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

A Dual printing system is a mechanism used in normal printer to reutilize not only print medium but also print media. This mechanism involves three different procedures which works according to sensors used in it. A waste paper sheet full of texts or images or scratches, is given as an input to the printer to erase all the rough text and reutilize the waste page. These texts or images or scratches are done by using pen or pencil or printer text. Also we can abstract and reuse printed text ink obtained during the Heating procedure. In this whole system three procedures are involved. In the first process, pencil made rough work on the paper/waste paper erased by using erasable toner. In second procedure normal printer ink made or ball pen made texts and images erased using heating mechanism. Printed text ink melted during heating procedure is fused by applying cold air through head2. After that fused ink particles are abstracted and collected in the black ink container and same container can be reused for next printing in the printer. If texts or images are not erased during erasable toner and heating process then third technique (white ink spreading) is applied over the pixels of the character or pattern to get blank sheet. By applying all above techniques printing medium and print media are reutilized without affecting paper quality to save paper cost, printer ink, carbon emission and environment.

Engineering and Technology

World University root node

2018 International Conference On Advances in Communication and Computing Technology, ICACCT 2018

DPS - Printer: Dual Printer System

Sensor network is made up of a large number of sensor nodes for communication. Some nodes can work as a gateway which transmits data to the sink. These nodes are considered as a bottleneck in multi-hop networks because they send data collected from other nodes, and hence it drains their energy quickly. This problem is known as Hot Spot problem. So, for balancing the energy throughout the network, sink and node mobility can be used. A mobile sink could collect data without the need of gateway, and hence the problem of a bottleneck in the multi-hop network can be minimized. Along with the sink, sensor nodes also can be made mobile. Energy efficiency of WSN is achieved by using mobile sensors and mobile sink with enhanced coverage, and better channel capacity. There are various routing protocols to reduce energy consumption in a mobile WSN. This review paper﻿ mainly concentrates on the study of different protocols that are used for efficient energy consumption using the mobile sink and mobile sensor nodes. © 2018, Springer Nature Singapore Pte. Ltd.

Lecture Notes in Networks and Systems

A Comparative Study of Various Routing Technique for Wireless Sensor Network with Sink and Node Mobility

Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts. The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer (Genetic GWO) algorithm for which a C-mixture parameter is used. The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints, such as the k-anonymity, l-diversity and the m-privacy. A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size. The minimum value of the fitness ensures the maximum utility and the maximum privacy. Experimentation was carried out using the adult dataset, and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9, respectively.

International Journal of Modeling, Simulation,and Scientific Computing

Genetic grey wolf optimization and C-mixturefor collaborative data publishing

Many gazettes, toys, sensors, instruments, etc., devices are mushrooming in the electronics market these days which are running with different operating systems and application frameworks. In Mobile phones also many platforms are available with its own stack, right from hardware level to application level. So it is difficult to all the mobile phones or devices with same characteristics, performance, and features. The conclusive success of a platform entirely depends on its security to the user data ultimately, it constructs the global market. We present here a comparative study of four Mobile Operating Systems architecture and security perspectives of each. Moreover, experiments are carried out with Windows and android smartphone to study the cryptographic performance in these devices which plays as decisive parameters for future applications and system developers to implement secured devices and applications. We show that although algorithms characteristics are fixed, due to different architectures and dependencies in different devices with different operating systems, same efficiency, and performance is not guaranteed by cryptographic as well as other data structure algorithms. © 2018, Springer Nature Singapore Pte Ltd.

Intelligent Computing and Information and Communication

Issues of Cryptographic Performance in Resource-Constrained Devices: An Experimental Study

For recommending the best result to the user as per his requirement, User Profiling plays an important role. In user profiling, the profiles are created from the past data of same user. Maintaining the security and privacy of this data becomes a big challenge for researchers. Here, we are proposing the algorithm for privacy and security purpose of different profiles, with the integration of Information Dispersal Algorithm. The use of vast data of profiles by the user from any location at any time would be achieved by the use of the private cloud. As the profiles of different devices are maintained on the central cloud server, the recommendation for user for particular device can be executed easily. © Springer Nature Singapore Pte Ltd. 2018.

Information and communication technology for sustainable development

Proposed use of information dispersal algorithm in user profiling

The main objective of this paper was to propose cloud supporter framework to support cloud for processing multiple tasks. Because of multiple tasks on cloud server, it works slower and sometimes gets failed. In this situation distributing or scheduling of tasks on the distributed computing system is the only solution to reduce workload on server. Computing capability of smart phones CPU can compete with the CPU of the computers. Smart phones are energy-efficient and cost-effective alternative to running certain tasks of traditional servers. Distributing tasks for computations must utilize all the resources equally, no resource should be under or over utilize this problem leads to focus on the load balancing technique to support the cloud for processing tasks. Our approach is to form a load balancing frame work as cloud supporter for processing tasks on smart phones when they are plugged into the power sources.

2018 3rd International Conference for Convergence in Technology, I2CT 2018

A Load Balancing Framework in Cloud to Support Task Processing Using Smartphone with CWC

Texture image classification is very useful in many domains. It has been tried using statistical., spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram., Haralick's features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore., Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas., they were comparable with the BoVwapproach.

Comparison of Pixel N-Grams with Histogram, Haralick's features and Bag-of-Visual-Words for Texture Image Classification

Changing perspective of Industries with Industry 4.

Changing perspective of Industries with Industry 4.0

Recent and Innovation Trends in Computing and Communication (IJRITCC)

Diversifying Web Service Recommendation Results using Optimized Method of Users Reviews and Usage History

With the recent advances in machines technology, there is revolution in healthcare industry also. Thus, it gave rise to the concept of Electronic Health Record (EHR) which stores patients demographics, lab tests results, medical history, habits etc. in electronic form. EHR is voluminous data which is difficult to store, maintain or alter. To provide extended life for people, health risk prediction model using this EHR is formulated in this work. Feature Selection is used to select only associated or relevant data from the dataset. Logistic Regression is used with improved loss functionality parameter which increases the accuracy, response time and performance of the system. Representation Learning enables formation of feature vector of the selected features thus calculating their scores. Further, risk prediction is performed by the neural network model. Deep Neural Network (DNN) is used with many hidden layers containing activation functions. Transfer learning is used to avoid re-Training of the whole system every time new data enters the model. Dataset used here is of hypertension. EHR dataset is also synthetically created for analysis. © 2018 IEEE.