Browsing by Author "Shetu, Syeda Farjana"

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A Survey of Botnet in Cyber Security
(Scopus, 2019-09-29) Shetu, Syeda Farjana; Saifuzzaman, Mohd.; Moon, Nazmun Nessa
Botnets is one of the most critical cyber security threats that confronted by organizations day by day. Botnet has used distinctive strategies, topologies and communication protocol in specific levels in their life cycle. Identifying of botnets has emerged as a very challenging topic, particularly for the reason that they can improve their method at any time. Nowadays, most of the recent botnet detection techniques cannot locate modern botnets in an early stage. Mainly, botnets follow command and control infrastructure. Nowadays, botnet is an interesting and very vital research topic for researchers in our cyber security. This Paper represents a completely comprehensive evaluation that extensively discusses the botnet problem; this survey classifies all of the possible botnet detection methods, also summarizes previously published studies and recent works.
Identifying the Writing Style of Bangla Language Using Natural Language Processing
(11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, IEEE, 2020-10-15) Shetu, Syeda Farjana; . Saifuzzaman, Mohd; Parvin, Masuma; Moon, Nazmun Nessa; Yousuf, Ridwanullah; Sultana, Sharmin
Bangla is one of the 8th major spoken languages around the world and like other widely spoken languages, it is a very morphologically rich language. It has two styles, one is standard literary style, known as Sadhu Bhasha and the other one is a standard colloquial style which is known as Cholito Bhasha. Mixing both the styles in a written document is considered as a grammatical error in Bangla language known as Guruchondali Dosh. This research aims to develop an algorithm to identify the style of a Bangla paragraph i.e. whether it is in Sadhu Bhasha or Cholito Bhasha from a given Bangla paragraph input. It's a contribution towards finding the Goruchondali Dosh which is a common grammatical mistake in written Bangla language as it was observed that a number of research work for identifying Bangla grammar mistakes is not so notable whereas it is a common trend in other language researchers.
Implementation of Low Cost Real-time Attendance Management System
(ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), IEEE, 2020-09-15) Hasan, Rakib; Islam, Sajedul; Rahman, Md. Habibur; Saifuzzaman, Mohd.; Shetu, Syeda Farjana; Moon, Nazmun Nessa
In this research, a systematic approach was adopted to expand the level of service to the real-time attendance system. The test was performed to assess the efficiency of the attendance system, which would be quite cost-effective but accurate. To that the expense of linking our biometric attendance system to a central server where the data is changed instantly. So, the possibility of data loss is nothing at all. Our proposed prototype may cost around 30$ while the available existing devices are high priced. Moreover, most devices generate abundant data in favor of an individual user where our proposed model provides well organized data to track down the attendance. Furthermore, a very low amount of energy is needed to run the entire system. The entire system can be installed anywhere with a very low expense
IOT Based Street Lighting Using Dual Axis Solar Tracker and Effective Traffic Management System Using Deep Learning
(11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. IEEE, 2020-10) Saifuzzaman, Mohd.; Shetu, Syeda Farjana; Moon, Nazmun Nessa; Nur, Fernaz Narin; Hanif Ali, Mohammad
With the rapid increase of population, nonrenewable energy loss has become one of the prior concerns worldwide in recent years. Researchers are working on the proper utilization of renewable energy to save the rest of the non-renewable energy sources for future generations. One of the purposes of this research is to develop an intelligent automation system that can decide luminous control operation based on light intensity. Here, LDR sensor is installed in such a way that it can be able to identify Night and Day mode using a certain intensity value, and as well as IR sensors take over the control of street light. The solar cell will be used for power supply here and DC current will work as a secondary backup. The article also involves automatic monitoring of traffic controls through a video system. The camera incorporates automatic text retrieval from video data utilizing lip reading by decoding facial expression utilizing deep learning methods. The proposed solution includes a test data collection, an interpretation of the picture frame, and a text production of the specified words. The test data collection will be structured in the proposed methodology by integrating all potential facial expressions of different words.
