Browsing by Author "Rahman, Md. Riazur"

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A Computer Vision System for Bangladeshi Local Mango Breed Detection using Convolutional Neural Network (CNN) Models
(Scopus, 2020) Haque, A.S. M. Farhan Al; Rahman, Md. Riazur; Marouf, Ahmed Al; Khan, Md. Abbas Ali
Magnifera Indica, traditionally known as mango, is a drupe found around the world in over 500 species. India has produced 19.5 million metric tons of mango in 2017. In Bangladesh, mango has been referred as the national tree and government has included endemic species of mango as geographical index (GI) of Bangladesh. Recognizing specific breeds has become a significant computer vision task. In this paper, we have proposed the convolutional neural network (CNN) based approach for detecting five mango species namely, Chosha, Fazli, Harivanga, Lengra and Rupali from 15000 different images. For better experimentation, we have applied three different models of CNN and analyzed the recognition rates with various criteria. For performance evaluation, we have utilized the classic metrics such as precision, recall, F1-score, ROC and accuracy. Among the experimented three models, the third model, outperformed in terms of accuracy with 92.80%.
A Context-Sensitive Approach to Find Optimum Language Model for Automatic Bangla Spelling Correction
(nternational Journal of Advanced Computer Science and Applications, 2018) Islam, Muhammad Ifte Khairul; Habib, Md. Tarek; Rahman, Md. Sadekur; Rahman, Md. Riazur; Ahmed, Farruk
Automated spelling correction is an important phenomenon in typing that has intense effect on aiding both literate and semi-literate people while using keyboard or other similar devices. Such automated spelling correction technique also helps students significantly in learning process through applying proper words during word processing. A lot of work has been conducted for English language, but for Bangla, it is still not adequate. All work done so far in Bangla is context-free. Bangla is one of the mostly spoken languages (3.05% of world population) and considered seventh language of all languages in the world. In this paper, we propose a context-sensitive approach for automated spelling correction in Bangla. We make combined use of edit distance and stochastic, i.e. N-gram language model. We use six N-gram models in total. A novel approach is deployed in order to find the optimum language model in terms of performance. In addition, for finding out better performance, a large Bangla corpus of different word types is used. We have achieved a satisfactory and promising accuracy of 87.58%.
An App-Based IoT-NFC Controlled Remote Access Security Through Cryptographic Algorithm
(Scopus, 2021) Khan, Md. Abbas Ali; Ali, Mohammad Hanif; Haque, A. K. M. Fazlul; Debnath, Chandan; Jabiullah, Md. Ismail; Rahman, Md. Riazur
In the twenty-first century, a human being is passing through the world with generosity of technology and most of it’s the systems are being operated by automated or remote access control. However, sensor technology is already playing a vital role to control the smart home, smart office, etc. However, it is about to beyond a smart city. Remote access control is a part of the leading technology. An app-based innovative remote access control framework is adding an extra security to make this technology more convenient, secured and illustrate the usability of a person along with an authenticated system of the executive. NFC is used as a communication technology, and a microcontroller camera is also used for detection. An authentication process drives through a smartphone application over the IoT framework. A definitive objective of this paper is to ensure the security of remote access control, notification to the comer and admin, accessibility, usability and permissibility to enter the premises. In order to maintain the integrity and the confidentiality of data cryptographic, techniques like computational 512 bits hash functions are considered and encrypt the hashed data once AES-192 is used. The additional part of this paper is to measure the performance of an employee.
FoNet - Local Food Recognition Using Deep Residual Neural Networks
(Proceedings - 2019 International Conference on Information Technology, IEEE, 2019-12-21) Jeny, Afsana Ahsan; Junayed, Masum Shah; Ahmed, Ikhtiar; Habib, Md. Tarek; Rahman, Md. Riazur
Food is an inseparable part of any culture of any country all over the world. Recognition ability for local foods reflects the cultural strength. Moreover, it would be so beneficial if some can come to know the information about a food by capturing the image of the food with any smart cellphone. In this paper, we have mainly focused on recognizing different local foods of Bangladesh. The Computer Vision community has given little attention to visual food analysis such as food detection, food recognition, food localization, and portion estimation. This is why, we have proposed a novel approach for local food recognition, where we have created and utilized a Deep Residual Neural Network for grouping six classes of food images. We have also used two convolutional neural networks separately for grouping six classes of food images. Then we compare our proposed model with these two deep learning models. Our proposed model has achieved a notable highest accuracy of 98.16%, which is promising enough.
Implementation of an Efficient Web-based Movie Ticket Purchasing System in the Context of Bangladesh
(Indonesian Journal of Electrical Engineering and Computer Science, 2020) Islam, Gazi Zahirul; Zinnia, Isrut Jahan; Hossain, Md. Fokhray; Rahman, Md. Riazur; Juman, Aman Ullah; Emran, Al Nahian Bin
The ‘Movie Ticket Purchase System’ is a web-based application. In this application, people can purchase movie tickets from all movie theatres in Bangladesh. Before purchasing a ticket, people have to do registration or login. This website builds by PHP and JavaScript for back-end; HTML, CSS for front-end. All steps of the software development life cycle are addressed properly to develop and implement the software. This website has three panels: one for the Admin, one for the Theatre Assistant and another for the Customer/User. Admin can insert the theatres, and Theatre Assistant handled maximum manual works on the website like movie add, delete, stop running, screen adds, etc. This is the first website in Bangladesh where people can purchase tickets from multiple movie halls and the site is only dedicated to this purpose. The website is very user-friendly and attractive that can give comfort to the end users. Also, the theater owners that have no digital platform for selling tickets can be a member of our service and get the opportunity of using digital platform.
Incorporating Supervised Learning Algorithms with NLP Techniques to Classify Bengali Language Forms
(Scopus, 2020-01-10) Parves, Abdul Bari; Imran, Abdullah Al; Rahman, Md. Riazur
Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Incorporating Supervised Learning Algorithms with NlP Techniques to Classify Bengali Language Forms
(ACM International Conference Proceeding Series, 2020-01-10) Parves, Abdul Bari; Imran, Abdullah Al; Rahman, Md. Riazur
Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Incorporating Supervised Learning Algorithms with NLP Techniques to Classify Bengali Language Forms
(ACM International Conference Proceeding Series, 2020-01-10) Parves, Abdul Bari; Imran, Abdullah Al; Rahman, Md. Riazur
Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Machine Vision Based Local Fish Recognition
(SN Applied Sciences, Springer, 2019-11-01) Sharmin, Israt; Islam, Nuzhat Farzana; Jahan, Israt; Joye, Tasnem Ahmed; Rahman, Md. Riazur; Habib, Md. Tarek
Bangladesh has its own abundance of water resources which helps to identify its customs that are related to freshwater fish. Due to environmental issues along with some other reasons, the amount of water resources of Bangladesh is reducing day-by-day. Consequently, many of our territorial freshwater fishes are getting abolished. Thus, the new generation people of Bangladesh lacks the knowledge of local freshwater fish. For this problem, a solution has been found with the collaboration of vision-based technology. As a solution, a machine-vision based local freshwater fish recognition system is presented that can be proceed with an image of fish captured with a mobile or handheld device and recognize the fish in order to introduce the fish. To demonstrate the utility of the proposed expert system, several experiments are performed. At first, a set of fourteen features, which consists of four types of features, are presented. Then the color image has been converted into gray-scale image and the gray-scale histogram is formed. Image segmentation takes place using histogram-based method and then the features are extracted. PCA is used for decreasing the feature numbers. Three classifiers are used for recognizing fish, where SVM gives the highest accuracy showing a value of 94.2%.