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- Analysis of Spamdexing Influence and Click-Fraud Link-Spam in Online Marketing
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Authors
Affiliations
1 Department of Computer Applications, Hindusthan College of Arts and Science, IN
1 Department of Computer Applications, Hindusthan College of Arts and Science, IN
Source
Software Engineering, Vol 10, No 3 (2018), Pagination: 41-44Abstract
The search engine advertising has become a dominant form of online advertising and is the predominant business model of search engines. The www leading search engine Google earned 16 billion US$ with search engine advertisement in the fiscal year 2007. The advertiser does not usually pay for the impression of the ad, but for a click on the advertisement. Fraudulent clicks present an inherent problem of this so called pay-per-click model. Click fraud represent any procedure that illegally exploits pay-per click markets. In particular, click fraud resolves around intentional clicks without intent to interact with the advertiser. In this context, a class action against several search engine providers in 2005 attracted attention (DELANEY 2005). To what extend, search engine providers are liable for the manipulation of clicks and click rates, was not finally judicially clarified. Google settled the class action by agreeing to pay its advertisers 90 billion US $. Click fraud represents a general threat for the pay-per-click model as well as a more specific threat for the business model of search engines. Search engine providers need to ascertain the reliability and correctness of the pay per-click model to preserve the trust of advertisers. Advertisers likewise need to consider click fraud in their decision process for the future configuration of advertising campaigns. In this contribution, we illustrate the main consequences of fraudulent clicks on frequently used measures of search engine advertising. Thereby, we support the early detection of and defense against click fraud in web.
Keywords
Spam, Link Spam, HITS, Pagerank, Hub, Authority, Searchengine, Click-Fraud.
- Groundwater Quality Assessment:A Review on Traditional and Soft Computing Approaches
Abstract Views :154 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, IN
2 Department of Chemistry, Gurukula Kangri Vishwavidyalaya, Haridwar, IN
1 Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, IN
2 Department of Chemistry, Gurukula Kangri Vishwavidyalaya, Haridwar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 32 (2020), Pagination: 1-12Abstract
Groundwater is indispensable natural resource for survival of human. It is a factor that comprehensively influences the socio-economic growth of any country. Due to uncertainties, interdependencies of parameters and situations of elements under consideration, groundwater quality assessment is a complex real world problem. In modern epoch of research, to solve real world problems or to handle ambiguous situations, traditional principles and approaches are hardly implemented. Soft computing may be appropriate to solve such complex problems and it provides an acceptable solution in an ambiguous environment. Artificial nervous system is helpful in learning and modeling non-linear and complex relationships found in groundwater quality assessment because many relationships between input and output are complex as well as non-linear. Fuzzy logic requires prior knowledge of physical, chemical and biological information about groundwater. It reduces the errors in the procedures used to solve the real world problem and gives accurate result considering hidden relationships or patterns between input and output. Genetic algorithm has been used to select the best result among available results of the groundwater quality. It chooses the best result according to the principles of genetics. In general, it is used to present high-quality solutions for adaptation and search problems. The objective of this research paper is to analyze the qualities of different groundwater quality estimation methods.Keywords
Groundwater Quality Assessment, Soft Computing, Artificial Neural Network, Fuzzy Logic, Traditional Methods.Full Text
References
- Finizio, A., Calliera, M., Vighi, M., 2001, Rating systems for pesticide risk classification on different ecosystems. Ecotoxicol. Environ. Saf. 49: 262–274.
- WHO 2011, Guidelines for Drinking-water Quality, 4th ed, ISBN 978 92 4 154815 1.
- IS 10500: 2012, Bureau of Indian Standards, Drinking Water — Specification, IInd Revision.
- Shwetank, 2019, Estimation of groundwater contamination using fuzzy logic: A case study of Haridwar, India, Groundwater for Sustainable Development. 8: 644–653.
- Md. Golzar Hossain, 2013, Factor and Cluster Analysis of Water Quality Data of the Groundwater Wells of Kushtia, Bangladesh: Implication for Arsenic Enrichment and Mobilization, Journal Geological Society Of India. 81: 377-384.
