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, commentaries, editorials, technical articles, and short communications with a quarterly frequency. Authors are not charged for article submission and processing.

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

Aims & Scope
Indexing
Editors in Chief
Editorial Board

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 (University of Minho, Portugal)
Jose Merseguer (Universidad de Zaragoza)
Jie Li (University of Tsukuba)
Lin Yun (Harbin Engineering University)
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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Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study
Appears in:
sis 18: e25

Authors:
Ravinder Singh, Yanchun Zhang, Hua Wang, Yuan Miao, Khandakar Ahmed

Published:
26th Oct 2020

Abstract:
Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. I…Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatio-temporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather.
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Evolving A Neural Network to Predict Diabetic Neuropathy
Appears in:
sis 18: e24

Authors:
Shiva Shankar Reddy, Gadiraju Mahesh, N. Meghana Preethi

Published:
26th Oct 2020

Abstract:
One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The mai…One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The main aim of this work is to identify various risk factors of DN and predict it accurately using ML techniques. Radial basis function (RBF) network is an artificial neural network proposed to obtain better results than traditional ML classification techniques. CART, random forest and logistic regression are existing classification techniques considered. Accuracy, recall, f1 score, area under ROC curve (AUC), Matthews correlation coefficient (MCC) and kolmogorov-smirnov (KS) statistic are performance metrics used to evaluate and compare algorithms. From comparative study it was observed that proposed technique RBF network performed better. The performance metric values obtained for RBF network are accuracy-68.18%, recall-0.909, f1score-0 .7407, AUC-0.6405, MCC-0.4082 and KS statistic-0.5417. Accordingly, the use of RBF network while predicting DN gives accurate and better results.
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SecHMS- A Secure Hybrid Monitoring Scheme for Cloud Data Monitoring
Appears in:
sis 18: e23

Authors:
Anuj Kumar Yadav, Ritika Ritika, M. L. Garg

Published:
23rd Oct 2020

Abstract:
Cloud computing enables the data owners to store and host their data on the cloud storage servers, which is to accessed by data consumers from the cloud storage servers whenever needed. The approach provides not only better accessibility but also unlimited storage capacity to the users. Even thou…Cloud computing enables the data owners to store and host their data on the cloud storage servers, which is to accessed by data consumers from the cloud storage servers whenever needed. The approach provides not only better accessibility but also unlimited storage capacity to the users. Even though there are numerous benefits of the approach, but security remains the prime concern for cloud computing and cloud service users. There are few solutions that provide protection in cloud computing, but even after having these solution, trust in cloud service providers remains the prime concern for cloud users. To overcome such a situation and to enhance the faith amongst the user and cloud service provider, a hybrid monitoring scheme (SecHMS) have been proposed and evaluated in this paper. The hybrid monitoring scheme (SecHMS) uses public-key cryptography and hashing technique to provide data security in cloud computing. The hybrid scheme (SecHMS) constantly monitors the stored data on behalf of end-users. As user’s data is getting continuously monitored, it leads to the enhancement of trust for the end-user in cloud computing systems. A thorough analysis has been done on different size files, and results have been demonstrated to show the correctness of SecHMS scheme.
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An analysis of security challenges and their perspective solutions for cloud computing and IoT
Appears in:
sis 18: e22

Authors:
Muhammad Hassan Ghulam Muhammad, Tahir Alyas, Faraz Ahmad, Fatima Hassan Butt, Wajahat Mahmood Qazi, Shazia Saqib

