the suitability of automated dataset translation to adapt it to email classification, written in other languages. Experiments on public dataset usage limitations for a specific organization are executed in this paper to evaluate the need of dataset updates for more accurate classification results.
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Kawuri, Gabriella Vindy, Ridi Ferdiana, and Lukito Edi Nugroho. "Auto-response Email based on User Habits with Privacy Model Approach and Cloud Adoption." Journal of Computer Science and Technology Studies 4, no. 2 (December 1, 2022): 128–39. http://dx.doi.org/10.32996/jcsts.2022.4.2.15.
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Email is an application that is used as a means of sending letters via a computer network or the internet. Many people whose work is done relate to other people, and email is the main channel through which work and related information can be distributed. An email has a feature that is to enable automatic responses. This feature is in the form of an automatic reply that will be sent in response to emails received when the email user is unable to respond. However, this feature still uses the default email to reply to receive emails. So in this study, the researcher proposes a new system to develop this feature by using user habits when the user replies to incoming emails. Using text processing techniques and sentiment analysis, we build a retrieval algorithm that finds similar cases outside of exact matches. This study uses a Cloud Adoption and is designed to use Power Automate to improve the performance of the system.
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Salazar-Campillo, Patricia. "Address forms and politeness markers in Spanish students' emails to faculty." Revista Electrónica de Lingüística Aplicada 21, no. 1 (January 31, 2023): 58–73. http://dx.doi.org/10.58859/rael.v21i1.501.
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In the present era of global online communication, email exchanges are one the most common means of interaction between students and professors (Tseng, 2015). However, emails may convey an impolite tone if students do not take status or power imbalance into account to show politeness (Economidou-Kogetsidis, 2011). The present study explored the informal second person pronoun of address (tú) and the formal one (usted) in first and follow-up requestive emails sent by Spanish students. In addition, some structural elements to show politeness in the emails were also examined. Although students had time to edit and correct their emails, our results indicate that in half of first-contact emails tú is employed, a percentage that increases in the follow-up email, therefore ignoring the degree of politeness expected in student-professor email communication. On the other hand, verbal and structural markers of politeness were broadly employed to indicate deference, especially in the first email.
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Panchal, Brijeshkumar Y., Janvi Patel, Kauresh V. Vachhrajani, Amit Gantara, Akshat Chhaya, and Vrushali Thakkar. "Survey on Carbon Dioxide Emissions Through Email Conversion." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 15, no. 01 (January 30, 2023): 122–27. http://dx.doi.org/10.18090/samriddhi.v15i01.16.
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In today’s scenario emissions of carbon dioxide and methane areca increasing day by day, leading to climate change. The aim of this paper is to explain carbon dioxide emissions through e-mail conversion. As an average spam email, a standard email, and an email with ‘long attachments’ emits 0.3g CO2e (carbon dioxide equivalent), 4g CO2e and 50g CO2e, respectively. The number of emails received by a common office worker is 121 and half of them are spam. The carbon footprint of emails received in a day is similar to 1,652 g CO2e. And those one year- received emails emit 0.6 tons of carbon dioxide. The carbon footprint of an Indian’s entire year is around 1.5 tons CO2e. So, three average workplace staff members yearly receive emails to exceed their carbon footprint for all the activities of another human for a year. An email with attachments releases 50g CO2e, that is an equivalent quantity as if one had used 5 plastic bags. So, overall, this paper shows how much carbon dioxide is emitted by an e-mail and how it works.In today’s scenario emissions of carbon dioxide and methane areca increasing day by day, leading to climate change. The aim of this paper is to explain carbon dioxide emissions through e-mail conversion. As an average spam email, a standard email, and an email with ‘long attachments’ emits 0.3g CO2e (carbon dioxide equivalent), 4g CO2e and 50g CO2e, respectively. The number of emails received by a common office worker is 121 and half of them are spam. The carbon footprint of emails received in a day is similar to 1,652 g CO2e. And those one year- received emails emit 0.6 tons of carbon dioxide. The carbon footprint of an Indian’s entire year is around 1.5 tons CO2e. So, three average workplace staff members yearly receive emails to exceed their carbon footprint for all the activities of another human for a year. An email with attachments releases 50g CO2e, that is an equivalent quantity as if one had used 5 plastic bags. So, overall, this paper shows how much carbon dioxide is emitted by an e-mail and how it works.
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Jebakumari, Mrs M., Mr T. Palaniraja, Mr K. Arun Patrick, and Ashwini . "Blocking Of Spam Mail Using K-Means Clustering Algorithm." International Journal of Information technology and Computer Engineering, no. 23 (April 20, 2022): 19–24. http://dx.doi.org/10.55529/ijitc23.19.24.
