Md. Nazrul Islam Mondal

Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction

Summary For the importance of protein subcellular localization in different branch of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays most vital role in protein subcellular localization prediction specially in case of machine learning technique. Single feature representation like pseudo amino acid composition (PseAAC), physiochemical property model (PPM), amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problem, we have proposed two feature fusion representations AAIDPAAC and PPMPAAC to work with Support Vector Machine classifier, which fused PseAAC with PPM and AAID accordingly. We have evaluated performance for both single and fused feature representation of Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.

A Rewriting Algorithm to Generate AROM-free Fully Synchronous Circuits

A Field Programmable Gate Array (FPGA) is used to embed a circuit designed by users instantly. FPGAs can be used for implementing hardware algorithms. Most of FPGAs have Configurable Logic Blocks (CLBs) to implement combinational and sequential circuits and block RAMs to implement Random Access Memories (RAMs) and Read Only Memories (ROMs). Circuit design that minimizes the number of clock cycles is easy if we use asynchronous read operations. However, most RAMs and ROMs in modern FPGAs support synchronous read operations, but do not support asynchronous read operations. It is one of the main difficulties for users to implement hardware algorithms using RAMs and ROMs with synchronous read operations. The main contribution of this paper is to provide one of the potent methods to resolve this problem. We assume that a circuit using asynchronous ROMs designed by a user is given. Our goal is to convert this circuit into an equivalent circuit with synchronous ROMs. We first clarify the condition that a given circuit with asynchronous ROMs can be converted into a circuit without asynchronous ROMs. For this purpose, we will show an algorithm that can generate a circuit with synchronous ROMs, whenever the original circuit with asynchronous ROMs satisfies this condition. Using our conversion algorithm, users can assume that FPGAs support asynchronous ROMs when they design their circuits. Finally, we will show that we can generate an almost equivalent circuit with synchronous ROMs by modifying the circuit even if it does not satisfy this condition.

An adaptive background modeling based on modified running Gaussian average method

Detecting moving objects is one of the most important research interest at present in computer vision due to its wide range of applications in traffic surveillance, human motion analysis and object tracking. Some approaches such as Gaussian running average provides faster background subtraction for object detection. However, it considers a fixed threshold for the background subtraction, which limits its application. In this research, a modification of Gaussian average technique has been proposed with the aid of an adaptive threshold and learning rate for traffic surveillance. The proposed approach develops a background model dynamically by extracting the edge information of individual frame. The application of adaptive threshold and learning rate over Gaussian average makes the approach more robust and suitable for video surveillance applications. The proposed approach has been tested on the real-time traffic data captured on a busy street using fixed camera. With the proposed technique, the moving vehicles are detected more accurately with little noise. The experimental results presented at the end reflect the suitability of the approach.

Determinant Factors of Diseases Suffering Duration among Elderly inBangladesh

Objective: This study aimed to identify the determinant factors of disease suffering duration among elderly population in the rural areas of Bangladesh. Methods: A cross-sectional study was conducted in three villages of Pabna District, Bangladesh. Data were collected from 250 (males, 168; females, 82) elderly aged 60 years and above using a structured questionnaire. To analyze the data, both bivariate and multivariate analyses were used as the statistical tools. Findings: The results revealed that most of the elderly (70.00%) were suffered from various types of long duration (>1 year) diseases. Respondents’ age, partnership status, family type, family size, education, working status, family income, and drug addiction were found significantly associated with diseases suffering duration. Finally, the binary logistic regression model identified almost all the factors are as important predictors diseases suffering duration. Conclusion: Health problems were found more prevalent among males than that of females. To reduce the disease suffering duration of the elderly, emphasis should be given to improve their financial condition and traditional family bond, and to create workplaces where they may involve.

Closest class measure based subspace detection for hyperspectral image classification

The objective of this study is to develop a hybrid nonlinear subspace detection technique in which Kernel Principal Component Analysis (KPCA) is combined with a Closest Class Pair (CCP) measure for the task of hyperspectral image classification. In the proposed approach, KPCA is applied first to generate the new features from original dataset then the CCP is applied to rank the features that are able to separate the complex or overlapping classes. Finally, the two ranked scores such as KPCA and CCP are combined to select a subset of features which is relevant and able to provide better discrimination among the input classes of interest. Experiments are performed on a real hyperspectral image acquired by the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and it can be seen that the proposed approach obtained the best classification accuracy 84.58%.

