EAI Endorsed Transactions on Scalable Information Systems

EAI Endorsed Transactions on Scalable Information Systems is an open access, peer-reviewed scholarly journal focused on scalable distributed information system, scalable, data mining, grid information systems and more. The journal publishes research articles, review articles and editorials with a quarterly frequency. Authors are not charged for article submission and processing.

Editor(s)-in-Chief: Hua Wang, Xiaohua Jia and Manik Sharma

Guest Editor(s): Gurdev Singh

Aims & Scope
Indexing
Editors in Chief
Editorial Board
Special Issues

As the data volumes continue to increase and the ways of information dispersion across the globe continue to diversify, new scalable methods and structures are needed for efficiently processing those distributed and autonomous data. Grid computing, P2P technology, distributed information retrieval technology and networking technology all must be merged to address the scalability concern.

The scope of the journal includes:

Scalable distributed information systems
Scalable grid information systems
Parallel information processing and systems
Web information searching and retrieval
Data mining
Content delivery networks (CDN)
VLDB
P2P systems
Scalable mobile and wireless database systems
Large scale sensor network systems
Index compression methods
Architectures for scalability
Scalable information system applications
Evaluation metrics for scalability
Information security

Ajay Kattepur (Tata Consultancy Services)
Aniello Castiglione (University of Salerno)
Chang Choi (Chosun University)
Cho-Li Wang (University of Hong Kong)
Daniel S. Katz (University of Chicago)
Fabrizio Silvestri (ISTI – CNR, Italy)
Giancarlo Fortino (University of Calabria)
Hamed Taherdoost (Hamta Business Solution Snd)
Heng Tao Shen (University of Queensland)
Houbing Song (Embry-Riddle Aeronautical University)
José Manuel Machado (Uniiversity of Minho, Portugal)
Jose Merseguer (Universidad de Zaragoza)
Jie Li (University of Tsukuba)
Lin Yun (Harbin Engineering University)
Mirjana Ivanovic (University of Novi Sad)
Phan Cong Vinh (Nguyen Tat Thanh University)
Raj Gururajan (University of Southern Queensland)
Sherman Chow (Chinese University of Hong Kong)
Silva Fábio (University of Minho, Portugal)
Steve Beitzel (Telcordia)
Tuan Phung-Duc (University of Tsukuba, JP)
Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)
Wang-Chien Lee (The Pennsylvania State University)
Weili Wu (The University of Texas at Dallas)
Xueyan Tang (Nanyang Technological University)
Vijayakumar Ponnusamy (SRM University, India)
J Amudhavel (KL University, India)
Yingshu Li (Georgia State University)
Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)
Karolj Skala (Ruđer Bošković Institute, Croatia)
Xiao-Zhi Gao (University of Eastern Finland, Finland)
Thaier Hayajneh (Fordham University, USA)
Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
Nuno M. Garcia (Universidade da Beira Interior, Portugal)
Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)
Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)
Hiep Xuan Huynh (Can Tho University, VietNam)
Dongming Li (Nanjing University of Posts and Telecommunications (NUPT), China)
Vincent A. Cicirello (Stockton University, USA)
Ji Zhang (University of Southern Queensland, Australia)
Xiaohui Tao (University of Southern Queensland, Australia)
Ye Wang (National University of Defense Technology, China)

Hua Wang¹

Hua Wang is now a full-time Professor at Victoria University. He was a professor at the University of Southern Queensland before he joined Victoria University in 2014. Hua has more than ten years teaching and working experience in Applied Informatics at both enterprise and university. He has expertise in Big Data, Health Informatics, Cloud Computing, Cyber Security, Artificial Intelligence. As a Chief Investigator, Hua has successfully received four Australian Research Council (ARC) Discovery grants and two Linkage grants since 2006. He is also a successor for international grants such as two Japan – Australian grants, one German – Australian grant, one Norway Government grant and grants from Hong Kong Chinese University, Hong Kong City University. Hua has published 258 refereed scholar papers in data security, access control, data mining, database, privacy preserving and Web services. Representative publications are on ACM Transactions on Internet Technology, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Automation Science and Engineering and World Wide Web; and Proceedings of ACL, AAAI, CIKM, ICDE, ICDM and PAKDD.

Xiaohua Jia²

Xiaohua Jia received his BSc (1984) and MEng (1987) from University of Science and Technology of China, and DSc (1991) in Information Science from University of Tokyo. He is currently Chair Professor with Dept of Computer Science at City University of Hong Kong. His research interests include cloud computing and distributed systems, data security and privacy, computer networks and mobile computing. Prof. Jia is an editor of IEEE Trans. on Parallel and Distributed Systems (2006-2009), IEEE Internet of Things (2013-2018), Journal of World Wide Web, Journal of Combinatorial Optimization, etc. He is the General Chair of ACM MobiHoc 2008, TPC Co-Chair of IEEE GlobeCom 2010 – Ad Hoc and Sensor Networking Symp, Area-Chair of IEEE INFOCOM 2015-2017, Track-Chair of IEEE ICDCS 2019, and General Chair of ACM ICN 2019. He is a Fellow of IEEE (Computer Society) and Distinguished Member of ACM.

Manik Sharma³

Dr. Manik Sharma is an MCA, UGC-NET qualified and has received his Ph.D. degree in “Distributed Query Optimization”. He has more than 12 years of teaching experience. He is working in the area of Data Mining, Machine Learning and Soft Computing with specialization in the diagnosis of diseases and modeling of DSS query optimizer. The thrust area is to improve the predictive accuracy in early diagnosis of several fatal human disorders. He has published 3 books related to Computer Network, Internet Applications, Management Information System and Database Theory. In addition, he has also published more than 25 research papers in various journals of International repute including Scopus and Thomson Reuters. He has also presented more than ten research articles in various national and international UGC and IEEE conferences. Moreover, he is an active member of International Program Committees, Technical Program Committees, and Advisory Committees of several academics conferences (United Kingdom, Singapore, Mauritius, Thailand, Vietnam, USA, Hong Kong, Canada, UAE, and India).

