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

Guest editor: Xuan Liu (Southeast University, China)

Call for Papers: Special issue on: Advance in Integrated Satellite-Aerial-Terrestrial Scalable Information Systems (Submission of manuscript: Mar 31, 2020; First notification: May 31, 2020; Submission of revised manuscript: Jul 31, 2020; Final notification: December 20, 2019; Publication date: Dec 31, 2020)

Guest editors: Thoudam Doren Singh (National Institute of Technology Silchar, India)

Ripon Patgiri (National Institute of Technology Silchar, India)

Call for Papers: Special issue on: Big Data: Exascale computation and beyond (Submission of manuscript: 17/02/2020; First notification: 16/03/2020; Submission of revised manuscript: 27/04/2020; Publication date: 11/04/2020)

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)

Hua Wang¹

Victoria University, Australia

Xiaohua Jia²

City University of Hong Kong

Manik Sharma³

DAV University, India

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

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Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters
Appears in:
sis 18: e16

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.
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Optimization of Governance Factors for Smart City Through Hierarchical Mamdani Type-1 Fuzzy Expert System Empowered with Intelligent Data Ingestion Techniques
Appears in:
sis 18: e15

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 »
Prediction of Cross Project Defects using Ensemble based Multinomial Classifier
Appears in:
sis 18: e14

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

Published:
9th Sep 2019

Abstract:
INTRODUCTION: 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…INTRODUCTION: 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 »
A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique
Appears in:
sis 18: e13

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 »
Detection and mitigation of DDoS attack in cloud computing using machine learning algorithm
Appears in:
sis 18: e12

Authors:
Aroosh Amjad, Tahir Alyas, Umer Farooq, Muhammad Arslan Tariq

Published:
12th Aug 2019

Abstract:
Cloud computing, with its staggering and on-demand services had revamped the technology so far. Cloud consumers are freely to use the applications and software on the premises of Pay-as-you go concept. This concept decreased the cost and make the services less expensive and more reliable. One of t…Cloud computing, with its staggering and on-demand services had revamped the technology so far. Cloud
consumers are freely to use the applications and software on the premises of Pay-as-you go concept. This concept decreased the cost and make the services less expensive and more reliable. One of the most important characteristic of cloud structure is on demand self-service. Cloud computing applications can be accessed anywhere at any time with much less cost. As cloud provide its consumers with its tremendous on demand services, besides this it is surviving from the excruciating security issues that are discourteous towards the cloud. There are, as many different attacks that results in making the servers down. One of the most hazardous attack is DDoS. This paper hiloghted the DDoS attack and its prevention technique which results in making the server side less vulnerable. The scenario includes, a transmission of million and trillion of packets in the form of DDoS at cloud-based websites, thus making it differentiated though different hosts. Making use of operating systems such as ParrotSec to make the attack possible. Last step includes detection and prevention through the most effective algorithms namely, Naïve Bayes and Random forest. This paper also focused the categories of attacks on cloud computing.
more »
Automated Skin Lesion Detection towards Melanoma
Appears in:
sis 18: e11

Authors:
Maryam Bibi, Anmol Hamid, Samabia Tehseen

Published:
7th Aug 2019

Abstract:
Skin cancer melanoma is one of the most dangerous cancers in the world. It is crucial to diagnose it in initial phases before it invades other organs. However, it requires an efficient and reliable diagnostic computer aided system for early detection. In this research study we aim to detect the ski…Skin cancer melanoma is one of the most dangerous cancers in the world. It is crucial to diagnose it in initial phases before it invades other organs. However, it requires an efficient and reliable diagnostic computer aided system for early detection. In this research study we aim to detect the skin cancer from two different image datasets. We also present the solution for
images that contain disk objects. In initial phase we perform pre-processing, which is followed by segmentation phase. Then candidate dataset is evaluated using different measures such as accuracy, specificity, sensitivity and similarity. Obtained results are compared with results of techniques used in academic literature. We claim that our techniques give
better accuracy for cancer detection.
more »
Simulation and Security Calibration of Weather Management System for the Least Rainy Areas of Pakistan through Quantum Key Distribution
Appears in:
sis 18: e10