Effective subspace detection based on cross cumulative residual entropy for hyperspectral image classification

Remote sensing hyperspectral images are blessings of technology through which the ground objects can be detected effectively with the cost of computer processing. For classification of hyperspectral images finding an effective subspace is very important to classify them efficiently. In recent years, many researchers have drawn their interest to extract data more effectively from hyperspectral dataset. In this research, an approach has been proposed to find the effective subspace by measuring the relevance of individual features through Cross Cumulative Residual Entropy from the Principal Component images. The Support Vector Machine has been used as the classifier for the assessment of the feature reduction performance. Experiment has been completed on real hyperspectral dataset and achieved 97% of accuracy which is better than the standard approaches studied.

Satellite image compression using integer wavelet regression

Multi-temporal Image Compression is now an immerging field considering the fact that terabytes of data is now available for download every day. Evantualy temporal data compression is becoming a critical issue for fast data transmission. Many works have been done regarding compression in the field of satellite images that utilizes the spectral and spatial redundancies using predictive and transformed based procedures for lossless data compression, but, most of the contributions are on individual data or on single data. The main objective of this paper is to exploit the temporal correlation between the images. The recent image will be predicted from the historical image that is already available to the user. This will substantially reduce the load in transmitting the images. This paper actually emphasis on the process of increasing temporal correlation, which consequently improves the compression gain. In sequential transmission, the transmitted data will be used in future as a reference. Therefore, a new lossless approach has been introduced where reversible integer wavelet transformation is used to improve the temporal correlation. The experimented results show that the proposed method outperformed many state of art lossless approaches including JPEG2000.

Gram-positive bacterial protein subcellular localization prediction using features fusion strategy

Prediction of protein subcellular localization is the most challenging field for the researchers because of its importance in different branch of molecular biology and drug discovery. Last two decades, a large number of machine learning approaches have been tested into sequence based features for the prediction of subcellular localization. Single features like amino acid composition (AAC), pseudo amino acid composition (PseAAC) and physiochemical property model (PPM)) contain insufficient information due to their single perspectives. To overcome this problem, the main contribution of our work is to propose two feature fusion representations AACPPM and PAACPPM which can be fused PPM with AAC and PseAAC respectively. Support Vector Machine (SVM) is applied as a classifier on to both single and fused feature representations of Gram-positive bacterial dataset. The actual accuracy of AACPPM is 72.4% which is 2% higher than single feature representations and 6% higher than X. Qu et al [1]. The locative accuracy of both AACPPM and PAACPPM is 73.2% which is also 2% higher than single feature representations.

Exploiting parallelism for faster implementation of Bubble sort algorithm using FPGA

Sorting is a classic problem that has been studied for decades. From the beginning of computing, many Sorting algorithms have been investigated. Bubble sort is a very common and powerful sorting technique used in different applications. For high speed data processing, we need faster and efficient environment for any sorting algorithm. In this purpose, FPGA based hardware accelerators can show better performance for high speed data processing than the general purpose processors. In this paper, the sequential and parallel bubble sort algorithm is implemented using FPGA. We show that parallel implementation of Bubble sort algorithm is almost 10 times faster than that of sequential implementation for 20 different data inputs. However, this implementation is faster for more data inputs.

Faster implementation of Booth's algorithm using FPGA

Modern world has become more dependent on electronics and hence speed is a major factor in the field of their functionalities. Modern CPUs work lot faster and efficiently than older versions. Still humans require more and more time efficiency in their daily computational works. In this paper, the main focus is on the increase of time efficiency in computing. Hence this paper shows a time performance comparison between FPGA and CPU implementation. In this regard, Booths multiplication algorithm has been implemented on both CPU and FPGA to compare the running time. The FPGA implementation is found out to be around 9 times faster than that of a modern CPU implementation.