1: Victoria University, Australia
2: City University of Hong Kong
3: DAV University, India

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Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model
Appears in:
sis 18: e20

Authors:
Akshay Aggarwal, Aniruddha Chauhan, Deepika Kumar, Mamta Mittal, Sharad Verma

Published:
9th Apr 2020

Abstract:
With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles manually is a time-consuming and laborious task, thus, …With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People
these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles
manually is a time-consuming and laborious task, thus, giving rise to the requirement for automated computational tools that
can provide insights about degree of fake ness for news articles. In this paper, a Natural Language Processing (NLP) based
mechanism is proposed to combat this challenge of classifying news articles as either fake or real. Transfer learning on the
Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task. This paper
demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform
significantly well on the downstream task of classification of news articles. In addition, LSTM and Gradient Boosted Tree
models have been built to perform the task and comparative results are provided for all three models. Fine-tuned BERT
model could achieve an accuracy of 97.021% on NewsFN data and is able to outperform the other two models by
approximately eight percent.
more »
Big Computing: Where are we heading?
Appears in:
sis 18: e19

Authors:
Sabuzima Nayak, Ripon Patgiri, Thoudam Doren Singh

Published:
9th Apr 2020

Abstract:
This paper presents the overview of the current trends of Big data against the computing scenario from different aspects. Some of the important aspect includes the Exascale, the computing power and the kind of applications which offer the Big data. This starts with the current computing hardware c…This paper presents the overview of the current trends of Big data against the computing scenario from
different aspects. Some of the important aspect includes the Exascale, the computing power and the kind of applications which offer the Big data. This starts with the current computing hardware constraint against
the need of the rising Big data applications. We highlight the issues and challenges of energy requirement, software complexity, hardware failure, fault tolerant computing, and communication. As the complexity of computation is going to rise in the future. The paper also highlights the future direction of Big computing systems for Bioinformatics, social media, hardware and software requirements, data intensive computation and then towards GPU era.
more »
Enhanced Bitcoin Protocol with Effective Block Creation and Verification by Trusted Miners
Appears in:
sis 18: e18

Authors:
R. Bala, R. Manoharan

Published:
19th Mar 2020

Abstract:
The Distributed nature of Bitcoin introduces security issues that necessitate security-specific enhancements in Bitcoin protocol. Therefore, proposing a method of incorporating criteria check and verification process for miners to participate in the mining process and join the mining pool respectiv…The Distributed nature of Bitcoin introduces security issues that necessitate security-specific enhancements in Bitcoin protocol. Therefore, proposing a method of incorporating criteria check and verification process for miners to participate in the mining process and join the mining pool respectively. The proposed idea mitigates double spending, block withholding, and 51 percentage attacks. In addition, an increase in the rate of Bitcoin users necessitates performance improvement. Hence,
proposing an effective approach of refining the existing block creation and verification strategy for improving transaction rate without compromising security.
more »
A Novel & Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery
Appears in:
sis 18: e17

Authors:
Shefali Dhingra, Poonam Bansal

Published:
17th Mar 2020

Abstract:
An efficient and novel image retrieval system is framed here, which retrieve images from massive datasets to overcome the constraints of efficiency and retrieval time. Thus to address this issue, an effective indexing technique is proposed on the hybrid system constituted by low level features of t…An efficient and novel image retrieval system is framed here, which retrieve images from massive datasets to overcome the constraints of efficiency and retrieval time. Thus to address this issue, an effective indexing technique is proposed on the hybrid system constituted by low level features of the image. Firstly, features are extracted from the combination color moment, LBP and segmentation to form a hybrid feature space. To reduce its dimensional space, principle component analysis is exercised which provide lesser and good quality features. On this space, two expedient indexing techniques are proposed: cluster based and similarity based. The approach that is proposed here is an innovative design of a hybrid content based image retrieval system, as in this framework all the skilled techniques are merged to form a competent and dynamic image retrieval system. Five touchstone datasets are used to test the performance of the system. Extensive experiments are carried out which shows that the system with cluster based indexing technique provides highlighted results as compared to similarity based technique and also surpasses the other latest state of art techniques in terms of precision and retrieval time.
more »
Learning Model for Phishing Website Detection
Appears in:
sis 18: e16

Authors:
A. Suryan, C. Kumar, M. Mehta, R. Juneja, A. Sinha

Published:
13th Mar 2020

Abstract:
Website portal empowered with information technology are of great importance in present scenario. With access to data all around the world, securing our information becomes an issue of topmost priority. Over the decade there have been numerous attacks by phishing websites and people have lost hug…Website portal empowered with information technology are of great importance in present scenario. With access to data all
around the world, securing our information becomes an issue of topmost priority. Over the decade there have been
numerous attacks by phishing websites and people have lost huge resources. Such malicious websites, also known as
phishing website, steal information of authenticate users and carry out illegal transactions by misusing the personal
information. Phishing website links and associated e-mails are sent to billions of users daily, thereby becoming a big
concern for cyber security. In this paper, we address the phishing problem using machine learning approach applied on our
proposed model, which uses 30 distinct features for phishing detection. We extracted multiple features from the website
link and applied appropriate algorithms to classify the link as legitimate or phishing links.
more »
FEECA: Fuzzy based Energy Efficient Clustering Approach in Wireless Sensor Network
Appears in:
sis 18: e15