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Email is a method of exchanging digital messages between people using digital devices such as computers, tablets and mobile phones. Email spam also known as junk email is unsolicited bulk messages send through email. The use of spam has been growing in popularity since 1990s and is a problem faced by most email users. Recipients of spam often have had their email addresses obtained by spambots which are automated programs that crawl the internet looking for email addresses. Origin blacklisting is used to detect and filter these kinds of emails. The sources of emails are provided by origin detection. In this origin identity, the spam blocking system finds the source and if any source matches with the user identity, the spammer is blocked to send email. But the system cannot check the contents of spam. The Propose system verifies the mail contents. If the spam content matches with the spam database cluster, then it blocked while sending to receiver. In this system blocks the mail and prevents the flow of data in a network. The porter stemmer and k-means clustering algorithms are using in this propose system.
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Surekha, P., M. Ganga Pavani, B. Jahanavi, B. Siri, and Tilottama Singh. "Voice-based e-mail for Information systems: An aid to visually impaired." E3S Web of Conferences 430 (2023): 01062. http://dx.doi.org/10.1051/e3sconf/202343001062.
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Artificial intelligence has the potential to revolutionize the way that visually impaired individuals interact with technology. With this in mind, a voice-based email system for visually impaired individuals has been proposed as a solution to provide a convenient and accessible way of managing email. The system leverages advanced Natural language processing (NLP)and Automatic Speech Recognition(ASR) technologies to convert spoken commands into email actions. The system supports voice commands for dictating emails, text-to-speech functionality for reading emails, and speech-to-text for writing emails, navigating the inbox, giving the count of unseen emails, and managing emails. This system is designed to be user-friendly, intuitive, and accessible, ensuring that blind individuals can easily navigate and use it with ease. Implementation of this system has the potential to greatly improve the daily lives of visually impaired individuals by providing a more convenient and accessible way to manage emails. This project presents an automation system for AI-powered voice-based email designed specifically information passing system to help blind individuals.
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Vivekanandam, B., and Balaganesh. "Spam Email Classification by Hybrid Feature Selection with Advanced Machine learning Algorithm – Future Perspective." Journal of Soft Computing Paradigm 4, no. 2 (July 6, 2022): 58–68. http://dx.doi.org/10.36548/jscp.2022.2.002.
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Recently, email has become a common way for people to communicate and share information both officially and personally. Email may be used by spammers to transmit harmful materials to Internet users. The data must be protected from unauthorized access, which necessitates the development of a reliable method for identifying spam emails. As a result, a variety of solutions have been devised. An innovative hybrid machine learning strategy for effectively detecting spam emails has been discussed in this study. This means that identifying spam and non-spam email is a difficult process. Spam email categorization has undergone a significant evolution in recent years, as shown by the research given below. For locating spam, this study uses a mixed approach. Different email categorization algorithms are used to rank them for future perspective.
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Joglekar, Pushkar, Janhavi Rajurkar, Madhuri Shinde, Pranav Tayde, and Ved Gadmade. "Machine Learning Based Email Spam Detection: Achieving High Accuracy and Efficiency." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 3575–80. http://dx.doi.org/10.22214/ijraset.2024.62390.
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Abstract: Email communication has become an essential aspect of modern-day interactions, but the proliferation of spam emails poses significant challenges to users' productivity and security. This research paper presents a comprehensive study on the development and implementation of an efficient email spam detection and categorization system. The project aims to categorize emails into predefined sections by using the Support Vector Machine (SVM) model, Flask, and the Gmail API, ensuring accuracy and efficiency in email classification. The methodology involves data preparation, processing, storage, and management, ensuring robust security and privacy considerations. The system's three-tiered classification strategy enhances the accuracy of spam and ham detection. Future enhancements include integrating advanced machine learning models, user feedback mechanisms, and multi-platform support to adapt to evolving email trends and user preferences. This research contributes to the field of email management by offering a new approach to combat spam effectively and enhance email organization for users in the digital age.
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Nmachi, Wosah Peace. "A Framework for Securing Email Entrances and Mitigating Phishing Impersonation Attacks." International Journal of Network Security & Its Applications 15, no. 6 (November 29, 2023): 15–35. http://dx.doi.org/10.5121/ijnsa.2023.15602.
