Browsing by Author "Sakib, Md. Mamun"

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A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms
(Scopus, 2024) Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption
A Novel Approach to Analyzing the Impact of Ai, Chatgpt, and Chatbot on Education Using Machine Learning Algorithms
(Institute of Advanced Engineering and Science (IAES), 2024-08-15) Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
CNN and Transfer Learning Modeling for Jujube Spices Recognition
(IEEE, 2023-11-23) Sakib, Md. Mamun; Hasan, Md. Mehedi; Bibi, Rabeya; Rahman, Md. Hamidur; Sattar, Abdus
Jujube make up a major portion of Bangladesh's total fruit production. It might be challenging to tell the differences between the many different species of jujube. The manual examination of jujube' physical qualities, which is time-consuming and prone to human mistakes, is the method of identification most commonly used in traditional methods. In this investigation, we make use of computer vision methods to zero in on particular jujube types that are native to the Bangladeshi region. In our approach, the question is solved with the assistance of a deep convolutional neural network (CNN) and Transfer Learning. Our method obtains an outstanding 98.0% accuracy on a test dataset after being trained on photographs of jujube taken in and around Bangladesh. Our work contributes to the growing body of research on applying computer vision and deep learning techniques to agricultural problems. Further research can be conducted to improve the accuracy of our system by collecting a larger dataset of jujube images, exploring the generalizability of our system to other regions and countries, and investigating the potential for using our system to recognize other fruit crops in Bangladesh or other countries.
Encryption Process of Blockchain Based Online Course Curriculum Education System
(IEEE, 2023-05-26) Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Rahman, Maruf Ur; Bibi, Rabeya; Talukder, Md. Maruf Hasan
Blockchain security is a comprehensive risk management system that includes assurance services, cybersecurity frameworks, and best practices to reduce the risks of fraud and cyberattacks. In shortened, an inherent data structure with security features is produced by blockchain technology where it is founded on cryptographic, decentralized, and consensus principles that uphold transactional trust. As our technology continuously updates and changes, new techniques and methods are appearing to secularly keep user details. The "Blockchain-based system" is among the finest. In a blockchain-based system is nearly impossible to hack or leak someone's information without permission. This is a more secure and reliable system than most of the other systems. But as this is a new and growing technology few online courses industry is using the system but not with its full function. We suggested including blockchain technology in the system of "Online courses". As a security precaution, the course modules and details will be entered into it, two keys are used throughout the entire program. The first is the public key, which is necessary for our course title and description, and the second is the private key, which is necessary for each course module and the payment system. Multiple machine learning models like Linear regression, random forest that are used to detect fraudulent transactions in the application. Every block and user details were hashed (64 bits) to keep them unique from each other. Numerous applications and software projects have been secured by blockchain technology. During our research, we experimented in a novel way and developed a fresh methodology that can be more secure than previously used applications. In this instance, our machine learning models have performed well so far, and we have also gained high accuracy from our dataset while.
Machine Learning Approach on Multiclass Classification of Internet Firewall Log Files
(IEEE, 2023-01-15) Rahman, Md Habibur; Islam, Taminul; Rana, Md Masum; Tasnim, Rehnuma; Mona, Tanzina Rahman; Sakib, Md. Mamun
"Firewalls are critical components in securing communication networks by screening all incoming (and occasionally exiting) data packets. Filtering is carried out by comparing incoming data packets to a set of rules designed to prevent malicious code from entering the network. To regulate the flow of data packets entering and leaving a network, an Internet firewall keeps a track of all activity. While the primary function of log files is to aid in troubleshooting and diagnostics, the information they contain is also very relevant to system audits and forensics. Firewall’s primary function is to prevent malicious data packets from being sent. In order to better defend against cyberattacks and understand when and how malicious actions are influencing the internet, it is necessary to examine log files. As a result, the firewall decides whether to 'allow,' 'deny,' 'drop,' or 'reset-both' the incoming and outgoing packets. In this research, we apply various categorization algorithms to make sense of data logged by a firewall device. Harmonic mean F1 score, recall, and sensitivity measurement data with a 99% accuracy score in the random forest technique are used to compare the classifier's performance. To be sure, the proposed characteristics did significantly contribute to enhancing the firewall classification rate, as seen by the high accuracy rates generated by the other methods.