Authors:
Anshu Kumar Dwivedi, A. K. Sharma

Published:
12th Mar 2020

Abstract:
With huge advancements in Microelectronics, Wireless Sensor Network (WSN) is conquering its domain with low powered tiny sensors. These sensors are commonly used in monitoring and surveillance of remote and urban areas. Since the power supply to these tiny sensors is a battery, for the maximal effi…With huge advancements in Microelectronics, Wireless Sensor Network (WSN) is conquering its domain with low powered tiny sensors. These sensors are commonly used in monitoring and surveillance of remote and urban areas. Since the power supply to these tiny sensors is a battery, for the maximal efficiency of WSN, there arises a need for a maximal
lifetime of these tiny sensors wherever they are deployed. Clustering is a promising solution but there is uncertainty in the selection of cluster heads. Fuzzy logic, however, can contribute to the selection of optimal candidates to play the role of such cluster heads. To muddle through the issue, we have proposed a fuzzy-based energy-efficient clustering approach
(FEECA) in wireless sensor networks and also designed a fuzzy inference system that defines influential parameters for selecting optimal candidates for cluster head (CH) role. To reduce the expenditure borne by communication, master nodes are selected among the chosen cluster heads so that their communication distance to the sink can be reduced. In the proposed scenario the area is divided into the diagonal form to reduce the load of the network. Each part consists of a Sensor Node (SN), cluster head, and master node. In the diagonal form, the network is divided so that the distance travelled by the data packet through the diagonal path is always smaller than the distance in which the horizontal path exists, based on the triangular inequality theorem. Simulation experiments were conducted and experimental results
unveiled better performance of proposed work in terms of stability period, more packet delivery to sink and extended lifetime.
more »
Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations
Appears in:
sis 18: e14

Authors:
Virendra P. Vishwakarma, Sahil Dalal

Published:
10th Mar 2020

Abstract:
The multifaceted light varying environment severely degrades the performance of person recognition using facial images. Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and fuzzy logic concepts. An efficient illumination normali…The multifaceted light varying environment severely degrades the performance of person recognition using facial images.
Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and
fuzzy logic concepts. An efficient illumination normalization method is presented with the help of a new modified S
membership function. The proposed method of illumination normalization retains the large scale facial features as well as
suppresses the variations related to change in light variations. Kernel extreme learning machine (KELM) which is a nonlinear and non-iterative learning algorithm of ANN is used for classification. Various kernel types and parameters are
experimented to find the best choice for robust classification. To assess the performance of proposed hybridization, Yale
and extended Yale B face databases have been used. Very promising results have been achieved which establish the worth
of the proposed method.
more »
Social Networks Mining and Analysis of Specific Groups (Political and Regional) by using API
Appears in:
sis 18: e13

Authors:
Farhan Khan, Saqib Saqib, Pireh Soomro, Asif Ali Laghari, Kamran Ali Memon

Published:
10th Mar 2020

Abstract:
People have informal conversations on social media sites (Twitter, Facebook, etc) that shed light on the current issues around the world – opinions, and concerns about the learning process. Data from such environments can provide valuable information to predict the future to make effective decisi…People have informal conversations on social media sites (Twitter, Facebook, etc) that shed light on the current issues
around the world – opinions, and concerns about the learning process. Data from such environments can provide valuable
information to predict the future to make effective decision making, however, the usage of such data is challenging. The
complexity of such data from social media content requires the use of human understanding as well as social network
analysis to make predictions. We focused on political groups around the world to discover linkages behind the Terrorism
around the World to make predictions depend upon the data available. In this research paper, we focused on tweets related
to the politicians and communities based on hashtags like #Syria #Weapons #Terrorist, etc. to identify the linkages of the
user who are talking about these issues depending upon the HashTag we used for the content analysis of social network by
using Gephi to make a prediction.
more »
Performance analysis of compression algorithms for information security: A Review
Appears in:
sis 18: e12

Authors:
Neha Sharma, Usha Batra

Published:
6th Mar 2020

Abstract:
Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques: - lossy a…Data compression is a vital part of information security, since compressed data is much more secure and convenient to
handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are
two types of compression algorithmic techniques: - lossy and lossless. These techniques can be applied to any data format
like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used
for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are
implemented and evaluated on parameters like- file size, saving percentage, time, compression ratio and speed. Detailed
analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However,
lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms
other algorithms.
more »
An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers
Appears in:
sis 18: e11

Authors:
Muhammad Abbas, Kamran Ali Memon, Noor ul Ain, Ekang Francis Ajebesone, Muhammad Usaid, Zulfiqar Ali Bhutto

Published:
27th Feb 2020

Abstract:
Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of these classifiers with training patterns of differen…Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one
parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of
these classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of the
kNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify wine
quality. The proposed method typically identifies the same number of nearest neighbours for each test example. Results
indicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the least
sensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees and
logistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root mean
square error (RMSE).
more »
Simplification of Punjabi Sentences: Converting Complex Participial Sentences into Simple Sentences
Appears in:
sis 18: e10

Authors:
R. M. Jindal, V. Rana, S. K. Sharma

Published:
27th Feb 2020

Abstract:
INTRODUCTION: In this world of internet and artificial intelligence, Natural Language Processing has emerged as most demanding research area. Under Natural Language Processing, sentence simplification is one of the research area that deals with simplification or conversion of complex sentences in…INTRODUCTION: In this world of internet and artificial intelligence, Natural Language Processing has emerged as most
demanding research area. Under Natural Language Processing, sentence simplification is one of the research area that
deals with simplification or conversion of complex sentences in to simple sentences.

OBJECTIVES: In this research
article, author has proposed a novel approach for conversion (simplification) of complex sentences (participial type) of
Punjabi language into easily understandable simple sentences.

METHODS: Author performed lexical and morphological
simplification by using morphological features of the language. Morphological features are used to identify the participial
type complex sentences.

RESULTS: on testing the proposed algorithm, a precision of 96.39%, recall of 91.37% and Fmeasure as 93.79% was reported.

