Browsing by Author "Mona, Tanzina Rahman"

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Early-Stage Diabetes Risk Prediction Using Supervised Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2023-01-04) Islam, Taminul; Sadik, Md Rezwane; Islam, Md. Fajle Rabbi; Mona, Tanzina Rahman; Rahman, Tanjila; Foysal, Md. Musfiqur Rahman
Diabetes is a common health problem worldwide; it is especially pervasive in Bangladesh. The condition manifests in a person when his blood sugar is consistently high. It also contributes to other health problems like blindness, renal failure, heart attack, and stroke. If you know about the early stage, you can take charge and maybe save someone's life. Sadly, this illness is spreading rapidly. The purpose of this research was to quantitatively evaluate the effectiveness of many widely used Machine Learning methods. The medical field is only one area that has benefited greatly from recent advancements in Machine Learning technology. Machine learning algorithms come in a wide variety. Nevertheless, in this research we employ five well-known machine learning algorithms to determine performance metrics: Gaussian Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, and the Decision Tree classifier. Using real data from diabetic patients in Bangladesh, these algorithms were developed and evaluated. There are 3837 patient records in the dataset, 3057 of which correspond to affected cases and 396 were normal. Out of 5 different machine learning algorithms, Random Forest achieved the highest 98% accuracy.
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.