Browsing by Author "Forhat, Rokeya"

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A Cloud of Things (CoT) Approach for Monitoring Product Purchase and Price Hike
(Lecture Notes in Networks and Systems, Springer, 2020-05-15) Sadeq, Muhammad Jafar; Kabir, S. Rayhan; Haque, Rafita; Ferdaws, Jannatul; Akhtaruzzaman, Md.; Forhat, Rokeya; Allayear, Shaikh Muhammad
Price hike is common and one of the major issues all over the world. The prices of daily necessities are increasing day by day. Many shops, restaurants and transport systems are charging extra price from the customers over the products’ expected or maximum retail price. Moreover, in special occasions, such as festivals, the sellers take high price from customers. Furthermore, unauthorized VAT or other taxes are charged on products on which such taxes are exempted by the government. This study proposes a Cloud of Things (CoT)-based model for monitoring the products’ price during transactions, where a cloud server maintains historical pricing data and maximum retail prices. Two algorithms run on the server based on live invoice data from sellers and customer feedback to detect unfair price hike.
Identification of Construction Era for Indian Subcontinent Ancient and Heritage Buildings by Using Deep Learning
(Scopus, 2021) Hasan, Md. Samaun; Kabir, S. Rayhan; Akhtaruzzaman, Md.; Sadeq, Muhammad Jafar; Alam, Mirza Mohtashim; Allayear, Shaikh Muhammad; Uddin, Md. Salah; Rahman, Mizanur; Forhat, Rokeya; Haque, Rafita; Arju, Hosne Ara; Ali, Mohammad
The Indian subcontinent is a south geographic part of Asia continent which consists of India, Bangladesh, Pakistan, Sri Lanka, Bhutan, Nepal, and Maldives. Different rulers or the empire of different periods have built various buildings and structures in these territories like Taj Mahal (Mughal Period), Sixty Dome Mosque (Sultanate Period), etc. From archaeological perspectives, a computational approach is very essential for identifying the construction period of the old or ancient buildings. This paper represents the construction era or period identification approach for Indian subcontinent old heritage buildings by using deep learning. In this study, it has been focused on the constructional features of British (1858–1947), Sultanate (1206–1526), and Mughal (1526–1540, 1555–1857) periods’ old buildings. Four different feature detection methods (Canny Edge Detector, Hough Line Transform, Find Contours, and Harris Corner Detector) have been used for classifying three types of architectural features of old buildings, such as Minaret, Dome and Front. The different periods’ old buildings contain different characteristics of the above-mentioned three architectural features. Finally, a custom Deep Neural Network (DNN) has been developed to apply in Convolutional Neural Network (CNN) for identifying the construction era of above-mentioned old periods.
Identification of Construction Era for Indian Subcontinent Ancient and Heritage Buildings by Using Deep Learning
(Springer, 2020-10-22) Hasan, Md. Samaun; Kabir, S. Rayhan; Akhtaruzzaman, Md.; Sadeq, Muhammad Jafar; Alam, Mirza Mohtashim; Allayear, Shaikh Muhammad; Uddin, Md. Salah; Rahman, Mizanur; Forhat, Rokeya; Haque, Rafita; Arju, Hosne Ara; Ali, Mohammad
The Indian subcontinent is a south geographic part of Asia continent which consists of India, Bangladesh, Pakistan, Sri Lanka, Bhutan, Nepal, and Maldives. Different rulers or the empire of different periods have built various buildings and structures in these territories like Taj Mahal (Mughal Period), Sixty Dome Mosque (Sultanate Period), etc. From archaeological perspectives, a computational approach is very essential for identifying the construction period of the old or ancient buildings. This paper represents the construction era or period identification approach for Indian subcontinent old heritage buildings by using deep learning. In this study, it has been focused on the constructional features of British (1858–1947), Sultanate (1206–1526), and Mughal (1526–1540, 1555–1857) periods’ old buildings. Four different feature detection methods (Canny Edge Detector, Hough Line Transform, Find Contours, and Harris Corner Detector) have been used for classifying three types of architectural features of old buildings, such as Minaret, Dome and Front. The different periods’ old buildings contain different characteristics of the above-mentioned three architectural features. Finally, a custom Deep Neural Network (DNN) has been developed to apply in Convolutional Neural Network (CNN) for identifying the construction era of above-mentioned old periods.
Integration of Block chain and Remote Database Access Protocol-Based Database
(2021) Sadeq, Muhammad Jafar; Kabir, S. Rayhan; Akter, Marjan; Forhat, Rokeya; Haque, Rafita; Akhtaruzzaman, Md.
Many companies are relying on software to manage their businesses. Usually, the software, especially those used by smaller companies, is not secure against unauthorized or unethical data manipulation on the database level. This paper recommends and demonstrates the use of block chain for securing small enterprises against hacking by alerting the management whenever a change is made to the data without using the authorized channels. This is done through block chain technology’s inherent hash replication and mining algorithm. The paper shows an application where this idea has successfully been implemented for a desktop application built upon a remote database access (RDA) protocol-based relational database management system (RDBMS) such as remote MySQL and remote Oracle database.
Integration of Blockchain and Remote Database Access Protocol-Based Database
(Fifth International Congress on Information and Communication Technology, Springier, 2020-10-10) Sadeq, Muhammad Jafar; Kabir, S. Rayhan; Akter, Marjan; Forhat, Rokeya; Haque, Rafita; Akhtaruzzaman, Md.
Many companies are relying on software to manage their businesses. Usually, the software, especially those used by smaller companies, is not secure against unauthorized or unethical data manipulation on the database level. This paper recommends and demonstrates the use of blockchain for securing small enterprises against hacking by alerting the management whenever a change is made to the data without using the authorized channels. This is done through blockchain technology’s inherent hash replication and mining algorithm. The paper shows an application where this idea has successfully been implemented for a desktop application built upon a remote database access (RDA) protocol-based relational database management system (RDBMS) such as remote MySQL and remote Oracle database.