Showing papers by "National Institute of Technology, Karnataka published in 2014"

Synthesis and characterisation of nickel oxide nanofibre webs with alcohol sensing characteristics

Abstract: In this study, nickel oxide (NiO) nanofibres were obtained by a sol–gel electrospinning process followed by calcination from an aqueous sol of poly(2-ethyl-2-oxazoline)/nickel acetate tetrahydrate. Thermogravimetric analysis was used to determine the degradation temperature of the composite fibres, so as to get nickel oxide nanofibres. X-ray photoelectron spectroscopy and X-ray diffraction studies revealed the complete elimination of the organic phase from NiO fibres during calcination. The change in grain size with calcination temperature was determined by X-ray diffraction. The defects in fibres resulted in the modification of their Raman spectra as compared with that of a single crystal. The magnetic properties of the fibres were reduced as the calcination temperature was increased; this is due to the presence of non-stoichiometric defects. As the calcination temperature was increased, the amount of defects was reduced, which induced a difference in the band gap energy of the fibres. Sensitivity of the NiO fibres towards five different alcohols was studied, and the sensitivity towards ethanol was the highest.

Optimal load balancing in cloud computing by efficient utilization of virtual machines

Abstract: Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. Now a day's clients from different parts of the world are demanding for the various services in a rapid rate. In this present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner. Further, the performance is analyzed using Cloudsim simulator and compared with existing Active-VM load balance algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all available virtual machines without under/over utilization.

Influence factor based opinion mining of Twitter data using supervised learning

Abstract: Social Networking portals have been widely used for expressing opinions in the public domain through internet based text messages and images. Among popular social networking portals, Twitter has been the point of attraction to several researchers in important areas like prediction of democratic electoral events, consumer brands, movie box office, stock market, popularity of celebrities etc. Sentiment analysis over Twitter offers a fast and efficient way of monitoring the public sentiment. In this paper, we introduce the novel approach of exploiting the user influence factor in order to predict the outcome of an election result. We also propose a hybrid approach of extracting opinion using direct and indirect features of Twitter data based on Support Vector Machines (SVM), Naive Bayes, Maximum Entropy and Artificial Neural Networks based supervised classifiers. We combined Principal Component Analysis (PCA) with SVM in an attempt to perform dimensionality reduction. This paper shows two different case studies of entirely different social scenarios, US Presidential Elections 2012 and Karnataka Assembly Elections 2013. We conclude the conditions under which Twitter may fail or succeed in predicting the outcome of elections. Experimental results demonstrate that Support Vector Machines outperform all other classifiers with maximum successful prediction accuracy of 88% in case of US Presidential Elections held in November 2012 and maximum prediction accuracy of 58% in case of Karnataka State Assembly Elections held in May 2013.

Review of Microstructure Evolution in Hypereutectic Al–Si Alloys and its Effect on Wear Properties

Abstract: Al–Si alloys with silicon content more than 13 % are termed as hypereutectic alloys. In recent years, these alloys have drawn the attention of researchers due to their ability to replace cast iron parts in the transportation industry. The properties of the hypereutectic alloy are greatly dependent on the morphology, size and distribution of primary silicon crystals in the alloy. Mechanical properties of the hypereutectic Al–Si alloy can be improved by the simultaneous refinement and modification of the primary and eutectic silicon and by controlling the solidification parameters. In this paper, the effect of solidification rate and melt treatment on the evolution of microstructure in hypereutectic Al–Si alloys are reviewed. Different types of primary silicon morphology and the conditions for its nucleation and growth are explained. The paper discusses the effect of refinement/modification treatments on the microstructure and properties of the hypereutectic Al-Si alloy. The importance and effect of processing variables and phosphorus refinement on the silicon morphology and wear properties of the alloy is highlighted.

Cadmium (II) and nickel (II) biosorption by Bacillus laterosporus (MTCC 1628)

Abstract: Biosorption of heavy metals is a promising technology that involves removal of toxic metals from industrial waste streams and natural waters. The study describes the sorption of cadmium (II) [Cd (II)] and nickel (II) [Ni (II)] by dead biomass of Bacillus laterosporus , MTCC 1628. The biosorption conditions for the removal of Cd (II) and Ni (II) were examined by studying the effect of pH, contact time, biosorbent dosage and initial metal ion concentration. Shake flask studies yielded adsorption equilibrium in almost 120 min, for both the metals. It was found from Langmuir model that the maximum adsorption capacity for Cd (II) and Ni (II) ions was 85.47 mg/g and 44.44 mg/g respectively. Kinetic evaluation of the experimental data showed that the biosorption process followed pseudo-second order kinetics. Thermodynamic analysis showed that biosorption is an endothermic process with Δ H ° of 5.45 kJ/mol for Cd (II) biosorption and 24.33 kJ/mol for Ni (II) biosorption. The surface characteristics of B. laterosporus biomass before and after metal biosorption were analyzed by using scanning electron microscope (SEM) with energy dispersive X-ray spectroscopy (EDAX) to study the changes in surface morphology and elemental constitution of the adsorbent. B. laterosporus exhibited a higher and better potential biosorbent for the removal of Cd (II) as compared to Ni (II) from aqueous solution.

SQL Injection detection using machine learning

Abstract: In the present world, the web is the firmest and most common medium of communication and business interchange. Every day, millions of data are loaded through various channels on the web by users and user input can be malicious. Therefore, security becomes a very important aspect of web applications. Since they are easily accessible, they are prone to many vulnerabilities which if neglected can cause harm. The attackers make use of these loopholes to gain unauthorized access by performing various illegal activities. SQL Injection is one such attack which is easy to perform but difficult to detect because of its varied types and channel. This may result in theft, leak of personal data or loss of property. In this paper we have analyzed the existing solutions to the problems such as AMNESIA [1] and SQLrand [3] and their limitations. We have devised a classifier for detection of SQL Injection attacks. The proposed classifier uses combination of Naive Bayes machine learning algorithm and Role Based Access Control mechanism for detection. The proposed model is tested based on the test cases derived from the three SQLIA attacks: comments, union and tautology.

