First International Conference on

12 - 13, April 2019Coimbatore I INDIA

First International Conference on

Advances in Intelligent Systems, Soft Computingand Optimization techniques 2019

03 - 04, April 2019 I Penang, Malaysia I www.icaisco.com

ICAISCO 2019

Book of Abstracts

The scope of the First International Conference on Advances in Intelligent Systems, Soft Computing and Optimization Techniques 2019

(ICAISCO 2019) is to offer a forum to share ideas, provide information, and enlarge peer research networks. ICAISCO 2019 opens an energetic

scientific forum by bringing researchers, engineers, and practitioners from diverse backgrounds all together to discuss and disseminate the

latest state-of-the-art achievements, challenges, and future directions in the fields of intelligent systems, soft computing and optimization.

ICAISCO 2019 will be kick- started by the welcome reception and an inaugural speech by an eminent professor followed by plenary speeches,

technical presentations including invited lectures, tutorials/workshops and oral/poster presentations. Several social programs also will be

given to the participants to offer the opportunities of strengthening peer network and interactions between the participants of the conference.

All the accepted papers of ICAISCO 2019 will be published in the SCOPUS indexed journals. ICAISCO 2019 will be held at The Light Hotel, Pinang,

Malaysia during 03 – 04, April 2019. ICAISCO is an annual event and is conducted in the first week of May every year.

The second edition of ICAISCO will be held during 06 - 07, May 2020. ICAISCO 2020 invites full length original research contributions from

engineering professionals from industries, R&D organisations, academic institutions, government departments and research scholars from across

the world. Full length original research contributions and review articles not exceeding seven pages in the single column format shall be submitted.

The manuscript template shall be found in the downloads section. The manuscript should contribute original research ideas, developmental ideas,

analysis, findings, results, etc. The manuscript should not have been published in any journals/magazines or conference proceedings and not under

review in any of them. Further the manuscript should contain the name of the corresponding author with e-mail id and affiliation of all authors. Soft

copy of the full length manuscripts (in .doc and .pdf) shall be submitted to icaisco2020@gmail.com.

Each submitted manuscript will receive a unique paper id. The manuscripts will be initially screened to check for the conference scope and

originality. All the submitted manuscripts will be sent for technical peer review process and the corresponding author will be notified the outcome of

the review process. ICAISCO adopts double blind review process. If reviewers recommended for further improvements in the manuscript, the

manuscript will be sent back to the corresponding author and the revised version of the manuscript shall be submitted within fifteen days on the

date of notification. The final decision on the manuscript will be announced after the third round of evaluation by the technical program committee.

The registered and presented papers of the

ICAISCO will be published into the book of

conference proceedings and the papers will

be published in Scopus indexed journals. The

list of the journals are available in the

conference website.

ICAISCO 2019

ICAISCO 2020 - Call for papers

Publications

Intelligent Systems

Artificial Intelligence

Intelligent Communications

Intelligent Electronics

Intelligent Electrical Systems

Intelligent Medical Systems

Intelligent Transportation Systems

Intelligent Control and Robotics

Intelligent Manufacturing Systems

Intelligent Property

Computational Intelligence

Pattern Recognition

Machine Learning

Multi-Agent Systems

Research manuscripts and proposals are invited in the following topics (but not limited to)

Topics

Soft Computing

Soft computing in Energy

Hardware/Software Co-Design

Fault Detection and Diagnosis

Bioinformatics

Design Management

Management Production Systems

Mathematical Models

Big Data Analysis

Peer review policy

Optimization Techniques

Fuzzy Logic and Reasoning

Neural Networks

Neuro-Fuzzy Systems

Genetic/Evolutionary Algorithms

Embedded Real Time Systems

Linear and Non-linear Programming

Discrete and Combinational Optimization

Optimization Software and Techniques

Communication and Informative

Cognitive Modeling

Mechatronics Design

Vision and Sensors

Web Intelligence and Interaction

Global Optimization

Control Theory and System Dynamics

Environment and Natural Resources

Data Mining

Learning and Adaptive Systems

Information Technology

Communication Engineering

Broad Band Communication

Computer and Intelligent Communication

Mobile and Optical Communication

Wireless Communication

Mobile and Optical Networks

Wireless Sensor Networks

Network Security

Advanced VLSI Systems

Embedded Wireless Systems

Medical Informatics

Hardware Engineering

Mechatranics

Robotics & Automation

Electrical Engineering

Electrical and Electronic Materials and

Process

Semiconductor Technology

Power Systems and Energy Engineering

Soft Computing Techniques in Power

Systems

Transmission and Distribution System and

Apparatus

Electromagnetics

Instrumentation & Feedback Control

Systems

Power Electronics & Energy Efficient Drives

Renewable Power Conversion Technologies

Power Quality Improvement Techniques

HVDC/FACTS

Electrical Machines and Industry

Applications

Bio-medical Engineering

Intelligent Systems

High Voltage Engineering & Insulation

Technology

Photo/Opto Electronics

Preface

The ICAISCO 2019 aims to offer a great opportunity to bring together professors, researchers and

scholars around the globe a great platform to deliver the latest innovative research results and the most

recent developments and trends in Electrical, Electronics and Computer Engineering and Technology

fields. The scope of the First International Conference on Advances in Intelligent Systems, Soft

Computing and Optimization Techniques 2019 (ICAISCO 2019) is to offer a forum to share ideas, provide

information, and enlarge peer research networks. ICAISCO 2019 opens an energetic scientific forum by

bringing researchers, engineers, and practitioners from diverse backgrounds all together to discuss and

disseminate the latest state-of-the-art achievements, challenges, and future directions in the fields of

intelligent systems, soft computing and optimization. The conference received more than 100

submissions from across the world and 36 papers have been accepted by the editorial board of ICAISCO

after the stringent screening and review process. 29 papers has been registered for ICAISCO 2019.

ICAISCO is an annual technical event and has been aimed to be conducted in the first week of May every

year. Being the first edition, ICAISCO received submission from different regions of the world and

scholars from 12 countries have registered and participated. All the papers registered for ICAISCO 2019

will be published in a Scopus indexed journal.

Dr. Thangaprakash Sengodan

Chair, ICAISCO 2019

Felicitations

I wish to extend my heartfelt felicitations to the executive committee of the first International Conference on Advances in Intelligent Systems, Soft Computing and Optmization Techniques 2019 (ICAISCO 2019). I am extremely happy and delighted to partner with ICAISCO 2019. Conferences like this provide an affable environment for the academicians and research scholars to freely exchange their views and ideas with the practicing engineers with considerable mutual benefits to shape up the future strategy for research in this field of science, engineering and technology. The whole spectrum of topics that are going to be discussed during ICAISCO 2019 will definitely provide an excellent opportunity to all delegates to exchange information on the latest developments.

I take this opportunity to convey my warm greetings and felicitations to the executive committee and participants and extend my best wishes for the success of the conference.

Dr. Nur Hafizah Ghazali

University Malaysia Perlis, Malaysia

Chair, ICAISCO 2019

Welcome note

As an organizing chair of ICAISCO 2019, I would like to welcome all the participants for the ICAISCO 2019 meeting at The Light Hotel, Penang, Malaysia. ICAISCO 2019 opens an energetic scientific forum by bringing researchers, engineers, and practitioners from diverse backgrounds all together to discuss and disseminate the latest state-of-the-art achievements, challenges, and future directions in the fields of intelligent systems, soft computing and optimization.

