Browsing by Author "Hossin, Md Altab"

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A New Method to Handle Facebook Users in the Distributed Database System
(Scopus, 2020) Rana, Md. Shohel; Hossin, Md Altab; Mahmud, S M Hasan; Jahan, Hosney; Hossen, Md. Anwar
The hasty growth of technology and social media has carried momentous changes to humanoid communication. Facebook, the largest online social media in the last few years has more than 200 million active users where more than 3.5 billion minutes are spent on Facebook daily. Since the competence of Facebook is subject to mostly on the processing of the massive volume of data. The volume of data is increasing day to day as well as the number of inactive and fake users. In this paper, we propose a new model using distributed database concept for management of users and their activities. This proposed mod-el helps to keep the system scalable, reliable, and faster and let the Facebook accessible from anywhere with high accessibility.
CSV-ANNOTATE: Generate annotated tables from CSV file
(IEEE, 2018-06-28) Mahmud, S M Hasan; Hossin, Md Altab; Jahan, Hosney; Noori, Sheak Rashed Haider; Bhuiyan, Touhid
The Semantic Web is a part of the current World Wide Web (WWW), which can facilitate a common mechanism to publish, share, and reuse data beyond the boundaries of web applications. It is widely believed that the majority of the datasets stored on the current web are in tabular data format (CSV, spreadsheets, SQL dumps, HTML tables etc), commonly in the comma-separated values (CSV) format. In order to prepare the CSV data semantically structured, interoperable, accessible and reusable for various web applications, they need to be extracted from the CSV files and converted into annotated table. Therefore, we propose an effective approach to generate annotated tables from CSV file. However, annotated table for CSV provides possibilities for data publishers to refer data validating, converting, displaying and inputting by following the Semantic Web standard. This research presents the conversion strategies of CSV file into annotated tables. Here, we design a parsing algorithm and development techniques to demonstrate the annotated tabular data model (column, row, and cell). An experiment is carried out to observe and compare the time efficiency of the annotation process. This method and findings provide a valuable reference for potential implementers to further operate the Semantic data.
PRMT: Predicting Risk Factor of Obesity among Middle-Aged People Using Data Mining Techniques
(Elsevier B.V., 2018-06-08) Hossain, Rifat; Mahmud, S.M. Hasan; Hossin, Md Altab; Noori, Sheak Rashed Haider; Jahan, Hosney
Obesity is an anatomical condition characterized by an extreme growth of body fat. The obesity rate is increasing gradually; from prior research, obesity is the serious health disease in the globe. This study collected 259 data from specified urban and rural areas regarding different risk factor of our daily activities. The purpose of the study is to simulate the risk factor by using statistical tools (SPSS), which helpsto predict the major risk factor of obesity by testing the class level attribute according to cross-sectional study with other attributes. By analyzing the P-value (p<0.05), the outcome of this process Age (0.002), Height (0.002), Weight 0.000), Healthy lifestyle (0.000), Marital status (0.001), BMI (0.000), Economic (0.028), Sleep per day (0.011) has a significant relationship with our obesity class. This study proposed a risk mining technique (PRMT)that foretells a model to analyze the risk factor of obesity class using different data mining classifiers, using WEKA to estimate the accuracy and error measurement. The outcome of this process Naïve Bayes is the best classifier for the 10-fold cross-validation study. The proposed model collaborates to predict human factor who want to control and mitigate this major cardiovascular disease.