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

School of Computer Engineering & Technology

World University root node

WSEAS Transactions on Signal Processing

The compression of digital imaging and communications in medicine images using wavelet coefficients thresholding and arithmetic encoding technique

Wire Electrical Discharge Machining (WEDM) is used as a valuable machining tool in the world of non-traditional machining due to various features which includes higher degree of accuracy, fine surface quality and good productivity. WEDM consists of large number of process parameters, thus it is difficult to obtain a combination of optimum parameters which provides higher accuracy. Optimization of a single response is often carried out with the well known technique Taguchi method. This method results in the solution which gives optimum value of each response. The experimentation has been completed with the help of Taguchi’s L25 Orthogonal Array. Grey Wolf Optimizer (GWO) algorithm is inspired by grey wolves. The Grey Wolf Optimizer algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In this paper, an attempt is made to study the effect of various process parameters such as pulse on time, pulse off time, wire feed, wire tension, upper flush and lower flush for high carbon high chromium steel and GWO is used to optimize the thinning in automotive sealing cover. ICMPC 2017

School of Mechanical Engineering

Materials Today: Proceedings

Process Parameter Optimization in WEDM by Grey Wolf Optimizer

The relationship between the values of local currencies in terms of foreign currencies and export competitiveness of any country is very complex. This relationship will become more complex if there is the heavy dependence on imported resources in the exported products. During last five years Indian rupee has weakened many times and reached to a level of 65.1806 for a dollar in March 2018. Since April 2013, the local currency lost around 18% to the US currency. Indian economy which as of now experienced vast financial and current account deficiency unfavorably influenced by exchange rate pressure. This paper endeavors to investigate the impacts of exchange rate movement in India and its effect on the Indian economy. The conditions which have been made for the economy because of the devaluation of rupee against dollar uncovers that there has been a solid and critical negative effect of this money unpredictability on numerous sectors.

School of Electronics & Communication Engineering

International Journal of Latest Technology in Engineering, Management {&amp;} Applied Science (IJLTEMAS)

The Macroeconomic Effects of Exchange Rate Movement in India

Economic Denial of Sustainability (EDoS) attack is one of the major web security attacks performed on cloud hosted websites that exploits cloud’s utility model by fraudulently consuming metered resources such as network bandwidth. In such attack, the malicious traffic imitates to be legitimate and hence goes undetected. A way to defend against such attack is to analyze the browsing behavior of the users and classify them. A training dataset to be used for this classification includes some features that are fuzzy which may lead to incorrect results. Hence, there is a need of feature selection mechanism that selects only important features from the feature set and discards the irrelevant one. This paper proposes to use fuzzy entropy based feature selection for classification of website users in EDoS defense. To evaluate the performance, the classification is done with and without doing feature selection. The classification accuracy shows that the proposed approach is capable of producing more accurate results with fewer features than original feature space. © Springer Nature Singapore Pte Ltd. 2018.

Communications in Computer and Information Science

Fuzzy entropy based feature selection for website user classification in EDoS defense

This paper illustrates a new method for depression detection using EEG recordings of a subject. It is meant to be used as a computerised aid by psychiatrists to provide objective and accurate diagnosis of a patient. First, data from the occipital and parietal regions of the brain is extracted and different channels are fused to form one wave. Then DFT, using FFT, is applied on the occipito-parietal wave to perform spectral analysis and the fundamental is selected from the spectrum. The fundamental is the wave with the maximum amplitude in the spectrum. Then classification of the subject is made based on the frequency of the fundamental using rule-based classifier. Detailed analysis of the output has been carried out. It has been noted that lower frequency of the fundamental tends to show hypoactivation of the lobes. Moreover, low-frequency characteristics have also been observed in depressed subjects. In this research, 37.5% of the subjects showed Major Depressive Disorder (MDD) and in all 80% of the subjects showed some form of depression. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Cognitive depression detection methodology using EEG signal analysis

In the current scenario, there is no computing system in practice which understands or provides analytical insights for the language used in the psychological documents maintained by psychiatrists. Most importantly, there is no standardized language or methodology used in psychiatric practice to diagnose a psychological disorder. The main aim of our system is to provide a standardized platform for an assistance in diagnosis using Natural Language Processing techniques and Sentiment Inference. The system would suggest to the psychiatrist, the psychological disorder identified and its severity from the document text. The severity is obtained via a gradient score, which is computed by the semantic based gradient processing module - the core of our research. The psychiatrist can approve/correct the system diagnosis if necessary and reassign the gradient values for phrases contributing towards disorder severity. In an automated process, model gets re-trained after a particular threshold is reached, continually improving its performance. © 2018 IEEE.

