Browsing by Author "Hakim, Md. Azizul"

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A Decision Support System of Selecting Groups (Science/ Business Studies/ Humanities) for Secondary School Students in Bangladesh
(IEEE, 2020-10-15) Hasan, Rifat; Ovy, Md. Khairul Alam; Nishi, Ifrat Zahan; Hakim, Md. Azizul; Hafiz, Rubaiya
As education is the only way to turn a person into human resource, every country tries to give her citizens proper scope of bringing out their inner ability by offering the appropriate education. According to the education system of Bangladesh, an 8 th grade completing student has to choose a group (science, Business Studies, humanity) for further studies. This group will be his/her initial highway for higher education. But it is a matter of sorrow that, in Bangladesh this crucial event is done by some rumors and some traditional old school ways, which are mostly wrong and destructive. From the perspective of this country, the only way of choosing those groups is previous result. Of course, result is one of the most important attribute, but it should not be the only thing. Again in this country, Science is thought to be superior than other groups. That's why, parents have the tendency to impose this group to their children without knowing their ability and interest and leads them towards an uncertain future. Therefore, the aim of this paper is to build a model of group selection by analyzing some random attributes of higher level students who have already gone through this event of selecting groups with the help of data mining and some machine learning algorithms, so that a newly 9 th grade student could have the proper direction of selecting a group which is best for him/her. For the purpose of experimentation we have used three machine learning algorithms: Naïve Bayes, Sequential Minimal Optimization (SMO) and Random Forest. Among these algorithms Random forest gives the best prediction result with an accuracy of 84.9%.
A Deep Learning Based Poisonous Frog Detection System
(IEEE, 2023-11-23) Jahan, Mushrat; Samia, Ismot Jahan; Younus, Saima Binta; Hakim, Md. Azizul; Jahan, Mushrat
Frog species across the world are vulnerable and in decline, despite the fact that frogs are an integral part of biological systems. Within this enchanting world, we encounter two intriguing groups: non-poisonous and poisonous frogs. This phenomenon prompts the development of an automated computer vision-based frog detection system that can distinguish between deadly and non-poisonous frogs, leading to the development of early treatment methods and a reduction in relative economic loss. In this study, we present a convolutional neural network-based technique for frog detection. The CNN model required numerous epochs to run in order to provide the best result. However, we must also consider the trade-off in convergence speed. In our exploration, we conducted experiments with different epochs. Interestingly, our findings revealed that running the model for 30 epochs yielded the highest accuracy, reaching an impressive 90.83%. Through rigorous and thorough experimentation, we evaluated confusion metrics and discovered that they yielded exceptional results.
A Web Based Utility Notification System
(Daffodil International University, 2019-05) Salekin, Serajush; Hassan, Md Shakib; Hakim, Md. Azizul
Public utilities are those services which are absolutely necessary for the community. Some of the services are like water supply, gas, electricity, transportation, communication, etc. which cannot be distributed without a serious successful economic living of the community. These services are so essential to the public that any interruption in their supply would throw the normal life of the community out of gear. For avoiding this interruption, we’ve came with an idea of a “Utility Notification System”. This system provide user the essential utility notification or message in their mobile minimum 24 hours before any interruption of utility. It saves a lots of time, money and a huge hassle.
Analysis of Student Sentiment During Video Class with Multilayer Deep Learning Approach
(Daffodil International University, 2022-08-08) Salehin, Imrus; Moon, Nazmun Nessa; Talha, Iftakhar Mohammad; Hasan, Md. Mehedi; Nur, Farnaz Narin; Hakim, Md. Azizul; Haque, Farhan Al
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine(SVM)and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
Bangla Font Recognition using Transfer Learning Method
(Daffodil International University, 2023-01-03) Hasan, Md. Sakibul; Rabbi, Golam; Islam, Rejoan; Bijoy, Md. Hasan Imam; Hakim, Md. Azizul
Font detection and similar font suggestions are essential in computer vision, document analysis, pattern recognition, web development, and core work for graphics designers and UX-UI engineers. Even though Bangla is one of the most communicated languages globally and the growing popularity of Bangla in online publications and social media platforms, there has been a noticeable demand for Bangla fonts. However, there is no appreciable work for font detection in Bangla, unlike other high resource languages like English, Chinese, Hindi, Arabic, Spanish, and German. This work represents a model to recognize Bangla fonts from images using the transfer learning method. In Image Processing and Classification, resources are insufficient for such work in Bangla, so it is required to build as much raw data as possible to train the model. Therefore, as part of the work, 6500 raw images of five different fonts are used, and with augmentation 26000 image data are created to train and 2600 images to validate the model. Three transfer learning models, which are VGG-16, VGG-19, and Xception are applied. Among them, VGG-16 archives the highest accuracy of 96.23%. This paper is the first publicly available work on Bangla font recognition using the Transfer Learning approach to the best of our knowledge.