Authors:
Nimra Dilawar, Sara Nadeem, Sania Arooj, Muhammad Rizwan, Fahad Ahmad

Published:
5th Aug 2019

Abstract:
Water plays a vital role for the survival of life. We get water in different forms of precipitation and rain is the most beneficial of all types. The demand for water is very high throughout the Pakistan, especially where we have limited natural water resources. Summer monsoon season has profound i…Water plays a vital role for the survival of life. We get water in different forms of precipitation and rain is the most beneficial of all types. The demand for water is very high throughout the Pakistan, especially where we have limited natural water resources. Summer monsoon season has profound impact in reducing water scarcity in Pakistan. Heavy rainfall results into floods whereas less rainfall creates drought which not only affects the economy but also puts human
life in risk. The aim of this research is to manage the movement of clouds from heavy rainfall areas to those areas where there is paucity of water, through satellite using electromagnetic waves. To accommodate such a sensitive satellite data which is paramount to a country, it is required to have some special systems based on quantum mechanics that are more efficient and pregnable than conventional computers. The consequences will be very high of not securing weather
management system, implies to country level disasters. In this paper we first discuss the technology behind quantum computer then proposed a secure architecture by employing quantum computers to ensure the security of data transmission for weather management system.
more »
Improving Semi-Supervised Classification using Clustering
Appears in:
sis 18: e9

Authors:
J. Arora, M. Tushir, R. Kashyap

Published:
5th Aug 2019

Abstract:
Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process. To reduce these efforts and improve the performance of classification process, this paper proposes a new framework, which combines …Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process. To reduce these efforts and improve the performance of classification process, this paper proposes a new framework, which combines a most basic classification technique with the semi-supervised
process of clustering. Semi-supervised clustering algorithms, aim to increase the accuracy of clustering process by effectively exploring available supervision from a limited amount of labeled data and help to label the unlabeled data. In our paper, a semi-supervised clustering is integrated with naive bayes classification technique which helps to better train the classifier. To evaluate the performance of the proposed technique, we conduct experiments on several real world
benchmark datasets. The experimental results show that the proposed approach surpasses the competing approaches in both accuracy and efficiency.
more »
Confine Ingenious Firefly Algorithm Correlating Water Drop Algorithm for Amassment in Networks
Appears in:
sis 18: e8

Authors:
Neha Sharma, Usha Batra, Sherin Zafar

Published:
24th Jul 2019

Abstract:
In present internet usage scenario users (clients or customers) are utilizing distinctive physical things for different purposes. This idea takes the users into the universe of Internet of Things (IOT) which is a course of action and development of fair innovation. It allows affiliation, game pla…In present internet usage scenario users (clients or customers) are utilizing distinctive physical things for
different purposes. This idea takes the users into the universe of Internet of Things (IOT) which is a course of
action and development of fair innovation. It allows affiliation, game plan and usage of different physical
things present in the propelled universe, utilizing heterogeneous frameworks and correspondence of
advances. The universe of IOT in different splendid circumstances with the infra-structure-less system
refereed as Mobile Ad-hoc Network (MANET) prompts an imaginative methodology altogether appealing for
current customers. The monetary imaginative worldview prompts associations among adaptable and compact MANET framework with IOT shaping a structure for the data innovation. MANET based IOT paradigm gives imperative flexibility to cutting edge clients and customers by diminishing costs of associations for the significant frameworks. This leads to new testing parameters in perspective of various organizations. In this examination work, authors have proposed a controlling response for the Internet of Things structure surrounding neoteric firefly routing algorithm associated with intelligent water drop routing methodology. Both firefly routing algorithm (FRA) and intelligent water drop routing algorithm (IWDRA) performs enhancement for the customary conventions of MANET. These enhancements will give enormous advances for various real life scenarios and issues. In the proposed methodology when FRA and IWDRA were connected in customary directing convention and mimicked for advancements, firefly calculation
demonstrated its value over the water drop approach. The presented results of plan's examination give a
ground-breaking procedure for growth through different execution factors specified as End-to-End Delay
(E2D), Normalized Routing Load (NRL), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR) and
throughput for the MANET based IOT worldview. The approval of the proposed methodology through
network simulator test system approach prompts new stage of reliable game plan for future system
establishment.
more »
The impact of Planned Special Events (PSEs) on urban traffic congestion
Appears in:
sis 18: e7