Dr. Mohammad Faridun Naim Tajuddin

University Malaysia Perlis, Malaysia

Organizing Chair, ICAISCO 2019

i | ICAISCO 2019

CONTENTS

CS 005 Dubai Real Estate: Technology Disruptions Reshaping the Market

Ashok Chopra 1

CS 013 Un-Normalized Hypergraph P-Laplacian Based Semi-Supervised Learning Methods Loc Tran, Linh Tran, An Mai, Tuan Tran 2

CS 014 Mathematical Modeling of Smart Irrigation System

Hesham Alhumyani, Sultan Alshamrani, Saleh Omran, Quadri Waseem 3

CS 016 Enhanced Bibliographic Data Retrieval and Visualization using Query Optimization and Spectral Centrality Measure Chitra A/P Ramasamy and Maslina Zolkepli 4

CS 001 Empirical Analysis of K-Means, Fuzzy C-Means and Particle Swarm Optimization for Data

Clustering Ahamed Shafeeq B M, Zahid Ahmed Ansari 5

CS 020 Intelligent Optimization Systems for Steam Boiler Maintenance: Real Case Study

N.F.A.Fuzi, Firas Basim Ismail 6

CS 022 Multilevel Edge Detection using an Improved Particle Swarm Optimization Khaled Beddakhe, Mohamed Tahar Kimour 7

CS 024 Organizational Culture Effect on Individuals’ Intentions for Knowledge Sharing

Arif Abdelwhab Ali 8

CS 002 Performance Comparison of MPICH and MPI4py on Raspberry Pi-3B Beowulf Cluster Saad Wazir , Ataul Aziz Ikram, Hamza Ali Imran, Hanif Ullah, Ahmed Jamal Ikram, Maryam Ehsan 9

CS 027 Current Biometric Systems for Attendance Taking Based on Hand Modalities

Seng Chun Hoo, and Haidi Ibrahim 10

CS 029 Features from Electroencephalogram (EEG) for Signal Analysis: A Literature Survey Chi Qin Lai, Haidi Ibrahim, Mohd Zaid Abdullah, Jafri Malin Abdullah, AiniIsmafairus Abd. Hamid, ShahrelAzmin Suandi, and Azlinda Azman 11

CS 031 Offline Handwritten Flowchart Recognition based on Faster-RCNN

Hung-Chun Chiu, Lijie Wen, Huaqing Wang, and Jianmin Wang 12

CS 032 Trapezoidal Approximation of Neutrosophic Numbers on Transportation problems M. Jagadeeswari, V. Lakshmana Gomathi Nayagam 13

CS 045 IOT Based Wearable Technology for Health Monitoring and Positioning System

Pooja Hulajatti, Pooja Deepsir 14

CS 049 Predictive Model Prototype for the Diagnosis of Breast Cancer using Big Data Technology Ankita SinhaBhaswati Sahoo, Siddharth Swarup Rautaray, Manjusha Pandey 15

CS 051 Extractive text summarization for single and multiple documents using lexical chains

Varsha Pandit, Nidhi Mishra 16

ii | ICAISCO 2019

CONTENTS

CS 054 Sentiment Analysis in Text Mining for Information Extraction using Machine Learning Approach Product Review J.Uma, Dr.K.Prabha, 17

CS 058 Recent Dimensions of Data Science: A Review

Sinkon Nayak, Mahendra Kumar Gourisaria , Manjusha Pandey and Siddharth Swarup Rautaray 18

CS 060 A New Method for Preventing Man-in-the-Middle Attack in IPv6 Network Mobility Senthilkumar Mathi andLingam Srikanth 19

CS 063 A Novel AdaBoost Approach to detect out the magnitude of Trash

Rahul Nijhawan, Muskan Choudhary and Pranshu Agarwal 20

CS 064 A Survey on Arabic Handwritten Character Recognition Amani Ali Ahmed Ali and Suresha M 21

CS 068 Intelligent Resource Provisioning and Decluttering cloud services using Virtual Agent

system S.Dhanasekaran, B.S.Murugan, S.Hariharasitaraman 22

CS 074 Hybrid Method of Local Binary Pattern and Classification Tree for Early Breast Cancer

Detection by Mammogram Classification Girija O K and M Sudheep Elayidom 23

CS 078 A Real Time Offline Positioning System for Disruption Tolerant Network

Arnika Patel and Dr. Pariza Kamboj 24

EC 002 A performance study of dimensionality reduction techniques for Telecommunication customer retention J.Vijaya, E.Ajith Jubilson and Ravi Sankar Sangam 25

EC 048 Defect Detection in Metal Sheets Using KNN

Anoopa Jose Chittilappilly & Kamalraj Subramaniam 26

EC 052 Risk Mitigation Model for Cyber Threat: A Case Study Approach Joshi Sujata, Pandey Roshan 27

EC 064 Bank Vault Security System Based on Infrared Radiation and GSM Technology

Mithun Dutta, Md. Ashiqul Islam, Mehedi Hasan Mamun, Kangkhita Kaem Psyche, Mohammad Al Mamun 28

EC 066 Energy Optimization and Sharing in Smart Grid with Renewable Energy System using

Cyber Physical System R.Karthikeyan, Dr.A.K.Parvathy, S.Priyadharshini 29

EC 069 Sonet DCS Self Healing Network Control architecture implementation by survivable Fiber

Optic Networks K.V.S.S.S.S.Sairam Chandra Singh , D K Shreekantha P Sai Vamsi K Annapurna K and Sarveswara Rao 30

Dubai Real Estate: Technology Disruptions Reshaping the Market

Ashok Chopra1

1Management & Commerce, Amity University, Dubai 1achopra@amityuniversity.ae

Abstract. Dubai has always in the fore front of initiative and innovation. Since Dubai economy has only Six percent

contribution of income from oil in its revenue. Dubai ranks 16th among innovation driven cities in world. Dubai realized

importance of real estate as its key sector which carry on growth engine in its revenue. It continued alluring investment from

global investors in its real estate market. Winning of Expo 2020 further added fuel to the fire of demand of real estate

investment. Lately market witnessed excess supply which prompted real estate agents starting accepting payment in digital

currencies (Crypto currencies). In the mean time crypto currencies which are based on Block chain as technologies started

witnessing unrealistic swings in market which lead to Governments and regulators not accepting crypto currencies as

alternative to current central system of currencies rather treat them as asset, stock or bond. Thus Crypto currencies even

after having suffix as currencies suffered severe blow to its acceptance. All this raised new doubt which is Block chain a

sound as technology acceptance in other areas. Are crypto currencies are using right platform and if so firstly when these

digital currencies would be accepted as alternative to current system, secondly if accepted would these make headway in

investment in real estate and save Dubai real estate which is currently witnessing erratic demand and supply situation. So in

essence in this case study we would understand basis of crypto currencies technology (Block chain), its acceptance and

stability as alternative to current system and how these crypto currencies can help save current scenario of Dubai real

estate.

