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

School of Computer Engineering & Technology

Meditation can significantly contribute to improving physical and mental health in modern stressful life. "OM" mantra is very easy to practice for meditation.This study is undertaken to classify the EEG band to observe abrupt changes in band as an effect of Om mantra meditation. Twenty-three naive meditators were experimented to chant OM mantra for 30 min and EEG signal recorded before and after meditation. The stationary wavelet transform is used to exact five bands from the EEG. The different statistical features were calculated. SVM classifier with Radial Basis Kernel is employed to classify the band. Results show the significant changes in the delta band which represent the brain in deep sleep. Thus OM meditation gives the experience of deep sleep. Thus study can be helpful to give new direction towards the meditation. © 2019 IEEE.

World University root node

Engineering and Technology

2019 IEEE 16th India Council International Conference, INDICON 2019 - Symposium Proceedings

SVM classification of EEG signal to analyze the effect of OM Mantra meditation on the brain

Every day, big amounts of unstructured data is generated. This data is of the form of essays, research papers, speeches, patents, scholastic articles, book chapters etc. In today's world, it is very important to extract key patterns from huge text passages or verbal speeches. This paper proposes a novel method for summarizing multilingual vocal as well as written paragraphs and speeches, using semantic Knowledge Graphs. Using the proposed model, big text extracts or speeches can be summarized for better understanding and analysis. The method uses speech recognition as well as Named Entity Recognition to identify entities from spoken content to create optimized Knowledge Graphs in the English Language. © 2019 IEEE.

2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019

A Knowledge Graph Based Approach for Automatic Speech and Essay Summarization

In recent years Mobile Wireless Sensor Networks have observed a substantial increase in the use of specific applications like surveillance, military applications, disaster management, wildlife monitoring, etc. The wireless nodes are small electronic devices embedded with various sensors to perform the given tasks. These sensor nodes are provided with a limited battery power which is a big constraint for the performance of the network in unattended environments. Also, mobility affects the battery life of the nodes to a great extent. Thus, it is necessary to design protocols to make the efficient use of available energy in the nodes and to acquire complete operability. This problem can be solved to some extent by clustering of the sensor nodes. This paper is a study about constraints of WSN and clustering techniques used for forming the network are presented. © 2019, Springer Nature Switzerland AG.

Lecture Notes on Data Engineering and Communications Technologies

A Survey on Mobility Driven Clustering Techniques for Wireless Sensor Networks

Now a day’s quantity of data growing day by day accordingly the size of storage media is also increasing rapidly. In most of the storage devices flash memories are used one of them is Solid State drive. Solid state drives i.e. SSDs are non-volatile data storage devices which store determined data in NAND or NOR i.e. in flash memories, which provides similar functionality like traditional hard disk (HDD). This paper provides comparative study of Solid-state drives over Hard-disk drives. Also, implementation of algorithm to enhance the security of Solid-state drives in terms of user authentication, access control and media recovery from ATA security feature set. This algorithm fulfils security principles like Authentication and Data Integrity.

Fulltext

International Journal of Engineering and Advanced Technology

Development of Algorithm to Enhance the Security Feature of SATA on Solid State Drives

Since last two decades, Wireless Sensor Network and Mobile Communication success rate is raised too high and demanding for overall communication network as it consists of heterogeneous wireless networks. These networks provides universal wireless access change, also provide dynamic scheduling between devices, networks and whole world for communication. It is applicable for prominently highly demanding domains like medical devices, sensors used for military applications, smart buildings, societies, and cities so on. At the same time new technological domain named IoT (Internet of Things) is making high impact on communication domain. It is an integration of several technologies. It is interchangeably can also be used as Wireless Sensor Network only the difference is that the IoT devices and networks has limited storage and computing capabilities and works on the internet. As IoT Ecosystem and WSN used for numerous Computation, Communication over the internet which ultimately increased network traffic and leads to congestion in the network which affects the Quality of Service (QoS) parameters of the network. In this paper, a novel method is proposed to handle the increased network traffic and Congestion network using the Krill Herd Aggregation Mechanism.

