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Search Results: 1 - 10 of 6729 matches for " Md. Khademul Islam Molla "
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Feature Selection for Intrusion Detection Using Random Forest  [PDF]
Md. Al Mehedi Hasan, Mohammed Nasser, Shamim Ahmad, Khademul Islam Molla
Journal of Information Security (JIS) , 2016, DOI: 10.4236/jis.2016.73009
Abstract: An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.
Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability
Md. Khademul Islam Molla,Poly Rani Ghosh,Keikichi Hirose
Discrete Dynamics in Nature and Society , 2011, DOI: 10.1155/2011/935034
Abstract: This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India. Fractional Gaussian noise (fGn) is used here as the reference signal. The climate signal and fGn (of same length) are combined to produce bivariate (complex) signal which is decomposed using BEMD into a finite number of sub-band signals named intrinsic mode functions (IMFs). Both of climate signal as well as fGn are decomposed together into IMFs. The instantaneous frequencies and Fourier spectrum of IMFs are observed to illustrate the property of BEMD. The lowest frequency oscillation of climate signal represents the annual cycle (AC) which is an important factor in analyzing climate change and variability. The energies of the fGn's IMFs are used to define the data adaptive threshold to separate AC. The IMFs of climate signal with energy exceeding such threshold are summed up to separate the AC. The interannual distance of climate signal is also illustrated for better understanding of climate change and variability. 1. Introduction The climate variability and change (CVC) element emphasizes research to improve descriptions and understanding of past and current climate, as well as to advance national modeling capabilities to simulate climate and project how climate and related Earth systems may change in the future. The CVC refers to shifts in the mean state of the climate or in its variability, persisting for an extended period (decades or longer). Research under this element encompasses time scales ranging from short-term climate variations of a season or less to longer-term climate changes occurring over decades to centuries. The CVC element places a high priority on improving understanding and predictions of phenomena that may cause high impacts on society, the economy, and the environment. Climate variability refers to variations in the mean state of climate on all temporal and spatial scales beyond that of individual weather events. It may be due to natural changes or to persistent anthropogenic changes in the composition of the atmosphere or in land use. A lot of people have been observing drastic climate changes throughout certain portions of the world for the past few decades. There is a perception that extreme natural disasters such as floods, droughts, and heat waves, have become more frequent. This
Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition
Md. Rabiul Islam,Md. Rashed-Al-Mahfuz,Shamim Ahmad,Md. Khademul Islam Molla
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/593018
Abstract: This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.
A Web-Based Face Recognition System Using Mobile Agent Technology
Md. Geaur Rahman,Somlal Das,A.R.S. Ahmed Siddique,Md. Khademul Islam Molla
Asian Journal of Information Technology , 2012, DOI: 10.3923/ajit.2010.91.97
Abstract: This study presents an application of mobile agent technology to solve the problem of web-based face recognition. Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult. The current face recognition models, which suffer from slow performance and platform dependence. The proposed system is a network-oriented system involving not only face detection but also facial feature extraction and graph matching schemes. For high performance in terms of reduced processing time and better flexibility, we introduce an innovative four-layer structural model and a three-dimension operational model. In addition, the proposed system integrates the computing advantages of mobile agents with several improved face recognition algorithms to enhance system robustness. Preliminary experimental results demonstrate the advantages and potentialities of the approach for face recognition on the web.
