June 2019 International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) by Editor IJITCE

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK

USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA.

India: Editor International Journal of Innovative Technology & Creative Engineering 36/4 12th Avenue, 1st cross St, Vaigai Coliny Ashok Nagar Chennai , India 600083 Email: editor@ijitce.co.uk

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

IJITCE PUBLICATION

International Journal of Innovative Technology & Creative Engineering Vol.9 No.6 June 2019

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

Dear Researcher,

Greetings! Article in this issue discusses about A Comparative analysis of Various Techniques for Web Traffic and Web User Pattern with Space Complexity, photocatalytic degradation of wastewater containing congo red by zinc oxide nano photocatalyst. Let us look around the world for latest inventions, Getting separated from friends or a family member at an event or festival can be annoying. LynQ helps you find your loved ones when you get separated from the group. The California-based startup Zipline intends to use drones to save lives. The company uses its drones in remote areas across the world to deliver vital supplies and even the delivery of blood. The Texas-based start-up ICON caught the attention of the world when it created a fully functioning 350-sq.-ft. home in 48 hours with the assistance of their Vulcan 3D printer. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA. Dr. S.Prasath Ph.D Assistant Professor, Department of Computer Science, Nandha Arts & Science College, Erode , Tamil Nadu, India

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Review Board Members Mr. Rajaram Venkataraman Chief Executive Officer, Vel Tech TBI || Convener, FICCI TN State Technology Panel || Founder, Navya Insights || President, SPIN Chennai Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

Contents A Comparative analysis of Various Techniques for Web Traffic and Web User Pattern with Space Complexity ……………….…..…..[694] PHOTOCATALYTIC DEGRADATION OF WASTEWATER CONTAINING CONGO RED BY ZINC OXIDE NANO PHOTOCATALYST ……………….…..…..[699]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

A Comparative analysis of Various Techniques for Web Traffic and Web User Pattern with Space Complexity Mr. N . Ulaganathan Ph.D. (Part-Time) Research Scholar Department of Computer Science Nandha Arts and Science College Erode, Tamil Nadu, India E-mail ID: ulaganathanjdk@gmail.com Dr. S. Prasath Research Supervisor & Ass.Professor, Department of Computer Science Nandha Arts and Science College Erode, Tamil Nadu, India E-mail ID: softprasaths@gmail.com quality. The key objective is to recognize the behavioral patterns in collected usage data and implement community Web directories depending on patterns. The method of collecting the patterns from data to web directories are called Usage Data Preparation.

Abstract: Classification is used to construct a representation for categorizing a set of pages. It is the process of mapping a page into various predetermined classes. In the Web domain, classification approaches are enabled to develop a profile of users related to a specific group and use particular server files. This in turn necessitated mining and collecting features depending on demographic data accessible on users or access patterns. The existing techniques such as Dominance Fuzzy Clustering and Distributed Probability Graph (, Map Reduce Pearson Correlation Fisher's Linear Discriminant Classifier (MPC-FLDC) technique and Poisson Fragment Frequency based Web Pattern Clustering technique are implemented in Java language by using Apache log samples dataset. Through the use of Apache log samples dataset in the experimental evaluation, web traffic patterns are effectively mined with the goal of tracking the location of web user. The proposed techniques are compared with existing methods. Keywords: Web Data, Clustering, Classifier, Space complexity.

Web traffic investigation applications are linked with large amount of data. Web traffic analysis is employed for extracting information and evaluating the performance for efficient input preprocessing. The data in the server logs are irregular, unrelated, noisy and unnecessary for an application of interest. Preprocessing of input data and feature collection approaches are utilized for choosing suitable attributes. The data networking world helps organizations to perform business by assisting companies to converse better with employees, customers and distributors. II. RELATED WORKS

I. INTRODUCTION

Web traffic is generally started through the use of web browsers. Traffic flow begins with a mouse click for delivering browser information to a server utilizing programmed rules and techniques to acquire user browser requests. Depending on these rules, the server chooses the type of action required. Web Traffic analysis is performed for maintaining and classifying the traffic. It also enhances the workload managing ability of the web server.

Marios Belk et al. [1] described user modeling mechanism for designing users cognitive styles depended on navigation patterns and click stream data. The gathering of users through measures acquired from psychometric tests and content navigation behavior with the aid of clustering methods are examined. Also, navigation metrics are employed in identifying specialized user groups with same navigation patterns associated to cognitive style. A psychometricdepended assessment is performed in mining the users cognitive styles. True positive rate is not calculated it decreases user modeling mechanism.