CONCLUSION: the developed system can be helpful for Aphasic and Dyslexia readers
and can be used as subpart for machine translation system, summarization system and other Natural Language Processing
applications.
more »
An Automated Recommender System for Educational Institute in India
Appears in:
sis 18: e9

Authors:
Mamata Garanayak, Sipra Sahoo, Sachi Nandan Mohanty, Alok Kumar Jagadev

Published:
14th Feb 2020

Abstract:
This study aims to suggest a recommender System for undergraduate students who desire to seek admission into engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a recommender system for admission into the top 10 IIT on a pilot basis …This study aims to suggest a recommender System for undergraduate students who desire to seek admission into
engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a
recommender system for admission into the top 10 IIT on a pilot basis in four common branches such as Electrical
Engineering, Computer Science and Engineering, Mechanical Engineering, Civil Engineering. Data were collected from
different authentic sources from 2016 to 2018. A model was built to predict the ranks for 2019 for each branch of every
IITs. This paper illustrates prediction using Time Series Forecasting and recommendation algorithm using classification
techniques. A comparative study of Random Forest Classification and K-Nearest Neighbor classification has been done.
Finally, the recommendation algorithm shown reliable results with high accuracy in prediction model. It can be diversify
and implement other streams as part of future work.
more »
A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks
Appears in:
sis 18: e8

Authors:
Shalu Mehta, Amita Malik

Published:
11th Feb 2020

Abstract:
In the WSN network, nodes are always deprived of battery and can’t be in operation for a long time. Some nodes may die sooner than others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid optimization algorithm. Our a…In the WSN network, nodes are always deprived of battery and can’t be in operation for a long time. Some nodes may die sooner than
others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid
optimization algorithm. Our algorithm makes the locally converging particle swarm optimization to a global optimization algorithm by
ensembling it with the Gravitational search algorithm. The perturbation in this algorithm is also reduced by logistic chaotic mapping.
The current solution has detected the holes and healed them with additional redundant nodes. The Delaunay triangulation method
detected the number of holes with the improved algebraic approach. The results are validated by extensive experimentation with
different sensing range and node's density. Our method seems to perform well than state of the art schemes. We have also shown that
increasing the redundant nodes, more coverage can be achieved.
more »
Semantic N-Gram Topic Modeling
Appears in:
sis 18: e7

Authors:
Pooja Kherwa, Poonam Bansal

Published:
11th Feb 2020

Abstract:
In this paper a novel approach for effective topic modeling is presented. The approach is different from traditional vector space model-based topic modeling, where the Bag of Words (BOW) approach is followed. The novelty of our approach is that in phrase-based vector space, where critical measure…In this paper a novel approach for effective topic modeling is presented. The approach is different from
traditional vector space model-based topic modeling, where the Bag of Words (BOW) approach is followed.
The novelty of our approach is that in phrase-based vector space, where critical measure like point wise
mutual information (PMI) and log frequency based mutual dependency (LGMD)is applied and phrase’s
suitability for particular topic are calculated and best considerable semantic N-Gram phrases and terms are
considered for further topic modeling. In this experiment the proposed semantic N-Gram topic modeling is
compared with collocation Latent Dirichlet allocation(coll-LDA) and most appropriate state of the art topic
modeling technique latent Dirichlet allocation (LDA). Results are evaluated and it was found that perplexity is
drastically improved and found significant improvement in coherence score specifically for short text data set
like movie reviews and political blogs.
more »
Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals
Appears in:
sis 18: e6

Authors:
Ritu Singh, Navin Rajpal, Rajesh Mehta

Published:
6th Feb 2020

Abstract:
Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel …Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to
interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities
of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet
domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised
classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor
(KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance
evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the
combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.
more »
An Experimental Analysis of Secure-Energy Trade-Off using Optimized Routing Protocol In modern-secure-WSN
Appears in:
sis 18: e5

Authors:
S. Venkataramana, B.V.D.S. Sekhar, Bh. V.S. Ramakrishnam Raju, V.V.S.S.S. Chakravarthy, G. Srinivas

Published:
6th Feb 2020

Abstract:
In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission of perceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securely to network. In this experimentation, we are proposing …In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission of
perceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securely
to network. In this experimentation, we are proposing a revised Low Energy Adaptive Clustering Hierarchy (LEACH)
protocol as LEATCH along secure information transmission (privacy and node authentication) in various levels using Quality
of Protection Modeling Language (QoPML), which balance the Security-Energy trade-offs. This research experimentally
analyzes the impact of data privacy, authentication operations on energy-utilization at sensor-node level while applying a
LEACH & LEATCH. The obtained outcomes indicate the optimized LEATCH is outperforming correlated to the basic Leach
with respect to minimal energy-utilization, time efficiency and expands life-time of modern-secure-WSNs.
more »
K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services
Appears in:
sis 18: e4

Authors:
Iqbal H. Sarker, Md. Faisal Faruque, Hamed Alqahtani, Asra Kalim

Published:
15th Jan 2020

Abstract:
Nowadays, eHealth service has become a booming area, which refers to computer-based health care and information delivery to improve health service locally, regionally and worldwide. An effective disease risk prediction model by analyzing electronic health data benefits not only to care a patient …Nowadays, eHealth service has become a booming area, which refers to computer-based health care and
information delivery to improve health service locally, regionally and worldwide. An effective disease risk
prediction model by analyzing electronic health data benefits not only to care a patient but also to provide
services through the corresponding data-driven eHealth systems. In this paper, we particularly focus on
predicting and analysing diabetes mellitus, an increasingly prevalent chronic disease that refers to a group
of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. K-Nearest
Neighbor (KNN) is one of the most popular and simplest machine learning techniques to build such a disease
risk prediction model utilizing relevant health data. In order to achieve our goal, we present an optimal KNearest Neighbor (Opt-KNN) learning based prediction model based on patient’s habitual attributes in various
dimensions. This approach determines the optimal number of neighbors with low error rate for providing
better prediction outcome in the resultant model. The effectiveness of this machine learning eHealth model
is examined by conducting experiments on the real-world diabetes mellitus data collected from medical
hospitals.
more »
Diagnosis Heart Disease Using Mamdani Fuzzy Inference Expert System
Appears in:
sis 18: e3