Keywords. Block chain Technology, Crypto currencies, Dubai Real Estate Market

MYAPCS005

UN-NORMALIZED HYPERGRAPH P-LAPLACIAN BASED SEMI-SUPERVISED LEARNING METHODS

Loc Tran

Laboratoire Chart EA4004, EPHE-PSL University, France tran0398@umn.edu

Linh Tran

Ho Chi Minh University of Technology, Vietnam linhtran.ut@gmail.com

An Mai

John von Neumann Institute, VNU-HCM, Ho Chi Minh City, Vietnam an.mai@jvn.edu.vn

Tuan Tran

John von Neumann Institute, VNU-HCM, Ho Chi Minh City, Vietnam tuan.tran2016@qcf.jvn.edu.vn

Abstract: Most network-based machine learning methods assume that the labels of two adjacent samples in the network

are likely to be the same. However, assuming the pair wise relationship between samples is not complete. The information a

group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the

information loss of the above assumption is to represent the dataset as the hyper graph. Thus, in this paper, we will present

the un-normalized hyper graph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo

dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of

these un-normalized hyper graph p-Laplacian based semi-supervised learning methods are significantly greater than the

accuracy performance measure of the un-normalized hyper graph Laplacian based semi-supervised learning method (the

current state of the arthypergraph Laplacian based semi-supervised learning method for classification problem with p=2).

Keywords: hyper graph, p-Laplacian, semi-supervised learning, un-normalized, classification

MYAPCS013

0

MATHEMATICAL MODELING OF SMART IRRIGATION SYSTEM

1Hesham Alhumyani, 2Sultan Alshamrani, 3Saleh Omran, 4Quadri Waseem 1,2,4College of Computer and Information Technology, 3Department of Mathematics

Taif University, Saudi Arabia {h.alhumyani, Salehomran, susamash, quadri.waseem}@tu.edu.sa;

Abstract. Nowadays water scarcity is a big issue for farming especially in the Arabian Peninsula and in Drought-affected

Countries. Water irrigation is very important part of land farming. Therefore, there is a need for a smart solution to help

farmers to irrigate the farmlands in a sophisticated and productive manner. We try to make a smart irrigation system based

on Internet-of-Things (IoT) in order to provide the sufficient amount of water automatically using the deployment concept of

Wireless sensor networks (WSNs) in an efficient mathematical way. However, we need to provide enough water with the

use of less water sprinklers in a cost-effective way that are positioned at the right locations with the maximum coverage

area. In this paper, we mainly focus on how to deploy the controlled water sprinklers in certain area properly using the best

technique of Sensor Deployment for more better coverage area. We mathematically modeled the problem using simplicial

complex analysis to determine the needed number of sprinklers in order to cover certain areas. We aim to cover target areas

using triangulations that satisfy Euler characteristic formula for a projective plan.

Keywords: Smart Irrigation, IoT, Wireless sensor networks (WSNs), Mathematical modeling.

MYAPCS014

0

ENHANCED BIBLIOGRAPHIC DATA RETRIEVAL AND VISUALIZATION USING QUERY OPTIMIZATION AND SPECTRAL CENTRALITY

MEASURE

Chitra A/P Ramasamy and Maslina Zolkepli Universiti Putra Malaysia

chitra8984@yahoo.com and masz@upm.edu.my Abstract. As the amount of data generated is growing exponentially, harnessing such voluminous data has become a major

challenge these years especially bibliographic data. This study proposing an enhance bibliographic data retrieval and

visualization using hybrid clustering method consists of K-harmonic mean (KHM) and Spectral Algorithm and eigenvector

centrality measure. A steady increase of publications recorded in the Digital Bibliography and Library Project (DBLP) can be

identified from year 1936 until 2018, reaching the number 4,327,507 publications. This study will be focusing on the

visualization of bibliographic data by retrieving the most influenced papers using hybrid clustering techniques and visualize it

in an understandable network diagram using the weightage node. This web based approach will be using Java programming

language and MongoDB (NoSQL database) to improve the retrieval performance by 80%, precision of the search result of

the bibliographic data by omitting non-significance papers and visualizing a clearer network diagram using centrality

measure for better decision making. This method will make ease for the young researchers, educators and students to dive

into the enormous real world social and biological network.

Keywords: Bibliographic, DBLP, Spectral Clustering, Centrality Measure, NoSQL

MYAPCS016

Empirical Analysis of K-Means, Fuzzy C-Means and Particle Swarm Optimization for Data Clustering

Ahamed Shafeeq B M1, Zahid Ahmed Ansari2

1Department of Computer Engineering School of Science and Engineering

Manipal International University,Malaysia 2Department of Computer Science & Engineering

P.A.College of Engineering,Mangalore,India Abstract. Clustering is a fundamental task in data mining technique which puts more similar data objects into one group and

dissimilar objects into another group. The aim of this paper is to compare the quality of clusters produced by K-Means,

Particle swarm optimization (PSO) and Fuzzy C-Means (FCM) for data clustering. The k-means algorithm is the most widely

used partitional clustering algorithm technique in the industries and academia. The algorithm is simple and easy to

implement. The main drawback of the K-Means algorithm is that it is sensitive to the selection of the initial cluster centers

and it may converge to local optima. Fuzzy C-means algorithm is a popular algorithm in the field of fuzzy clustering. Fuzzy

clustering using FCM can provide a data partition that is both better and more meaningful than hard clustering approaches.

Particle Swarm Optimization (PSO) is an evolutionary computational technique which was motivated by the organisms

behavior such as schooling of fish and flocking of birds. The quality of the clusters produced by above three algorithms is

estimated using Silhouette Coefficient. The experimental results show that the performance of PSO clustering is better than

FCM & K-Means clustering. The difference in time taken by the algorithms for execution is negligible.

Keywords: Clustering, Particle Swarm Optimization, Fuzzy, K-Means, Silhouette coefficient.

MYAPCS01

8

Intelligent Optimization Systems for Steam Boiler Maintenance: Real Case Study

N.F.A.Fuzi1(a), Firas Basim Ismail1

1Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia.

a)Corresponding author: fazreenfuzi@yahoo.com Abstract. Steam boiler is a crucial equipment in the power plant. Recent years, most researcher found out that steam boiler

in a power plant have an issue of major outage. To avoid repetition of outage or failure, preventive maintenance scheduling

or known as scheduled maintenance had been used widely in the power plant as an alternative based on failure history of

the equipment. However, the main issue in scheduled maintenance is the redundancy of maintenance activities. Based on

selected adopted case study, we found out that there are some repetition activities in the scheduling. As a potential solution

to this problem, the Analytic Hierarchy Process (AHP) and the Particle Swarm Optimization (PSO) are implemented to the

adopted case study to optimize the maintenance operational duration and cost before implementing to the real case study.

MYAPCS020

Multilevel Edge Detection using an Improved Particle Swarm Optimization

Khaled Beddakhe1, Mohamed Tahar Kimour2

1,2 Laboratory of Research on Embedded Systems (LASE), University of Badji Mokhtar – Annaba, Annaba, BP 12, Algeria

1 khaled777@dr.com 2 tahar.kimour@univ-annaba.dz

Abstract. This paper presents a multilevel edge detection technique using a novel improvement of particle swarm

optimization algorithm, which computes the best thresholds that segment the image, while considering the spatial

information of the pixel contained in such image. Using Kapur’s entropy as the optimized objective function, we introduced

appropriate mechanisms to PSO aiming at speeding up the process of finding a solution, escaping the local optima, and

enhancing accuracy. Then, edge detection is easily performed based on the thresholds provided by such a solution.