User communities are created by data collected from Web proxies while users browse the Web. Many hybrid representations are designed over time as search engines integrated directory features to address the problems like categorization and site

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 Mohammad Amin Omidvar et al. [2] considered the effectiveness of distinct variables on diverse dependent variables. These variables are times series and a time series regression is discussed. The time series regression is a significant and primary index on Google analytic. In addition, the most appropriate data provided to acquire outcomes of true positive rate has not been addressed.

sequence. Finally, Distributed Probability Graph Arc (DPG) model examines the access history of web user for predicting the future access of web user. Due to this model, the cache utilization and latency are minimized in a significant manner. The description about the process of proposed DFC-DPG framework is discussed below. 3.2 Web User Information Collection

Neha Goel et al. [3] analyzed Web Log Expert for discovering the user behavior accessing an astrology website. A comparison of accessible log analyzer tools is performed. Web Log Analyzer tools are sector of Web Analytics Software which accepts log file as input and examined input for producing outcomes. Web Log Expert considered web logs of the website and results are analyzed for inclusion in user website. It, in turn sustained in recognizing the customer behavior. The process of user behavior discovery is not accurate in terms of results. II.

During the implementation of proposed DFC-DPG framework, web user information collection is carried out as first process. The proposed DFC-DPG framework collects the information of web user through the sever log files from the web server data base. The web server log file is considered as text file which contains one line for each web user queries. Every line in log file includes information such as host making the request, timestamp, requested URL, HTTP reply code and bytes in reply which is visited by the user. The other log file is considered as Parse Log which is obtained from web server log file. The obtained files contained IP address, hostname, date, time and request. These data is stored on a web database for successfully handling the data in an effective manner.

METHODOLOGY

In order to overcome the limitations in the existing methodology proposed a method for performing effective web mining. 3.1 Dominance

Distributed Framework

3.3 Linear-Temporal

Logic Model Checking Approach Sergio Hernandez [16] developed lineartemporal logic (LTL) model checking approach for investigating structured e-commerce web logs. Based on the mapping log records with ecommerce structure and web logs were changed into event logs to extract the user behavior. Various predefined queries were developed to recognize behavioral patterns of a user during sessions. Certain enhancements in the website design were made to improve its performance efficiency. The product classification and the potential of users assisted in navigating website with respect to such association.

Fuzzy Clustering and Probability Graph

Dominance Fuzzy Clustering and Distributed Probability Graph (DFC-DPG) framework is developed with the aim of extracting the similar web pages which is visited by user with improved clustering efficiency, less latency and space complexity. The implementation of proposed DFC-DPG framework contains four phases such as web user data collection, dominance rank model, fuzzy clustering approach and Distributed Probability Graph Arc model. The DFC-DPG framework is used in order to discover the information about the activities of web user from weblog data base. Initially, the web user data are collected by using server log files. The development of Dominance Rank model in proposed DFC- DPG framework separates the relevant and irrelevant web user data. Following this, fuzzy clustering is carried out on relevant data to form the cluster which contains the users with similar access

A number of query patterns were changed into LTL formula to enable the extraction of significant correlations among sequences of events acquired from user behaviour. This helped in identifying how various website sections are visited and navigational patterns are associated to buying actions. Several problems, issues and enhancements with respect to product categorization and organization of website sections were resolved. LTL model was also capable of

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 executing in parallel with the aid of parallel servers. But, it was not a sufficient model to perform effective traffic pattern mining for web user tracking.

volume are predicted in an efficient manner. As a result, the prediction rate is enhanced while performing the web traffic pattern mining on weblog database.

3.4 Proposed Technique

The web usage mining approach was implemented to predict the online navigational behavior of web users but it failed to perform the effective prediction of web traffic patterns at the required level. However, a novel method was implemented with the objective of providing better results in the web usage pattern detection by the implementation of client-side logging. It failed to minimize the time consumption for detecting the web usage patterns. Hence, the proposed Map Reduce Pearson Correlation Fisher's Linear Discriminant Classifier technique is introduced with the objective of effectively predicting the web traffic patterns from weblog database with improved accuracy and less time. In the proposed technique, the frequent or the non frequent web patterns on weblog database are effectively classified with higher accuracy by using Fisher’s Linear Discriminant Classifier. Thus, the performance of Pearson Correlation Analysis effectively predicted the web traffic patterns with minimized time consumption.