Author:
Ruwangi Fernando

Published:
24th Jul 2019

Abstract:
The transport infrastructure of many cities has not been able to keep up with the pace of growth in the motorization rate or to counteract the intensification of urban traffic. Rising in traffic congestion in cities not only impacts the productivity costing billions but also responsible for more th…The transport infrastructure of many cities has not been able to keep up with the pace of growth in the motorization rate or to counteract the intensification of urban traffic. Rising in traffic congestion in cities not only impacts the productivity costing billions but also responsible for more than 40% of all CO2 emissions which results in global warming. While expansion
and construction of new roads may be considered in some cases, in most, better management of existing infrastructure to lower traffic congestion is the only option.
The current state of the art commercial solutions can predict the recurring traffic situations, as this behavior can be easily learned from historical data. The challenge is to predict the non-recurrent congestion caused by events such as accidents, adverse weather, construction zones and Planned Special Event (PSE). Past research has shown, PSEs such as concerts or
sports games, festivals and conventions has a huge impact on everyday urban transportation. Therefore, the aim of the proposed research is to investigate the impact PSEs on urban traffic congestion.
The proposed research is applying spatial-temporal big data mining methods to predict the impact of PSEs on the urban concession. Specifically, the proposed research will consider the characteristics of PSEs such as location, type, duration, audience and time and day of the event in the proposed analysis approach which enables to predict of urban congestion for future PSEs.
more »
Topic Modeling: A Comprehensive Review
Appears in:
sis 18: e6

Authors:
Pooja Kherwa, Poonam Bansal

Published:
24th Jul 2019

Abstract:
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented i…Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its
applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges
of topic modelling, which will definitely give researchers an insight for good research.
more »
A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
Appears in:
sis 18: e5

Authors:
J. Arora, M. Tushir

Published:
24th Jul 2019

Abstract:
Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data. Also the intuitionistic fuzzy sets, a generalization of fuzzy sets, have been proven to deal better with the…Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data. Also the intuitionistic fuzzy sets, a generalization of fuzzy sets, have been proven to deal better with the problem of uncertainty present in the data. In this paper, we have proposed to embed the concept of intuitionistic fuzzy set theory with semi-supervised approach to further improve the clustering process. We evaluated the performance of the proposed methodology on several benchmark real data sets based on several internal and external indices. The proposed Semi-Supervised Intuitionistic Fuzzy C-means clustering is compared with several state of the art clustering/classification algorithms. Experimental results show that our proposed algorithm is a better alternative to these competing approaches.
more »
Empirical Analysis of Recent Advances, Characteristics and Challenges of Big Data
Appears in:
sis 18: e4

Authors:
Burhanullah Khattak, Aurangzeb Khan, Khairullah Khan, Wahab Khan, Muhammad Kamran, Muhammad Fahad

Published:
24th Jul 2019

Abstract:
Here in this study, we provide an empirical analysis of recent advances, characteristic and challenges of big data. Initially, we acquaint the readers with the general background, history, and characteristics of big data including volume, velocity, value and variety etc. The scope of applications f…Here in this study, we provide an empirical analysis of recent advances, characteristic and challenges of big data. Initially, we acquaint the readers with the general background, history, and characteristics of big data including volume, velocity, value and variety etc. The scope of applications for big data including political services and government monitoring, enterprise
management, scientific research, public utilities, public administration and internet of things are illustrated. A detailed analysis is presented regarding opportunities and challenges faced by the public and private sectors during analysis phase of big data management such as storing, visualizing, capturing and so on. In addition, we investigated and reported a detailed empirical analysis of the most recent management tools like Hadoop and MapReduce, along with their different components,
usage, and limitation. Finally, open issues and future directions for this new and dynamic area of research are provided. The primary objective of this empirical analysis is to present a broad-spectrum perspective of this emerging research area with the goal to present big data related concepts in a coherent manner to the beginners.
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A Step towards the Efficiency of Collisions in the Wireless Sensor Networks
Appears in:
sis 18: e3

Authors:
Poonam K.K, Asif Ali Laghari, Rashid Ali Laghari

Published:
10th Jul 2019

Abstract:
Wireless Sensor Networks (WSNs) have an ideal tactic for implementing the number of applications due to evading complex wirings connections and their maintenance. On the other hand, the main source of data communication in other networks is protocols. The Medium Access control Protocol defines when…Wireless Sensor Networks (WSNs) have an ideal tactic for implementing the number of applications due to evading complex wirings connections and their maintenance. On the other hand, the main source of data communication in other networks is protocols. The Medium Access control Protocol defines when a wireless sensor (WS) transmits its recognizer
data. However, MAC protocols suffer from collisions that ultimately affect the performance of WSNs. Consequently, care must be taken for improving the efficiency of medium access protocol. This paper presents a model of the Lightweight medium access protocol (LMAC) protocol, which is energy efficient protocol suitable to analyze the probability of collisions for SN (Sensor Nodes) when the data is transmitted at the same time. This paper we consider connected
topologies that consist of 5 nodes. For analysis, we have used UMC PRO 0.2 (UPPAAL Model Checker Probabilistic) and results show that the chances of collisions decreases if the weight of nodes before they transmit data is increased. After that, we also compare the results of five nodes with their time slot 5, 6, and 7 depend on the chances of the collisions and suggest an optimistic choice of efficiency and also decrease the cost of networks a network set-up that improves the
performance and reduces the cost of the network.
more »
Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system
Appears in:
sis 18: e2