- N. Subba Rao, 2014, Spatial control of groundwater contamination, using Principal Component Analysis, J. Earth Syst. Sci. 123(4): 715–728.
- Poonam Tirkey, 2017, Assessment of Groundwater Quality and Associated Health Risks: A case study of Ranchi city, Jharkhand, India, Groundwater for Sustainable Development. 8
- Y. Ouyang, J.E. Zhang, L. Cui, 2014, Estimating impacts of land use on groundwater quality using trilinear analysis, Environ. Monit. Assess. 186: 5353–5362.
- M.M. Awawdeh, R.A. Jaradat, 2010, Evaluation of aquifers vulnerability to contamination in the Yarmouk river basin, Jordan, based on DRASTIC method, Arab. J. Geosci. 3: 273–282.
- Javier Montero, 2010, Grading ideas about the concept of Soft Computing, International Journal of Computational Intelligence Systems. 3(2): 144-147.
- P.P. Mujumdar, K. Sashi kumar, 2002, A fuzzy risk approach for seasonal water quality management of river water, Water Resour. Res. 38: 1004.
- S.N. Sivanandam, S. Sumathi, S.N. Deepa, 2007, Introduction to fuzzy logic using MATLAB, Springer-Verlag Berlin Heidelberg, ISBN-10 3-540-35780-7.
- UNESCO 2012, Managing Water Report under Uncertainty and Risk — The United Nations World Water Development. Report 4 Volume 1
- Shreedhar Maskey, Yonas B. Dibike, Andreja Jonoski, Dimitri Solomatine, 2000, Groundwater Model Approximation with Artificial Neural Network for Selecting Optimum Pumping Strategy for Plume Removal, Proc 2nd Joint Workshop "Artificial Intelligence in Civil Engineering", Cottbus, Germany, 67-80, ISBN 3-934934-00-5
- Emery Coppola Jr., Mary Poulton, Emmanuel Charles, John Dustman, Ferenc Szidarovszky, 2003, Application of Artificial Neural Networks to Complex Groundwater Management Problems, Natural Resources Research. 12(4): 303-320.
- Goloka B. Sahoo, Chittaranjan Ray, Edward Mehnert, Donald A. Keefer, 2006, Application of artificial neural networks to assess pesticide contamination in shallow groundwater, Science of the Total Environment. 367: 234–251.
- Ioannis N. Daliakopoulos, Paulin Coulibaly, Ioannis K. Tsanis, 2005, Groundwater level forecasting using artificial neural networks, Journal of Hydrology. 309: 229–240.
- Muhammad Ali Shamim, A.R.Ghumman, Usman Ghani, 2004, Forecasting Groundwater Contamination Using Artificial Neural Networks, International Conf. on Water Resources & Arid Environment.
- Raj Mohan Singh, Bithin Datta, Ashu Jain, 2004, Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks, J. Water Resour. Plann. Manage. 130: 506-514.
- Tutu H., Cukrowska E.M., Dohnal V., Havel J., 2005, Application of artificial neural networks for classification of uranium distribution in the Central Rand goldfield, South Africa, Environmental Modeling and Assessment. 10: 143–152
- Eddy El Tabach, Laurent Lancelot, Isam Shahrour, Yacoub Najjar, 2007, Use of artificial neural network simulation metamodelling to assess groundwater contamination in a road project, Mathematical and Computer Modelling. 45: 766–776.
- Purkait B., Kadam S. S., Das S. K., 2008, Application of Artificial Neural Network Model to Study Arsenic Contamination in Groundwater of Malda District, Eastern India, Journal of Environmental Informatics. 12(2): 140-149.
- Shouju Li, He Yu, Yingxi Liu, 2008, Aquifer Parameter Identification with Hybrid Ant Colony System, Nonlinear Dynamics and Systems Theory. 8(4): 359–374.
- Tapesh K Ajmera, A. K. Rastogi, 2008, Artificial Neural Network Application on Estimation of Aquifer Transmissivity, Journal of Spatial Hydrology. 8(2): 15-31.