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Emails are used every day for communication, and many countries and organisations mostly use email for official communications. It is highly valued and recognised for confidential conversations and transactions in day-to-day business. The Often use of this channel and the quality of information it carries attracted cyber attackers to it. There are many existing techniques to mitigate attacks on email, however, the systems are more focused on email content and behaviour and not securing entrances to email boxes, composition, and settings. This work intends to protect users' email composition and settings to prevent attackers from using an account when it gets hacked or hijacked and stop them from setting forwarding on the victim's email account to a different account which automatically stops the user from receiving emails. A secure code is applied to the composition send button to curtail insider impersonation attack. Also, to secure open applications on public and private devices.
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T. Vacalares, Sophomore, Brian Paul E. Sta. Ana, and Daryl Q. Dranto. "Bank Emails: The Language of Legit and Scam." International Journal of Research and Review 11, no. 7 (July 16, 2024): 192–203. http://dx.doi.org/10.52403/ijrr.20240721.
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Amidst the pandemic, the surge in unsolicited scam emails has made internet technology a significant concern. Distinguishing between scams and authentic/legit emails has become increasingly difficult. This research aimed to identify typical characteristics and language patterns found in emails. It also aimed to identify the common linguistics and technical features of the scams and legitimate bank emails. Two legitimate emails and two scam or fraudulent emails from different banks in the Philippines (i.e., LandBank and BDO). A qualitative method was used to analyze and interpret the language features of the two emails. This led to the identification of shared characteristics found in scams and legitimate emails. A scam email typically includes embedded malicious links, misspellings, formatting issues, suspicious email domains, frequent use of contractions and redundancy, tautology, and imperative mood. On the other hand, legitimate emails exhibit proper punctuation, accurate email domains, appropriate formatting, absence of spelling mistakes, avoidance of contractions and redundancy, and use indicative mood rather than imperative. Scam emails utilize modal verbs to manipulate recipients into divulging personal information. Recognizing these email patterns is crucial for educating users and reducing phishing risks. It is important to note that the interpretation and findings of this study are based on the entire dataset collected. Keywords: Bank Emails, Legit Emails, Linguistic Features, Scam Emails, Phishing
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Tamhankar, Dr Ishaan. "“A Combine Model for Email Classification in Hindi Language using Supervised Learning (NB, K-NN, DT, SVM)”." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 23 (April 8, 2022): 17–23. http://dx.doi.org/10.55529/jaimlnn.23.17.23.
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Email communication is necessary in today's environment, yet unwanted emails create issue in such communication. The current study focuses on developing an Email classification model for the use of classifiers approaches. The goal of this research is to classification of emails based on features. For classification especially in Hindi language of the email dataset Different machine learning classifiers such as Naïve Bayes, Decision Tree, K-Nearest Neighbor and Support Vector Machine used in research work as well as we used combined model also for optimum accuracy.
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Zhu, Fucheng. "Automatic Classification for Unlabeled Email Messages into Folders." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 120–26. http://dx.doi.org/10.54097/hset.v34i.5432.
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Imagine returning from an excused absence because of Covid-19 or any force majeure alike, and having to immediately face 300+ unread emails; getting overwhelmed by emails has become part of office workers’ daily routine. Numerous pieces of research have shown effective methods to categorize email messages, detect potential harassment, and even automatically send a reply. But still, email is an interesting type of text to analyze and gives rise to many challenges. First discussing the challenge in the problem, this paper aims to research, study, and propose a method that can deal with a specific challenge: making folders out of income email messages and then classifying emails automatically. By cooperating basic methods, techniques, and algorithms, an intuitive program is developed that can perform the task with the given public email dataset. The method is then expected to raise prospects for future investigations and improvements in performance and robustness.
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Kannan, Lucky, and Jebakumar R. "Public Sender Score System (S3) by ESPs for Email Spam Mitigation with Score Management in Mobile Application." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 17 (October 13, 2020): 204. http://dx.doi.org/10.3991/ijim.v14i17.16609.
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Many businesses use email as a medium for advertising and they use emails to communicate with their customers. In the email world, the most common issue that remains unresolved even now is spamming or in other terms unsolicited bulk email. Currently, there is no common way to regulate the practices of an email sender. This proposed system is to formulate a protocol common for all the ESPs or inbox providers and a centralized system that will easily find the spammers and block them. By this method, the Email Service Providers (ESPs) or Inbox Providers need not wait for the sender behaviour and then take actions on the sender or sender domain or sender IP address. Instead, they can get the sender history of reputation from blockchain where the ESPs or Inbox Provider provides a score based on the emails they have received from the sender. The ESPs can get the Public Sender Score(S3) from the mobile application or web application which provides the score management user interface and APIs. The email marketers can also monitor their score through the application.