III.

EXPERIMENTATION AND RESULTS

An effective Clustering framework is implemented in Java language using Apache log samples dataset. The Apache log samples datasets identifies the access activities of several web users namely IP address, Date, Time of Access, Port Number and accessed Web page. The tables and the graphs generated depend on the performance values obtained from experiments to assure the effectiveness of the proposed technique.

4.1 Performance Analysis of Space Complexity The space complexity is defined as the amount of memory space required to store the similar web pages from the web server log files. The space complexity is measured as the difference between the entire memory space and the unused memory space on weblog database. The mathematical expression of space complexity is given as SC = Total NeNory space − unused NeNory space …………….…(4.1)

Then, the proposed MPC-FLDC technique is carried out to analyze the web traffic pattern analysis within three phases such as preprocessing, Fisher’s Linear Discriminant Classifier and Pearson Correlation Analysis. After performing the web pattern classification, the proposed MPC-FLDC technique carried out Pearson Correlation Analysis in order to achieve effective web traffic pattern prediction (daily/hourly traffic) in weblog database. The web traffic patterns are represented as the web pages which are browsed more number of times by a web user. With the classified frequent web patterns, the web traffic patterns are predicted by using Pearson Correlation Analysis. Through the Pearson Correlation Analysis, the degree of web pages correlation is estimated among different sessions in order to perform web traffic predictions in a significant way.

In the above equation (4.1), the space complexity is represented as ‘SC’ which is measured in terms of Mega Bytes (MB). The lower value of space complexity enhanced the performance of DFC-DPG framework.

The establishment of Pearson Correlation computes the similarity of web pages between the user sessions. From the determined degree of correlation i.e. similarity, the daily and hourly traffic

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 I. CONCLUSION

Table 4.1 Space Complexity Number of web patterns 30 60 90 120 150 180 210 240 270 300

LTL DFC-DPG based framework model 16 18 21 22 22 24 23 26 28 29 29 32 30 33 31 34 32 35 33 36

The proposed framework performing the web user data analysis in an effective manner. The fuzzy clustering approach is carried out on the obtained relevant data regarding the user to form the clusters with similar user interest web pages. The DPG model is performed for changing the web user session into the graph which results in the reduction of latency. The experiment result shows that the proposed DFC-DPG framework groups the similar user interest web pages with the improvement of clustering efficiency with other existing methods. An effective PFF- WPC technique is implemented to track the web user location through the performance of web traffic pattern mining. By carrying the Poisson fragment process, the web pages are grouped at different sessions which result to attain effective web user tracking. Through the deployment of frequency-based web patterns clustering, frequent or non-frequent web patterns are clustered from web pages. For the detected frequent web patterns, temporal similarity is determined to find the web traffic patterns. In this proposed technique, the clustering efficiency has improved and computational complexity is reduced for web user when compared to existing methods.

Fuzzy Proposed Clustering technique 23 14 28 19 29 20 31 21 36 26 38 27 40 28 42 29 43 30 45 31

Fig. 4.1 Space Complexity

Space Complexity DFC-DPG

framework

LTL

20 1 2 3 4 5 6 7 8 9 10

Propose d

According to the different number of web patterns, the experimental result of space complexity is determined as shown in table 4.1. While carrying out the experiment, the number of web patterns considered ranges from 30 to 300 which are taken as input. After the experiment, the proposed is compared with the existing methods for analyzing the results of the space complexity. From table 4.1, it is comparatively proposed framework needs less memory space to store the web pages than the other existing methods. In the above Fig.4.1explains the performance analysis of space complexity with the number of web patterns for proposed framework and three existing methods. This proposed framework relatively consumed less memory space for storing web pages when compared with the existing methods.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019

PHOTOCATALYTIC DEGRADATION OF WASTEWATER CONTAINING CONGO RED BY ZINC OXIDE NANO PHOTOCATALYST S.V.Elangovan*1, N.Sivakumar 2, V.Chandramohan 3, T.S.Senthil4 1. Department of Chemistry, Velalar College of Engineering and Technology, Erode. 2. Department of Chemistry, Chikkanna Govt. Arts College, Tiruppur. 3. Department of Chemistry, PSG College of Arts and Science, Coimbatore. 4. Department of Physics, Erode Sengunthar Engineering College, Erode. *Corresponding Author E.mail: kanjielango@gmail.com,