Published:
23rd Oct 2020

Abstract:
INTRODUCTION: With the on-going revolution inthe Internet of Things and Cloud Computing has made the potential of every object that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction very helpful and serviceable in their daily ro…INTRODUCTION: With the on-going revolution inthe Internet of Things and Cloud Computing has made the potential of every object that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction very helpful and serviceable in their daily routines.
OBJECTIVES: The objective of this research to identify the complex configured network system is a soft target to security threats, therefore we need a security embedded framework for IoT and cloud communication models. Another objective is to provide protection of information from unauthorized access controls in IoT-cloud integrated framework and secure data from spying.
METHODS: This paper has applied an integrated IoT-cloud theoretical solution, whose activities are mainly decided by a centralized controller to provide safeguard against data attacks. Our theoretical integrated IoT-cloud theoretical solution is able to achieve unauthorized access control and data breach. RESULTS: Internet of things and cloud computing has intensively used by several real-time applications. After the thercical analysis, the different vulnerabilities explained after detail literature review to prevent unauthorized access and unauthorized data breach.
CONCLUSION: Internet of things have changed the shape of communicationand centralized data controller is the main entity that is robust against eavesdroppers. In case, any eavesdropper tries to be a normal user and attempts to access a personal file then he has been entertained with a misleading file that he considers as an authentic file but in actual it is not. Desirable IoT proposed solutions need to be design and deploy, which can guarantee: anonymity, confidentiality, andintegrity in heterogeneous environments.
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An approach for fast compressed text matching and to avoid false matching using WBTC and wavelet tree
Appears in:
sis 18: e21

Authors:
Shashank Srivastav, P. K. Singh, Divakar Yadav

Published:
23rd Oct 2020

Abstract:
Text matching is a process of finding the frequency of occurrences of text pattern in a corpus. It's very costly to store, process, and retrieve a vast volume of text data. In this paper, we present a method to keep the massive text corpus in lesser memory …Text matching is a process of finding the frequency of occurrences of text pattern in a corpus. It's very costly to store, process, and retrieve a vast volume of text data. In this paper, we present a method to keep the massive text corpus in lesser memory space by using text compression and to retrieve the results by matching directly on this compressed corpus without decompression using compressed pattern matching (CPM). The proposed approach also helps to minimize the time taken to perform matching without compromising the false matching results. We used word-based tagged coding to perform text compression and Wavelet Trees for representing the compressed text in memory. The proposed Text Matching in Compressed text using Parallel Wavelet Tree (TMC_PWT) method is quite fast in comparison to other existing text matching algorithms that support CPM. In the context of CPM, the proposed method provides a good compression ratio and does not suffer from the problem of false matching.
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A Game Theoretical Model for Anticipating Email Spear-Phishing Strategies
Appears in:
sis 18: e20

Authors:
Franklin Tchakounte, Virgile Simé Nyassi, Duplex Elvis Houpa Danga, Kalum Priyanath Udagepola, Marcellin Atemkeng

Published:
18th Sep 2020

Abstract:
A solution to help victims against phishing is anticipating and leveraging impacts related to phisher actions.In this regard, this work reshapes game theoretical logic between Intrusion Detection System (IDS) agents andinsiders to email spear-phishing interactions. The email spear-phishing attack i…A solution to help victims against phishing is anticipating and leveraging impacts related to phisher actions.In this regard, this work reshapes game theoretical logic between Intrusion Detection System (IDS) agents andinsiders to email spear-phishing interactions. The email spear-phishing attack is designed as a non-cooperativeand repeated game between opponents. Additionally, this work relies on Quantal Response Equilibrium (QRE)to build a game theoretical approach to predict the phisher’s future intent based on past actions of bothplayers. This approach is coupled with a recommendation strategy of appropriate allocation of resources toinvest to strengthen user protection. Simulations on spear-phishing scenarios demonstrate the ability of thefinal system to intuitively guess the most likely phisher decisions. This work provides intelligence to spear-phishing detectors and humans such that they can anticipate next phisher actions.
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Challenges of Complying with Data Protection and Privacy Regulations
Appears in:
sis 18: e19

Authors:
A. M. Lonzetta, T. Hayajneh

Published:
18th Sep 2020

Abstract:
As we move into a more digitized society, the collection and use of data continues to increase. This influx in data, partnered with challenges complying with data protection and privacy regulations and the absence of a comprehensive global data protection and privacy strategy, ha…As we move into a more digitized society, the collection and use of data continues to increase. This influx in data, partnered with challenges complying with data protection and privacy regulations and the absence of a comprehensive global data protection and privacy strategy, has contributed to data breaches and data misusage. In order to reduce these incidents, updates must be made to existing regulations and included in future regulations. A global agency should also be created to identify the main data protection and privacy objectives to develop a comprehensive strategy and oversee data protectionand privacy. Our paper presents an overview of existing data protection and privacy regulations, the challenges of complying with the regulations, and recommendations to achieve long-term data protect and privacy.
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Scaffolding computer programming languages learning with tailored English vocabulary based on learners' performance states
Appears in:
sis 18: e18