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Fang, Yong, Yue Yang, and Cheng Huang. "EmailDetective: An Email Authorship Identification And Verification Model." Computer Journal 63, no. 11 (July 13, 2020): 1775–87. http://dx.doi.org/10.1093/comjnl/bxaa059.
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Abstract Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author’s writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model’s accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors.
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Cranor, Lorrie Faith. "email." XRDS: Crossroads, The ACM Magazine for Students 2, no. 2 (November 1995): 27. http://dx.doi.org/10.1145/332180.332194.
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Duncan, D. J. "Email." Interdisciplinary Studies in Literature and Environment 21, no. 1 (April 16, 2014): 18–19. http://dx.doi.org/10.1093/isle/isu012.
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V, Mr Chandrasekhar. "Customer Support Emails by RPA Methodology." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4252–60. http://dx.doi.org/10.22214/ijraset.2023.52597.
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Abstract: In today’s internet era electronic mail (Email) is a widely used communication channel commonly used to control customer inquiries such as complaints, feedbacks, reviews, and suggestions. With an increase in the number of customers, there is a significant increase in the emails being received daily, which needs to be segregated, to ensure that a proper reply is sent to all the senders in an organization and to prevent overwhelming and messy email accumulation. And the problem with the traditional method is that a manual workforce team cannot sit and segregate every email as the humongous number of emails get generated daily. apart from that it is quite a tiresome job and cannot be done by a single employee or a team of employees. So, the businesses industry and other industries can simply automate the email processing task of segregating common emails into specified folders as per the organization’s requirements, by Introducing a Robotic Process Automation (RPA) system which can generate and send customer support email. The work facilitates the process which is done by a robot without human intervention by using UiPath studio.S
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Mathur, Shubham, and Aakash Purohit. "Performance Evaluation of Machine Learning Algorithms on Textual Datasets for Spam Email Classification." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4726–34. http://dx.doi.org/10.22214/ijraset.2022.46072.
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Abstract: Email is one of the most popular modes of communication we have today. Billions of emails are sent every day in our world but not every one of them is relevant or of importance. The irrelevant and unwanted emails are termed email spam. These spam emails are sent with many different targets that range from advertisement to data theft. Filtering these spam emails is very essential in order to keep the email space fluent in its functioning. Machine Learning algorithms are being extensively used in the classification of spam emails. This paper showcases the performance evaluation of some selected supervised Machine Learning algorithms namely Naive Bayes Classifier, Support Vector Machine, Random Forest, & XG-Boost for spam email classification on a combination of three different datasets. For feature extraction, both Bag of Words & TF-IDF models were used separately and performance with both of these approaches was also compared. The results showed that SVM performed better than all the other algorithms when trained with TF-IDF feature vectors. The performance metrics used were accuracy, precision, recall, and f1-score, along with the ROC curve.
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Englehardt, Steven, Jeffrey Han, and Arvind Narayanan. "I never signed up for this! Privacy implications of email tracking." Proceedings on Privacy Enhancing Technologies 2018, no. 1 (January 1, 2018): 109–26. http://dx.doi.org/10.1515/popets-2018-0006.
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Abstract We show that the simple act of viewing emails contains privacy pitfalls for the unwary. We assembled a corpus of commercial mailing-list emails, and find a network of hundreds of third parties that track email recipients via methods such as embedded pixels. About 30% of emails leak the recipient’s email address to one or more of these third parties when they are viewed. In the majority of cases, these leaks are intentional on the part of email senders, and further leaks occur if the recipient clicks links in emails. Mail servers and clients may employ a variety of defenses, but we analyze 16 servers and clients and find that they are far from comprehensive. We propose, prototype, and evaluate a new defense, namely stripping tracking tags from emails based on enhanced versions of existing web tracking protection lists.
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Burgess, Anthony, Thomas Jackson, and Janet Edwards. "Email training significantly reduces email defects." International Journal of Information Management 25, no. 1 (February 2005): 71–83. http://dx.doi.org/10.1016/j.ijinfomgt.2004.10.004.
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Liu, Yanhua, Guolong Chen, and Yiyun Zhang. "An Anonymous Email Identification Solution based on Writing Structural Patterns." International Journal of Grid and High Performance Computing 7, no. 2 (April 2015): 37–49. http://dx.doi.org/10.4018/ijghpc.2015040103.