Authors:
Iftikhar Naseer, Bilal Shoaib Khan, Shazia Saqib, Syed Nadeem Tahir, Sheraz Tariq, Muhammad Saleem Akhter

Published:
15th Jan 2020

Abstract:
The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference (DHD-MFI) based expert system which intelligently diagnos…The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information
technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference
(DHD-MFI) based expert system which intelligently diagnoses heart disease. In an explorative pattern, the current research
has taken six conducive variables for the purpose of fuzzy logic technical enhancement in the diagnosis of heart disease. The
input fields comprise of age, chest pain, electrocardiography, blood pressure systolic, diabetic and cholesterol are
transmitted with the help of Fuzzy rules which are framed in the light of low, normal, high and very high intensity among
the input variations. The single output is obtained as a clinical decision support system for the heart diagnosis by using the
Mamdani Inference method. The proposed DHD-MFI based expert system gives 94% overall accuracy.
more »
Diagnosis of Arthritis Using Adaptive Hierarchical Mamdani Fuzzy Type-1 Expert System
Appears in:
sis 18: e2

Authors:
Shahan Yamin Siddiqui, Syed Anwar Hussnain, Abdul Hannan Siddiqui, Rimsha Ghufran, Muhammad Saleem Khan, Muhammad Sohail Irshad, Abdul Hannan Khan

Published:
18th Nov 2019

Abstract:
The adroit system is frequently used in artificial intelligence in medicine (AIM). They comprise medical information about a dedicated task and prone to purpose with data from case studies to produce lucid results. Though there are many irregularities, the information with an adroit network is deri…The adroit system is frequently used in artificial intelligence in medicine (AIM). They comprise medical information about a dedicated task and prone to purpose with data from case studies to produce lucid results. Though there are many irregularities, the information with an adroit network is derived with a set of expert rules to produce accurate results. Arthritis is the stiffness of one or more joints and about three fourth of the victims are suffering from it. Late detection of that chronic disease may cause the severity of the sickness at greater risk. So the idea is to contemplate a mechanism for the detection of arthritis using an adaptive hierarchical Mamdani fuzzy expert system (DA-AH-MFES). It is a befitting source to process ambiguity and inaccuracy. Physical and some medical parameters with the expertise of doctors can be
mapped using MFES. The ability of MFES completely depends on the rules which are finalized by a discussion with an expert. The expert system has eight input variables at layer-I and four input variables at layer-II. At layer-I input variables are rest pain, morning stiffness, body pain, joint infection, swelling, redness, past injury and age that detects output condition of arthritis to be normal, infection and/or other problem. The further input variables of layer-II are RF, ANA,
HLA-B27, ANTI-CCP that determine the output condition of arthritis. The performance of proposed Diagnose arthritis disease using an adaptive hierarchical mamdani fuzzy expert system is evaluated with expert observations of Cavan General Hospital Lisdaran, Cavan, Ireland and Jinnah Hospital Lahore, Pakistan. The accuracy of the expert system (DAAH-MFES) is 95.6%.
more »
Efficient and Flexible Role-Based Access Control (EFRBAC) Mechanism for Cloud
Appears in:
sis 18: e1

Authors:
Shilpi Harnal, R.K. Chauhan

Published:
18th Nov 2019

Abstract:
The cloud reduces the user’s burden to many folds. But cloud providers and cloud users with dynamic relationship, are in distinct security domains. Amongst various challenges with cloud, the crucial one is to detect and protect the user’s data from unauthorized accesses. In cloud, users are not leg…The cloud reduces the user’s burden to many folds. But cloud providers and cloud users with dynamic relationship, are in distinct security domains. Amongst various challenges with cloud, the crucial one is to detect and protect the user’s data from unauthorized accesses. In cloud, users are not legendary by their predefined identities. Instead, they are providing
accesses based on their characteristics and attributes. This work is focusing on available access control mechanisms and one that applicable for cloud environment. The paper also proposes an Efficient and Flexible Role-Based Access Control (EF-RBAC) mechanism for the cloud computing environment to achieve confidentiality and security. RBAC limits the
accesses for resources within an organization to authorized users only and also guarantees that a user can solely access specific information they are authorized for by the organization policy. The proposed scheme adds flexibility to the RBAC for better cloud user’s experience.
more »

Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models
Appears in:
sis 20(24): e8

Authors:
Kiran Kumar Paidipati, Arjun Banik

Published:
6th Nov 2019

Abstract:
In India, due to the blessing by the outbreak of the National Food Security Mission, the production of cereals such as wheat, rice etc, has increased in an alarming rate. In this Study, forecasting is done with the help Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Ne…In India, due to the blessing by the outbreak of the National Food Security Mission, the production of cereals such as wheat, rice etc, has increased in an alarming rate. In this Study, forecasting is done with the help Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM-NN) models on the basis of the historical data of rice cultivation from the year 1950-51 to 2017-18. The well fitted ARIMA models for the parameters such as Area under Cultivation (0,1,1), Production (0,1,1) and Yielding (2,2,1) are obtained from the significant spikes of their respective Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots. But, the models fitted with a supervised deep learning neural network known as LSTM-NN are found much better time series forecasting model than the ARIMA models. The performances of these models validated with the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. From the study, the LSTM-NN’s are more flexible and able to develop accurate models for predicting the behavior of agricultural parameters than the ARIMA models.
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Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking
Appears in:
sis 20(24): e7

Authors:
S. Khurana, R. K. Singh

Published:
6th Nov 2019

Abstract:
Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migratio…Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision
problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of
optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.
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Machine Learning Based Hybrid Model for Fault Detection in Wireless Sensors Data
Appears in:
sis 20(24): e6