Authors:
Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali Raza

Published:
18th Sep 2020

Abstract:
Due to the ubiquitous nature of mobile devices, they are now considered as an emerging platform for facilitating both teaching and learning experiences. In this paper, we presented a tailored mobile learning system, namely the Integrated English and Programming Language Learning System (IEPLS), whi…Due to the ubiquitous nature of mobile devices, they are now considered as an emerging platform for facilitating both teaching and learning experiences. In this paper, we presented a tailored mobile learning system, namely the Integrated English and Programming Language Learning System (IEPLS), which aims at learning English vocabulary before studying programming language concepts. The IEPLS supports programming language learning in three ways; (a) Recommending to learn specific English vocabulary used in programming language concepts (b) Adaptation to the learning flow of the learner and (c) Motivating and encouraging learners to learn items based on individual learner's performance. The IEPLS was used by one hundred and fifty undergraduate students for six months. Evaluation results revealed the attainment of IEPLS in supporting learners in learning programming languages backed by precise English vocabulary.
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Master-Slave TLBO Algorithm for Constrained Global Optimization Problems
Appears in:
sis 18: e17

Authors:
Sandeep U. Mane, Amol C. Adamuthe, Rajshree R. Omane

Published:
9th Sep 2020

Abstract:
INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance …INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system’s computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.
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Cross-domain sentiment classification initiated with Polarity Detection Task
Appears in:
sis 18: e16

Authors:
Nancy Kansal, Lipika Goel, Sonam Gupta

Published:
11th Aug 2020

Abstract:
INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposi…INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposing a polarity detection task. METHODS: We have proposed the CDSC-PDT method that is the polarity Detection Task (PDT) followed by the CDSC task. The proposed PDT task extracts the polarity of reviews from the source domain using the contextual and relevancy information of words in documents and this automatic labeled dataset is further used to train classifiers to make the further classification. RESULTS: Proposed method is comparable to the traditional learning method giving the highest precision 85.7%. CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), hence it overcomes the human intervention and is also time saving and cheap process, unlike traditional CDSC tasks.
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An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews
Appears in:
sis 18: e15

Authors:
A. K. Yadav, D. Yadav, A. Jain

Published:
22nd Jul 2020

Abstract:
In today’s society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at an early stage. It thus intimates users’ percepti…In today’s society, sentiment analysis has gained due importance as it provides useful information about products that are
used by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at an
early stage. It thus intimates users’ perception and charts out a path that is beneficial for the market to grow as a whole.
Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the
detailed components based products and their associated features, a novel method is desired to meet these criteria. So far,
no such method is explored that analyses the product’s components and their features simultaneously, on the basis of
sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of
product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation
of double negative sentences, detecting questions and emotions from the review sentences are measured which increases
efficiency of the FBA method. The comparison of product’s components reviews is done with other existing algorithmsTF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.
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ANFIS based hybrid approach identifying correlation between decision making and online social networks
Appears in:
sis 18: e14

Authors:
Areeba Rahman, Nageen Saleem, Aysha Shabbir, Maryam Shabbir, Muhammad Rizwan, Shahid Naseem, Fahad Ahmad

Published:
22nd Jul 2020

Abstract:
The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hence have raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing a significant role in supporting consumer’s rationa…The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hence
have raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing a
significant role in supporting consumer’s rational DM. We use a hybrid approach i.e. an online survey and fuzzy model
development to indicate how OSNs have resulted in giving an impact on the decision making. The research depicts that OSNs
support and empower users in the DM process specifically in Rationality, Design, & Choice and the model predicts the DM
strategy of each user according to their present decisions using fuzzy logic. Our results also reveal that different types of users
(observers, seekers, and advisers) have significantly different participation styles, which in turn have an impact on the efficacy
of the DM process. We discussed the implications for OSN designers and developers based on the findings from the research.
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Revealing the Demonstration of Blockchain and Implementing Scope in COVID-19 Outbreak
Appears in:
sis 18: e13