According to our numerical experiments, our method exhibits good performance and segmentation accuracy.

Keywords: Image Segmentation, Multilevel Thresholding, Particle swarm optimization (PSO), Entropy.

MYAPCS022

Organizational Culture Effect on Individuals’ Intentions for Knowledge Sharing

Arif Abdelwhab Ali

Faculty of Computer Sciences and Information Technology University of Albutana, Rufaa, Sudan

arifkoo@yahoo.com

Abstract. Knowledge sharing is a complex process that involves various factors and influenced by different elements across

organizations. The effect of these factors also differ based on the type of industry and the organization’s nature. Individual

intention for knowledge sharing is a crucial factor that indicates the actual occurrence of knowledge sharing behavior. It

would be useful if organizations specify what factors have the capability to boost the staff intention for knowledge sharing.

The cultural element in organizations may influence individual intention for knowledge sharing. This paper investigates the

effect of the organizational culture on the individual intention for the knowledge sharing practices. The data for this study are

collected from the Oil and Gas industry using online questionnaire. Based on the study objective, the proposed model is

designed alongside the study hypotheses. The preliminary analysis is done using SPSS version 20.0. Whereas model

validation and hypotheses testing are done through Structural Equation Modeling technique using AMOS version 20.0. The

preliminary analysis includes generation of the sample profile, conduction of reliability analysis through Cronbach’s alpha

method as well as calculation of the correlation matrix for all variables. The second stage of analysis concerns about

validation of the proposed model and hypotheses testing results. The findings confirmed that supportive organizational

culture plays a positive role in boosting the intention of individuals for knowledge sharing practice in organizations.

Keywords: knowledge sharing; individual intention; knowledge management, organizational culture; knowledge sharing

practice;

MYAPCS024

Performance Comparison of MPICH and MPI4py on Raspberry Pi-3B Beowulf Cluster

Saad Wazir

EPIC Lab, FAST-National University of Computer & Emerging Sciences Islamabad, Pakistan

swazir.mscs18seecs@seecs.edu.pk

Ataul Aziz Ikram EPIC Lab, FAST-National University of Computer & Emerging Sciences

Islamabad, Pakistan ata.ikram@nu.edu.pk

Hamza Ali Imran

EPIC Lab , FAST-National University of Computer & Emerging Sciences Islambad, Pakistan

himran.mscs18seecs@seecs.edu.pk

Hanif Ullah Research Scholar,Riphah International University

Islamabad, Pakistan hukhan.dev@gmail.com

Ahmed Jamal Ikram

EPIC Lab, FAST-National University of Computer & Emerging Sciences Islamabad, Pakistan

ahmed.jamal.ikram@gmail.com

Maryam Ehsan Information Technology Department University of Gujrat Gujrat, Pakistan

maryam.ehsan@uog.edu.pk Abstract. Moore’s Law is running out. Instead of making powerful computer by increasing number of transistor now we are

moving toward Parallelism. Beowulf cluster means cluster of any Commodity hardware. Our Cluster works exactly similar to

current day’s supercomputers. The motivation is to create a small sized, cheap device on which students and researchers

can get hands on experience. There is a master node, which interacts with user and all other nodes are slave nodes. Load is

equally divided among all nodes and they send their results to master. Master combines those results and show the final

output to the user. For communication between nodes we have created a network over Ethernet. We are using MPI4py,

which a Python based implantation of Message Passing Interface (MPI) and MPICH which also an open source

implementation of MPI and allows us to code in C, C++ and Fortran. MPI is a standard for writing codes for such clusters.

We have written parallel programs of Monte Carlo’s Simulation for finding value of pi and prime number generator in Python

and C++ making use of MPI4py and MPICH respectively. We have also written sequential programs applying same

algorithms in Python. Then we compared the time it takes to run them on cluster in parallel and in sequential on a computer

having 6500 core i7 Intel processor. It is found that making use of parallelism, we were able to outperform an expensive

computer which costs much more than our cluster.

Keywords: MPI, Beowulf Cluster, SBC, HPC, MPI

MYAPCS02

6

Current Biometric Systems for Attendance Taking Based on Hand Modalities

Seng Chun Hoo, and Haidi Ibrahim

School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia.

Correspondence should be addressed to Haidi Ibrahim; haidi_ibrahim@ieee.org

Abstract. Significant progress in the field of biometric technology has provides option to educators in automatically marking

the attendance of students in a class. As a result, the educator has more time to conduct a class which in turn benefits the

students during the teaching and learning process. Besides, it also helps to curb fraudulent cases as the attendance is taken

depending on the distinctiveness of each student’s biometric trait. In this paper, a survey has been focused on the different

types of biometric attendance systems based on hand modalities such as fingerprint, finger vein and palm vein. From the

analysis, for fingerprint based attendance system, the average accuracy obtained is 97% while average processing time is

around 5 seconds per person. Palm vein attendance system has lower accuracy at only 70%. Hence, the outcome from the

survey will help future researcher in developing attendance system based on hand modalities with improved accuracy and

faster response time.

Keywords : Biometric based attendance systems, fingerprint, finger vein, palm vein, identification, verification.

MYAPCS027

Features from Electroencephalogram (EEG) for Signal Analysis: A Literature Survey

Chi Qin Lai1, Haidi Ibrahim1, Mohd Zaid Abdullah1, Jafri Malin Abdullah2,AiniIsmafairus Abd. Hamid2,

ShahrelAzmin Suandi1, and Azlinda Azman3 1School of Electrical & Electronic Engineering, Engineering Campus, UniversitiSains Malaysia (USM), 14300

NibongTebal,Penang, Malaysia 2Department of Neurosciences, School of Medical Sciences, UniversitiSains Malaysia(USM), 16150 Kubang Kerian,

Kelantan, Malaysia 3School of Social Sciences, UniversitiSains Malaysia (USM), 11800 Pulau Pinang, Malaysia

Corresponding author: Haidi Ibrahim (email: haidi_ibrahim@ieee.org)

Abstract. Electroencephalogram (EEG) is a recording from human brain, which normally been used to study brain activities.

As EEG is a multichannel signal with high temporal resolution, EEG gives us a huge amount of data. Thus, to fully utilize the

information from EEG, researchers have provided better alternatives such as automated processing that can analyze EEG

more efficiently. In the development of these approaches, informative features, which can be extracted mathematically, have

to be extracted from the EEG. Therefore, in this paper, a literature study is done to explore features that have been used by

researchers in order to represent the information in the EEG. Amongst these features are the spectral analysis features,

entropy, and statistical features.

Keywords: EEG · Features · Power Spectral Density · Entropy · Statistical Features.

