Digital Technologies Research and Applications

Volume 1 Issue 1: March 2022

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Short Communication Article ID: 31

Fan-Beam Projection-Based Feature Extraction for Facial Expression Recognition

by A. Sherly Alphonse
Volume 1 Issue 1 (2022). 2-5. DOI: https://doi.org/10.54963/dtra.v1i1.31

This paper presents a novel method of feature extraction using Fan beam projection-based data. The Fanbeam projection covers the image completely and hence gathers all the important information. Even though the image quality is distorted, this type of feature extraction method helps to gather all the important information as there is a huge volume of projection data. Also, the use of multiple detectors speeds up the entire process. All the projections of the image together form a sinogram image which is unique for each facial expression image. Hence, the sinogram image is divided into grids and the histogram formation results in a feature vector for each image. The classification of these feature vectors using Radial Basis Function-based Extreme learning Machine (RBF-ELM) results in high classification accuracy for all the datasets.

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Article Article ID: 26

A Predictive Investment Scheme for Dhaka Stock Exchange

by A. H. M. Kamal, Md. Kaysar Abdullah
Volume 1 Issue 1 (2022). 6-12. DOI: https://doi.org/10.54963/dtra.v1i1.26
Stock market plays a vital role in industrial development of a country. People invest money to make profit from market. Inexperience investors cannot yield profit due to their weak predictions. This research tries to understand the nature of those investors and their demands. Most investors first analyze the prospect of companies based on rate of up-down in prices of share, given bonus, companies’ goodwill, temptation by others, etc.. This research presents a good prediction methodology for the stock market investors and thus, will help them to achieve a profit. It will improve the stability of a market.

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Article Article ID: 28

Hybridized Deep Neural Network Using Adaptive Rain Optimizer Algorithm for Multi-Grade Brain Tumor Classification of MRI Images

by V. V. S. Sasank, S. Venkateswarlu
Volume 1 Issue 1 (2022). 13-30. DOI: https://doi.org/10.54963/dtra.v1i1.28

Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.

View Volume 1 Issue 1: March 2022

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Editorial Article ID: 29

Explainable Artificial Intelligence: A New Era of Artificial Intelligence

by Ashraf Darwish
Volume 1 Issue 1 (2022). 1. DOI: https://doi.org/10.54963/dtra.v1i1.29
Recently, Artificial Intelligence (AI) has emerged as an emerging with advanced methodologies and innovative applications. With the rapid advancement of AI concepts and technologies, there has been a recent trend to add interpretability and explainability to the paradigm. With the increasing complexity of AI applications, their a relationship with data analytics, and the ubiquity of demanding applications in a variety of critical applications such as medicine, defense, justice and autonomous vehicles , there is an increasing need to associate the results with sound explanations to domain experts. All of these elements have contributed to Explainable Artificial Intelligence (XAI).