Subband DCT and EMD Based Hybrid Soft Thresholding for Speech Enhancement
Erhan Deger,Md. Khademul Islam Molla,Keikichi Hirose,Nobuaki Minematsu,Md. Kamrul Hasan
Advances in Acoustics and Vibration , 2014, DOI: 10.1155/2014/765454
Abstract: This paper presents a two-stage soft thresholding algorithm based on discrete cosine transform (DCT) and empirical mode decomposition (EMD). In the first stage, noisy speech is decomposed into eight frequency bands and a specific noise variance is calculated for each one. Based on this variance, each band is denoised using soft thresholding in DCT domain. The remaining noise is eliminated in the second stage through a time domain soft thresholding strategy adapted to the intrinsic mode functions (IMFs) derived by applying EMD on the signal obtained from the first stage processing. Significantly better SNR improvement and perceptual speech quality results for different noise types prove the superiority of the proposed algorithm over recently reported techniques. 1. Introduction In many speech related systems, the desired signal is not available directly; rather it is mostly contaminated with some interference sources. These background noise signals degrade the quality and intelligibility of the original speech, resulting in a severe drop in the performance of the post applications. Speech enhancement aims at improving the perceptual quality and intelligibility of such speech signals degraded in noisy environments, mainly through noise reduction algorithms [1]. Due to its significant importance in today’s information technology, many methods have been developed for this purpose. A major problem in most algorithms is that the enhanced speech signal has distortions compared to the original one which results in loss of some speech details. The residual noise is another problem which affects the performance of the postprocessing systems. Soft thresholding is a powerful technique used for removing the noise components by subtracting a constant value from the coefficients of the noisy speech signal obtained by the analyzing transformation. However, such type of direct subtraction results in a degradation of the speech components. Unlike the conventional constant noise-level subtraction rule [2, 3], a new soft thresholding strategy based on frequency frames was proposed in [4]. The later one is able to remove the noise components while giving significantly less damage to the speech signal. This enables even signals with high SNRs to be processed effectively. However due to the thresholding criteria, a noticeable amount of noise still remains in the enhanced signal. Another disadvantage is the lack of robustness of the algorithm to different noise types. The empirical mode decomposition (EMD), recently pioneered by Huang et al. [5] as a new and powerful data
Single Channel Speech Enhancement Using Adaptive Soft-Thresholding with Bivariate EMD
Md. Ekramul Hamid,Md. Khademul Islam Molla,Xin Dang,Takayoshi Nakai
ISRN Signal Processing , 2013, DOI: 10.1155/2013/724378
Abstract: This paper presents a novel data adaptive thresholding approach to single channel speech enhancement. The noisy speech signal and fractional Gaussian noise (fGn) are combined to produce the complex signal. The fGn is generated using the noise variance roughly estimated from the noisy speech signal. Bivariate empirical mode decomposition (bEMD) is employed to decompose the complex signal into a finite number of complex-valued intrinsic mode functions (IMFs). The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. Each IMF is divided into short time frames for local processing. The variance of IMF of fGn calculated within a frame is used as the reference term to classify corresponding noisy speech frame into noise and signal dominant frames. Only the noise dominant frames are soft-thresholded to reduce the noise effects. Then, all the frames as well as IMFs of speech are combined, yielding the enhanced speech signal. The experimental results show the improved performance of the proposed algorithm compared to the recently reported methods. 1. Introduction The research on speech enhancement is motivated by the rapidly growing market of speech communication applications, such as teleconferencing, hands-free telephony, hearing-aids, and speech recognition. In hands-free communication systems, the microphone(s) is typically placed at a certain distance from the speaker. In adverse acoustic environment, various noise sources make the speech signal corrupted. Although, the human auditory system is remarkably robust in most adverse situations, noise effects heavily affect the performance of automatic speech recognition (ASR) systems. The performance of an ASR system trained in one specific environment will drop considerably when used in another acoustic environment [1]. Several approaches have already been proposed to improve the speech enhancement results. Although the microphone array based approach exhibits better results, at the same time speech processing research community is trying to reduce the number of microphones (channels). The spectral subtraction is one of the early methods to reduce the noise effects from the observed speech signals. In this method, the noise reduction is achieved by appropriate adjustment of the set of spectral magnitudes [2]. Its basic requirement is the noise spectrum which is determined from the nonspeech segments [3]. In such single channel speech enhancement system, the residual noise is a usual issue. It decreases the speech intelligibility and hence further processing is
Empirical mode decomposition analysis of climate changes with special reference to rainfall data
Md. Khademul Islam Molla,M. Sayedur Rahman,Akimasa Sumi,Pabitra Banik
Discrete Dynamics in Nature and Society , 2006, DOI: 10.1155/ddns/2006/45348
Abstract: We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzing time-series data representing nonstationary and nonlinear processes. This method could decompose any time-varying data into a finite set of functions called “intrinsic mode functions” (IMFs). The EMD analysis successively extracts the IMFs with the highest local temporal frequencies in a recursive way. The extracted IMFs represent a set of successive low-pass spatial filters based entirely on the properties exhibited by the data. The IMFs are mutually orthogonal and more effective in isolating physical processes of various time scales. The results showed that most of the IMFs have normal distribution. Therefore, the energy density distribution of IMF samples satisfies χ2-distribution which is statistically significant. This study suggested that the recent global warming along with decadal climate variability contributes not only to the more extreme warm events, but also to more frequent, long lasting drought and flood.