Carrot Cure: A CNN based Application to Detect Carrot Disease
(Daffodil International University, 2022-06-04) x Ray, Shree. Dola; Natasha, Mst. Khadija Tul Kubra; Hakim, Md. Azizul; Nur, Fatema
Carrot is a famous nutritional vegetable and developed all over the world. Different diseases of Carrot has become a massive issue in the carrot production circle which leads to a tremendous effect on the economic growth in the agricultural sector. An automatic carrot disease detection system can help to identify malicious carrots and can provide a guide to cure carrot disease in an earlier stage, resulting in a less economical loss in the carrot production system. In this paper, we have developed a web application “Carrot Cure” based on Convolutional Neural Network (CNN) which can identify a defective carrot and provide a proper curative solution. Images of carrots affected by cavity spot and leaf bright as well as healthy images were collected. In this research, we’ve employed Convolutional Neural Network to include birth neural purposes and a Fully Convolutional Neural Network model (FCNN) for infection order. We’ve explored different avenues regarding different convolutional models with colorful layers and the proposed Convolutional model achieved the perfection of virtually 99.8%, which is surely useful for the drovers to distinguish carrot illness and boost their advantage. Index Terms—Carrot Disease Detection, Image Processing, Web Application, Convolutional Neural Network, CNN Model, Deep Learning Approach
CNN Based Automatic Computer Vision System for Strain Detection and Quality Identification of Banana
(2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), IEEE, 2021-09-08) Al Haque, A. S. M. Farhan; Hakim, Md. Azizul; Hafiz, Rubaiya
Banana is amongst the most appealing and nutritious fruits worldwide, cultivated almost every part of the world round the year. Bangladesh ranks 14 th worldwide in producing this appealing fruit putting a substantial mark on the national economic growth. Classification and recognition of specific strains and identifying the quality of different agricultural products has been a challenge for mass production. With the continuous evolution of technological advancements now it has become a beneficial machine vision task to classify different strains and also determine the quality of the fruit to trash the affected ones that will minimize the loss to a great extent. In this paper, we have proposed a Convolutional Neural Network (CNN) based model that classifies five strains of different bananas namely cavendish, lady finger, shabri, green and the red banana and also identifies the rotten ones with great accuracy. We have successfully deployed the two deep learning models to find significant accuracy varying different parameters. We have also utilized the widely accepted precision, recall, F1-score and ROC evaluation metrics. The second model has outperformed the other in terms of accuracy with 93.4±0.8% and identifying the rotten bananas with an accuracy of 98.3±.8%.
Three Phase Transmission Line Fault Analysis by Using Matlab
(Daffodil International University, 2018-12) Hakim, Md. Azizul
Power system fault analysis is a process which determines the bus voltages and line currents during the occurrence of various types of faults. Faults on power systems can be divided into three-phase balanced faults and unbalanced faults. Three types of unbalanced fault occurrence on power system transmission lines are single line to ground faults, line to line faults, and double line to ground faults. Fault studies are used to select and set the proper protective devices and switchgears. The determination of the bus voltages and line currents is very important in the fault analysis of power system. The process consists of various methods of mathematical calculation which is difficult to perform by hand. The calculation can be easily done by computer which is generated by a Simulink developed using MATLAB. However, in the conventional short circuit study there arises an error while doing Y-bus to Zbus inversion for a big bus system. To minimize the error, this paper provides a solution which is solved through “MATLAB SIMULINK”.
Vulnerability Detection of US Power Grid Network Based on Cascading Failure Through Underlying Principles of Complex Network Analysis
(2023-07) Hakim, Md. Azizul; Farha, Amena Begum; Jahan, Fatema; Safty, Md.; Farabe, Sadman Al
Complex network (CN) concepts have revolutionized the way large and complex technological networks are perceived and understood. Electrical power grids are a prime example of such networks. Removal of small number of grids and power lines can cause a serious blackout throughout a country which refers to cascading failure in the power grid network. Complex network concepts can help to solve cascading problem by analyzing the robustness and performance of a power grid network by representing it a simple graph network to find the impact of the removal of some grids (nodes) and power lines (edges) on the overall performance of the network. In this research, we have used complex network concepts to find out the cascading failure in the US power grid network. We have analyzed the cascading breakdown by employing various measures of network centrality. Our findings indicate that eliminating only 10% of nodes with both high degree and high betweenness in the power grid network has a significant impact, leading to a loss of 96% efficiency and posing a significant risk of a widespread blackout across the entire country. The outcomes of this study offer valuable understanding regarding the durability and resilience of the power grids in the United States. Furthermore, help to identify the critical weaknesses that require attention to enhance the network’s overall performance and stability.