MYAPCS029

Offline Handwritten Flowchart Recognition based on Faster-RCNN

Hung-Chun Chiu1, Lijie Wen1, Huaqing Wang1, and Jianmin Wang1 School of Software, Tsinghua University,

Beijing {qhj16,whq16}@mails.tsinghua.edu.cn, {wenlj,jimwang}@tsinghua.edu.cn

Abstract. Although handwritten flowchart recognition has been studied for many years, it is still a tough problem. Most

traditional methods are based on grouping strokes, which is known as online handwritten flowcharts recognition. However,

these methods cannot be applied directly on the offline handwritten flowcharts which are lack of the stroke information.

Hence it is in a great demand to design an effective algorithm to recognize the offline handwritten flowcharts. Inspired by

RPN (Region Proposal Network) in Faster-RCNN, a framework is proposed to detect different symbols in offline handwritten

flowcharts. With the predicted localization, a pixel searching algorithm is presented to refine the results of symbols. Finally,

all the symbols in flowcharts are connected, which turns offline flowcharts into images and structural XML. Experiments are

conducted on several datasets. The evaluation results show the accuracy of nodes recognition, text recognition and arrow

recognition hits 99%, 96.5% and 84% respectively, and the results of all types of symbols are better than those of other

online and offline methods.

Keywords: Offline · Handwritten flowcharts · Flowchart recognition · Flowchart generation · Faster-RCNN.

MYAPCS031

Trapezoidal Approximation of Neutrosophic Numbers on Transportation problems

1M. Jagadeeswari, 2V. Lakshmana Gomathi Nayagam

1Department of Mathematics, National Institute of Technology, Tiruchirappalli, 2Department of Mathematics, National Institute of Technology, Tiruchirappalli

Abstract. Zadeh introduced theory of fuzzy sets in 1965 as generalization of crisp sets to represent uncertain, incomplete,

imprecise and inconsistent information and Atanassov proposed intuitionistic fuzzy sets in 1986 as generalization of fuzzy

sets. Smarandache defined neutrosophic sets in 1995 which generalizes both fuzzy sets and intuitionistic fuzzy sets.

Compared to all other logics, neutrosophic logic introduces a model to represent the degree of indeterminacy hidden in

propositions due to unexpected parameters without constraints. Some authors proposed different types of neutrosophic sets

and their operations, applications in real life. In this study, we define some linear and non linear neutrosophic numbers,

approximation of these numbers by trapezoidal neutrosophic numbers, and some theorems related to them. In addition, an

example is provided to illustrate the implications of the proposed

numbers.

Keywords: Neutrosophic Number, Quadratic Neutrosophic Number, Approximation, Magnitude of Neutrosophic Number,

Transportation problem.

MYAPCS032

IOT Based Wearable Technology for Health Monitoring and Positioning System

Pooja Hulajatti1, Pooja Deepsir2

K.L.S Gogte Institute of Technology, Belgaum,India

Abstract. In this paper an idea of a wearable health monitoring and tracking device is proposed. The wearable device is

basically designed keeping hospital scenario in mind, especially for patients with Alzheimer’s, Dementia or similar kind of

diseases. The wearable device proposed is capable of both outdoor using GPS module and as well as indoor tracking using

RFID technology but with a new approach of moving objects (that need to be tracked) that switch their position. It can not

only track the patient but it is also capable of monitoring the patient’s basic health parameters such as temperature, heart

rate, glucose level and blood pressure using Non-Invasive techniques. It will intimate about location and health condition of

the patient to the caretaker or doctor in case of emergency through the app designed/SMS gateway[1]. The device will be

highly efficient, low cost and responsive rather than interactive unlike the existing devices. Feature addition to this could be

the shortest distance recognition to reach the patient in case of medical emergency.

Keywords: Indoor Positioning System (IPS), Radio Frequency Identification (RFID), Outdoor positioning, Global Positioning

System (GPS), Health monitoring system, Global System for Mobile communication (GSM), Heart rate sensor, Blood

pressure module, etc.

MYAPCS045 0

Predictive Model Prototype for the Diagnosis of Breast Cancer using Big Data Technology

Ankita SinhaBhaswati Sahoo, Siddharth Swarup Rautaray, Manjusha Pandey

KIIT Deemed University, Bhuneshwar E-mail: ethosankita@gmail.com,bhaswati.sahoofcs@kiit.ac.in, siddharthfcs@kiit.ac.in

manjushafcs@kiit.ac.in

Abstract. Big Data is the collection of millions of datasets from many different sources such as social media, banking, sales,

marketing etc. In every field, Big Data Technologies is used for analyzing, pre-processing, storing, and generating new

patterns for the benefits of the organization. The era of big data technology is nowadays booming[1]. Healthcare is one the

most important application of Big Data. In Healthcare data exist in different forms like heart rate, blood pressure, blood test,

sugar test, cholesterol and many more. Diagnosis of diseases at an early stage is also very important in

HealthCare services. Cancer diseases is an abnormal cell that negatively affects our body texture and regular functioning

body organs. Due to cancer, the death rate is the increased as it gets diagnosed at a later stage. Early diagnosis of cancer

increases the survival rate of a patient. This paper focuses on the prediction model for the diagnosis of breast cancer at an

early stage as it increases the chances for successful treatment. Data mining algorithm combined with Apache Hadoop is

promising independent tool for the prediction of breast cancer.

Keywords: Big Data· HealthCare· Prediction· Cancer· Breast Cancer.

MYAPCS049

Extractive text summarization for single and multiple documents using lexical chains

Varsha Pandit1, Nidhi Mishra2

1M. Tech scholar,Poornima University, Jaipur, India varshapandit06@gmail.com

2Associate Professor, Poornima University, Jaipur, India nidhi.mishra@poornima.edu.in

Abstract: Extractive Text summarization is the process which extracts the sentences which is important from text files. It

selects the sentences which is important and then combine those sentences which generates the summary. Our proposed

work includes computation of lexical chains and WordNet. The Summary is generated by using lexical chains which is better

than the other approaches used to generate summary. The combination of noun, proper noun, adverb, verb and adjective is

taken to generate better result. Our proposed work is not only deal with single text files but also deal with multi-text files. In

this paper, comparison of our work with previous work is presented which shows that our work is better than the prior work

and increase the recall value in less time.

Keywords: Extractive text summarization; lexical chains; WordNet ; Recall

MYAPCS051 20

SENTIMENT ANALYSIS IN TEXT MINING FOR INFORMATION EXTRACTION USING MACHINE LEARNING APPROACH PRODUCT

REVIEW

J.Uma, Research Scholar, Department of computer science Periyar University PG Extesion centre, Dharmapuri.

Dr.K.Prabha,

Assistant Professor, Department of computer science Periyar University PG Extesion centre, Dharmapuri

Abstract. Digital advancement in computerized information procurement strategies have prompted gigantic volume of

information. In excess of 80 percent of the present information is made out of unstructured or semi-organized information.

The recuperation of comparable examples and patterns to see the content information from gigantic volume of information is

a major issue. Content mining is a procedure of separating intriguing and nontrivial designs from gigantic measure of content

reports. There falsehoods numerous systems and instruments to mine the content reports and find the data for future and

process in basic leadership. The decision of choosing the privilege and proper content mining method recuperates the

speed and moderates the time and exertion required to get profitable data. This paper quickly talks about and examines the

content mining procedures and their applications. With the headway of innovation, an ever increasing number of information

is accessible in computerized shape. Among which, the greater part of the information (approx. 85%) is in unstructured

literary frame. In this manner, it has turned out to be basic to construct better systems and calculations to get valuable and

fascinating information from the huge measure of literary information. Subsequently, the field of data extraction and content

mining ended up well known regions of research, to get fascinating and needful data.

