Browsing by Author "Atik, Syeda Tanjila"

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A Comparative Study of Different CNN Models in City Detection Using Landmark Images
(Scopus, 2020) Junayed, Masum Shah; Jeny, Afsana Ahsan; Neehal, , Nafis; Atik, Syeda Tanjila
Abstract Navigation assistance using different local Landmarks is an emerging research field now-a-days. Landmark images taken from different camera angles are being vividly used alongside the GPS (Global Positioning System) data to determine the location of the user and help user with navigation. However, determining the location of the user by recognizing the landmarks from different images, without the help of GPS, can be a worthy research trend to explore. Hence, in this paper, we have conducted a comparative study of 3 different popular CNN models, namely - Inception V3, MobileNet and ResNet50, and they have achieved an overall accuracy of 99.7%, 99.5% and 99.7% respectively while determining cities using landmark images.
A Model for Identifying Historical Landmarks of Bangladesh from Image Content Using a Depth-Wise Convolutional Neural Network
(Springer, 2019-04-12) Jeny, Afsana Ahsan; Junayed, Masum Shah; Atik, Syeda Tanjila; Mahamd, Sazzad
At present, tourism is considered to be one of the key factors shaping the development of a country’s economy. Most of the tourists tend to explore places that they find fascinating after watching pictures of that places over Internet. Anyone can know about a famous place by simply typing the name of that place in an internet browser. But problem arises when he/she comes across the image of a beautiful landmark which is anonymous as most of the time web images do not convey any text caption. Most of models provided for image identification so far exhibit much complex structure and increased time complexity. In this paper, we have proposed a CNN model based on MobileNet and TensorFlow for detecting some historical landmarks of Bangladesh from their image. We have examined 750 images from five different places and comparing other state-of-art models, our model holds relatively simpler structure and has achieved a significantly higher average accuracy of 99.2%. This model can be further enhanced to facilitate image classification in other related areas.
A Variable Length Key Based Cryptographic Approach on Cloud Data
(Scopus, 2019-12-21) Ghosh, Pronab; Jabiullah, Md. Ismail; Hasan, Md. Zahid; Atik, Syeda Tanjila
Security has emerged to be a concerning issue in cloud computing as numerous sensitive data are processed and transferred over the cloud servers. In this paper, a variable length key based security mechanism has been designed, developed, and implemented by using the Advanced Encryption Standard (AES) protocol for the secured delivery of cloud data which also works as SaaS (Software as a Services). The length of the used key is given as input by the user followed by the selection of a secret key which is fed into the AES cryptographic system with the desired cloud data message thus producing the Cipher text that is to be transmitted to the destination. In the receiver end, the reverse process is performed with the same key on the received Cipher text and the plaintext is retrieved. Using Python to implement and validate the security process, several messages from cloud users are used and the result for each input is analyzed. The result of our study is then compared with the two existing approaches which clearly shows the advancement of the proposed approach. This process can be applied in any secured electronic message transactions for cloud data.
AcneNet - A deep CNN Based Classification Approach for Acne Classes
(Scopus, 2019) Junayed, Masum Shah; Jeny, Afsana Ahsan; Neehal, Nafis; Atik, Syeda Tanjila
Skin diseases are very common and nowadays easy to get remedy from. But, sometimes properly diagnosing these diseases can be quite troublesome due to the stiff hard-to-discriminate nature of the symptoms they exhibit. Deep Neural Networks, since its recent advent, has started outperforming different algorithms in almost every sectors. One of the problem domains, where Deep Neural Networks are really thriving today, is Image Classification and Object and Pattern Discovery from images. A special type of Deep Neural Network is Convolutional Neural Networks (CNN), which are being extensively used for different sorts of computer vision and image classification related problems. Hence, we have proposed a novel approach, where we have developed and used a Deep Residual Neural Network model for classifying five classes of Acnes from images. Our model has achieved an approximate accuracy as much as 99.44% for one class, and the rest were also above 94% with fairly high precision and recall score.
An Adaptive Routing Algorithm for on-chip 2D Mesh Network with an Efficient Buffer Allocation Scheme
(IEEE, 2018-09-20) Atik, Syeda Tanjila; Imran, M.M.; Mahi, Julkar N; Jeba, Jenia A; Chowdhury, Z I; Kaiser, M S
Network-on-Chip (NoC) is thought to be an effective packet-switched on chip arrangement for System-on-Chip (SoC) paradigm. Using router in a NoC, higher throughput is facilitated which is desirable for dealing with the difficulty of current systems. For proper usage of the communication bandwidth and to diminish transportation delay, an intelligent routing mechanism is required. Also a proper buffer management is essential to reduce packet drops and area overhead. Several routing algorithms and buffering techniques are proposed so far. In this paper, we propose a modified XY routing algorithm coupled with an on-demand buffer allocation concept for a mesh on-chip network and compare the performance of our algorithm with other frequently used algorithm named as Odd-Even and DyAD routing. Simulation results indicate that the proposed algorithm achieves better performance than traditional XY-routing and other algorithms in terms of latency and throughput. Again, the effect of using different number of virtual channels in the router buffer is also studied here.
Expert Cancer Model Using Supervised Algorithms with a Lasso Selection Approach
(International Journal of Electrical and Computer Engineering (IJECE), 2021) Ghosh, Pronab; Karim, Asif; Atik, Syeda Tanjila; Afrin, Saima; Saifuzzaman, Mohd.
One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
Expert Cancer Model Using Supervised Algorithms with a Lasso Selection Approach
(International Journal of Electrical and Computer Engineering (IJECE), Elsevier, 2021) Ghosh, Pronab; Karim, Asif; Atik, Syeda Tanjila; Afrin, Saima; Saifuzzaman, Mohd.
One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
PassNet - Country Identification by Classifying Passport Cover Using Deep Convolutional Neural Networks
(2018 21st International Conference of Computer and Information Technology, ICCIT 2018, IEEE, 2019-02-04) Jeny, Afsana Ahsan; Junayed, Masum Shah; Atik, Syeda Tanjila
Globalization has enhanced the transportation system resulting in the increased mobility of the people all around the world. More people are now travelling outside their own border for self-refreshment or business purpose. The task of the immigration officers in the airport thus has become more challenging now-a-days. Moreover, there are many countries whose people are restricted from travelling to other certain countries. Hence, the country identification by analyzing only the passport cover can reduce both time and hassle greatly by detecting those unauthorized travelers. In our paper, we present a model for country recognition by analyzing any passport cover using a deep Convolutional Neural Network (CNN) model based on the Residual Network architecture with 50 layers termed as ResNet50. We have experimented our model with images of seven passport covers of seven different countries from different angles and found an average accuracy rate of 98.56%. Our model can also be enhanced to detect fake and forged passports.