Authors:
Mansi Gupta, Vimal Kumar

Published:
15th Jul 2020

Abstract:
Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization. It can also be used as decentralized storage. Blockchain offers a wide range of digital services both in financial and nonfinancial sectors. In this paper, we discuss the ba…Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization.
It can also be used as decentralized storage. Blockchain offers a wide range of digital services both in financial and nonfinancial sectors. In this paper, we discuss the basic concepts and terminologies associated with blockchain and discuss the
application areas both in financial and non- financial sectors such as Digital voting, Digital identity, Education, etc. and
discuss the existing framework in these areas, also the progress and challenges of global acquisition. The main highlight of
this paper is COVID-19 and we discussed existing solutions to fight the COVID-19 pandemic by using such technology.
This survey paper will be helpful for a data scientist as well as researchers to explore in the area COVID-19.
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A lightweight technique for detection and prevention of wormhole attack in MANET
Appears in:
sis 18: e12

Authors:
Zulfiqar Ali Zardari, Kamran Ali Memon, Reehan Ali Shah, Sanaullah Dehraj, Iftikhar Ahmed

Published:
8th Jul 2020

Abstract:
A mobile ad-hoc network (MANET) is an ordinary and self-orbiting communication network that is capable of managing mobile nodes. Many proposed protocols on MANET address its vulnerability against different threats and attacks. The malicious node exploits these vulnerabilities to lunch attacks, es…A mobile ad-hoc network (MANET) is an ordinary and self-orbiting communication network that is capable of managing
mobile nodes. Many proposed protocols on MANET address its vulnerability against different threats and attacks. The
malicious node exploits these vulnerabilities to lunch attacks, especially when nodes have mobility and network do not have
constant topology, like wormhole attack. This paper presents a lightweight technique that detects the wormhole attacks in
MANET. In the proposed technique, the source node calculates the average sequence number of the reply (RREP) packets.
If the sequence number of the corresponding node exceeds the calculated average value of the sequence number, then all
traffic is discarded, and the node is marked as malicious. The proposed technique is less complex, power-efficient, and
enhances network lifetime as more data packets are delivered to the destination node. This technique is validated through
comprehensive simulations results in NS2.
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Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods
Appears in:
sis 18: e11

Authors:
Shiva Shankar Reddy, Nilambar Sethi, R. Rajender

Published:
1st Jul 2020

Abstract:
Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that efficiently predicts diabetic retinopath…Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at
the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that
efficiently predicts diabetic retinopathy. Machine learning techniques like Decision tree, Random forest, Adaptive
boosting and Bagging are used as primary algorithms to train predictive models. An algorithm namely ‘Support Vector
Machine using Gaussian kernel for retinopathy prediction’ is proposed in this work. The proposed algorithm is compared
with the primary algorithms based on five evaluation metrics namely accuracy, Youden’s J index, concordance, Somers’ D
statistic and balanced accuracy. From the results obtained the proposed algorithm obtained better values for all considered
evaluation metrics. Thus the use of SVM with Gaussian kernel is proposed to be used for prediction of diabetic
retinopathy.
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Enhancing Security of Android Operating System Based Phones using Quantum Key Distribution
Appears in:
sis 18: e10

Authors:
Nageen Saleem, Areeba Rahman, Muhammad Rizwan, Shahid Naseem, Fahad Ahmad