Abstract ZnO nanocrystals were prepared and the crystalline size, morphology and optical properties of the synthesized ZnO nanocrystals were done by X-ray diffraction (XRD), scanning electron microscope (SEM) with EDXA, TEM and UV-Visible spectroscopy. The photocatalytic activity of ZnO nanocrystals were analysed using Congo Red dye with UV light irradiation. The optimization of various operational parameters like pH, the initial concentration of congo red dye solution, UV irradiation time, the photocatalyst dose on the photocatalytic degradation to achieve high degradation rate of Congo Red has been performed. It is optimized that at pH= 8, catalyst load of 75mg/50ml and UV irradiation time of 90 min for maximum dye decolourisation. The Kinetic study of photocatalytic degradation reveals that the degradation follows pseudo first- order kinetics according to the Langmuir- Hinshelwood equation.

Key words: Photocatalytic degradation, Congo Red, ZnO nanocrystals, Langmuir-Hinshelwood equation. 1. Introduction The explosion in population, industrialization increase the production and discharging of wastewater. The wastewater from textile industry with organic dyes into the water sources causes water pollution [1-4]. A various techniques are applied for removal of synthesized organic compounds from wastewater, such as coagulation, membrane separation, chemical and electrochemical process, and adsorption [5]. Each method having its own limitations. Recently Advanced

Oxidation Processes (AOPs), which contain a set of physico-chemical treatment methods used as a promising process for environmental remediation. In this work we focused on the preparation of an efficient, and eco-friendly photocatalyst materials for degradation organic compounds from waste water. 2.Materials The zincoxide photocatalyst was prepared by wet chemical method using zinc aceatate and sodium hydroxide as starting materials and precipitate formed was filtered and washed many times with water to remove impurities and annealed at 250º C in a muffle furnace. The prepared ZnO nanocrystals were characterized by Xray diffraction (Rigaku D max-C) with Cu Kα radiation (1.5406 Å). The surface morphology and composition of the synthesized ZnO samples were determined by using scanning electron microscope (SEM with EDXA, Sirion). The optical absorption spectra of ZnO nanoparticles were recorded using a UV-VIS spectrophotometer (JASCO V570) [6]. Photocatalytic decolourisation experiments were performed using specially designed home made UV chamber equipped with UV bulb of 8W (Philips) .The absorbance values after irradiation were measured using UV–Visible spectrophotometer (Systronics 119); pH meter (Elico) was used to adjust the pH of the solution. In the present study the congo red dye was used as model organic pollutant to estimate the photocatalytic activity. The solutions of different concentrations were prepared from the stock solution of dye. The required pH of the solution was altered by the addition of 0.1N

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 hydrochloric acid and sodium hydroxide solutions. All laboratory reagents used were of analytical grade.

3.Characterization To identify the crystal structure of the ZnO nanocrystals X-Ray diffraction analysis have been used. Fig. 1 shows the X-ray diffraction pattern of the ZnO nanocrystals prepared using 0.5 M and annealed at 250 ºC temperatures. The results of XRD showed that the prepared ZnO nanocrystals are hexagonal wurtzite structure and it does not contain any characteristics peaks other than ZnO peaks, which confirm that the synthesized nanocrystals are pure. The diffraction peaks at 2θ (degree) of 31.98, 34.57, 36.25, 36.39, 47.7, 56.7, 62.9, and 68.51 are respectively indexed to (100), (002), (111), (101), (102), (110), (103), and (201) planes of ZnO. They are in good accordance with the JCPDS card No. 36-1451. The average grain size of the samples was calculated by Scherrer formula, D=0.89λ / (βCosθ), where λ is the wavelength (Cu Kα), β is the full width at halfmaximum (FWHM) and θ is the diffraction angle [3].

Figure 2: SEM image of ZnO annealed at 250 ºC

Figure 2. indicate the SEM image of ZnO nanocrystals prepared using 0.5M and annealed at 250ºC. From the SEM image it was identified that prepared sample are in nano in size.

