Call for Comming Issue

Volume 1, Issue 4 September 2013

S.No. Title Page No.
Effect of nanoSiO2 additive of some mechanical and water absorption of polyvinyl alchol /chitosan blends
Author: Nadia Abbas Ali, Ekram Atta .AL-Ajaj, F.T.Mohammed Noori
Abstract: Polyvinyl alcohol/Chitosan blend (PVA/CC(50:50) and (PVA/CC/10wt% SiO2) composite, were prepared by casting method on
a glass plate . Poly (vinyl alcohol)-chitosan blend films were prepared with a thickness about 250 micron. The crystalline
structure of PVA and fingerprint of semi-crystalline of chitosan was confirmed by X-ray diffraction. The tensile results show
that the tensile strength and Young’s modulus of these hybrid films were greatly improved compared to the neat PVA film
which was 10.86 MPa, 82GPa respectively and for the sample containing 10 wt% silica was 20.6 MPa and 227GPa respectively
where compared to PVA/CC which was 15.93 MPa and 131GPa respectively. Strong interfacial bonding between the silica and
the PVA/CC , and homogenous distribution of the silica particles in PVA/CC are supportive of markedly improved mechanical
strength. The solubility measurements showed that the hybrid has an enhanced water resistance, the solubility decreased with
the addition of silica. Blending PVA and chitosan improved strength and young modules of the film and increased water uptake
because hydrophobicity of two polymers blend films.
Internet Mining, website Mining and internet Structure Mining
Author: Mr. Prakhar Suman
Abstract: Web mining a hot analysis space of knowledge mining are often classified into three main areas: internet Usage Mining, website Mining and internet Structure Mining. Internet usage mining may be a quite internet mining, which exploits data processing techniques to find valuable data from navigation behavior of World Wide internet users. There are generally 3 tasks in internet Usage Mining: Preprocessing, Pattern analysis and data discovery. Preprocessing cleans log file of server by removing log entries like error or failure and recurrent request for a similar URL from a similar host etc. the most task of Pattern analysis is to filter uninteresting data and to see and interpret the attention-grabbing pattern to users. The statistics collected from the log file will facilitate to find the data. This information collected are often accustomed take call on varied factors like Excellent, Medium, Weak users and wonderful, Medium and Weak web pages supported hit counts of the net page within the computing machine. The design of the web site is restructured supported user’s behavior or hit counts that provides fast response to the net users, saves memory area of servers and so reducing communications protocol requests and bandwidth utilization. This paper addresses challenges in 3 phases of internet Usage mining along side internet Structure Mining .
Analysis of web browsing behavior in the internet
Author: Mr. Parash Singh
Abstract: The complicated link structures of enormous websites and therefore the day to day increasing usage had attracted the web site administrator’s attention towards the understanding of user’s requirements associated with their web site usage. Applied math analysis of data so has become a vital parameter for many of the administrators. Through applied math analysis ways, analyzing page browsing time provides valuable info concerning usage of website and its users to directors. This paper analyzes and monitors the user’s net browsing behavior on net pages with totally different modes of action on consumer facet so calculates browsing time of every online page consequently. This paper overcomes the inaccuracies in browsing time calculation considering server log information.
Sequent lowest Optimization (SMO) algorithmic employed in acting SVM
Author: Mr. Anil Sinha
Abstract: This paper highlights the prediction of Learning Disabilities (LD) in school-age kids victimization 2 classification methods, Support Vector Machine (SVM) and call Tree (DT), with a stress on applications of knowledge mining. Regarding 100% of children registered at school have a disorder. Learning disability prediction at school age kids may be a terribly sophisticated task as a result of it tends to be known in grammar school wherever there is nobody sign to be known. By victimization any of the 2 classification strategies, SVM and DT, we will simply and accurately predict LD in any kid. Also, we will confirm the deserves and demerits of those 2 classifiers and also the best one are often designated for the use within the relevant field. During this study, sequent lowest Optimization (SMO) algorithmic program is employed in acting SVM and J48 algorithm is employed in constructing call trees.





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