Published:
15th Jun 2020

Abstract:
The Android-based devices are gaining popularity now a day. With the widespread use of smartphones both in private and work-related areas, securing these devices has become of paramount importance. These devices are prone to various security issues of malicious attacks and performance problems. O…The Android-based devices are gaining popularity now a day. With the widespread use of smartphones both in private and
work-related areas, securing these devices has become of paramount importance. These devices are prone to various
security issues of malicious attacks and performance problems. Owners use their smartphones to perform tasks ranging
from everyday communication with friends and family to the management of banking accounts and accessing sensitive
work-related data. These factors, combined with limitations in administrative device control through owners and securitycritical applications, make Android-based smartphones a very attractive target for attackers and malware authors of any
kind and motivation. Applications keep and manage diverse intrinsic data as well as sensitive private information such as
address books. Smartphones enable swift and easy data exchange via 3G, 4G, and Wi-Fi. Thus, personal information
stored on smartphones is prone to leakage. Up until recently, the Android Operating System’s security model has
succeeded in preventing any significant attacks by malware. This can be attributed to a lack of attack vectors which could
be used for self-spreading infections and low sophistication of malicious applications. The research provides a distinctive
solution to the security threats being found in the Android operating system. This paper presents a data security and quality
enhancement method based on amalgamating quantum attributes into the Android operating system that could effectively
solve the issue raised. The paper provides a proposed architecture of Quantum Key distribution being embedded within the
Android OS to improve efficiency. However, QKD is a new technology. The research unleashes the possible ways in
which quantum could be effectively embedded in smartphones to resolve certain data security problems. Quantum key
distribution implements the Android to guard and use in the case of a run-time kernel compromise. That is, even with a
fully compromised kernel, an attacker cannot read key material stored in Quantum key.
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An Automatic Conversion of Punjabi Text to Indian Sign Language
Appears in:
sis 18: e9

Authors:
Amandeep Singh Dhanjal, Williamjeet Singh

Published:
12th Jun 2020

Abstract:
An escalating focus worldwide on the development of persons with disabilities results in the disability-inclusive 2030 UN agenda for sustainable development. This paper discusses the development of Punjabi text to Indian Sign Language (ISL) conversion system aiming for the better communication an…An escalating focus worldwide on the development of persons with disabilities results in the disability-inclusive 2030 UN
agenda for sustainable development. This paper discusses the development of Punjabi text to Indian Sign Language (ISL)
conversion system aiming for the better communication and education of hearing-impaired people. The developed system
accepts Punjabi text as input and deliver output as 3D animation. The currently created corpus involves more than 100
commonly used Punjabi words under various categories. This system has been tested on all the prepared words. Results of
the developed system are very encouraging and the work is still going on with full enthusiasm. Since there is no universal
or written form of sign language available, so generating synthetic sign animation is the best solution. In near future it can
be implemented as a tutoring system to promote sign language education among common people of Punjab.
more »
Performance Evaluation of Symmetrical Encryption Algorithms with Wavelet Based Compression Technique
Appears in:
sis 18: e8

Authors:
Neetu Gupta, Ritu Vijay, Hemant Kumar Gupta

Published:
28th May 2020

Abstract:
To overcome the different issues connected with security and limited bandwidth during transmission of images, compression and encryption play an important role. To measure the effect on the performance of compression technique followed by encryption techniques is a challenging task. In this paper, …To overcome the different issues connected with security and limited bandwidth during transmission of images, compression and encryption play an important role. To measure the effect on the performance of compression technique followed by encryption techniques is a challenging task. In this paper, biorthogonal discrete wavelet transform (DWT) technique is proposed for compression which is followed by advanced encryption standard (AES) and data encryption standard (DES) technologies to achieve secure transmission of data. The performances of DWT-AES and DWT-DES Compression-encryption (CE) algorithms are analyzed based on statistical and differential parameters. We illustrate the authentication of results by applying the proposed CE algorithms over five standard test images and also by comparing with different state of the art methods. The results show that combination of DWT-DES CE methodology provides high quality of reconstructed image with robustness against different attacks.
more »
A Visualization Based Analysis on Dynamic Bandwidth Allocation Algorithms for Optical Networks
Appears in:
sis 18: e7

Authors:
Noor ul Ain, Kamran A. Memon, Muhammad Abbas, Nazish Nawaz Hussaini, Zulifqar Ali Bhutto, Kamlesh Kumar Soothar