250C 20

Two theta (deg)

Figure 1: XRD pattern of ZnO annealed at 250 ºC

Figure 3: EDXA spectrum of ZnO annealed at 250 ºC

Energy Dispersive X-ray analysis (EDXA) spectra showed that the prepared ZnO sample contains only Zn and O elements no other impurity elements were detected which is depicted in Figure 3. So, the synthesized sample are pure in nature.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 4. Photocatalytic activity 1.4

Photocatalytic decolourisation of Congo Red dye under UV irradiation The photocatalytic decolourisation of the direct anionic dye, Congo Red in aqueous solution using Zincoxide photocatalysts under UV light irradiation in a own designed photoreactor. To optimize the effect of the different important parameters such as dye solution concentration, pH, photocatalyst dose for the decolourisation of dye, UV irradiation time on the decolourisation of dye were carried out. 4.1. Effect of initial dye concentration The effect of initial dye solution concentration on the decolourisation was studied by varying the concentration of dye from 10mg/L to 50mg/L by keeping other parameters as 100mg/50ml of ZnO nano photo-catalyst under UV light and neutral pH. The absorbance values were measured in the wavelength of 497nm using UV spectrophotometer (Systronics) [7].

1.2

(10ppm) (20ppm) (30ppm) (40ppm) (50ppm)

1.0

ln(Co/Ct)

To 50ml of dye solution, Zincoxide nano photocatalyst was added and suspension was irradiated under UV light. The aqueous suspension was magnetically stirred throughout the work using magnetic stirrer. At prefixed uniform time intervals small quantity of samples were taken out with the help of a syringe and then filtered. Then absorbance values of centrifugate were measured at 497nm and decolorization efficiency was calculated [6]. The results of photocatalytic activity were recorded by varying the pH of the solution (pH 2– 10), concentration of dye (10–50 mg/l) and catalyst loading (50mg–300 mg/50ml) for Congo red.

0.8 0.6 0.4 0.2 0.0 0

100

120

UV irradiation Time (min)

Figure 4: Effect of initial dye concentration After 30 mg/ L the decolourisation percentage was not at all appreciable. so, for further studies 10 mg/L was fixed as optimum dye solution concentration. It is not beneficial for industries. Hence 10 mg/L of dye solution concentration was fixed as optimum level for further studies. 4. 2 Effect of pH of the dye solution Wastewaters discharged with dyes are of different pH, so it is very essential to study the effect of pH in the photocatalytic reactions. The pH of the solution indicates the surface charge properties of the photocatalyst. The formation of hydroxyl radicals is also a function of pH. To study the impact of the initial pH of dye solution on the decolourisation of congo red, the analysis were carried out. The rate of decolourisation with 10 mg/L of dye solution at different pH was irradiated by UV light for 75min and the results were shown in Figure 5.

0.14

701

0.12

% Decolourisation

The results are shown in Figure 4. The rate of decolourisation decreases with increase of congo red dye solution concentration. After UV irradiation 70.36% decolourisation was observed for dye solution of 10mg/L at 75 min and for 50mg/L shows only 33.5% decolourisation in the same time interval. It is possible that the initial concentration of dye solution increases, the light penetration to the photocatalyst surface decreases due to presence of high concentration of dye molecules. So there is a decrease in the number of dye molecules getting exited. But there is increasing the number of photons absorbed by the photo catalyst in lower concentration. Hence at high concentration lower decolourisation is observed [8].

0.10

% Decolourisation 0.08

0.06

0.04 4

Figure: 5. Effect of pH of the solution on rate of decolourisation of Congo Red

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 for photocatalytic decolourisation is 75mg/50ml for further studies.

% Decolourisation

The rate of decolourisation increases from 0.0474 mg/L/min to 0.1356 mg/L/min when pH is raised from 4 to 10, i.e., rate of decolourisation is low in acidic pH and increases with increase of pH. Amphoteric nature of Zincoxide contributes highly on the surface charge of the photocatalyst. The point zero charge (pzc) for ZnO is 9.0Âą0.3 [9]. Based on pzc, below pH 9, ZnO photocatalyst surface is positively charged. ZnO dissolved at low pH. Hence, in acidic pH ranges the decrease in the photocatalytic activity was observed. In alkaline solutions photodegradation efficiency was higher than that in acidic solutions is due to formation of more efficient hydroxyl radicals in alkaline solution [10].Though pH 10 shows high colour removal pH 8 is optimized for our other studies to avoid high usage of acid and bases for pH alterations. As per World Health Organization most of the industries releases basic pH coloured waste water. Hence pH 8 is fixed as optimum for further studies.