Authors:
P. Raghu Vamsi, Anjali Chahuan

Published:
5th Nov 2019

Abstract:
Wireless Sensor Networks (WSN) refers to a group of spatially deployed and dedicated sensors for sending, recording, and monitoring the physical conditions of the environment and transmitting the collected data to a central location. The major challenge is to extract high level knowledge from such …Wireless Sensor Networks (WSN) refers to a group of spatially deployed and dedicated sensors for sending, recording, and monitoring the physical conditions of the environment and transmitting the collected data to a central location. The major challenge is to extract high level knowledge from such data. Detecting abnormality in such data can help finding the faulty sensor and also the sensor collecting the most interesting reading from the dataset. This paper proposes a machine learning
based hybrid model for knowledge discovery that works best with multivariate time-series data. The Intel Berkeley Research lab (IBRL) dataset is one of the most trending dataset collected by a WSN is considered for the study. The spatial-temporal correlation was also taken as reference to find anomalies in the dataset using three models - 1) Histogram Based Outlier Score (HBOS), 2) Minimum Covariant Determinant (MCD) and 3) Isolation Forests (IF). Further, the electrical configuration about components of WSN has been used to find faults among the outliers found in the dataset. The results show that the proposed hybrid model with Isolation Forest outperformed with a precision of 94.86%. The
experiment was also able to spot the least trustful or faulty sensors among the deployed sensors in IBRL dataset.
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Optimised Transformation Algorithm For Hadoop Data Loading in Web ETL Framework
Appears in:
sis 20(25): e6

Authors:
Gaurav Gupta, Neelesh Kumar, Indu Chhabra

Published:
2nd Oct 2019

Abstract:
Web ETL unlike conventional ETL framework requires considerable improvements in all the three layers i.e. Extraction, Transformation and Loading due to the inherent nature of web input data. Websites are huge and are unique source of information, out of such huge information available on the websit…Web ETL unlike conventional ETL framework requires considerable improvements in all the three layers i.e. Extraction, Transformation and Loading due to the inherent nature of web input data. Websites are huge and are unique source of information, out of such huge information available on the websites, finding and analysing the required and relevant data is critical as the data may be foul consisting of redundant data or misspelled. Determining integrated record that stands for identical real world entities in abundant ways is the major problem to be analysed for any database. Hence, Web ETL transformation layer functionality of data transformation becomes mandatory in determining the pertinent information to be examined. Since the data on the web is “very voluminous” hence loading only clean data in data warehouse is necessary for fast processing to achieve accurate result. The present research focuses on data transformation in web ETL framework
and proposes a modified technique to employ token wise sentence sorting to remove redundant records from the patent database along with Levenshtein distance used for string matching. Afterwards the cleaned data is transformed and loaded from this staging area to hadoop environment. The integration of proposed transformation technique with hadoop system
delimits the constraint of data processing, storage and retrieval of large data structure from conventional data warehouse system.
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A Competent and Novel Approach of Designing an Intelligent Image Retrieval System
Appears in:
sis 20(24): e5

Authors:
Shefali Dhingra, Poonam Bansal

Published:
30th Sep 2019

Abstract:
A prominent technique used to search imperceptible images from any vast repository is denoted by Content based image retrieval (CBIR) system. The most eminent features of CBIR system are Texture, Color and Shape and in this paper these features are extracted using Local binary pattern, Color moment…A prominent technique used to search imperceptible images from any vast repository is denoted by Content based image retrieval (CBIR) system. The most eminent features of CBIR system are Texture, Color and Shape and in this paper these features are extracted using Local binary pattern, Color moment and Automatic segmentation process respectively. The
features of these descriptors are combined for the formation of a hybrid feature vector by utilizing the process of normalization. Then, for enhancing the retrieval accuracy of the proposed system, Extreme learning machine (ELM) has been utilized as a classifier. The proposed hybrid CBIR system with ELM leads to an evolution of an intelligent system. Various
evaluation metrics like Precision, Recall, Accuracy, etc. have been calculated on the proposed system on standard datasets. Average precision of 90%, 78%, 72% and 88.70% has been obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM datasets which is significantly higher than the related techniques.
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Effect of Packet Loss and Reorder on Quality of Audio Streaming
Appears in:
sis 20(24): e4

Authors:
Asif Ali Laghari, Rashid Ali Laghari, Asif Ali Wagan, Aamir Iqbal Umrani

Published:
24th Sep 2019

Abstract:
In this paper, Audio QoE assessment experiments were conducted by using network parameters such as packet loss and reorder. The network model was created by using three computers, which includes sender, receiver, and middle between Linux based router machines was configured. NetEm tool was used to …In this paper, Audio QoE assessment experiments were conducted by using network parameters such as packet loss and reorder. The network model was created by using three computers, which includes sender, receiver, and middle between Linux based router machines was configured. NetEm tool was used to disturb the on-going network traffic to real-time packet loss and reorder in the broadcasting environment. VLC player was used to broadcasting and recording audio from
sender to receiver machines and audios were presented to users to listen, and assign ratings according to their level of satisfaction. The results show the user’s satisfaction level is decreased when packet loss and reorder level is increased in audio streams. The user accepted a certain level of network traffic disturbance, however, the increment of disturbance in
network traffic damage the audio quality below the then acceptable level. This research work will help to cloud service providers to provide QoE to users according to singed in service level agreements.
more »
Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters
Appears in:
sis 20(24): e3

Authors:
Umer Farooq, Khalil Ahmad

Published:
13th Sep 2019

Abstract:
Emerging technologies such as Cloud Computing, Internet of Things (IoT) and Big Data are developing a digital ecosystem. This ecosystem is catering diverse types and volumes of data that represents information segments. The essence of these segments become vital when transformed into knowledge unit…Emerging technologies such as Cloud Computing, Internet of Things (IoT) and Big Data are developing a digital ecosystem. This ecosystem is catering diverse types and volumes of data that represents information segments. The essence of these segments become vital when transformed into knowledge units to provide a more meaningful and productive perspective. The transformed knowledge at this stage is heterogenetic in nature, consisting of functional and structural properties which needs to be arranged to formulate robust and efficient knowledge repositories. The heterogenetic knowledge can be transformed into classification clusters using structural properties by controlling the
degree of heterogeneity. In this paper, Fuzzy Inference System (FIS) based classification approach is proposed for heterogenetic knowledge clustering.
more »
Prediction of Cross Project Defects using Ensemble based Multinomial Classifier
Appears in:
sis 20(25): e5

