http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5543 2026-05-18T15:41:26Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17207 Enhanced CNN DCT Steganography: Deep Learning BasedImageSteganography Akter, Erin Protecting data on the internet from attacks and unauthorized access requires informationsecurity. The complementary techniques of cryptography and steganography are highlightedfor ensuring the confidentiality and integrity of shared data. Cryptography converts messagesinto ciphertext to conceal their content, whereas steganography conceals the existenceof datawithin seemingly innocent carriers like text or graphics. Combining the two methods results inacomprehensive solution that protects communication from potential adversaries by addinglevels of obscurity and secrecy. By fusing DGP with an XOR-based embedding technique, thestudy suggests a solution that improves data security while providing remarkableimperceptibility.Secure communication has become increasingly important and challenginginthe digital era, which has resulting in the disclosure of sophisticated information-hidingstrategies.Using deep learnig technologies, this work offers an innovative solution for imagesteganography that enhances the security and usability of encoding confidential data intooverphotos.Conventional steganographic techniques frequently have capacity, resilience, andsmall limitations that leave them open to discovery and extraction by unauthorized parties. I suggesta deep learning-based image steganography method that uses convolutional neural networks(CNN) to discover the best embedding strategies for secret images in order to overcomethesedifficulties. Because the model was trained on a variety of cover and secret image datasets, itwas possible to integrate the secret data while preserving visual consistency. By offering a reliable method for securely transmitting data or information to others, this studyadvances the subject of data and information security. Other ambiguous phrases usedhereinclude digital watermarking and secure transmission. Additionally, copyright protectionhasbeen obtained by applicants. Thesis Report 2025-09-17T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17206 Cyberbullying Text Classification Using Machine Learning Approaches Asad, Hafiz Al Cyberbullying has become a significant issue in today’s society, particularly among adolescents and teenagers. The rise of social media platforms and online communication tools has made it easier for individuals to harass others anonymously, often without accountability. In recent years, natural language processing (NLP) techniques have been employed to detect and classify instances of cyberbullying. These methods analyze the language used in online interactions to identify patterns and indicators of bullying behavior. This study focuses on evaluating the effectiveness of NLP techniques in detecting and categorizing cyberbullying incidents. To achieve this, the research draws on various data sources, such as chat logs, social media posts, and other forms of online communication, to understand the diverse forms of cyberbullying. The ultimate goal is to enhance our understanding of cyberbullying dynamics and explore how NLP applications can help mitigate its adverse effects. The research employs supervised learning techniques, which use labeled data to train algorithms for accurate predictions and classifications. As technology advances, it has impacted both the positive and negative aspects of life, with machine learning systems becoming increasingly effective in detecting aggressive language associated with cyberbullying. This study categorizes cyberbullying into seven groups: “Not abusive,” “gender,” “ethnicity,” “political,” “insult,” “age,” and “religion.” Among the machine learning classifiers tested, the Support Vector Machine (SVM) achieved the highest accuracy of 91.07% in identifying abusive or cyberbullying-related texts. Thesis Report 2025-09-19T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17205 Shipment Management System Palah, Md Jahid Hasan A Shipment Management System (SMS) is a software solution that helps businesses manage the process of shipping goods efficiently. It integrates with carriers, tracks shipments in real-time, and automates order processing, documentation, and delivery notifications. Key features include order and inventory management, route optimization, shipment tracking, and analytics. The system improves efficiency, reduces costs, enhances customer experience, and ensures compliance with regulations. It integrates with e-commerce platforms and third-party carriers to streamline logistics operations. Thesis Report 2025-09-19T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17204 Internship on Information System Audit Habibullah, Md. There is a growing interest in information system (IS) auditing among students and recent graduates. My work as an IS auditor at ACNABIN Chartered Accountants, a firm with eight esteemed partners, is summarized in this report. With a detailed description of the tasks, skills, and information I acquired and applied throughout my internship, this report offers a comprehensive summary of the job I accomplished. Thesis Report 2025-09-17T00:00:00Z