Published:
21st May 2020

Abstract:
This paper presents the bibliometric analysis of Dynamic Bandwidth Allocation (DBA) algorithms in the telecommunication field using CiteSpace and Web of Science (WoS) analysis for the period of decade (2009-2018). The publication count and citation counts in the last five years have significantly i…This paper presents the bibliometric analysis of Dynamic Bandwidth Allocation (DBA) algorithms in the telecommunication field using CiteSpace and Web of Science (WoS) analysis for the period of decade (2009-2018). The publication count and citation counts in the last five years have significantly increased reflecting this as active research. The visualization results identified the most influential DBA algorithms centered on 1) EPON technology addressing DBAs meeting QoS requirement (68 counts), 2) DBAs for Elastic Optical Networks (58 counts) and (3 DBAs for long reach PON (54 counts). Assi CM and Kramer G are the leading contributors and Plos One and Journal of optical communication and networking are proffered Journals. The correlation between the research contribution and expenditures countries do is 0.945. Wherein, China in countries and Beijing university of Posts and Telecommunications among research institutes contribute the most in the DBA PON research across the world.
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The Design of Wireless Portable Electrocardiograph Monitoring System Based on ZigBee
Appears in:
sis 18: e6

Authors:
Saleemullah Memon, Kamlesh Kumar Soothar, Kamran Ali Memon, Arif Hussain Magsi, Asif Ali Laghari, Muhammad Abbas, Noor ul Ain

Published:
14th May 2020

Abstract:
The emerging trend in wireless hospitals requires the development of bio-signal acquisition devices to be easily integrated into a single clinical routine. The wired devices used in hospitals and clinics, which makes setup very inconvenient, costly, and difficult to manage patients. It is crucial t…The emerging trend in wireless hospitals requires the development of bio-signal acquisition devices to be easily integrated into a single clinical routine. The wired devices used in hospitals and clinics, which makes setup very inconvenient, costly, and difficult to manage patients. It is crucial to develop and design a clinical diagnostic system that takes bio-signal from the body of the patient and transmits wirelessly to the physicians/hospital. In this paper, we have designed a novel portable system for ECG signal acquisition, processing, and wireless transmission using a ZigBee module. Our portable design enhances mobility, small in size, and very cheap for long term monitoring of cardiac patients. The results show that the ECG wave successfully transmitted to the physician, and heartbeat values are visible to the patient. Furthermore, the results of the ECG wave are unclear when the position of electrodes is dislocated.
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Maiden Application of Meta-Heuristic Techniques with Optimized Integral minus Tilt-Derivative Controller for AGC of Multi-area Multi-Source System
Appears in:
sis 18: e5

Authors:
Sanjeev Kumar Bhagat, Naladi Ram Babu, Lalit Chandra Sakia, Dhenuvakonda Koteswara Raju

Published:
14th May 2020

Abstract:
This article presents a new meta-heuristic algorithm optimized secondary controller called Integral minus Tilt-Derivative (I-TD) for automatic generation control of three area multi-source system. Area-1 comprises of thermal and solar thermal units, area-2 comprises of two thermal units and area-3 …This article presents a new meta-heuristic algorithm optimized secondary controller called Integral minus Tilt-Derivative (I-TD) for automatic generation control of three area multi-source system. Area-1 comprises of thermal and solar thermal units, area-2 comprises of two thermal units and area-3 comprises of thermal and wind systems. Comparison of system responses using proposed I-TD controller and some other commonly used controller revels better dynamics characteristics of the proposed one. Dynamic responses of the system corresponding to various meta heuristic optimization technique like firefly algorithm (FA), grey-wolf optimization (GWO), grass-hopper algorithm (GHA) explore that GHA provides slightly better dynamics than the other and also converges faster. Further, sensitivity analysis suggests that system dynamics with GHA optimized I-TD controller at various loading conditions are robust and are not reset again.
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Spatial Ambiguities Optimization in GIR
Appears in:
sis 18: e4

Authors:
Arun Kumar Yadav, Jay Kant Pratap Singh Yadav, Divakar Yadav

Published:
14th May 2020

Abstract:
INTRODUCTION: Huge amount of geographically referenced information is available on World Wide Web and has become an excellent source for retrieval of desired information. Extraction of relevant information from such a huge unstructured source is not an easy task. OBJECTIVES: In this paper, we p…INTRODUCTION: Huge amount of geographically referenced information is available on World Wide Web and has become an excellent source for retrieval of desired information. Extraction of relevant information from such a huge unstructured source is not an easy task.

