oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 268 )

2018 ( 370 )

2017 ( 352 )

2016 ( 527 )

Custom range...

Search Results: 1 - 10 of 25656 matches for " Md. Al Hasan "
All listed articles are free for downloading (OA Articles)
Page 1 /25656
Display every page Item
mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue  [PDF]
Md. Al Mehedi Hasan, Shamim Ahmad
Natural Science (NS) , 2018, DOI: 10.4236/ns.2018.109035
Abstract: Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as “acetylation”, “crotonylation”, “methylation” and “succinylation” and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.
Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)  [PDF]
Md. Al Mehedi Hasan, Mohammed Nasser, Biprodip Pal, Shamim Ahmad
Journal of Intelligent Learning Systems and Applications (JILSA) , 2014, DOI: 10.4236/jilsa.2014.61005
Abstract:

The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid of this problem, several types of intrusion detection methods have been proposed and shown different levels of accuracy. This is why the choice of the effective and robust method for IDS is very important topic in information security. In this work, we have built two models for the classification purpose. One is based on Support Vector Machines (SVM) and the other is Random Forests (RF). Experimental results show that either classifier is effective. SVM is slightly more accurate, but more expensive in terms of time. RF produces similar accuracy in a much faster manner if given modeling parameters. These classifiers can contribute to an IDS system as one source of analysis and increase its accuracy. In this paper, KDD’99 Dataset is used and find out which one is the best intrusion detector for this dataset. Statistical analysis on KDD’99 dataset found important issues which highly affect the performance of evaluated systems and results in a very poor evaluation of anomaly detection approaches. The most important deficiency in the KDD’99 dataset is the huge number of redundant records. To solve these issues, we have developed a new dataset, KDD99Train+ and KDD99Test+, which does not include any redundant records in the train set as well as in the test set, so the classifiers will not be biased towards more frequent records. The numbers of records in the train and test sets are now reasonable, which make it affordable to run the experiments on the complete set without the need to randomly select a small portion. The findings of this paper will be very useful to use SVM and RF in a more meaningful way in order to maximize the performance rate and minimize the false negative rate.

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network  [PDF]
Md. Hasan Hasnain Nashif, Md. Badrul Alam Miah, Ahsan Habib, Autish Chandra Moulik, Md. Shariful Islam, Mohammad Zakareya, Arafat Ullah, Md. Atiqur Rahman, Md. Al Hasan
Journal of Information Security (JIS) , 2018, DOI: 10.4236/jis.2018.93015
Abstract: Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.
Dyeing of S/J Cotton Knit Fabric with Natural Dye Extracts from Green Walnut Shells: Assessment of Mordanting Effect on Fastness Properties  [PDF]
Zakaria  , Md. Eanamul Haque Nizam, Md. Hasan Al Mamun, Md. Abu Yousuf, Ramjan Ali, Lutfor Rahman, Md. Raza Miah
Journal of Textile Science and Technology (JTST) , 2017, DOI: 10.4236/jtst.2017.32002
Abstract: In this study, aqueous extraction method is used because of its high extraction ratio, light fastness and also functional properties. In 1st phase, for dyeing S/J cotton knit fabric with green walnut power ferrous sulfate is considered as a mordant. In this study, three different mordanting methods such as pre-, meta-, and post-mordanting are conveyed the dyeing process with the state of metallic mordant and without metallic salt mordants. In 2nd phase, in dyeing for fixation ferrous sulfate was considered as mordants. Furthermore, the analysis and evaluation of each colour dyed material was done through following two terms for instance CIELAB (L*, a*, and b*) and K/S values. According to AATCC test methods, colour fastness to washing, crocking, perspiration of the dyed samples is determined whereas according to the ISO standard, the colour fastness to light was estimated and tested. When dyeing was carried out on S/J cotton knit fabric through considering optimum parameter like at 80°C for 60 min and at pH 4 which showed optimum results. From the results we can see, very good wash fastness was obtained while there is no fading of the colour, whereas the outstanding and moderate level of colour fastness to light and crocking is achieved.
RETRACTED: Natural Dyes Extract from Chinese Tallow (Triadica sebifera) Tree Leaves Extraction in Alkaline Medium and Their Application on Silk Fabrics  [PDF]
Md. Hasan-Al Mamun, Md. Anwar Hossain, Monir Khan, Asheke Mostofa, Md. Zakaria, Most Sabina Yeasmin
Journal of Textile Science and Technology (JTST) , 2018, DOI: 10.4236/jtst.2018.41001
Abstract:

Short Retraction Notice

The first author (Md. Hasan-Al Mamun) didn’t get permission to publish this paper from her supervisor.

This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.

Please see the article page for more details. The full retraction notice in PDF is preceding the original paper which is marked \"RETRACTED\".