Keywords: Text mining, Text Mining Process, Summarization, and Text categorization, Information Extraction, Information

Retrieval, sentiment analysis, Machine Language.

MYAPCS054

2

0

Recent Dimensions of Data Science: A Review

Sinkon Nayak1, Mahendra Kumar Gourisaria2 , Manjusha Pandey3 and Siddharth Swarup Rautaray4

KIIT Deemed to be UNIVERSITY, BHUBANESWAR, INDIA sinkonnayak07@gmail.com , mkgourisariafcs@kiit.ac.in, manjushafcs@kiit.ac.in, siddharthfcs@kiit.ac.in

Abstract. Now a day’s huge amount of data has been generated and collected in every instance of time. Data has been

generated and collected very rapidly, so to analyze them is the toughest task to do Data are generated and collected in a

huge amount from unlike sources such as social media, business transactions, public data etc. These greater amount of

data are may be structured, semi structured and unstructured one. The data in which analysis is to perform now-a-days are

not only of massive amount but also varies each other by its types, at which speed it is generated and by its value and also

varies by different characteristics which is termed as Big data. So to examine this vast amount of data and get the relevant

information from it, analysis should be done and to analyze these huge amounts of data is a greater challenge these days.

So to analyze this vast amount of data we need the help of several data analytics tools and methods so that it will be easier

to deal with it. This survey paper talk about different tools and techniques used for big data analytics. This survey paper tries

to provide a clear idea about the genesis of Big data, features of Big data, different tools and techniques used to analyze

these huge collection of data.

Keywords: Big data· Techniques· Clustering· Classification· Architecture

MYAPCS058 0

A New Method for Preventing Man-in-the-Middle Attack in IPv6 Network Mobility

Senthilkumar Mathi1 andLingam Srikanth2

Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore,

Amrita Vishwa Vidyapeetham, India 1m_senthil@cb.amrita.edu2, cb.en.p2cse17014@cb.students.amrita.edu

Abstract. IPv6 is the next generation version of the Internet Protocol, which is soon bound to take over IPv4, its

predecessor completely. It has various features over IPv4 such as error detection and communication and is comparatively

more secure than the predecessor due to the usage of IPsec and ICMPv6. The neighbor discovery protocol, specific to IPv6

offers some applications for neighbor discovery, reachability, address resolution but more the number of applications, more

chance for vulnerabilities. Even though the IPv6 is said to be more secure than IPv4, it falls prey to some attacks which lead

to fatal consequences. One such attack is the man-in-the-middle attack where an attacker positioned manipulates the

communication in between the nodes. In spite of using IPsec, the attacker can cause a hindrance to the network. The man-

in-the-middle attack has many types and solutions proposed to prevent it. This paper explores man-in-the-middle attack

along with the existing solutions and proposes a new method to prevent it.

Keywords: IPv6, Neighbour discovery protocol, Fire brigade attack, Man-in-the-middle attack, Authentication.

MYAPCS060

A Novel AdaBoost Approach to detect out the magnitude of Trash

Rahul Nijhawan, Muskan Choudhary and Pranshu Agarwal Deptt. Of Computer Science Engg, Graphic Era University, Dehradun, India

{rahulnijhawan2010@gmail.com, muskanchoudhary1724@gmail.com, pranshuagarwal66@gmail.com}

Abstract. The waste generated by day to day use of domestic premise is called domestic refuse. This waste is not domestic

if it is taken under commercial arrangement. In this paper a novel AdaBoost approach is used for feature extraction.

AdaBoost is one of the most out-performing boosting algorithms. It has a solid theoretical basis and has made great success

in practical applications. AdaBoost is used to improve the performance of any machine learning algorithm. Due to the

absence of dataset, another dataset was built for testing the execution of the anticipated model. This was tested on our

dataset and also compared with other algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN),

Neural Network (NN) and Tree. We give a convergence result for the algorithm showing that prior knowledge can

substantially improve classification performance. After testing, we can visualize that highest accuracy (99.5%) was achieved

by novel AdaBoost approach.

Keywords: AdaBoost, Garbage, SVM, ANN, Stacking, Tree, KNN.

MYAPCS063

A Survey on Arabic Handwritten Character Recognition

Amani Ali Ahmed Ali1 and Suresha M.2

1Department of Computer Science & MCA, Kuvempu University, Shimoga, India. 1Department of Computer Science, Taiz University, Taiz, Yemen. dramaniali2@gmail.com

2Department of Computer Science & MCA, Kuvempu University, Shimoga, India. Abstract. There are much heavy studies on HCR which indicated to handwritten character recognition for nearly previous

four decades. The research on some of common script like Arabic, Indian and Chinese has been done. This manuscript

presents a survey of character recognition on Arabic script, most of the popular published paper methods are summarized,

also analyzed different methods for building a robust system of HCR and included some future research on recognition

direction of handwritten character. The paper analyzed and presented various algorithms with respect to pre-processing

methods, segmentation methods, feature extraction methods and various classification approaches of the Arabic character

recognition.

Keywords: Character Recognition of Arabic Handwritten, Artificial Neural Network, Freeman Chain Code, Fuzzy Systems,

Genetic Algorithms, Hidden Markov Model, Neural Network, Support Vector Machine.

MYAPCS064

Intelligent Resource Provisioning and Decluttering cloud services using Virtual Agent system

S.Dhanasekaran1, B.S.Murugan2, S.Hariharasitaraman3

1,2,3Department of CSE, School of Computing, Kalasalingam Academy of Research and Education,Srivilliputtur,Tamilnadu.

1srividhans@gmail.com, 2muruganbs@gmail.com, 3hariharasitaraman@gmail.com Abstract – Intelligent Cloud Service decluttering and resource provisioning in Virtual Agent system is a process of searching,

collecting, arranging, and dispatch the cloud services to the different clients based on their demands. Cloud Service

Decluttering (CSD) can be done by calculating free cloud resource and CPU utilization of each virtual machine by means of

Intelligent Agent Virtual Agent system. In this strategy, decluttering and resource provisioning is based on available cloud

resources. Here resource provisioning is done by two basic categorizations. First one to check the required cloud resources

and the number of jobs which is less than the available cloud resources. Provisioning is done if the condition is satisfied. The

second category checks where the required cloud resource is greater than the available free cloud resources. If the

condition satisfied we cannot allocate the job directly. The performance will get low if the jobs were allocated. Hence we split

job according to the need and available cloud resource and then allocate to corresponding node where need is less than the

free available cloud resources. The aim is to achieve maximum utilization of Cloud resource in effective manner.

Keywords: Cloud computing, resource provisioning, Cloud Service organizing, Agent.

MYAPCS068

Hybrid Method of Local Binary Pattern and Classification Tree for Early Breast Cancer Detection by Mammogram Classification

Girija O K1 and M Sudheep Elayidom2

1School of Engineering, CUSAT, Kerala, India. girijaok@gmail.com 2School of Engineering, CUSAT, Kerala, India.