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A method to analyze anonymous emails in digital forensics is presented in this paper. The frequent pattern-growth algorithm is used in the proposed method to analyze an email and obtain the structural email writing pattern of the user. The influence of a user's writing structural pattern on the analysis of an anonymous email varies. The analytic hierarchy process is used to calculate the weight of a user's different writing structural patterns. For a given anonymous email, matching the writing structural pattern and weight calculation can help investigators improve their decision making and determine the author of an anonymous email in forensic work.
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Panwar, Manish, Jayesh Rajesh Jogi, Mahesh Vijay Mankar, Mohamed Alhassan, and Shreyas Kulkarni. "Detection of Spam Email." American Journal of Innovation in Science and Engineering 1, no. 1 (December 30, 2022): 18–21. http://dx.doi.org/10.54536/ajise.v1i1.996.
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Spam, often known as unsolicited email, has grown to be a major worry for every email user. Nowadays, it is quite challenging to filter spam emails since they are made, created, or written in such a unique way that anti-spam filters cannot recognize them. In order to predict or categorize emails as spam, this paper compares and reviews the performance metrics of a few categories of supervised machine learning techniques, including Svm (Support Vector Machine), Random Forest, Decision Tree, Cnn, (Convolutional Neural Network), Knn(K Nearest Neighbor), Mlp(Multi-Layer Perceptron), Adaboost (AdaptiveBoosting), and Nave Bayes algorithm. Thegoal of this study is to analyze the specificsor content of the emails, discover a limited dataset, and create a classification model that can predict or categorize whether spam is present in an email. Transformers’ Bidirectional Encoder Representations) has been optimized to perform the duty of separating spam emails from legitimate emails (Ham). To put the text’s context into perspective, Bert uses attention layers. Results are contrasted with a baseline Dnn (deep neural network) modelthat consists of two stacked Dense layers and a Bilstm (bidirectional Long Short-Term Memory) layer. Results are also contrasted with a group of traditional classifiers, including k- Nn (k-nearest neighbours) and Nb (Naive Bayes). The model is tested for robustness andpersistence using two open-source data sets, one of which is utilized to train the model.
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Okokpujie, Kennedy, Chinyere G. Kennedy, Kamsiyochukwu Nnodu, and Etinose Noma-Osaghae. "Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)." International Journal of Sustainable Development and Planning 18, no. 1 (January 31, 2023): 255–63. http://dx.doi.org/10.18280/ijsdp.180127.
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With the advancement in information communication technology (ICT), cyber-attacks have become a global phenomenon, with email phishing at the topmost. Academic institutions' ICT infrastructures are one of many targets, thus the need to facilitate cybersecurity awareness among students. This research is aimed at investigating students’ susceptibility to phishing attacks for sustainable safe electronic mail (email) usage in the academic environment. Two email phishing tests were carried out during this research work to discover how students reacted to phish emails and understand how students respond to phish emails where all group members are recipients. Finally, questionnaires are administered to participants after completing the exercise to ascertain the students' awareness of phishing attacks based on received emails. The results show that 70.6% of college students surveyed are susceptible to this form of attack due to unawareness. In conclusion, recommendations are outlined on securing the academic community and ICT infrastructures to achieve a sustainable and Safe email usage environment.
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Reddy, Gaddam Chakradhar, Ramanadham Rohith Kumar, Pikkili Siva Kasi, Navuluri Sarath Chandra, R. Pavan Kumar, and P. Prabakaran. "An Evaluation on the efficiency of E-Mail Spam Detection Using Naive Bayes Classifier." International Journal of Innovative Research in Engineering and Management 8, no. 2 (April 27, 2022): 648–52. http://dx.doi.org/10.55524/ijirem.2022.9.2.103.
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Nowadays, electronic mail is ubiquitous, being used everywhere from the business sector to the classroom. Emails can be broken down into two distinct categories: ham and spam. Email spam, also known as junk email or unwanted email, is a form of email that can be used to harm any user by wasting his or her time, draining system resources, and stealing sensitive data. Every day, the proportion of spam emails increases dramatically. Today's email and IoT service providers face a large and formidable task in detecting and filtering spam. One of the most prominent and widely-known approaches of detecting and avoiding spam is email filtration. It's also one of the most discussed tactics out there. Several machine learning and deep learning techniques, including Naive Bayes, decision trees, neural networks, and random forests, have been employed to reach this objective. After completing a survey of the available machine learning approaches, this article groups them into the most acceptable categories for usage in spam screening on email and IOT platforms. Accuracy, precision, memory requirements, and other metrics are used to thoroughly assess the methodologies. In the final section, we examine both the overall takeaways and directions future studies could go in.