MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

World University root node

School of Computer Engineering & Technology

International Journal of Science and Research 5 (11),

Search Recommendation with Mining Query Facets

Pure water is very important for human life. All agriculture, industrial and commercial sector need water of required quality. Pollution of water is due to all these sectors. Waste from all sectors is entering freshwater everyday. The demand for water is increasing while water availability is less because of improper waste disposal. The removing pollutant from all water resources is costly so there is need for new technologies, which are less expensive, require less maintenance and are energy effective. Chromium is widely used in electroplating, alloying, leather tanning, corrosion protection, etc. [1]. Chromite mining and processing units produce waste and effluent containing chromium in two oxidation states, for example, Cr(VI) and Cr(III) in aqueous solution. Cr(VI) oxidizing properties is dangerous due to carcinogenicity [2]. The potable water allows only 0.05 mg/lit Cr(VI) because of toxicity [3]. Higher concentrations are in the industrial and mining effluents, which crosses permissible limit. © 2017 by Apple Academic Press, Inc.

School of Petroleum Engineering

Process Modeling, Simulation, and Environmental Applications in Chemical Engineering

Removal of Cr(VI) from wastewater using red gram husk as adsorbent

Video compression is a key enabling technology for multimedia communication. The need for high-quality video processing in multimedia products is increasing, leading to enhancement in coding techniques. In future, multimedia systems require efficient video coding algorithms that provide high and efficient compression. The H.264 is one of the latest coding standards that provide high compression efficiency and network friendly representation compare to previous standards. In this paper, we present an architecture for Context-Based Adaptive Binary Arithmetic coding (CABAC) decoder which is used in H.264 as entropy decoding. CABAC gives higher compression efficiency by bringing higher implementation cost and complexity. In this work, we are focusing on designing an efficient and fast hardware and software co-design for CABAC which can be useful for the real-time application. A complex part of CABAC is design over FPGA hardware and remaining parts are on processing system (PS). For this work, we are using zynq-7000 based Zedboard which consists two parts one is Programing Logic (FPGA) and second is Processing system (Arm Cortex-9). We create an IP for a part of CABAC which can communicate with PS by AXI bus.

School of Electronics & Communication Engineering

Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016

Hardware software co-design for CABAC entropy decoder

International Journal of scientific research and management (IJSRM)

Evaluation of different Hypervisors Performances using Different Benchmarks

Survey on Energy Efficient \&amp; Secured Data Routing in Wireless Sensor Network

X.509 certificates enable to affirm the distinguishing proof of the parties involved in the communication. As of now, majority of individuals and communities are using X.509 certificates to demonstrate their ID during on-line exchanges, so the unwavering quality and risk level of these certificates come into a question. Hence, we have proposed a framework which assesses risk associated with X.509 Certificates with the help of certain trust criteria and characteristics. For evaluating risk related with certificate we use classification Technique with machine learning algorithm, which categorizes risk of certificate in three levels-High Risk, Medium Risk and Low risk. Our system is useful to find out risk associated with certificate while user carries out important transactions. User needs to input the certificate and system will provide risk associated with that certificate and if it is a high risk or medium risk certificate it will mention due to which parameter it bears risk. Our framework has application in browser-server communication and detecting phishing websites which have Https URLs.

Fulltext

Procedia Computer Science, Elsevier

Risk Rating System of X.509 Certificates

The paper deals with the analysis of shaft-rotor system which is under parallel misalignment. Misalignment is one of the most commonly observed faults in rotating machines. Misalignment is the second common mechanical fault in rotating machine applications after unbalance. The aim of the paper is to investigate the misalignment problem in rotating system using its vibration signature analysis. Parallel misalignment was given to the well aligned shaft-rotor system.Experimental studies were performed on a shaft-rotor system at to predict the misalignment in system. Analysis of shaft-rotor system with parallelmisalignment is done by using FFT (Fast Fourier Transform) analyser. In experimental results acceleration pick occurs which shows misalignment in system. Vibration analysis therefore helps to detect misalignment in the system.

School of Mechanical Engineering

International Engineering Research Journal

Experimental Study of ParallelMisalignment in Shaft Rotor System using Vibration Signature Analysis

Classification is inherently multi-objective problem. It is one of the most important tasks of data mining to extract important patterns from large volume of data. Traditionally, either only one objective is considered or the multiple objectives are accumulated to one objective function. In the last decade, Pareto-based multi-objective optimization approach have gained increasing popularity due to the use of multi-objective optimization using evolutionary algorithms and population-based search methods. Multi-objective optimization approaches are more powerful than traditional single-objective methods as it addresses various topics of data mining such as classification, clustering, feature selection, ensemble learning, etc. This paper proposes improved approach of non-dominated sorting algorithm II (NSGA II) for classification using neural network model by augmenting with local search. It tries to enhance two conflicting objectives of classifier: Accuracy and mean squared error. NSGA II is improved by augmenting backpropagation as a local search method to deal with the disadvantage of genetic algorithm, i.e. slow convergence to best solutions. By using backpropagation we are able to speed up the convergence. This approach is applied in various classification problems obtained from UCI repository. The neural network modes obtained shows high accuracy and low mean squared error. © Springer Science+Business Media Singapore 2016.

Advances in Intelligent Systems and Computing

Hybrid Multi-objective Optimization Approach for Neural Network Classification Using Local Search

Precision Agriculture Robot for Seeding Function,” International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India

Virendra. V. Shete, Shruti. R. Danve,“

A number of algorithms are available in the areas of data mining, machine learning and pattern recognition for solving the same kind of problem. But there is a little guidance for suggesting algorithm to use which gives best results for the problem at hand. This paper shows an approach for solving this problem using meta-learning. The paper uses three types of data characteristics. Simple, information theoretic, and statistical data characteristics are used. Results are generated using nine different algorithms on thirty eight benchmark datasets from UCI repository. The proposed approach uses K-nearest neighbor algorithm for suggesting the suitable algorithm. Classifier accuracy is taken as a basis for recommending the algorithm. By using meta-learning, accurate method can be recommended as per the given data, and cognitive overload for applying each method, comparing with other methods and then selecting the suitable method for use can be reduced. Thus it helps in adaptive learning methods. The experimentation shows that predicted accuracies are matching with the actual accuracies for more than 90 % of the benchmark datasets used. Thus it is concluded that the number of attributes, the number of instances, the number of classes, maximum probability of class and class entropy are playing a major role in classifier accuracy and algorithm selection for thirty eight datasets used for experimentation. © 2016 IEEE.