2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018

Intelligent AI Assisted Psychological Disorder Analysis Using Sentiment Inference

Nowadays in educational institutions teachers provide problem statement to students and students submit their solution in accordance with problem statement provided. Grading of these solutions done manually. It is a time-consuming task. To find out the correctness of the solution they required to run multiple test cases. Also, teachers have to ensure that each computer in the lab has editor and compiler to run the code. This paper will provide guidelines for system interface to teachers in which teachers are able to create new programming assignments and assign it to a particular class. Also, this system helps teachers for grading of programming assignments. It will provide a detailed report of class for each programming assignments. It will also reduce the overhead of installing the software required to edit and run the code. This the system provide an easy interface to students to edit and to compile programs. It will also provide the detailed analysis of evaluated assignments. © 2017 IEEE.

Proceedings of 2017 International Conference on Intelligent Computing and Control, I2C2 2017

Online interactive educational system for submission and evaluation of programming assignments

Energy plays very important role in any kind of network. As most of routing protocols and strategies are designed by considering the well-connected network but because of the mobility of nodes these routing protocols do not work effectively. For such kind of intermittent connectivity, Delay Tolerant Network is used. Amongst all the mobile nodes, many have a very limited amount of energy and to perform any kind of operation energy is required. So, it is necessary to reduce the consumption of energy. The effective and efficient use of energy increases the lifetime of node and network. Hence, there are some approaches which proposed different schemes to reduce the energy consumption of node and also increase the delivery probability. By using different aspects of the conservation of energy the energy efficiency has been increased. Amongst them, few recent approaches are explained in this papers which consider the energy prominently. © Springer Nature Singapore Pte Ltd. 2018.

Lecture Notes in Networks and Systems

Energy based recent trends in delay tolerant networks

Data analysis plays a significant role in handling a large amount of data in the healthcare. The previous medical researches based on handling and assimilate a huge amount of hospital data instead of prediction. Due to an enormous amount of data growth in the biomedical and healthcare field the accurate analysis of medical data becomes propitious for earlier detection of disease and patient care. However, the accuracy decreases when the medical data is partially missing. To overcome the problem of missing medical data, we perform data cleaning and imputation to transform the incomplete data to complete data. We are working on heart disease prediction on the basis of the dataset with help of Naïve bayes and KNN algorithm. To extend this work, we propose the disease risk prediction using structured data. We use convolutional neural network based unimodel disease risk prediction algorithm. The prediction accuracy of CNN-UDRP algorithm reaches more than 65%. Moreover, this system answers the question related to disease which people face in their life. © 2018 IEEE.

Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018

Disease risk prediction by using convolutional neural network

Data clustering and classification technique algorithms often need to possess enough and prominent number of features in the data. Repeating and dominant features are useful in clustering or segmenting the image. The image segmentation method based on k-mean clustering, hierarchical clustering, and expectation maximization derives the optimum cluster centers based on the number of features such as similar intensity region. Deriving such number optimum number of clusters and its centers is an optimization problem. The aim of this paper is to improve the image segmentation using nature inspired techniques. Image segmentation which is complex optimization problem can be solved by this simple nature inspired PSO (Particle swarm optimization) model which is formulated in this paper. PSO model is generic model which is used to solve number of scientific problems. This paper formulates simple PSO model to solve the image segmentation problem. The proposed algorithm randomly assigns the centers to swarm and best value of objective function is initialized best on the color histogram of an image. This is discussed in section 2 and 3 of paper. Section 4 and 5 discusses and results and concluding remarks on results. © 2018 Association for Computing Machinery.