OBJECTIVES: In this paper, we propose a location based search engine, designing two indexes namely spatial index and inverted index. Using these indexes, we are able to solve toponym ambiguity and overlapping of documents.

METHODS: To handle toponym ambiguity problem, we designed and implemented architecture for directional web document search. The architecture poses spatial followed by textual indexing for toponym resolution and to reduce overlapping of documents in geographical locations.

RESULTS: The proposed architecture was implemented and tested on spatial and textural data sets.

CONCLUSION: The performance is measured in terms of precision and false positive parameters and found that the proposed architecture performs better for geo/geo and geo/non-geo search queries.
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Analytical Method to Compute the Cloud Computing Data Security Issues by Using Encryption Algorithms
Appears in:
sis 18: e3

Authors:
Abdul Hannan Khan, Syed Anwer Hasnain, Shahan Yamin Siddiqui, Muhammad Sohail Irshad, Muhammad Sajid, Shahid Iqbal

Published:
13th May 2020

Abstract:
All the associations are genuine thoughts to receive the cloud computing administrations, seeing its advantages as far as cost, openness, accessibility, adaptability and profoundly computerized procedure of upgrading. Cloud computing upgrade the present capacities progressively without further spec…All the associations are genuine thoughts to receive the cloud computing administrations, seeing its advantages as far as cost, openness, accessibility, adaptability and profoundly computerized procedure of upgrading. Cloud computing upgrade the present capacities progressively without further speculation. Cloud computing is a band of assets, applications, and administrations. With the execution of cloud computing, associations have solid worries about the security of their information. The research focuses on the security issues of cloud services. The AES and RSA algorithms will be verified on five parameters key generation time, encryption execution time, memory usage, file uploading time and file downloading time. The CloudSim simulator will be used to find results that the RSA is performing much better than AES in terms of encryption execution time.
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Secure new node ID assignment for internet integrated wireless body area networks
Appears in:
sis 18: e2

Authors:
Amit Kumar Gautam, Rakesh Kumar

Published:
13th May 2020

Abstract:
Internet integrated sensor networks have gained much importance and exponential growth in wireless body area networks (WBAN) over the last few years. These networks are used in health services to remotely monitor the health of patients and send/receive sensitive, time critical medical data. Because…Internet integrated sensor networks have gained much importance and exponential growth in wireless body area networks (WBAN) over the last few years. These networks are used in health services to remotely monitor the health of patients and send/receive sensitive, time critical medical data. Because of the wireless and broadcast nature of WBAN, it is easy for an adversary to get, inject, or update the information transmitted in the medium or launch many security attacks. To protect the sensitive data of the patient, we propose a secure communication strategy for different sensors to form a WBAN. Here, a new node ID is assigned for sensors by using public key cryptography to communicate in the network. An energy utilization and communication cost analysis show that our approach incurs less communication overhead as compared with the recently proposed secure solutions in WBAN. The complexity analysis show that the proposed model works efficiently for secure communication in WBAN.
more »
Eurasian Wolves-Cuckoo Search Optimizer for Localization of Mobile Sensor Nodes using Single Beacon Node in WSN
Appears in:
sis 18: e1

