Abdullah - al - mamun

Rajshahi University of Engineering and Technology, Computer Science & Engineering, Graduate Student

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Abdullah al Mamun is a professional Software Engineer and Researcher. Currently, he is working as a Manager at Robi Axiata Limited. 10 Years of professional job career, he was responsible as Senior Software Engineer of Datasoft Systems Bangladesh Limited and Assistant Director of Primeasia University. He graduated from Rajshahi University of Engineering & Technology (RUET) with and B.Sc in Computer Science and Engineering (CSE). As a professional Software Engineer, he has experience in analysis, design, development, testing, and implementation of application designing and development platforms, and project management. Also, he is interested in research in Artificial Intelligence, Neural Networks, Deep Learning, Pattern Recognition, Hidden Markov models, and Machine Learning. His research has been published in 6 different international journals and conferences, including IEEE. He reviewed a book that was published on Amazon Kindle. He is a member of the Bangladesh Computer Society(BCS) and the Institution of Engineers, Bangladesh (IEB). In his long career, he got different academic and professional excellence awards.
Supervisors: Artificial intelligence, Machine learning, Neural Networks, and Pattern recognition

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An approach to empirical Optical Character recognition paradigm using Multi-Layer Perceptorn Neural Network

by Abdullah - al - mamun and Tanjina Alam

In this paper we are represent the architecture of Optical Character Recognition that converting ... more In this paper we are represent the architecture of
Optical Character Recognition that converting from visual
character to the machine readable format. To present this
architecture, several stages are associate like take the character
input image, preprocessing the image, feature extraction of the
image and at last take a decision by the artificial computational
model same as biological neuron network. Decision making
system by the Artificial Neural Network associated with two
steps; first is adapted the artificial neural network throughout
the Multi-Layer Perceptron learning algorithm and second is
recognition or classification process for the character image to
comprehensible for the machine in a way that what character is
it. Our proposal architecture achieved 91.53% accuracy to
recognize the isolated character image and 80.65% accuracy for
the sentential case character image.

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Hypothetical Pattern Recognition design using Multi-Layer Perceptorn Neural Network for supervised learning

Humans are capable to identifying diverse shape in the different pattern in the real world as eff... more Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain (called, Artificial Neural Network) that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because, the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research, now a day’s pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural network(in the algorithm of artificial Intelligence) as the best possible way of utilizing available resources to make a decision that can be a human like performance.

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Emblematical image based pattern recognition paradigm using Multi-Layer Perceptron Neural Network

The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is profic... more The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is proficiently identify an image based object like optical character, hand character image, fingerprint and something like this. To present the model of image based pattern recognition perspective by a machine, different stages are associated like image acquiring from the digitizing image sources, preprocessing image to remove unwanted data by the normalizing and filtering, extract the feature to represent the data as lower dimension space and at last return the decision using Multi-Layer Perceptron neural network that is feed feature vector from got the feature extraction process of a given input image. Performance observation complexity is discussed rest of the description of pattern recognition model. Our goal of this paper is to introduced symbolical image based pattern recognition model using Multi-Layer Perceptron learning algorithm in the field of artificial neural network (like as human-like-brain) with best possible way of utilizing available processes and learning knowledge in a way that performance can be same as human.

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A Novel Training Based Concatenative Bangla Speech Synthesizer Model

In the modern era of information technology, information is carried out in various ways to lead h... more In the modern era of information technology, information is carried out in various ways to lead human life easily. Information can be exchanged among people in various
ways and speech is the primary communication process among
human beings. A TTS (Text-to-Speech) is used to convert input
text to speech, and it’s very popular application for computer
users. Although different types of speech synthesis technologies are available for the English, France, Chinese and so many other languages, but in Bengali language, it’s so scarce. This paper represents the implemented process of training based Concatenative Bangle Speech Synthesizer System and its
performance. The synthetic utterances are built by concatenating different speech units selected from recorded database from the training session for concatenative speech synthesizer system. Here training based means any person can train his/her voice and that will be stored on database and next time that person will input a text to convert speech and listen according to his/her trained voice. So this process is known as independent voice. And to train the voice a set of Bengali keyword is stored on the database as segmented audio file. At last the performance of this Bangla speech synthesizer system implemented by the concatenative speech synthesizer technology is analyzed which has provided 85% accuracy to listener to identify the sentence.

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Performance analysis of isolated Bangla speech recognition system using Hidden Markov Model.

here we present a model of isolated speech recognition (ISR) system for Bangla character set and ... more here we present a model of isolated speech recognition (ISR) system for Bangla character set and analysis the performance of that recognizer model. In this isolated Bangla speech recognition is implemented by the combining MFCC as feature extraction for the input audio file and used Hidden Markov Model (HMM) for training & recognition due to HMMs uncomplicated and effective framework for modeling time-varying sequence of spectral feature vector. A series of experiments have been performed with 10-talkers (5 male and 5 female) by 56 Bangla characters (include, Bangla vowel, Bangla consonant, Bangla

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Automatic Speaker Recognition system using Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) approach

In this paper, automatic speaker recognition system is implemented by combining feature extractio... more In this paper, automatic speaker recognition system is implemented by combining feature extraction and feature matching technique. Feature extraction method that is implemented by the Mel Frequency Cepstral Coefficients (MFCC). The Vector Quantization (VQ) is done using k-means algorithm that is used for the feature matching technique due to high accuracy but its simplicity. A speaker recognition experiments was performed using a 5talkers (3 male and 2 female) with different conditions of Bangla sentence utterances. So, in this proposal achieved 96% efficiency for text dependent or independent speaker recognition system with 60 -90 seconds duration test voice sample.

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