Abstract. Breast cancer is an alarming disease due to mutation in breast cells and it is one type of the cancer among

women which highly leads to their death. One of the most effective tools for early detection of breast cancer is

mammography, which a screening tool is used to ex- amine the human breast by using low-dose amplitude X-rays.

Computer Aided Diagnosis (CAD) is used as an important tool to help the medical professionals for classifying breast

tissues into different class. Computer Aided Diagnosis (CAD) can be used to reduce human error in reading the

mammograms and it shows effective results in classification of benign and malignant abnormalities. The proposed method

presents a new classification approach to detect the abnormalities in the mammograms using Local Binary Pattern and

Decision Tree Classification. A uniform Local Binary Pattern(uLBP) is an extension of the original Local Bi-nary Pattern in

which only patterns that contain at most two transitions from 0 to 1 (or vice versa) are considered. In uniform Local Binary

Pattern (LBP) mapping, there is a separate output label for each uniform pattern and all then on uniform patterns are

assigned to a single label. These patterns are utilized to detect breast cancer by classification employing the Decision Tree

Classification. Specificity and sensitivity are the two statistical measures used in this proposed method to verify and

measure the significance of the test related to abnormalities in the breast tissues. Thus, it can be measurement of

performance test for the purpose of classify the patients who do and do not suffer from the cancer. The miniMIAS

mammography database is employed for testing the accuracy

of the proposed method and the results are promising.

Keywords: Breast Cancer, Mammography, Classification, Local Binary Pat-tern, Classification Tree, Early Detection.

MYAPCS074

A Real Time Offline Positioning System for Disruption Tolerant Network

Arnika Patel1 and Dr. Pariza Kamboj2,

Computer Engineering Department, SCET, GTU, Surat, India {arnikapatel9@gmail.com}

Abstract. Sparse mobile ad-hoc network called Disruption tolerant network may violate one or more assumptions of existing

TCP/IP protocol, therefore it might not serve well the Disruption tolerant network. In this network, end-to end disconnection

may be more common than connection. So positioning of mobile devices to have latest location in Disruption tolerant

network becomes more important. In this paper, we have implemented an offline positioning system and tested it in real

time. Here positioning of the users are carried out using Cell-Id, MNC (Mobile Network Code), MCC (Mobile Country Code),

LAC (Location Area Code). Comparison of online and offline positioning system in real time environment shows that

positioning system has comparable accuracy in offline mode. We have also carried out energy consumption in online and

offline modes and result shows that energy consumed is higher in online mode.

Keywords: Disruption tolerant network, Offline, Positioning, Cell-Id, MNC,MCC, LAC, Energy Consumption

MYAPCS078

A performance study of dimensionality reduction techniques for Telecommunication customer retention

J.Vijaya1, E.Ajith Jubilson2 and Ravi Sankar Sangam3

VIT University-Andra Pradesh, INDIA, vijaya.stephen@vitap.ac.in, ajith.jubilson@vitap.ac.in, ravisankar.s@vitap.ac.in

Abstract. Customer retention is one of the main objectives of Customer Relation Management(CRM) and customer churn

occurs when the customer terminates from a company or a service. The telecommunication sector has an important place in

people's business and daily life and has become one of the most important industrial enterprises of our time. Now a day's

exibility of customer mobility in the telecommunications sector is caused by the customer churn easier. Best feature

identification is one of the problems encountered in predicting telecommunication sector customer retention process. The

proposed churn prediction model consist of two stages: In stage one, feature selection techniques like Rough set based

attribute selection (RSAS), Information gain (IG) and feature deduction techniques like Principle component analysis (PCA),

Linear discriminant analysis (LDA) used to find the reduced attributes. In next stage the identified features combines

classifications of Decision tree (DT), K-nearest neighbor (KNN), Support vector machines (SVM) and Naive Bayes (NB) to

address the limitation of high dimensional classification. The proposed method achieves efficient Accuracy, True churn (TC),

Specificity, False churn (FC) and Precision which can be used to predict customer churn efficiently. It has been concluded

that the overall performance of RSAS based classifier is much better than other algorithms.

Keywords: CRM, Churn, Telecommunication, Feature Selection, RSAS, IG, Feature Reduction, LDA, PCA, Classifier, DT,

KNN, SVM, NB

MYAPEC002

Defect Detection in Metal Sheets Using KNN

Anoopa Jose Chittilappilly1 & Kamalraj Subramaniam2

1,2Department of Electronics & Communication Engineering Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

1anoopajosec@gmail.com, 2kamalrajece@gmail.com Abstract. For identifying the defects in industrial applications image restoration and classification algorithms are used. The

processes involved are acquisition of metal sheet image, converting original image into gray scale, restoring the image,

feature extraction and defect detection. Image restoration being the important preprocessing technique has been addressed

with the Non-Local Means (NLM) based algorithm. To improve the performance of the defect detection system, the k-

Nearest Neighbor (KNN) algorithm has been suggested. Accuracy, sensitivity, specificity, precision, recall, F-Measure and

G-Means are the indices that evaluated the performance of the detection algorithm.

Keywords: Image Restoration, Classification, Non-Local Means (NLM), Average RGB Features, k-Nearest Neighbor (KNN).

MYAPEC04

8

Risk Mitigation Model for Cyber Threat: A Case Study Approach

Joshi Sujata1, Pandey Roshan 2

1Professor, Symbiosis Institute of Telecom Management, Symbiosis International (Deemed University), Pune, India,

sjoshi@sitm.ac.in.

2 Student, Symbiosis Institute of Telecom Management, Symbiosis International (Deemed University), Pune, India,

roshan.pandey1820@sitm.ac.in Abstract. Organizations today are relying heavily on digital processes to manage their businesses hence Digital Risk

Management (DRM) has gained immense importance. As organizations are moving towards digital transformation, it is

becoming increasingly important for them to understand the digital risks associated with this move .The objective of this

study is to understand the risks in the area of cyber threat by studying several organizational use cases and propose a

mitigation model to curtail the risks in these areas. The paper adopts a case study based approach and discusses use cases

of cyber threats, its impact and solutions for its mitigation. The study aims at providing insights to managers in the area of

cyber risk and proposes solutions for mitigation of those risks. It will help managers cultivate a fresh approach to understand

and adapt to emerging digital threats and develop a resilient digital risk management strategy for the organization.

Keywords: Digital risk, Cyber threat, Mitigation model

MYAPEC052 0

Bank Vault Security System Based on Infrared Radiation and GSM Technology

Mithun Dutta1*, Md. Ashiqul Islam2, Mehedi Hasan Mamun3,

Kangkhita Kaem Psyche4, Mohammad Al Mamun5

1,5Lecturer, Rangamati Science and Technology University, Rangamati, Bangladesh 2,3Student, Daffodil International University, Savar, Dhaka, Bangladesh

4Jahangirnagar University, Savar, Dhaka, Bangladesh mithundutta92@gmail.com

Abstract: Recently banking security systems are getting more awareness and importance. Through this project a security

system has been designed to protect the bank vault from theft or unauthorized access. This banking security system with

high security based on Infrared ray and GSM technology, sensors(Sound sensor, Motion sensor, Laser sensor and Gas

sensor), IP camera, microcontroller, keyboard, and LCD. Warning SMS will be send to dedicated phone number through

GSM technology. In this paper highly reliable, multi level and most efficient vault security system has been designed. The

system may includes a biometric system, i.e., a fingerprint scanner or an iris scanner with a password/PIN, which are

responsible for the security of the main door of the vault and the system also includes Infrared radiation between the walls of

bank vault room to provide fine security of the vault. Walls of the vault room is specially designed in two sections maintaining

parallel gap where the Infrared radiation will pass from the Infrared radiation sources to prevent any accident like wall break

robbery. To monitor the unauthorized people in the vault area a passive infrared sensor is fixed. If any unauthorized motion

is detected from the camera the picture will be mailed to security office and the alarms will be on. The proposed security

system of bank vault has been designed and it has been tested several times and found most effective security system.