Authors:
Ravi Sharma, Shiva Prakash

Published:
13th May 2020

Abstract:
Wireless Sensor Networks (WSNs) is a widely used technology for remote area monitoring in collaboration with the Internet of Things (IoT). The fundamental research challenge of mobile sensor nodes for the WSN community is localization. The sensor node localization of the WSN is related to the NP-ha…Wireless Sensor Networks (WSNs) is a widely used technology for remote area monitoring in collaboration with the Internet of Things (IoT). The fundamental research challenge of mobile sensor nodes for the WSN community is localization. The sensor node localization of the WSN is related to the NP-hard problem, and because of this, determining the actual coordinate of the sensor node is quite complex. The computational intelligence approach is assisted in obtaining an optimal solution to the given NP-hard problem. Most researchers today are more concerned about three beacon-based localization approaches, but the fewest researchers are concerned about two or single beacon-based localization approaches. This paper provides a single beacon-based localization approach using the hybrid approach of the Eurasian Wolves Optimizer (EWO) and the Cuckoo Search Optimizer (CSO) algorithm called the EW-CSO computational intelligence algorithm for randomly deployed mobile sensor nodes. The simulation results of the computational intelligence algorithms show that the proposed work using EW-CSO performs better in terms of mean localization error, computational cost, and number of localized nodes from the EWO and EW- Particle Swarm Optimization (EW-PSO) algorithms. It also reduced the line of sight problem for mobile sensor nodes with efficient use of network resources.
more »

Effects of data protection laws on data brokerage businesses
Appears in:
sis 20(27): e12

Authors:
G. Birckan, M. L. Dutra, D. D. J. de Macedo, A. F. G. Viera

Published:
23rd Jul 2020

Abstract:
For years now, players known as data brokers have operated on an unregulated market, by building businesses from the aggregation and processing of data, away from public scrutiny. Due to the genesis of laws that ended up exposing their model, along with debates on data privacy, a new generation o…For years now, players known as data brokers have operated on an unregulated market, by building businesses from the
aggregation and processing of data, away from public scrutiny. Due to the genesis of laws that ended up exposing their
model, along with debates on data privacy, a new generation of data protection acts, much stricter, are being enacted
worldwide. The shift away from the data commoditization paradigm might, at first, indicate the extinction of such businesses.
In this paper, we present related concepts and a brief history that led to that current scenario, along with some collaborativeoriented proposals for the adequacy of the data brokerage industry to a transparent perspective.
more »
Finding Frequent Subgraphs and Subpaths through Static and Dynamic Window Filtering Techniques
Appears in:
sis 20(27): e11

Authors:
Bhargavi B., K. Swarupa Rani

Published:
16th Apr 2020

Abstract:
Big data era has large volumes of data generated at high velocity from different data sources. Finding frequent subgraphs from the graph streams can be a challenging task as streams are non-uniformly distributed and continuously processed. Its applications include finding strongly interacting gro…Big data era has large volumes of data generated at high velocity from different data sources. Finding frequent
subgraphs from the graph streams can be a challenging task as streams are non-uniformly distributed and
continuously processed. Its applications include finding strongly interacting groups in social networks and
sensor networks. To find frequent subgraphs, we proposed static single-window technique and dynamic
sliding window techniques. We also proposed enhancements by extending proposed static approach with its
variations and extending dynamic approach in variations of incremental strategy to find frequent subgraphs.
We also solved the sub problem to extract frequent subpaths from sequence of paths. Its applications include
finding congested sections in traffic analysis. We applied our proposed static and dynamic techniques to
extract the frequent subpaths from sequence of paths. We experimented the proposed dynamic and static
approaches with real and benchmark datasets.
more »
Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model
Appears in:
sis 20(27): e10

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 20(27): e9

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 20(27): e8

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 20(27): e7

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 20(27): e6

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 20(27): e5

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 20(27): e4

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 20(27): e3

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 »

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:
5045

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:
2033

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.
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Preventing DDoS using Bloom Filter: A Survey
Appears in:
sis 18(19): e3

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
1960

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 »
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:
1933

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:
1731

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 »
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:
1551

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 »
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:
1537

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 »
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:
1478

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 »
Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM
Appears in:
sis 20(25): e1

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
1201

Abstract:
The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The…The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite
comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is
taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.
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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:
1123

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

Last Published: 2020-10-15

EAI Endorsed Transactions on Scalable Information Systems

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³