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

Theoretical Computer Science

Today ovarian cancer is second most perilous cause of cancer deaths in women after breast cancer. In this work, we have developed system which acquires ultrasound images and using image processing and machine learning algorithms accurately classify benign and malignant tumors in ovarian cancer. This technique denoise image using wavelet transform, grey level texture features extracted using GLCM(grey level co-occurrence algorithm), extracted features will be trained through SVM(Support vector machine) and selected non-redundant features selected through Relief-F will be further train and test through SVM for output. Proposed technique was validated by 60 malignant and 60 benign images of patients. On evaluating classifier for 14-texture descriptors give 74% and relief-F gives 82% accuracy. After selecting 6 features from 14 features it will give accuracy 86% and relief-F gives 92% accuracy. Thus, the features are significant for result and preliminary results depict that the proposed technique can be reliable for ovarian tumor classification as this system is fully automated, advantageous and cost-effective too. © 2015 IEEE.

Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2015

Identification of ovarian mass through ultrasound images using machine learning techniques

Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.

Journal of Institution of Engineers (series B)- Springer

Weighted Hybrid Decision Tree model for Random Forest classifier

Radar X-band electromagnetic interference shielding (EMS) is one of the prime requirements for any air vehicle coating; with limitations on the balance between strength and thickness of the EMS material. Nanocomposite of multiwalled-carbon-nanotubes (MWCNT) has been homogeneously integrated (0 – 9 wt%) with polymer, poly (vinylidene fluoride, PVDF) to yield 300 micron film. The PVDF + 9 wt% MWCNT sample of density 1.41 g/cm3 show specific shielding effectiveness (SSE) of 17.7 dB/(g/cm3) (99.6% EMS), with maintained hardness and improved conductivity. With multilayer stacking (900 microns) of these films of density 1.37 g/cm3, the sample showed increase in SSE to 23.3 dB/(g/cm3) (99.93% EMS). Uniform dispersion of MWCNTs in the PVDF matrix gives rise to increased conductivity in the sample beyond 5 wt% MWCNT reinforcement. The results are correlated to the hardness, reflection loss, absorption loss, percolation threshold, permittivity and the conductivity data. An extremely thin film with maximum EMS property is hence proposed.

School of Mechanical Engineering

Fulltext

AIP Advances

Fabrication and evaluation of thin layer PVDF composites using MWCNT reinforcement: Mechanical, electrical and enhanced electromagnetic interference shielding properties

Classification of imbalance data is the major challenge to the community these days. Machine learning algorithms can evolve a one-sided classifier when data is imbalance. The vital challenge in imbalance dataset problem is that sometimes the minority (tiny) classes are more useful, but standard classifiers tend to be biased toward the majority (huge) classes and ignore the tiny ones. In this paper we compared the existing methods to handle imbalance dataset and provide a new hybrid approach which will improve the accuracy of classifier on imbalanced data. © 2016 IEEE.

International Conference on Communication and Signal Processing, ICCSP 2016

A new approach for handling imbalanced dataset using ANN and genetic algorithm

Now-a-days many unauthorized users try to get the protected information and therefore it is necessary to secure our data. Besides, there are also scenarios that data hiding needs to be done in the encrypted domain or combined with the encryption, especially in the age of big data and cloud computing. In previous method first encryption was done and then the room was vacated to hide the data. But there were some errors in data extraction and image recovery. In this paper a novel technique is proposed termed as reversible data hiding (RDH) by which we can hide our data. RDH is applied to encrypted images by which we can properly recover our data and the cover image. The hidden data can be in the form of text or image. In the proposed method we first vacate room to hide data and after encrypting image using certain encryption key the data hider reversibly hides the data whether text or image using data hiding key. In previous method only text was hidden. But the proposed paper is extended to hide the image also in the same reserved space which was used to hide text. The hidden image is also recovered without any errors. The results given in the paper proves that we can exactly recover the hidden data, hidden image and the cover image as and when needed by the receiver. The traditional RDH algorithm used is histogram shifting. By this method we can properly recover hidden data and cover image without any errors. Large amount of data can be hidden as compared to previous method. © 2015 IEEE.

School of Electronics & Communication Engineering

Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015

Reversible Data Hiding in Cloud Based Applications

International Journal of Science, Engineering and Management

Pixel N-grams: Size, Location and Resolution Invariance for Shape Classification

A survey of cloud supporter: a load balancing framework in cloud using smartphone

Annual Fedderation University Conference

Pixel N-grams for Mammograms Classification

International Journal for Innovative Research in Science & Technology

A Review of Automotive Radiator Performance