Machine Learning Approach to Predict SGPA and CGPA
(2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), IEEE, 2021-07-30) Saifuzzaman, Mohd.; Parvin, Masuma; Jahan, Israt; Moon, Nazmun Nessa; Nur, Fernaz Narin; Shetu, Syeda Farjana
The prediction of SGPA and CGPA is beneficial to university students. Students will easily get an estimate of their final outcome from this project. As a result, the students will be able to brace themselves for a successful outcome. Students pass the day by participating in a variety of events. Students use social media sites such as Facebook, Instagram, and Twitter. They engage in various hobbies such as playing mobile games, listening to music, among others. As a result, they were able to move several times with these tasks. As a result, if a student spends so much time doing any of those things, she will not be able to achieve a successful grade because of the experiment; students can develop a research routine or guideline that they can apply to their other tasks. Additionally, students' behaviors will forecast their outcomes. The Authors will now see machine learning in Python being used all over the place. After that, The Authors created a smart SGPA and CGPA prediction project, as well as the results on students. The findings are predicted using the Nave Byes algorithm. The Nave Byes algorithm is a simple but effective prediction algorithm. It is a machine learning algorithm as well. As a result, students will be given an estimate of their final exam scores. They can prepare them to make a good result by following the routine of the SGPA & CGPA prediction project.
Predicting Satisfaction of Online Banking System in Bangladesh by Machine Learning
(2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), IEEE, 2021-07-30) Shetu, Syeda Farjana; Jahan, Israt; Islam, Mohammad Monirul; Hossain, Refath Ara; Moon, Nazmun Nessa; Nur, Fernaz Narin
Online banking refers to using your smartphone, tablet, or another internet-connected computer to browse and access your bank account. It is quick and free, and it usually allows you to perform a variety of activities, such as paying bills and exchanging currency, without having to visit or call your branch. As a developing nation, Bangladesh is seeing an increase in online banking. People are still reliant on online banking because it makes a man's life much easier. During the Corona incident, the use of online banking increased at an unprecedented pace. Online banking services such as Rocket, bKash, and Nagad are now available in the region. While online banking makes life easier, third-party money laundering incidents do occur from time to time. As a result, some people are unhappy with online banking. However, some people say that they are happy with their online banking experience. This work tries to address this critique and give the right advice to the customer. Customer satisfaction and frustration with online banking have been predicted using Machine Learning techniques in this study. Seven traditional machine learning classification algorithms Logistic Regression, Random Forest, Naïve Bayes, support vector machine, Neural network, Decision tree, K nearest neighbor algorithms to complete this research work and find the concluded delimiter.
Prediction of Pneumonia Disease of Newborn Baby Based on Statistical Analysis of Maternal Condition Using Machine Learning Approach
(2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, 2021-03-15) Hasan, Md. Mehedi; Faruk, Md. Omar; Biki, Bidesh Biswas; Riajuliislam, Md; Alam, Khairul; Shetu, Syeda Farjana
Pneumonia is one of the common diseases amongst children in Bangladesh. Many children die from pneumonia in Bangladesh. Pneumonia is an infection that infects the air sacs in one or both lungs. In Bangladesh, nearly 50,000 children die of pneumonia every year. For diseases forecasting, Machine learning algorithms are popular and used extensively. Machine Learning allows us to fulfill such a task with much consequence. We established our dataset from the particular obtainable from our survey. For prognosticating pneumonia, we employed six traditional Machine Learning algorithms. They are K- Nearest Neighbor (KNN), Naive Bayes classifier, Decision Tree, Support Vector Machine (SVM), Neural Network algorithm, and Random Forest. For implementing these algorithms, we applied Scikit-leam, Pandas, NumPy, and for visualizing our data, we have used Matplotlib and seaborn. By proper interpretation, we considered the best performing algorithm for the prediction of pneumonia. We have measured to classify whether pneumonia declines under pneumonia (Positive) and pneumonia (Negative) class. Among all the algorithms, we have chosen the best algorithm which is provided us best accuracy and F1-score. By the best accomplishing algorithm, our model can predict pneumonia quite well.
Student’s Performance Prediction Using Data Mining Technique Depending On Overall Academic Status and Environmental Attributes
(Springer, 2021) Shetu, Syeda Farjana; Saifuzzaman, Mohd; Moon, Nazmun Nessa; Sultana, Sharmin; Yousuf, Ridwanullah
In the education sector, it has been a challenging task to identify the students individually to take appropriate actions to get a very deserving outcome from them. On the other hand, a student getting higher education should have the knowledge about the market demands and where are their weaknesses. If it is possible to get some data from students’ academic record and their percepts on some factors related to academic performances those may help to understand the reasons for success and failure which would be very useful in the educational environment and student success rate. We collect data from students from different institutes. First, we create an online survey form to get data, and then we process them to get some valuable information. After getting those data, we visualize then analyze them from different prospects. We try to get some exact knowledge which can be crucial for students’ success or failure in an academic environment. We apply the data mining technique decision tree algorithm (j48) to develop a model that shows us the hierarchy of different attributes related to students’ academic performance and their personal behaviors that affect a student’s academic status. Then we try to get information about which attributes have a positive and which attribute has a negative impact on students’ academic growth.