Authors:
Lipika Goel, Mayank Sharma, Sunil Kumar Khatri, D. Damodaran

Published:
9th Sep 2019

Abstract:
BACKGROUND: The availability of defect related data of different projects leads to cross project defect prediction an open issue. Many studies have focused on analyzing and improving the performance of Cross project defect prediction. OBJECTIVE: The multinomial classification has not been much e…BACKGROUND: The availability of defect related data of different projects leads to cross project defect prediction an open issue. Many studies have focused on analyzing and improving the performance of Cross project defect prediction.

OBJECTIVE: The multinomial classification has not been much explored. This paper instanced on multiclass/multinomial classification of defect prediction of cross projects.

METHOD: The ensemble based statistical models – Gradient Boosting and Random Forest are used for classification. An empirical study is carried out to determine the performance of multinomial classification for cross project defect prediction. Depending on the number of defects, class level information is classified into one of three defined multiclass class 0, class 1, and class 2.

RESULTS & CONCLUSION: Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data.
more »
Optimization of Governance Factors for Smart City Through Hierarchical Mamdani Type-1 Fuzzy Expert System Empowered with Intelligent Data Ingestion Techniques
Appears in:
sis 19(23): e8

Authors:
Areej Fatima, Sagheer Abbas, Muhammad Asif, Muhammad Adnan Khan, Muhammad Saleem Khan

Published:
9th Sep 2019

Abstract:
A Smart City is an urban area that uses the Internet of things (IoT) sensors to collect data and information to enhance the operational aptitude, in a way to manage assets and resources efficiently. Smart governance is a factor of a smart city for intelligent utilization of ICT to enhance the basic…A Smart City is an urban area that uses the Internet of things (IoT) sensors to collect data and information to enhance the operational aptitude, in a way to manage assets and resources efficiently. Smart governance is a factor of a smart city for intelligent utilization of ICT to enhance the basic leadership. The smart government may be considered as a reason for creating smart governance, through the application of rising information and communication technology for administering. Smart Governance is totally dependent on the information that is being recorderded. Smart consists of multiple factors that an essential role in smart city activities, which require complex collaborations between governments, citizens and different partners. In this article, a new computational method is proposed for the evaluation of the Governance factors of the smart city using Hierarchical Mamdani Type-1 Fuzzy Expert System and empowered with fuzzy based data ingestion techniques.
more »
A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique
Appears in:
sis 20(25): e4

Author:
Rachhpal Singh

Published:
19th Aug 2019

Abstract:
The gene expression data selection is an ill-posed problem. The features selection techniques are found to be an efficient way to evaluate the dimensions of huge gene expression data. This feature selection techniques guide the relevant gene selection. In this paper, a hybrid method (MPG) is propos…The gene expression data selection is an ill-posed problem. The features selection techniques are found to be an efficient way to evaluate the dimensions of huge gene expression data. This feature selection techniques guide the relevant gene selection. In this paper, a hybrid method (MPG) is proposed to get selection of gene expression by using Mutual
information way with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A simulation environment is developed, which reveals the decrease in gene expression data dimensions and also removes the duplication among the classified gene data sets significantly. The proposed approach suitable for gene data set analysis using different classifier
techniques and show the higher efficiency and accuracy of proposed data sets as compared to traditional selection
mechanisms.
more »

The impact of using social media and internet on academic performance case study Bahrain Universities
Appears in:
sis 17(13): e2

Author:
Abdulla Jaafar Desmal

Downloads:
4564

Abstract:
The internet and social media provide students with a range of academic benefits and opportunities to enhance their learning process. The main goal of this research is to examine the impact of using the social media on the academic performance. The new social networks, such as Instagram, Facebook, …The internet and social media provide students with a range of academic benefits and opportunities to enhance their learning process. The main goal of this research is to examine the impact of using the social media on the academic performance. The new social networks, such as Instagram, Facebook, Twitter, etc., can affect the behavior and academic performance of the universities' students; therefore the selected universities were Ahlia University, Applied Science University and University of Bahrain. The sample was (150) students distributed equally among the three universities. The research questions will answer (1) what is the evolution of ICTs and the Internet in the World; (2) what is the impact caused by ICT in education; (3) what are the effects of social media on the academic performance of students at Bahrain Universities; (4) what are the social networking sites that are more popular among students at Bahrain Universities. The results show that the social media has a positive impact on academic performance and 57% of students prefer the mobile application WhatsApp as a social media for their academic purpose.
more »
A Narrative Literature Review and E-Commerce Website Research
Appears in:
sis 18(17): e1

Author:
K.M. Rahman

Downloads:
1570

Abstract:
In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To…In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show less-focused research areas in addition to future directions.
more »
A Parking Management System based on Background Difference Detecting Algorithm
Appears in:
sis 15(7): e3

Authors:
Xiaoling Wu, Yanwen Wang, Hainan Chen, Kangkang Liang, Lei Shu

Downloads:
1546

Abstract:
The number of vehicles in cities has increased dramatically due to rapid economic development. However, the infrastructure for accommodating these vehicles has grown relatively slow. Alleviating the pressure on the urban transport system and solving the ‘parking difficulty’ problem have thus becom…The number of vehicles in cities has increased dramatically due to rapid economic development. However, the infrastructure for accommodating these vehicles has grown
relatively slow. Alleviating the pressure on the urban transport system and solving the ‘parking difficulty’ problem have thus become hot topics recently. In this paper, an intelligent parking system based on geomagnetic field variations is presented to solve
this problem. An algorithm which detects the presence of vehicles in parking spaces in a parking lot is designed and field test results are presented. Our results show that this system has an acceptably high accuracy with low cost, high feasibility, high efficiency and hence is recommended for wide use.
more »
A Hybrid Approach for Breast Cancer Classification and Diagnosis
Appears in:
sis 19(20): e2