International Journal of Recent Technology and Engineering (IJRTE)

Congestion Control for Better Performance of WSN Based IoT Ecosystem using KHA Mechanism

With passage in technical and scientificknowledge, the execution and accomplishment of mobile devicesis increasing. The usage of smartphones is becoming generousnow a days. Since the performance and use of these smart devicesis expanding to a larger extent because of their capability toreserve a vast amount of data varying from a simple multimediafile such as images, videos to those important informationregarding contacts, messages etc. These smart devices aresometimes also used to store more complex information such asthose important and sensitive details regarding bank accounts,passwords, ATM pins etc. Since all these information can lead tovarious kinds of thefts. Therefore protecting this informationfrom unwanted theft of mobile smart devices and tracking thestolen device is a must. This paper presents an approach to anapplication that will help to protect the valuable data of theowner and helps to track the device when it is stolen by anyunwanted means. This application will use the internal camera ofthe smartphones that will start capturing the activity when theapplication starts. The application will run in the backgroundand will not be available in android launcher so that the one whohas stolen the device will never get to know that he has beencaptured along with his location.

International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878

Anti-Theft Hybrid Solution for Tracking &amp; Locating Mobile Devices with Data Security

Heart failure is a severe medical case, where the heart is not able to function properly to maintain blood flow. The surgical heart transplant procedure accomplished with the last stage of failure of the heart. Machine learning approaches account an ability to handle large datasets systematically and are extensively used in a biomedical research field. With the help of machine learning algorithms, tools are developed that helps specialist as a successful mechanism. The objective of this study is to learn different machine learning approaches for analyzing the heart transplantation dataset by using suitable classification algorithm. Also, the theoretical and the experimental comparative study of different machine learning techniques, using heart transplantation data. This study provides basic guidelines on machine learning technique. The results provide an overview of machine learning technique. We have used a WEKA machine learning software for evaluation and analysis to get an easy way to understand the result. © Springer Nature Singapore Pte Ltd. 2019.

Lecture Notes in Networks and Systems

Comparative Study of Machine Learning Approaches for Heart Transplantation

Unlike traditional Denial of Service (DoS) attacks, application layer DoS attacks are nearly undetectable at the network layer. CIC DoS is one of the intrusion detection dataset which includes application layer DoS attacks. Therefore in this study, we handle this dataset to detect application based DoS attacks by using Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting, Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) algorithms. The experimental results show that the performance of the LGBM based model is better than the other algorithms.

UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering

Intrusion Detection Using Machine Learning and Deep Learning Techniques

In this research paper, we have done the implementation and analysis of true color image compression and decompression technique. The implemented paper divides the color image into RGB component then after applying three-level Discrete Wavelet Transform, RGB components are split into nine higher frequency sub-bands and one lower order sub-band. The lower frequency sub-band is compressed into T-Matrix using One Dimension Discrete Cosine Transform. At the same time, higher frequency sub-bands are compressed using scalar quantize and eliminate zero and store data algorithm are applied to remove zeros in sub-band matrixes. Last, the encoded mode adopted arithmetic encoding. This algorithm has use two level of quantization this show significance improve in performance of compression algorithm. The decompression process is reverse process of encoder. The decompression algorithm decoded high-frequency subbands using return zero matrix algorithm and recover low-frequency sub-bands and other sub-bands using applying inverse process. © Springer Nature Switzerland AG 2019.

Lecture Notes in Computational Vision and Biomechanics

True color image compression and decompression using fusion of three-level discrete wavelet transform—discrete cosine transforms and arithmetic coding technique

The extraction of Bag of Visual Words (BoVW) features from retinal images for automated classification has been shown to be effective but computationally expensive. Histogram and co-variance matrix features do not generally result in models that have the same predictive accuracy as BoVW and are still computationally expensive. The discovery of features that result in accurate image classification on computationally constrained devices such as smartphones would enable new and promising applications for image classification. For example, smartphone retinal cameras can conceivably make diabetic retinopathy widely available and potentially reduce undiagnosed retinopathy if it could be achieved with computationally simple classification algorithms. A novel image feature extraction technique inspired by N-grams in text mining, called 'Pixel N-grams' is described that can serve this purpose. Results on mammogram and texture classification have shown high accuracy despite the reduced computational complexity. However retinal scan classification results using Pixel N-grams lag behind BoVW approaches. An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.