Automatic Phoneme Recognition using Mel-Frequency Cepstral Coefficient and Dynamic Time Warping

A phoneme recognition process is performed by using the Mel-Frequency Cepstral Coefficient (MFCC) feature extraction technique and an unknown test pattern is compared with the pre-recorded reference pattern by using the Dynamic Time Warping (DTW) algorithm to determine the similarity between them

Impacts of ICT Integration in the Higher Education Classrooms: Bangladesh Perspective

For the last few years, ICT integration in education has been the topic of discussion for researchers. Very few researches have been conducted on ICT integration in the context of higher education, especially in Bangladesh. The purpose of this study is to explore the ICT integration in higher education teaching - learning in Bangladesh. This study is qualitative in design. Data were collected from students and teachers in the University of Dhaka through semi-structured interview schedule, focused group discussion and classroom observation schedule. The major findings of this study reveal that ICT is not integrated effectively in higher education teaching-learning. Furthermore, several obstacles have been identified that impede the effective integration of ICT. The obstacles are teachers’ lack of knowledge and skills, teachers’ lack of time to take preparations for class, lack of adequate equipment and access to internet and inadequate technical support. It is asserted that proper teachers’ training about integrating ICT in education will be able to change the scenario to a great extent. This study has, therefore, implications for policy developers, teachers and students of various departments. Keywords: ICT, Pedagogy, Social Interaction, Technology, Teaching – learning

Unveiling the Potential of Big Data Analytics for Transforming Higher Education in Bangladesh; Needs, Prospects, and Challenges

Big Data Analytics has gained tremendous momentum in many sectors worldwide. Big Data has substantial influence in the field of Learning Analytics that may allow academic institutions to better understand the learners needs and proactively address them. Hence, it is essential to understand Big Data and its application. With the capability of Big Data to find a broad understanding of the scientific decision making process, Big Data Analytics (BDA) can be a piece of the answer to accomplishing Bangladesh Higher Education (BHE) objectives. This paper reviews the capacity of BDA, considers possible applications in BHE, gives an insight into how to improve the quality of education or uncover additional values from the data generated by educational institutions, and lastly, identifies needs and difficulties, opportunities, and some frameworks to probable implications about the BDA in BHE sector. Keywords; Big Data Analytics, Learning Analytics, Quality of Education, Challenges, Higher Education, Banglades

PHOTOLYTIC DEGRADATION STUDY ON FILM COATED ATENOLOL TABLET AVAILABLE IN MARKET

Objective: Despite its sensitivity to light, atenolol has been manufactured as a film-coated tablet with normal blister packaging by several pharmaceutical companies in Bangladesh. The aim of this study was to investigate the extent of light-induced potency degradation of a randomly selected film coated brand of atenolol.Methods: Atenolol tablets were exposed to different lighting conditions such as normal room light, direct sunlight and different incandescent lights (25W and 40W). Subsequently, UV spectroscopy technique was employed to determine the relative reduction of light absorbance compared to their respective controls. Thereafter, photolytic degradation was calculated by means of the potency reduction of tablets.Results: In all lighting conditions, atenolol tablets underwent exposure dependent gradual decrease in potency. Except for normal room light condition, a significant decrease in potency was found even after 4 to 6 h of exposure to all lighting conditions. After 6 h, potency reduction was found at 40-47%, 26-38% and 34-36% in the samples exposed to direct sunlight, 25 W bulb, and 40 W bulb respectively. Although the shelf life of the film coated tables was 2 y, surprisingly, statistically significant reduction in potency was observed within only 30 d in room light condition.Conclusion: In order to protect from light, blister packaging is not sufficient for film coated atenolol tablets. Photo-stability of all brands of atenolol must be ensured either by protective packaging materials or by optimizing the formulations

Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning

Handwriting Recognition has been a field of great interest in the Artificial Intelligence domain. Due to its broad use cases in real life, research has been conducted widely on it. Prominent work has been done in this field focusing mainly on Latin characters. However, the domain of Arabic handwritten character recognition is still relatively unexplored. The inherent cursive nature of the Arabic characters and variations in writing styles across individuals makes the task even more challenging. We identified some probable reasons behind this and proposed a lightweight Convolutional Neural Network-based architecture for recognizing Arabic characters and digits. The proposed pipeline consists of a total of 18 layers containing four layers each for convolution, pooling, batch normalization, dropout, and finally one Global average pooling and a Dense layer. Furthermore, we thoroughly investigated the different choices of hyperparameters such as the choice of the optimizer, kernel initializer, activation function, etc. Evaluating the proposed architecture on the publicly available 'Arabic Handwritten Character Dataset (AHCD)' and 'Modified Arabic handwritten digits Database (MadBase)' datasets, the proposed model respectively achieved an accuracy of 96.93% and 99.35% which is comparable to the state-of-the-art and makes it a suitable solution for real-life end-level applications.Comment: Accepted in 25th ICCIT (6 pages, 4 tables, 4 figures

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.Comment: Accepted in ECCE 2023, 6 pages, 6 figures, 4 table