Keywords: GSM, Sensors, Infrared Radiation, IP camera, PIN, Microcontroller.

MYAPEC064

Energy Optimization and Sharing in Smart Grid with Renewable Energy System using Cyber Physical System

R.Karthikeyan1, Dr.A.K.Parvathy2, S.Priyadharshini3

1Assistant Professor, Department of Electrical and Electronics Engineering, Velammal Institute of Technology, Tamil Nadu, India

karthikeyan0505@gmail.com

2Professor,Department of Electrical and Electronics Engineering, Hindustan university of Science and Technology, Tamil Nadu, India

akparvathy@hindustanuniv.ac.in

3Assistant Professor, Department of Electrical and Electronics Engineering, Hindustan university of Science and Technology, Tamil Nadu, India ,

Abstract— The recent development in Micro smart grid technology has improved energy efficiency and renewable energy

utilization rate to serve local load with dispersed resources. We Propose a Hierarchical Household Load priority Load

scheduling algorithm using cyber physical controller for Hybrid Energy Management in micro Smart Grids to maximize the

utilization rate of the Renewable energy resources connected to the system. The Household appliances are connected to

bundle with different priority according to the energy consumption pattern and the customer sophistications. The inclusion of

energy storage systems in micro grids provides the energy management with additional degrees of freedom, and therefore,

makes the micro grid more flexible to the changeable situations. This paper focuses on implementing adaptable energy

management system for micro grids in order to coordinate generation, demand, and storage, as well as compensate the

effects of the variability of renewable energy generators and load fluctuations, in view of the user necessities. The

considered hybrid energy systems comprise of renewable sources (solar photovoltaic and wind turbine), conventional

systems (utility grid connection), battery-based energy storage systems and loads. In this work, a modular structure of

energy management system is introduced in order to conduct the function of optimizing the energy dispatch of distributed

resources. The Scheduling of the appliance are done according the priority and the availability of the renewable resources

such as Photovoltaic Cells and Wind energy added to the grid. The proposed scheduling algorithm used Minority Game

Technique to improve the utilization factor of the renewable resources. The System further uses Non- Intrusive Load

monitoring is essential to obtain specific power consumption by individual appliances which will be used to allocate priority

during the load scheduling.

Keywords: Smart Grid, Load Scheduling, Non-Intrusive, Renewable Resources, Hybrid Energy Management, Cyber

Physical System

MYAPEC066

Sonet DCS Self Healing Network Control architecture implementation by survivable Fiber Optic Networks

K.V.S.S.S.S.Sairam Chandra Singh , D K Shreekantha P Sai Vamsi K Annapurna K and Sarveswara Rao

125 Dept. of ECE, NMAMIT, India 46 Dept. of CSE, NMAMIT, India

Dept. of ISE, NMAMIT, India Dept. of CSE, CEC, India

drsairam@nitte.edu.in,chandrasingh146@gmail.com, mbharish8@gmail.com

Abstract. Optical Communication System implies Elements, Devices and Systems. Optical Networks consists of three

domains such as Networking, Switching and Routing. Survivability plays an important role in order to protect the network

from failures. Hence survivability in optical network determines the interconnectivity between user and system

configurations. Today’s Optical Network Environment (ONE) presents N X N and carries the demands in terms of THz. Thus

two modes are described to plan and design of SFON, They are Physical Layer Mode (PLM) and Logical Layer Mode (LLM).

PLM consists of cross connectivity of digital signals, bandwidth estimation, user survivability impact ratio, network path

estimation and network capacity model. In LLM it describes the user node and flow survivability path, scaled the node

position dependency and independency between different topologies to evaluate the throughput of the system Tier

Configurations (TC). Further these modes present the Local and Global fairness (LGF) scenario regarding the packet

transfer between Single Period Demand (SPD) to Multi Period Demands(MPD). The DCS Network, Restoration methods are

processed by using five phases viz. Detection, propagation , Routing ,Rerouting and Normal Path. Further these methods

can be implemented by Self Healing Centralized Demand (SHCD) and Distributed Demand(DD) , Static versus Dynamic

rerouting path and signal restoration level by using Network Restoration i.e. Digital Cross Connectivity.

Key Words : Optical Network Environment (ONE) , Physical Layer Mode (PLM) , Logical Layer Mode (LLM) , Tier

Configurations (TC) , Local and Global fairness (LGF), Single Period Demand (SPD) , Multi Period Demands(MPD) , Digital

Cross Connectivity System (DCS), Self Healing Centralized Demand (SHCD) , Distributed Demand(DD).

MYAPEC069

Conference Chair (s)

Dr. Nur Hafizah Ghazali

University Malaysia Perlis, Malaysia

Dr. Thangaprakash Sengodan

SVS College of Engineering, India

Organizing Chair

Dr. Mohammad Faridun Naim Tajuddin

University Malaysia Perlis, Malaysia

Editors

Dr. Ramani Kannan

University Technology Petronas, Malaysia

Dr. Sanjeevkumar Padmanaban

Aalborg University, Denmark

Finance Chair

Mr. Azralmukmin Azmi

University Malaysia Perlis, Malaysia

Review Committee Chair(s)

Dr. Baharudin Bin Ismail

University Malaysia Perlis, Malaysia

Dr. Marimuthu C

Nandha Engineering College, India

Publicity Chair(s)

Mr. Mohammad Fayzul Mohammad

University Malaysia Perlis, Malaysia

Mr. Ahmad Asri Abd Samat

University Technology Mara, Malaysia

Technical Program Committee

Dr. Shahrin Md Ayob, University Technology Malaysia, Malaysia

Dr. Amjad Anvari, Aalborg University, Denmark

Dr. Sardar Ethem Hamamci, Inonu University, Turkey

Dr. Sanjay Misra, Covenant University, OTA, Nigeria

Dr. Omar Ellabban, Texas A&M University, Qatar

Dr. Mani V N, Senior Scientist, DEIT, Hyderabad, Govt. of India

Dr. R. N. Patel, National Institute of Technology, Raipur

Dr. Mandal S K, NITTTR, Kolkatta

Dr. Krishnan A, KSR College of Engineering, India

Dr. Senthikumar S, SVS College of Engineering, India

Dr. Anand Nayyar, Duy Tan University, Vietnam

Dr. R. Murugappan, KIST, Kwait

Dr TOH Tien Choon, Universiti Tunku Abdul Rahman, Malaysia

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