Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

Studies on Advanced Oxidation Processes for the Removal of Acetamiprid from Wastewater

Pollution of water with pesticides has become a threat to the man, material and environment. The pesticides released to the environment reach the water bodies through run off. Industrial wastewater from pesticide manufacturing industries contains pesticides at higher concentration and hence a major source of water pollution. Pesticides create a lot of health and environmental hazards which include diseases like cancer, liver and kidney disorders, reproductive disorders, fatal death, birth defects etc. Conventional wastewater treatment plants based on biological treatment are not efficient to remove these compounds to the desired level. Most of the pesticides are phyto-toxic i.e., they kill the microorganism responsible for the degradation and are recalcitrant in nature. Advanced oxidation process (AOP) is a class of oxidation techniques where hydroxyl radicals are employed for oxidation of pollutants. AOPs have the ability to totally mineralise the organic pollutants to CO2 and water. Different methods are employed for the generation of hydroxyl radicals in AOP systems. Acetamiprid is a neonicotinoid insecticide widely used to control sucking type insects on crops such as leafy vegetables, citrus fruits, pome fruits, grapes, cotton, ornamental flowers. It is now recommended as a substitute for organophosphorous pesticides. Since its use is increasing, its presence is increasingly found in the environment. It has high water solubility and is not easily biodegradable. It has the potential to pollute surface and ground waters. Here, the use of AOPs for the removal of acetamiprid from wastewater has been investigated. Five methods were selected for the study based on literature survey and preliminary experiments conducted. Fenton process, UV treatment, UV/ H2O2 process, photo-Fenton and photocatalysis using TiO2 were selected for study. Undoped TiO2 and TiO2 doped with Cu and Fe were prepared by sol-gel method. Characterisation of the prepared catalysts was done by X-ray diffraction, scanning electron microscope, differential thermal analysis and thermogravimetric analysis. Influence of major operating parameters on the removal of acetamiprid has been investigated. All the experiments were designed using central compoiste design (CCD) of response surface methodology (RSM). Model equations were developed for Fenton, UV/ H2O2, photo-Fenton and photocatalysis for predicting acetamiprid removal and total organic carbon (TOC) removal for different operating conditions. Quality of the models were analysed by statistical methods. Experimental validations were also done to confirm the quality of the models. Optimum conditions obtained by experiment were verified with that obtained using response optimiser. Fenton Process is the simplest and oldest AOP where hydrogen peroxide and iron are employed for the generation of hydroxyl radicals. Influence of H2O2 and Fe2+ on the acetamiprid removal and TOC removal by Fenton process were investigated and it was found that removal increases with increase in H2O2 and Fe2+ concentration. At an initial concentration of 50 mg/L acetamiprid, 200 mg/L H2O2 and 20 mg/L Fe2+ at pH 3 was found to be optimum for acetamiprid removal. For UV treatment effect of pH was studied and it was found that pH has not much effect on the removal rate. Addition of H2O2 to UV process increased the removal rate because of the hydroxyl radical formation due to photolyis of H2O2. An H2O2 concentration of 110 mg/L at pH 6 was found to be optimum for acetamiprid removal. With photo-Fenton drastic reduction in the treatment time was observed with 10 times reduction in the amount of reagents required. H2O2 concentration of 20 mg/L and Fe2+ concentration of 2 mg/L was found to be optimum at pH 3. With TiO2 photocatalysis improvement in the removal rate was noticed compared to UV treatment. Effect of Cu and Fe doping on the photocatalytic activity under UV light was studied and it was observed that Cu doping enhanced the removal rate slightly while Fe doping has decreased the removal rate. Maximum acetamiprid removal was observed for an optimum catalyst loading of 1000 mg/L and Cu concentration of 1 wt%. It was noticed that mineralisation efficiency of the processes is low compared to acetamiprid removal efficiency. This may be due to the presence of stable intermediate compounds formed during degradation Kinetic studies were conducted for all the treatment processes and it was found that all processes follow pseudo-first order kinetics. Kinetic constants were found out from the experimental data for all the processes and half lives were calculated. The rate of reaction was in the order, photo- Fenton>UV/ H2O2>Fenton> TiO2 photocatalysis>UV. Operating cost was calculated for the processes and it was found that photo-Fenton removes the acetamiprid at lowest operating cost in lesser time. A kinetic model was developed for photo-Fenton process using the elementary reaction data and mass balance equations for the species involved in the process. Variation of acetamiprid concentration with time for different H2O2 and Fe2+ concentration at pH 3 can be found out using this model. The model was validated by comparing the simulated concentration profiles with that obtained from experiments. This study established the viability of the selected AOPs for the removal of acetamiprid from wastewater. Of the studied AOPs photo- Fenton gives the highest removal efficiency with lowest operating cost within shortest time.

Sono, Photo and Sonophoto Catalytic Removal of Chemical and Bacterial Pollutants from Wastewater

The term ‘water pollution’ broadly refers to the contamination of water and water bodies (e.g. lakes, rivers, oceans, groundwater etc). Water pollution occurs when pollutants are discharged directly or indirectly into water bodies without adequate treatment to remove the harmful contaminants. This affects not only the plants and organisms living in these bodies of water but also the entire natural biological communities and the biodiversity.Advanced Oxidation Processes (AOPs) have been tested as environment-friendly techniques for the treatment of contaminated water, in view of their ability to convert pollutants into harmless end products. These techniques refer to a set of treatment procedures designed to remove organic or inorganic contaminants in wastewater by oxidation. The contaminants are oxidized by different reagents such as air, oxygen, ozone, and hydrogen peroxide which are introduced in precise, preprogrammed dosages, sequences and combinations under appropriate conditions. The procedure when combined with light in presence of catalyst is known as photocatalysis. When ultrasound (US) is used as the energy source, the process is referred as sonication. Sonication in presence of catalyst is referred as sonocatalysis. Of late, combination of light and sound as energy sources has been tested for the decontamination of wastewater in the presence of suitable catalyst. In this case, the process is referred as sonophotocatalysis. These AOPs are specially advantageous in pollution control and waste water treatment because unlike many other technologies, they do not just transfer the pollutant from one phase to another but completely degrade them into innocuous substances such as CO2 and H2O.

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.

Fast computation of concept lattices using data mining techniques

We present a new algorithm called TITANIC for computing concept lattices. It is based on data mining techniques for computing frequent itemsets. The algorithm is experimentally evaluated and compared with B. Ganter's Next-Closure algorithm.

Mining minimal non-redundant association rules using frequent closed itemsets

The problem of the relevance and the usefulness of extracted association rules is of primary importance because, in the majority of cases, real-life databases lead to several thousands association rules with high confidence and among which are many redundancies. Using the closure of the Galois connection, we define two new bases for association rules which union is a generating set for all valid association rules with support and confidence. These bases are characterized using frequent closed itemsets and their generators; they consist of the non-redundant exact and approximate association rules having minimal antecedents and maximal consequences, i.e. the most relevant association rules. Algorithms for extracting these bases are presented and results of experiments carried out on real-life databases show that the proposed bases are useful, and that their generation is not time consuming.

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Semantic web mining and the representation, analysis, and evolution of web space

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: Growing numbers of researchers work on improving the results of Web Mining by exploiting semantic structures in the Web, and they use Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The second aim of this paper is to use these concepts to circumscribe what Web space is, what it represents and how it can be represented and analyzed. This is used to sketch the role that Semantic Web Mining and the software agents and human agents involved in it can play in the evolution of Web space.

Mining association rules in folksonomies

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.

Semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.