An Analysis of the Use of CMC in Synchronized Distance Training for Global Organizations
Kamrul Hasan Talukder,Md. Khademul Islam Molla,Abu Shamim Mohammad Arif
Asian Journal of Information Technology , 2012,
Abstract: Multinational companies and professional training institutes face several challenges when they deploy new policies, novel ideas and new courses to their audience in global environments. To constitute such training, local expertise is not always available. Moreover, the travel of the instructors/trainer increases cost and time distracting experts from their primary focus of research. An emerging solution to this problem is the utilization of computer mediated synchronized distance technology to impart the necessary training at minimum resources and maximum effectiveness. However, there is a need to carefully manage these training programs, in order that the instructors and the trainees avail of the benefits of distance learning avoiding the innate ambiguity that such a training method might harbor. Different organizations have deployed various types of tools and technologies to conduct efficient distance training. It is the prerogative of the management to select the proper technology to empower the instructor and to structure the course materials depending on the type of course, background of the trainees and consider the other factors in order to reap the benefits, which such training has to provide. This study presents an analysis of the use of Computer Mediated Communications (CMC) in synchronized distance training for global organizations. We also present how CMC can be used in the stated field effectively with some important recommendations to enhance the performance of the uses of CMC in this case.
Single-Channel Speech Enhancement by NWNS and EMD
Somlal Das,Mohammad Ekramul Hamid,Keikichi Hirose,Md. Khademul Islam Molla
Signal Processing : An International Journal , 2010,
Abstract: This paper presents the problem of noise reduction from observed speech by means of improving quality and/or intelligibility of the speech using single-channel speech enhancement method. In this study, we propose two approaches for speech enhancement. One is based on traditional Fourier transform using the strategy of Noise Subtraction (NS) that is equivalent to Spectral Subtraction (SS) and the other is based on the Empirical Mode Decomposition (EMD) using the strategy of adaptive thresholding. First of all, the two different methods are implemented individually and observe that, both the methods are noise dependent and capable to enhance speech signal to a certain limit. Moreover, traditional NS generates unwanted residual noise as well. We implement nonlinear weight to eliminate this effect and propose Nonlinear Weighted Noise Subtraction (NWNS) method. In first stage, we estimate the noise and then calculate the Degree Of Noise (DON1) from the ratio of the estimated noise power to the observed speech power in frame basis for different input Signal-to-Noise-Ratio (SNR) of the given speech signal. The noise is not accurately estimated using Minima Value Sequence (MVS). So the noise estimation accuracy is improved by adopting DON1 into MVS. The first stage performs well for wideband stationary noises and performed well over wide range of SNRs. Most of the real world noise is narrowband non-stationary and EMD is a powerful tool for analyzing non-linear and non-stationary signals like speech. EMD decomposes any signals into a finite number of band limited signals called intrinsic mode function (IMFs). Since the IMFs having different noise and speech energy distribution, hence each IMF has a different noise and speech variance. These variances change for different IMFs. Therefore an adaptive threshold function is used, which is changed with newly computed variances for each IMF. In the adaptive threshold function, adaptation factor is the ratio of the square root of added noise variance to the square root of estimated noise variance. It is experimentally observed that the better speech enhancement performance is achieved for optimum adaptation factor. We tested the speech enhancement performance using only EMD based adaptive thresholding method and obtained the outcome only up to a certain limit. Therefore, further enhancement from the individual one, we propose two-stage processing technique, NWNS+EMD. The first stage is used as a pre-process for noise removal to a certain level resulting first enhanced speech and placed this into second stage for furthe
Developing an Agent-Mediated E-Commerce Environment for the Mobile Shopper
Md. Geaur Rahman,A.R.S. Ahmed Siddique,Somlal Das,A.K.M. Akhtar Hossain,Md. Khademul Islam Molla
Asian Journal of Information Technology , 2012, DOI: 10.3923/ajit.2010.85.90
Abstract: This study presents a novel framework for developing an agent-mediated E-commerce environment for the mobile shopper. Intelligent agents represent both shoppers and the store to negotiate for desired products based on shopper preferences. In this system, buyer and seller agents are created to simulate the e-market. Seller agents advertise his products to sell and buyer agents search the products to buy. The seller and buyer agent paradigm is gaining in importance and visibility as a programming paradigm for several classes of agent-based system. Its inherent advantages, such as communicative with others, less time consuming, dynamic service updates etc. make it an attractive alternative to the traditional paradigm communication-agent system. In addition, the proposed system integrates the computing advantages of mobile agents with several improved agent-mediated algorithms to enhance system robustness. Preliminary experimental results demonstrate the advantages and potentialities of the approach for the mobile shopper on the e-market.
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