Applied Science and Engineering Journal for Advanced Research 2024-05-12T06:23:48+00:00 Dr. Ashutosh Kumar Bhatt Open Journal Systems <p>Applied Science and Engineering Journal for Advanced Research is a bi-monthly, online, double blind peer reviewed open access international journal. This journal publish research papers from all the discipline of applied sciences, Medicine and engineering related subjects. Published papers are freely accessible online in full-text and with a permanent link to the journal's website.</p> <p><strong>JOURNAL PARTICULARS</strong></p> <p><strong>Title:</strong> Applied Science and Engineering Journal for Advanced Research<br /><strong>Frequency:</strong> Bimonthly (6 issue per year)<br /><strong>ISSN (Online):</strong> <a href="" target="_blank" rel="noopener">2583-2468</a><br /><strong>Publisher:</strong> Singh Publication, Lucknow, India. (Registered under the Ministry of MSME, Government of India. Registration number: “UDYAM-UP-50-0033370”)<br /><strong>Chief Editor:</strong> Dr. Ashutosh Kumar Bhatt<br /><strong>Copyright:</strong> Author<br /><strong>License:</strong> Creative Commons Attribution 4.0 International License<br /><strong>Starting Year:</strong> 2022<br /><strong>Subject:</strong> Applied Science and Engineering <br /><strong>Language:</strong> English<br /><strong>Publication Format:</strong> Online<br /><strong>Contact Number:</strong> +91-9555841008<br /><strong>Email Id:</strong><br /><strong>Journal Website:</strong> <a href=""></a><br /><strong>Publisher Website:</strong> <a href="" target="_blank" rel="noopener"></a><br /><strong>Address:</strong> 78/77, New Ganesh Ganj, Opp. Rajdhani Hotel, Aminabad Road, Lucknow-226018, Uttar Pradesh, India.</p> Password Complexity Prediction Based on RoBERTa Algorithm 2024-05-10T09:10:25+00:00 Yuhong Mo Shaojie Li Yushan Dong Ziyi Zhu Zhenglin Li <p>Corresponding author email: In the digital age, password security is a top priority for protecting personal information. Machine learning techniques provide us with intelligent and efficient means to enhance password security. In this paper, we adopt RoBERTa algorithm and use the password complexity text dataset for password complexity prediction, and the confusion matrix and accuracy rate of the three classifications are derived through two model trainings. The confusion matrix shows that the vast majority of the classification results are accurate, and the accuracy of the two classifications is over 99.741% and 99.11%, respectively. This indicates that the model is able to effectively predict password complexity, provide users with accurate feedback, and prompt users to enhance password security in a timely manner. Through this study, we can better understand how to use machine learning technology to improve password security and protect personal private information from malicious intrusion. In our daily life, we should pay attention to the complexity of password settings and realise the importance of password security for personal information protection. We look forward to the launch of more similar studies in the future to further strengthen cybersecurity protection measures and work together to build a more secure and reliable digital environment.</p> 2024-05-11T00:00:00+00:00 Copyright (c) 2024 Yuhong Mo, Shaojie Li, Yushan Dong, Ziyi Zhu, Zhenglin Li Spam Detection and Classification Based on DistilBERT Deep Learning Algorithm 2024-05-10T11:28:03+00:00 Tianrui Liu Shaojie Li Yushan Dong Yuhong Mo Shuyao He <p>This paper discusses the importance of spam classification in the field of information security. With the popularity of the Internet and email, spam has become one of the major issues affecting user experience and information security. The study begins with preprocessing text data in various ways, including converting to lowercase, removing irrelevant content, links, punctuation, etc., and filtering deactivated words and words of length 1. By applying the DistilBERT model to the text classification task, the results show that it achieves 93% accuracy in spam classification, effectively distinguishing between spam and non-spam emails. The confusion matrix showed that 18,500 emails were correctly classified and a small number of spam emails were misclassified as non-spam emails. Overall, the DistilBERT model showed high accuracy in spam classification, but more algorithms are still expected to emerge to improve the prediction accuracy. This study provides a useful reference for improving spam filtering systems in the future, which is expected to further enhance user experience and information security.</p> 2024-05-11T00:00:00+00:00 Copyright (c) 2024 Tianrui Liu, Shaojie Li, Yushan Dong, Yuhong Mo, Shuyao He