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Search Results: 1 - 10 of 190719 matches for " G. Raju "
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Comparison of Alkaline Treatment of Lead Contaminated Wastewater Using Lime and Sodium Hydroxide  [PDF]
Sudhakar M. Rao, G. C. Raju
Journal of Water Resource and Protection (JWARP) , 2010, DOI: 10.4236/jwarp.2010.23032
Abstract: A lead-acid storage battery manufacturing industry in India produces several thousand liters of lead con-taminated acidic wastewater on a daily basis and uses hydrated lime to render the lead-contaminated acidic wastewater alkaline (pH = 8.0). Alkaline treatment of the acidic wastewater with lime though a cost-effective method, generates copious amount of lead-contaminated gypsum sludge. Other alkali agents such as sodium hydroxide, sodium carbonate and dolomite are also used for alkali treatment of the acid wastewaters. The present paper compares the relative efficiency of hydrated lime and 0.05 M to 1 M NaOH solutions with re-spect to 1) amounts of sludge produced, 2) immobilization of the soluble lead in the acidic wastewater (AWW) and 3) increase in TDS (total dissolved solids) levels upon treatment of AWW with NaOH solutions and lime. The study also performs equilibrium speciation upon alkaline treatment of AWW with lime and NaOH (sodium hydroxide) solutions using the Visual MINTEQ program to understand the chemical reac-tions occurring during treatment process.
Wavepackets in the Recognition of Isolated Handwritten Characters
G. Raju,K. Revathy
Lecture Notes in Engineering and Computer Science , 2007,
Abstract:
MINING MAXIMAL WEB ACCESS PATTERNS- A NEW APPROACH
RAJIMOL A and RAJU G.
International Journal of Machine Intelligence , 2011,
Abstract: Web access pattern mining is an application of sequence mining on web log data to generate interesting useraccess behavior on World Wide Web. In this paper we present a new method for the efficient mining of maximal web accesspatterns. The method is a variation of recently published, FOL-Mine (First Occurrence List Mine) [1] for mining web accesspatterns. It is a top-down method that uses the concept of first occurrence to reduce search space and thus improving theperformance
AUTOMATIC EAR LOCALIZATION USING AN EFFECTIVE SKIN SEGMENTATION ALGORITHM AND CORRELATION COEFFICIENT IN 2D IMAGES.
LIJA JACOB and RAJU G
International Journal of Machine Intelligence , 2011,
Abstract: Studies have shown that human ear is one of the representative human biometrics with uniqueness and stability. Automatic Localization of 2-D ear from a side face image is one of the challenging problems. This paper presents an efficient technique for automatic ear localization from a side face image. The localization is done using an effective Skin Segmentation algorithm and Template Matching using Correlation Coefficients. The ear is localized automatically through Skin Segmentation followed by automatic ear localization. The proposed technique is tested using an ear database which contains 100 ear images and 95% of the images responded with correct automatic ear localization.
A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules
Binu Thomas,G. Raju
ISRN Artificial Intelligence , 2013, DOI: 10.1155/2013/316913
Abstract: In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm. 1. Introduction Classification is a Data Mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants in a bank as low, medium, or high credit risks. A classification task begins with a data set in which the class assignments are known. A classification model that predicts credit risk could be developed based on observed data for many loan applicants over a period of time. In addition to the historical credit rating, the data might track employment history, home ownership or rental, years of residence, number and type of investments, and so on. Credit rating would be the target, the other attributes would be the predictors, and the data for each customer would constitute a case. Classification techniques include decision trees, association rules, fuzzy systems, and neural networks. Classification has many applications in customer segmentation, business modeling, marketing, credit analysis, web mining and biomedical, and drug response modeling. Classification models include decision trees, Bayesian models, association rules, and neural nets. Although association rules have been predominantly used for data exploration and description, the interest in using them for prediction has rapidly increased in the Data Mining community. When
Ulam's Conjecture is True for Connected Graphs
Raju Renjit. G
Computer Science , 2007,
Abstract: This submission has been withdrawn at the request of the author.
P is not equal to NP
Raju Renjit. G
Computer Science , 2006,
Abstract: This submission has been withdrawn at the request of the author.
Software Aging Analysis of Web Server Using Neural Networks
G. Sumathi,R. Raju
Computer Science , 2012, DOI: 10.5121/ijaia.2012.3302
Abstract: Software aging is a phenomenon that refers to progressive performance degradation or transient failures or even crashes in long running software systems such as web servers. It mainly occurs due to the deterioration of operating system resource, fragmentation and numerical error accumulation. A primitive method to fight against software aging is software rejuvenation. Software rejuvenation is a proactive fault management technique aimed at cleaning up the system internal state to prevent the occurrence of more severe crash failures in the future. It involves occasionally stopping the running software, cleaning its internal state and restarting it. An optimized schedule for performing the software rejuvenation has to be derived in advance because a long running application could not be put down now and then as it may lead to waste of cost. This paper proposes a method to derive an accurate and optimized schedule for rejuvenation of a web server (Apache) by using Radial Basis Function (RBF) based Feed Forward Neural Network, a variant of Artificial Neural Networks (ANN). Aging indicators are obtained through experimental setup involving Apache web server and clients, which acts as input to the neural network model. This method is better than existing ones because usage of RBF leads to better accuracy and speed in convergence.
Super Object Oriented Programming
Raju Renjit. G
Computer Science , 2005,
Abstract: This submission has been withdrawn at the request of the author.
Incorporating LINQ, State Diagrams Templating and Package Extension Into Java
Raju Renjit. G
Computer Science , 2005,
Abstract: This submission has been withdrawn at the request of the author.
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