Investigation of third-order nonlinear and optical power limiting properties of terphenyl derivatives

Abstract: A series of new chalcones containing terphenyl as a core and with different functional groups has been successfully synthesized by Claisen–Schmidt condensation method in search of new nonlinear optical (NLO) materials. Molecular structural characterization for the compounds was achieved by FTIR and single crystal X-ray diffraction. The third-order NLO absorption and refraction coefficients were simultaneously determined by Z-scan technique. The measurements were performed at 532 nm with 7 ns laser pulses using a Nd:YAG laser in solution form. The Z-scan experiments reveal that the compounds exhibit strong nonlinear refraction coefficient of the order 10 –11 esu and the molecular two photon absorption cross section is 10 –46 cm 4 s/photon. The results also show that the structures of the compounds have great impact on NLO properties. The compounds show optical power limiting behavior due to two-photon absorption (TPA).

A novel sentiment analysis of social networks using supervised learning

Abstract: Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013.

Essentials of Cloud Computing

Abstract: Cloud computingaccessing computing resources over the Internetis rapidly changing the landscape of information technology. Its primary benefits compared to on-premise computing models are reduced costs and increased agility and scalability. Hence, cloud computing is receiving considerable interest among several stakeholdersbusinesses, the IT industry, application developers, researchers, and students. To successfully embrace this new computing model, these stakeholders need to acquire new cloud computing skills and knowledge. This book is designed to provide readers with a clear and thorough understanding of the key aspects of cloud computing. Presented in an easy-to-understand style, Essentials of Cloud Computing begins with an introduction to basic cloud computing concepts. It then covers cloud computing architecture, deployment models, programming models, and cloud service types, such as Software as a Service (SaaS) and Infrastructure as a Service (IaaS). It also discusses the clouds networking aspects, major service providers, open source support, and security issues. The book concludes with a discussion of several advanced topics, such as mobile clouds, media clouds, and green clouds. This book is intended for beginners as well as experienced practitioners who want to learn more about cloud computing. It includes many case studies, programming examples, and industry-based applications. Each chapter concludes with review questions that help readers check their understanding of the presented topics. Essentials of Cloud Computing will help readers understand the issues and challenges of cloud computing and will give them the tools needed to develop and deploy applications in clouds.

Analysis of surface roughness and hardness in titanium alloy machining with polycrystalline diamond tool under different lubricating modes

Abstract: The present work deals with the investigation on machining of difficult-to-machine material titanium alloy (Ti-6Al-4V) using poly crystalline diamond (PCD) tool under different coolant strategies, namely dry, flooded and MQL. Taguchi technique has been employed and the optimization results indicated that MQL lubricating mode with cutting speed of 150 m/min, feed rate of 0.15 mm/rev, nose radius of 0.6 mm and 0.25 mm depth of cut is necessary to minimize surface roughness and dry mode with cutting speed of 150 m/min, feed rate of 0.15 mm/rev, nose radius of 0.6 mm and 0.75 mm depth of cut is necessary to maximize surface hardness. The results indicate the substantial benefit of the minimum quantity of lubrication (MQL) and justify PCD inserts to be the most functionally satisfactory commercially available cutting tool material for machining titanium alloys for better surface finish and hardness.

Tubular array, dielectric, conductivity and electrochemical properties of biodegradable gel polymer electrolyte

Abstract: A supercapacitor based on a biodegradable gel polymer electrolyte (GPE) has been fabricated using guar gum (GG) as the polymer matrix, LiClO4 as the doping salt and glycerol as the plasticizer. The scanning electron microscopy (SEM) images of the gel polymer showed an unusual tubular array type surface morphology. FTIR, DSC and TGA results of the GPE indicated good interaction between the components used. Highest ionic conductivity and lowest activation energy values were 2.2 × 10−3 S cm−1 and 0.18 eV, respectively. Dielectric studies revealed ionic behavior and good capacitance with varying frequency of the GPE system. The fabricated supercapacitor showed a maximum specific capacitance value of 186 F g−1 using cyclic voltammetry. Variation of temperature from 273 K to 293 K did not significantly influence the capacitance values obtained from AC impedance studies. Galvanostatic charge–discharge study of supercapacitor indicated that the device has good stability, high energy density and power density.

Structural, optical and mechanical properties of ternary CaO–CaF 2 –P 2 O 5 glasses

Abstract: The ternary phosphate glass series (50−x)CaO-xCaF2-50P2O5 (x = 0–20 mol%) were synthesized using melt quench technique. Structural, optical and mechanical properties were investigated with increase in CaF2 content. Using X-ray diffraction (XRD), synthesized glasses were confirmed to be amorphous in nature. Replacement of oxygen ions by fluorine ions increased the values of density. Decrease in refractive index due to the low polarizability of fluorine ions in the glass matrix was observed. In Fourier transform infrared (FTIR) spectra, the slight variation in νas (PO2) band position and intensity could be attributed to replacement of fluorine ions for oxygen ions in phosphate glass structure. These data were well supported by Raman spectra. Optical band gap energy increased from 3.44 eV to 3.64 eV with increase in CaF2 content, and Urbach energy decreased suggesting that the fluorine ions reduced the tail energy states in the band gap compared to the oxygen ions. Mechanical parameters such as Vickers hardness, fracture toughness and brittleness were evaluated from the Vickers micro indentation measurements. Increase in Vickers hardness, decrease in fracture toughness and increase in brittleness were observed with increase in CaF2 content.