Ashfaq Ali Shafin

Work place: Department of Computer Science and Engineering, Stamford University Bangladesh, Dhaka, Bangladesh



Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Vision, Natural Language Processing, Image Compression, Image Manipulation, Image Processing


Ashfaq Ali Shafin is appointed as a Lecturer for the Department of Computer Science and Engineering at Stamford University Bangladesh. In 2018, he earned his Bachelor of Science degree in Computer Science and Engineering from Ahsanullah University of Science and Technology (AUST), securing the second position in a class of 112 students. At present, he is exploring the research fields of Machine Learning, Natural Language Processing (NLP), Digital Image processing (DIP), and Computer Vision (CV).

Author Articles
Automatic Environmental Sound Recognition (AESR) Using Convolutional Neural Network

By Md. Rayhan Ahmed Towhidul Islam Robin Ashfaq Ali Shafin

DOI:, Pub. Date: 8 Oct. 2020

Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.

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Statistical and Machine Learning Analysis of Impact of Population and Gender Effect in GDP of Bangladesh: A Case Study

By Md. Rayhan Ahmed Ashfaq Ali Shafin

DOI:, Pub. Date: 8 Feb. 2020

Gross Domestic Product (GDP) per capita is a critical degree of a nation's monetary growth that records for its number of people. A balanced participation ratio of both males and females in the industry by ensuring skilled and technical education for all provides a stable economic development in a country. Population and Gender impact on GDP prices in Bangladesh were investigated in this study. To address the effect of gender factors in GDP prices, we considered the following parameters: year, combined population, male population, and female population. Based on these parameters, the global domestic product-current prices of Bangladesh were analyzed. For the predictive analysis, we have used various machine learning algorithms to make prediction and visualization of the predicted output. A quantitative analysis was also performed to examine the correlation among different gender factors with the growth of GDP. Based on analysis and study results, we can say that the machine learning approach could be applied efficiently in numerous applications of GDP forecasting.

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