Authors:
Bibhuprasad Sahu, Sachi Nandan Mohanty, Saroj Kumar Rout

Downloads:
1350

Abstract:
Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It …Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It can be cured if the disease identified at early stage. Accurate classification of benign tumours can avoid patients undergoing unnecessary treatments. Data Analytics and machine learning methods provides framework for prognostic studies by errorless classification of data instances into relevant based on the cancer severity. In this study we have purposed a prediction model by combining artificial intelligent based learning technique with multivariate statistical method. For automation of the diagnosis process data mining plays an significant role. The data sets available in different repositories are noisy in nature. This study suggests a hybrid feature selection method to be used with PCA (Principal Component Analysis) and Artificial Neural Network (ANN). Preprocessing of data and extracting the most relevant features done by PCA. The proposed algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases. In classification phase 10 fold cross validation was used. The suggested algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved better accuracy with sensitivity and F measure comparison with others and by enhancing this concept we can provide a future scope to produce sophisticated learning models for diagnosis.
more »
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
Appears in:
sis 18(18): e5

Authors:
M. Sharma, G. Singh, R. Singh

Downloads:
1346

Abstract:
The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solu…The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.
more »
Preventing DDoS using Bloom Filter: A Survey
Appears in:
sis 18(19): e3

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
1331

Abstract:
Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, causes loss of business re…Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, causes loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending the DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.
more »
Machine Learning and Predictive Analysis of Fossil Fuels Consumption in Mid-Term
Appears in:
sis 17(15): e4

Authors:
Mahmood Amerion, Mohammadmehdi Hosseini, Abdorreza Alavi Gharahbagh, Mohsen Amerion

Downloads:
1226

Abstract:
In economies that are dependent on fossil fuel revenues, Realization of long-term plans, mid-term and annual budgeting requires a fairly accurate estimation of the amount of consumption and its price fluctuations. Accordingly, the present study is using machine learning techniques to predict the us…In economies that are dependent on fossil fuel revenues, Realization of long-term plans, mid-term and annual budgeting requires a fairly accurate estimation of the amount of consumption and its price fluctuations. Accordingly, the present study is using machine learning techniques to predict the usage of fossil fuels (Diesel, Black oil, Heating oil, and Petrol) in mid-term. Exponential Smoothing, a model of time series and the Neural Network model have been applied on the actual usage data obtained from Shahroud area from 2010 to 2015. For estimation of predictive value by Neural Network method, the training and testing samples, the highest and lowest errors with a range of 41% -0.89% and 88% -3% for the Mean Absolute Percent Deviation are the most appropriate predictions for Petrol consumption. And in the Single Exponential Smoothing, the forecast rate for each product is estimated on a
quarterly as well as monthly basis.
more »
Stock Price Prediction using Artificial Neural Model: An Application of Big Data
Appears in:
sis 19(20): e1

Authors:
Malav Shastri, Sudipta Roy, Mamta Mittal

Downloads:
1153

Abstract:
In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment sc…In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.
more »
Auto recalls and software quality in the automotive sector
Appears in:
sis 18(17): e3

Authors:
J. Pancik, A. Vemola, R. Kledus, M. Semela, A. Bradac

Downloads:
958

Abstract:
Software for automobiles is one from innovative factors in the automotive industry. Automobile is represented as an amount of embedded systems (embedded systems) and it is a very complex computing system. It is currently estimated that the average car has built-in software in the range of 100 milli…Software for automobiles is one from innovative factors in the automotive industry. Automobile is represented as an amount of embedded systems (embedded systems) and it is a very complex computing system. It is currently estimated that the average car has built-in software in the range of 100 million lines and in 2020 is already expected 300 million lines of code. The contribution is devoted to the management of safety and reliability of the software development for embedded systems designed for electromechanical (mechatronic) systems through quality assurance of embedded software. The contribution defines the term software quality assurance strategy, explains the role of standards such as ISO 26262 (Road vehicles - Functional safety), ISO 15504 (Automotive SPICE 3.0)
more »
Single and Multiple UAV Cyber-Attack Simulation and Performance Evaluation
Appears in:
sis 15(4): e4

Authors:
Ahmad Y. Javaid, Weiqing Sun, Mansoor Alam

Downloads:
913

Abstract:
Usage of ground, air and underwater unmanned vehicles (UGV, UAV and UUV) has increased exponentially in the recent past with industries producing thousands of these unmanned vehicles every year.With the ongoing discussion of integration of UAVs in the US National Airspace, the need of a cost-effect…Usage of ground, air and underwater unmanned vehicles (UGV, UAV and UUV) has increased exponentially in the recent past with industries producing thousands of these unmanned vehicles every year.With the ongoing discussion of integration of UAVs in the US National Airspace, the need of a cost-effective way to verify the security and resilience of a group of communicating UAVs under attack has become very important. The answer to this need is a simulation testbed which can be used to simulate the UAV Network (UAVNet). One of these attempts is - UAVSim (Unmanned Aerial Vehicle Simulation testbed) developed at the University of Toledo. It has the capability of simulating large UAV networks as well as small UAV networks with large number of attack nodes. In this paper, we analyse the performance of the simulation testbed for two
attacks, targeting single and multiple UAVs. Traditional and generic computing resource available in a regular computer laboratory was used. Various evaluation results have been presented and analysed which suggest the suitability of UAVSim for UAVNet attack and swarm simulation applications.
more »

Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 7

Last Published: 9th Mar 2020

EAI Endorsed Transactions on Scalable Information Systems

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³