M. A. H. Akhand

Work place: Department of Computer Science and Engineering, Khulna University of Engineering & Technology Khulna, Bangladesh

E-mail: akhand@cse.kuet.ac.bd

Website: https://scholar.google.com/citations?user=NKBjhR8AAAAJ&hl=en

Research Interests: Computing Platform, Computer Networks, Neural Networks, Evolutionary Computation, Artificial Intelligence,


M. A. H. Akhand received his B.Sc. degree in Electrical and Electronic Engineering from Khulna University of Engineering and Technology (KUET), Bangladesh in 1999, the M.E. degree in Human and Artificial Intelligent Systems in 2006, and the Ph.D. in System Design Engineering in 2009 from University of Fukui, Japan.

He joined as a lecturer at the Department of Computer Science and Engineering at KUET in 2001, and is now a Professor and Head. He is also head of Computational Intelligence Research Group of this department. He is a member of Institution of Engineers, Bangladesh (IEB). His research interest includes artificial neural networks, evolutionary computation, bioinformatics, swarm intelligence and other bio-inspired computing techniques. He has more than 50 refereed publications.

Author Articles
Robust Face Detection integrating Novel Skin Color Matching under Variant Illumination Conditions

By Asif Anjum Akash M. A. H. Akhand N. Siddique

DOI: https://doi.org/10.5815/ijigsp.2021.02.01, Pub. Date: 8 Apr. 2021

Integration of skin color property in face detection algorithm is a recent trend to improve accuracy. The existing skin color matching techniques are illumination condition dependent, which directly impacts the face detection algorithm. In this study, a novel illumination condition invariant skin color matching method is proposed which is a composite of two rules to balance the high and low intensity facial images by individual rule. The proposed skin color matching method is incorporated into Haar Feature based Face Detection (HFFD) algorithm for face detection and is verified on a large set of images having variety of skin colors and also varying illumination intensities. Experimental results reveal the effectiveness and robustness of the proposed method outperforming other existing methods.

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Recognizing Bangla Handwritten Numeral Utilizing Deep Long Short Term Memory

By Mahtab Ahmed M. A. H. Akhand M. M. Hafizur Rahman

DOI: https://doi.org/10.5815/ijigsp.2019.01.03, Pub. Date: 8 Jan. 2019

Handwritten numeral recognition (HNR) has gained much attention in present days as it can be applied in range of applications. Research on unconstrained HNR has shown impressive progress in few scripts but is far behind for Bangla although it is one of the major languages. Bangla contains similar shaped numerals which are difficult to distinguish even in printed form and this makes Bangla HNR (BHNR) a challenging task. Our goal in this study is to build up a superior BHNR framework and consequently explore the profound design of Long Short Term Memory (LSTM) method. LSTM is a variation of Recurrent Neural Network and is effectively used for sequence ordering with its distinct features. This study considered deep architecture of LSTM for better performance. The proposed BHNR with deep LSTM (BNHR-DLSTM) standardizes the composed numeral images first and then utilizes two layers of LSTM to characterize singular numerals. Benchmark dataset with 22000 handwritten numerals having various shapes, sizes and varieties are utilized to examine the proficiency of BNHR-DLSTM. The proposed method indicates agreeable recognition precision and beat other conspicuous existing methods.

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Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

By Ryusuke Hata M. A. H. Akhand Md. Monirul Islam Kazuyuki Murase

DOI: https://doi.org/10.5815/ijisa.2018.05.01, Pub. Date: 8 May 2018

The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.

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Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition

By M. A. H. Akhand Mahtab Ahmed M. M. Hafizur Rahman

DOI: https://doi.org/10.5815/ijigsp.2016.09.06, Pub. Date: 8 Sep. 2016

Recognition of handwritten numerals has gained much interest in recent years due to its various potential applications. Bengali is the fifth ranked among the spoken languages of the world. However, due to inherent difficulties of Bengali numeral recognition, a very few study on handwritten Bengali numeral recognition is found with respect to other major languages. The existing Bengali numeral recognition methods used distinct feature extraction techniques and various classification tools. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this paper, we have investigated a CNN based Bengali handwritten numeral recognition scheme. Since English numerals are frequently used with Bengali numerals, handwritten Bengali-English mixed numerals are also investigated in this study. The proposed scheme uses moderate pre-processing technique to generate patterns from images of handwritten numerals and then employs CNN to classify individual numerals. It does not employ any feature extraction method like other related works. The proposed method showed satisfactory recognition accuracy on the benchmark data set and outperformed other prominent existing methods for both Bengali and Bengali-English mixed cases. 

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Acoustic Modeling of Bangla Words using Deep Belief Network

By Mahtab Ahmed Pintu Chandra Shill Kaidul Islam M. A. H. Akhand

DOI: https://doi.org/10.5815/ijigsp.2015.10.03, Pub. Date: 8 Sep. 2015

Recently, speech recognition (SR) has drawn a great attraction to the research community due to its importance in human-computer interaction bearing scopes in many important tasks. In a SR system, acoustic modelling (AM) is crucial one which contains statistical representation of every distinct sound that makes up the word. A number of prominent SR methods are available for English and Russian languages with Deep Belief Network (DBN) and other techniques with respect to other major languages such as Bangla. This paper investigates acoustic modeling of Bangla words using DBN combined with HMM for Bangla SR. In this study, Mel Frequency Cepstral Coefficients (MFCCs) is used to accurately represent the shape of the vocal tract that manifests itself in the envelope of the short time power spectrum. Then DBN is trained with these feature vectors to calculate each of the phoneme states. Later on enhanced gradient is used to slightly adjust the model parameters to make it more accurate. In addition, performance on training RBMs improved by using adaptive learning, weight decay and momentum factor. Total 840 utterances (20 utterances for each of 42 speakers) of the words are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent existing methods.

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Bangla Handwritten Character Recognition using Convolutional Neural Network

By Md. Mahbubar Rahman M. A. H. Akhand Shahidul Islam Pintu Chandra Shill M. M. Hafizur Rahman

DOI: https://doi.org/10.5815/ijigsp.2015.08.05, Pub. Date: 8 Jul. 2015

Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters. Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla. Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition. In this paper, a CNN based Bangla handwritten character recognition is investigated. The proposed method normalizes the written character images and then employ CNN to classify individual characters. It does not employ any feature extraction method like other related works. 20000 handwritten characters with different shapes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.

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Producer-Scrounger Method to Solve Traveling Salesman Problem

By M. A. H. Akhand Pintu Chnadra Shill Forhad Hossain A. B. M. Junaed Kazuyuki Murase

DOI: https://doi.org/10.5815/ijisa.2015.03.04, Pub. Date: 8 Feb. 2015

Algorithms inspired from natural phenomena are seem to be efficient to solve various optimization problems. This paper investigates a new technique inspiring from the animal group living behavior to solve traveling salesman problem (TSP), the most popular combinatorial optimization problem. The proposed producer-scrounger method (PSM) models roles and interactions of three types of animal group members: producer, scrounger and dispersed. PSM considers a producer having the best tour, few dispersed members having worse tours and scroungers. In each iteration, the producer scans for better tour, scroungers explore new tours while moving toward producer’s tour; and dispersed members randomly checks new tours. For producer’s scanning, PSM randomly selects a city from the producer’s tour and rearranges its connection with several near cities for better tours. Swap operator and swap sequence based operation is employed in PSM to update a scrounger towards the producer. The proposed PSM has been tested on a large number of benchmark TSPs and outcomes compared to genetic algorithm and ant colony optimization. Experimental results revealed that proposed PSM is a good technique to solve TSP providing the best tours in most of the TSPs.

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