Isolation, Identification and Antimicrobial Susceptibility Profile Analysis of Vibrio cholerae O1 from Stool Samples of Bangladesh  [PDF]
Muhammad Ekhlas Uddin, Tanzina Akter, Papia Sultana, Papia Sultana, Md. Ikramul Hasan, Mahbuba Akter Lubna, Hasan Al Monem, Md. Anowar Khasru Parvez, Shamsun Nahar, Md. Sumon Khan
Advances in Microbiology (AiM) , 2018, DOI: 10.4236/aim.2018.83013
Abstract: Cholera is a severe diarrheal disease which is usually caused by toxigenic strain of Vibrio cholerae O1 and O139. Cholera is still one of the major health concerns in developing countries like Bangladesh due to poor sanitation and unavailability of safe drinking water. This experiment was confronted to identify V. cholerae O1 from stool samples as well as to determine the antibiotic susceptibility pattern of the isolated strains. A total of 140 stool samples from people infected with diarrheal disease were collected from July 2016 to December 2016. Among all, 58 samples were found positive for V. cholerae which were further subjected to sero-grouping by specific anti-sera and antimicrobial sus-ceptibility test by Kirby Bauer disc diffusion method. The zones of inhibition were measured and interpreted by following the recommendations of the criteria of Clinical and Laboratory Standards Institute (CLSI). It was found that 43 (74.1%) isolates of V. cholerae were O1 serogroup of Ogawa serotype and the rest 15 (25.9%) were O1 serogroup of Inaba serotype. People aged between 41 - 50 were most susceptible to V. cholerae O1 having about 39.7% of positive cases. The isolates were highly susceptible to Ciprofloxacin and Gentamicin with 100% susceptibility whereas 100% resistant was found towards Nalidixic acid. Though most of the isolates in our study were susceptible against tested antibiotics, the continuous surveillance is required to see the changing pattern of serogroups or serotypes and antimicrobial profile in this region.
Predicting Rainfall Using the Principles of Fuzzy Set Theory and Reliability Analysis  [PDF]
Mahbub Hasan, Salam Md. Mahbubush Khan, Chandrasekhar Putcha, Ashraf Al-Hamdan, Chance M. Glenn
American Journal of Computational Mathematics (AJCM) , 2013, DOI: 10.4236/ajcm.2013.34043
Abstract:

The paper presents occurrence of rainfall using principles of fuzzy set theory and principles of reliability analysis. Both the abstract and the rest of the paper are discussed from these two points of view. First, a fuzzy inference model for predicting rainfall using scan data from the USDA Soil Climate Analysis Network Station at Alabama Agricultural and Mechanical University (AAMU) campus for the year 2004 is presented. The model further reflects how an expert would perceive weather conditions and apply this knowledge before inferring a rainfall. Fuzzy variables were selected based on judging patterns in individual monthly graphs for 2003 and 2004 and the influence of different variables that caused rainfall. A decrease in temperature (TP) and an increase in wind speed (WS) when compared between the ith and (i ? 1)th day were found to have a positive relation with a rainfall (RF) occurrence in most cases. Therefore, TP and WS were used in the antecedent part of the production rules to predict rainfall (RF). Results of the model showed better performance when threshold values for 1) Relative Humidity (RH) of ith day; 2) Humidity Increase (HI) between the ith and (i ? 1)th day; and 3) Product (P) of decrease in temperature (TP) and an increase in wind speed (WS) were introduced. The percentage of error was 12.35 when compared the calculated amount of rainfall with actual amount of rainfall. This is followed by prediction of rainfall using principles of reliability analysis. This is done by comparing theoretical probabilities with experimental probabilities for the occurrence of two main events, namely, Relative Humidity (RH) and Humidity Increase (HI) being in between specified threshold values. The experimental values of probability are falling in between μ ? σ and μ + σ for both RH and HI parameters, where μ is the mean value and σ is the standard deviation.

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.
The Prospects of Blue Economy to Promote Bangladesh into a Middle-Income Country  [PDF]
Md. Monjur Hasan, B. M. Sajjad Hossain, Md. Jobaer Alam, K. M. Azam Chowdhury, Ahmad Al Karim, Nuruddin Md. Khaled Chowdhury
Open Journal of Marine Science (OJMS) , 2018, DOI: 10.4236/ojms.2018.83019
Abstract: This paper draws attention to the prospects of sea-based economy to promote Bangladesh in a middle-income country through the sustainable use of marine resources. About three-fourths of the earth is covered by the seas. It plays the vital role in two important functions from ancient time known as the means of communication and the source of huge living and non-living natural resources. At present, the countries are becoming very much concerned about their marine resources to resolve many of the present and future challenges of their economies. Generally for Bangladesh, ocean is contributing a significant role to its overall socio-economic progress through rising up the economic activities across the country and especially to the coastal zone at southern part. This paper investigates how much Bangladesh is capable to take of or handle the challenges to become a middle income country through the Sustainable Development Goals (SDGs). In addition, it has attempted with a closer-look to find out the barriers or limitations of these activities from different angles if exist.
Study of the Bulk Magnetic and Electrical Properties of MgFe2O4 Synthesized by Chemical Method  [PDF]
Sheikh Manjura Hoque, M. Abdul Hakim, Al Mamun, Shireen Akhter, Md. Tanvir Hasan, Deba Prasad Paul, Kamanio Chattopadhayay
Materials Sciences and Applications (MSA) , 2011, DOI: 10.4236/msa.2011.211209
Abstract: Nanocrystalline Magnesium ferrite has been prepared by chemical co-precipitation technique. Structural characterization has been performed by X-ray diffraction. Formation of ferrites has also been studied by using FTIR. Frequency dependence of real and imaginary part of initial permeability has been presented for the samples sintered at different temperatures. Real part of initial permeability, increases with the increase of grain growth. The loss component repre- sented by imaginary part of initial permeability decreases with frequency up to the measured frequency of this study of 13 MHz. Curie temperatures have been determined from the temperature dependence of permeability. Curie temperatures for the samples of this composition do not vary significantly with the variation of sintering temperatures. B-H loop measurements have been carried out by B-H loop tracer. Transport property measurements haven been carried out by electrometer and impedance analyzer.
Page 1 /25656
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.