Abstraction Based Data Mining

Data Mining is the process in which useful information is extracted from large dataset. Such huge datasets consists of typically large number of patterns and features. This consumes a lot of storage space and since all of the data cannot be stored in main memory, it has to be fetched from secondary storage as required increasing the disk I/O operations. This situation can be resolved by using abstraction in data mining. Abstraction in simple terms refers to compact representation of dataset. Such an abstraction helps in reducing the time and space requirements of the overall decision making process. It is also important that the abstraction generated from the data are generated in as minimum number of scans as possible. In this paper we implemented existing algorithms which generate compact representations of patterns in data mining operations and analysed and compared the results of implementation to determine their efficiency. DOI: 10.17762/ijritcc2321-8169.16041

The prevalence of headache may be related with the latitude: a possible role of Vitamin D insufficiency?

According to recent observations, there is worldwide vitamin D insufficiency (VDI) in various populations. A number of observations suggest a link between low serum levels of vitamin D and higher incidence of chronic pain. A few case reports have shown a beneficial effect of vitamin D therapy in patients with headache disorders. Serum vitamin D level shows a strong correlation with the latitude. Here, we review the literature to delineate a relation of prevalence rate of headaches with the latitude. We noted a significant relation between the prevalence of both tension-type headache and migraine with the latitude. There was a tendency for headache prevalence to increase with increasing latitude. The relation was more obvious for the lifetime prevalence for both migraine and tension-type headache. One year prevalence for migraine was also higher at higher latitude. There were limited studies on the seasonal variation of headache disorders. However, available data indicate increased frequency of headache attacks in autumn–winter and least attacks in summer. This profile of headache matches with the seasonal variations of serum vitamin D levels. The presence of vitamin D receptor, 1α-hydroxylase and vitamin D-binding protein in the hypothalamus further suggest a role of vitamin D deficiency in the generation of head pain

Transfer Function and Impulse Response Synthesis using Classical Techniques

This thesis project presents a MATLAB based application which is designed to synthesize any arbitrary stable transfer function. Our application is based on the Cauer synthesis procedure. It has an interactive front which allows inputs either in the form of residues and poles of a transfer function, in the form of coefficients of the numerator and denominator of the transfer impedance or in the form of samples of an impulse response. The program synthesizes either a single or double resistively terminated LC ladder network. Our application displays a chart showing the variation of stability of an impulse response with the addition of delay. An attempt is made to synthesize usually unstable impulse responses by calculating the delay that would make them stable

Machine Learning Approaches to Cyber Security, Markov Chain (MC)Model and Support Vector Machine (SVM) Approach

In this thesis I presented machine learning application for cyber security. In particular anomalies are detected with Markov chain model technique and Support Vector Machine (SVM) method. Markov chain model form with normal or anomaly free data and considered as reference for anomaly detection. For anomaly detection ground truth (anomaly free) data is important which is lack in availability so I generated data in MATLAB and used to make MC (Markov Chain) model and for anomaly detection. I also used data generated by software SADIT downloaded from github. For SVM technique Kernel function used is ‘radial basis function’. SVM technique trained and tested with data generated by SADIT. Three dataset created of SADIT data to perform experiment using SVM method. MC model technique gives good performance for low noise level data but not gives good result for large noise level data. So MC model technique is robust for low noise level data. SVM technique not giving same results for all datasets i.e. for some datasets its performance is good and for some datasets its performance is not good. This is because features of data, so feature selection is important for SVM i.e. features for which SVM gives good performance that features should be selected

Hydrodynamic Dispersion in Unsaturated Porous Media

101 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1970.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

ANTIBACTERIAL AND ANTIBIOFILM ACTIVITY OF QUERCUS INFECTORIA GALLS ON ROTHIA DENTOCARIOSA ISOLATED FROM DENTAL CARIES

Objective: The present study aimed to study Quercus infectoria gall extract for phytochemical analysis, antibacterial, and antibiofilm activity against Rothia dentocariosa isolated from dental caries. Methods: R. dentocariosa was isolated, characterized, and identified by 16S rRNA sequence and also checked for biofilm formation ability. Phytochemical analysis of Q. infectoria aqueous gall extracts was carried out. Antibacterial and antibiofilm activity was performed using agar well diffusion method and microtiter plate assay, respectively. Results: Bacterial isolate from dental caries was identified as R. dentocariosa by 16s rRNA sequencing technique with accession number MH824681 obtained from NCBI. Phytochemical analysis of Q. infectoria aqueous gall extract revealed the presence of alkaloids, phenol, tannin, glycosides, phenolic compound, and flavonoids. Significant antibacterial activity was observed with 19.00 (±7.07) mm diameter zone of inhibition. The biofilm inhibition assay was performed by microtiter plate method indicated 92.89% inhibition of bacteria at the concentration of 100 mg/mL of aqueous extract. Conclusion: The results indicated the efficacy of Q. infectoria gall extracts that could be explored as an alternative to current treatment

Monodispersed PtPdNi Trimetallic Nanoparticles-Integrated Reduced Graphene Oxide Hybrid Platform for Direct Alcohol Fuel Cell

The direct alcohol fuel cell has recently emerged as an important energy conversion device. In the present article, superior alcohol (ethanol, ethylene glycol, and glycerol) electrooxidation performance using trimetallic platinum–palladium–nickel (PtPdNi) alloy nanoparticles of diameters from 2–4 nm supported on a reduced graphene oxide (rGO) electrocatalyst is demonstrated. A simple and single-step solvothermal technique is adopted to fabricate the alloy/rGO hybrid electrocatalysts by simultaneous reduction of metal ions and graphene oxide. The detailed electrochemical investigation revealed that the performance of the trimetallic/rGO hybrid toward electrooxidation of different alcohols is higher than that of bimetallic alloy/rGO hybrids and the state-of-the-art Pt/C catalyst. The incorporation of Ni into the PtPd alloy is found to change the surface of the electronic structure PtPd alloy leading to higher electrochemical surface areas and improved kinetics. In addition, the hydrophilic nature of Ni not only facilitates alcohol electrooxidation but also electrooxidation of residual carbon impurities formed on the catalyst surface, thus reducing catalyst poisoning, demonstrating its role in the development of anode catalysts for the alcohol fuel cells