- Mohammad Chowdhury, Ali Alouani, Faisal Hossain, 2010, Comparison of ordinary kriging and artificial neural network for spatial mapping of arsenic contamination of groundwater, Stoch Environ Res Risk Assess. 24:1–7.
- Mohamed Seyam, Yunes Mogheir, 2011, Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity, Journal of Environmental Protection. 2: 56-71.
- Kyung Hwa Cho, Suthipong Sthiannopkao, Yakov A. Pachepsky, Kyoung-Woong Kim, Joon Ha Kim, 2011, Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network, Water Research. 45: 5535 -5544.
- M. R. Mustafa, M. H. Isa, R. B. Rezaur, 2012, Artificial Neural Networks Modeling in Water Resources Engineering: Infrastructure and Applications, World Academy of Science, Engineering and Technology, Vol:6 2012-02-24, 317-325.
- Khamis Al-Mahallawi, Jacky Mania, Azzedine Hani, Isam Shahrour, 2012, Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas, Environ. Earth Sci., 65: 917–928.
- P. Abbasi Maedeh, N. Mehrdadi, G.R. Nabi Bidhendi, H. Zare Abyaneh, 2013, Application of Artificial Neural Network to Predict Total Dissolved Solids Variations in Groundwater of Tehran Plain, Iran, International Journal of Environment and Sustainability. 2(1): 10-20.
- Maria Laura Foddis, Philippe Ackerer, Augusto Montisci, Gabriele Uras, 2013, Polluted aquifer inverse problem solution using artificial neural networks, AQUA mundi, Am07054: 015 – 021.
- Dixon B., 2003, Prediction of ground water vulnerability using an integrated GIS-based Neuro-Fuzzy techniques, Journal Of Spatial Hydrology. 4(2), 1-38.
- J. Ganoulis, P. Anagnostopoulos, H. Mpimpas, 2003, “Fuzzy numerical simulation of water quality”. In: Proc. 30th IAHR Congress, Theme B, August 2003, Thessaloniki, Greece. IAHR, Spain. pp: 165–174.
- Vito F. Uricchio, Raffaele Giordano, Nicola Lopez, 2004, A fuzzy knowledge-based decision support system for groundwater pollution risk evaluation, Journal of Environmental Management. 73, 189–197.
- Sayed Farhad Mousavi, Mohammad Javad Amiri, 2012, Modeling Nitrate Concentration of Groundwater Using Adaptive Neural-Based Fuzzy Inference System, Soil & Water Research. 7: 73–83.
- Yani Geng, Jun Zhang, Qi Zhou, Chundi Xu, Yaqian Zhao, 2011, Fuzzy synthetic evaluation of Weihe water quality, Environmental Engineering and Management Journal. 10(10): 1477-1484
- Ayse Muhammetoglu, Ahmet Yardimci, 2006, A Fuzzy Logic Approach To Assess Groundwater Pollution Levels Belowagricultural Fields, Environmental Monitoring and Assessment. 118: 337–354.
- SHI JianSheng, MA Rong, LIU JiChao, ZHANG YiLong , 2013, Suitability assessment of deep groundwater for drinking, irrigation and industrial purposes in Jiaozuo City, Henan Province, north China, Chinese Science Bulletin. 58(25): 3098-3110.
- Natarajan Venkat Kumar, Samson Mathew, Ganapathiram Swaminathan, 2010, A Hybrid Approach towards the Assessment of Groundwater Quality for Potability: A Fuzzy Logic and GIS Based Case Study of Tiruchirappalli City, India, Journal of Geographic Information System. 2: 152-162.
- S. Venkatramanan, S. Y. Chung, R. Rajesh & S. Y. Lee, T. Ramkumar, M. V. Prasanna-2015-Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea, Environ Sci Pollut Res. 22(15):11209-23.
- Lalithamma.G.A, Dr. P.S. Puttaswamy, 2013, Literature Review of Applications of Neural Networkin ControlSystems, International Journal of Scientific and Research Publications. 3(9): 1-6
- Sivanandam, S.N., Sumathi, S., Deepa, S.N., 2007. Introduction to Fuzzy Logic Using MATLAB. Springer-Verlag Berlin Heidelberg ISBN-10 3-540-35780-7.