30 0

100

150

200

250

300

Catalyst(mg)

Figure 6 : Effect of catalyst dose

4. 3 Effect of catalyst loading 4. 4 UV Irradiation Time

Further increase in catalyst dose had no effect on decolurisation percentage of dyes. The decolurisation percentage of dye using 25mg/50ml of ZnO nanophotocatalyst is 34.79. It increases to 74.9 when catalyst dose is increased to 150mg/L after this catalyst dose the decolurisation percentage is decreasing shown in Figure: 6. So the optimum catalyst dose for the decolourisation of the dye Congo red is 75mg/50ml. The photocatalytic degradation of other organic pollutants has shown that the catalyst dose is an important parameter [11]. This can be explained on the basis that optimum catalyst dose is found to be dependent on initial solute concentration, because, When increase the amount of catalyst, total number of active surface area increases, so high number of active sites also available on catalyst surface. The higher dose of catalyst is not useful because it develops aggregation or reduces the irradiation field due to light scattering. Hence the optimum catalyst dose

The time required to attain maximum decolourisation was determined by changing the time with 10 mg/L dye concentration, pH of dye solution is adjusted to 8 using HCl, NaOH solutions with catalyst dose of 75mg/ 50 ml of ZnO nanocatalyst as optimum level. The results revealed that when time increases, the percentage of decolourisation increases from 5min to 90 min. After that irradiation of UV light source is not benefitted for decolurising the dye solution to great extent which is shown in Figure. 7. So the optimum time for decolourisation of Congo Red is fixed as 90min. This optimum time of UV irradiation was used for further studies [12,13].

% Decolurisation

To optimize the amount of catalyst used for photo decolourisation of dye, experiments were conducted using 10mg/L of dye solution and catalyst concentration varied from 25mg/50ml to 300mg/50ml for 120min of UV irradiation. The experimental results reveals that the rate of reaction increases when catalyst concentration increases from the catalyst load of 25mg/ 50 ml to 150mg/50ml, and further the catalyst dose of 75mg/50ml the rate of decolourisation remains almost constant.

20 0

100

150

200

250

UV Irradiation Time (min)

Figure 7 : Effect of UV irradiation Time on dye decolourisation

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.6 JUNE 2019 4.5. Kinetics of photodecolourisation Langmuir-Hinshellwood model

using

Inorder to find the mechanism of photo decolourisation of Congo Red by the nano Zincoxide photocatalyst the following kinetics have been considered. Langmuir-Hinshellwood kinetic mechanism were most widely used by researchers to explain the photocatalytic decolourisation of dyes [14,15,16]. The variation of 1/Kapp and initial concentration of Congo red is given in Figure 8. It was observed that dye concentration decreases linearly related to the elapse of UV irradiation time. Based on the LangmuirHinshellwood mechanism R2 value (R2= 0.9928), the decolourisation of Congo red using ZnO nanophotocatalyst follows the pseudo- first-order kinetics.

180

160

1/Kapp

140

In heterogeneous photocatalysis of azo dyes, the electron-hole pairs will be initially produced by irradiation of a semiconductor with a photon of energy equivalent to or greater than its band gap width [17]. The electrons and holes may migrate to the semiconductors on the catalyst surface where they take part in redox reactions with the adsorbed azo dyes [18]. The oxidizing radicals could attack the azo dye molecule and disintegrate it possibly into CO2 and H2O molecules which are nontoxic [17â&#x20AC;&#x201C;19]. 5. Conclusion In this work, ZnO nano photocatalyst was prepared and characterized and the photocatalytic decolorization of congored, was examined. The results clearly indicate the importance of the optimum degradation parameters to get a high decolourisation rate. The L-H kinetics of photodecolourisation of congo red dye exhibit Pseudo-first order reaction rate for the prepared zincoxide nanocrystals. Photocatalytic reaction mechanism was clearly explained. 10ppm concentration exhibit highest decolourisation under optimized conditions.

120

100

Initial concentration (ppm)

Figure 8 : Langmuir â&#x20AC;&#x201C;Hinshellwood kinetics

Figure 9: Mechanism of Photocatalytic activity of ZnO nanophotocatlyst

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