Authors:
Nadia Tabassum, Muhammad Saleem Khan, Sagheer Abbas, Tahir Alyas, Atifa Athar, Muhammad Adnan Khan

Published:
10th Jul 2019

Abstract:
Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud servi…Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and
maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud services over the internet. The cloud service providers cannot promise accurate reliability of their services i.e. problem in provisioning of software or hardware failure etc. Reliability of cloud computing services depends on the ability of fault tolerance during the execution of services. There are so many factors can cause faults, such as network failure, browser crash, request time out or hacker attacks. When users are facing these types of faults, they usually resubmit their requests. However, if there is any key element involved in faults or errors, additional action may be needed to deal with system logs. If there is anomaly behavior occurred in faulted virtual machine, these VMs may need extra attention from cloud system protection and security point of view. In this paper, provision of reliability management in hyper-convergence cloud infrastructure is proposed and self-healing techniques in software as a service on the basis of failure in cloud services. Intelligent cloud service reliability framework will increase the reliability during execution of cloud service.
more »
Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM
Appears in:
sis 18: e1

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Published:
10th Jul 2019

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.
more »

Ambient Air Quality Estimation using Supervised Learning Techniques
Appears in:
sis 19(22): e8

Authors:
Jasleen Kaur Sethi, Mamta Mittal

Published:
29th Jul 2019

Abstract:
The exponential increase of population in the urban areas has led to deforestation and industrialization that greatly affects the air quality. The polluted air affects the human health. Due to this concern, the prediction of air quality has become a potential research area. For the assessment of …The exponential increase of population in the urban areas has led to deforestation and industrialization that
greatly affects the air quality. The polluted air affects the human health. Due to this concern, the prediction of
air quality has become a potential research area. For the assessment of air quality an important indicator is Air Quality Index (AQI). The objective of this paper is to build prediction models using supervised learning.
Supervised Learning is broadly classified into: classification, regression and ensemble techniques. This study has been carried out using various techniques of classification, regression and ensemble learning. It has been observed from experimental work that Decision Trees from classification, Support Vector Regression from regression and Stacking Ensemble from ensemble techniques work more effectively and efficiently than the rest of the other techniques that fall under these categories.
more »
Improving Performance in Component Based Distributed Systems
Appears in:
sis 19(22): e7

Authors:
Fahd N. Al-Wesabi, Huda G. Iskandar, Mokhtar M. Ghilan

Published:
4th Jul 2019

Abstract:
Assuring high performance is one of the most important factors when building computerized systems. In distributed component-based systems different configurations and workloads are common. Over the years, many approaches were proposed to analyse, predict, measure and evaluate the performance of com…Assuring high performance is one of the most important factors when building computerized systems. In distributed component-based systems different configurations and workloads are common. Over the years, many approaches were proposed to analyse, predict, measure and evaluate the performance of component-based distributed systems in an attempt
to improve its performance. Higher performance results in better utilization of system resources, high throughput and quick response time of user requests. In this paper, we extend the work of the E-Avala approach to improve the overall performance
of the proposed approach by adding power processing capabilities. The proposed approach has been implemented and compared with previous approach using Arena simulation with varying configuration parameters. Comparative results show that the proposed approach has given better performance with different levels of processing powers.
more »
Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment
Appears in:
sis 19(22): e6

Authors:
Shekha Chenthara, Khandakar Ahmed, Frank Whittaker

Published:
4th Jul 2019

Abstract:
Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcare providers and patients to efficiently store and share their private healthcare information from any place and at any time as per demand. Generally, Cloud services provide the infrastructure by…Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcare
providers and patients to efficiently store and share their private healthcare information from any place and at any time as per demand. Generally, Cloud services provide the infrastructure by reducing the cost of storing, processing and updating information with improved efficiency and quality. However, the privacy of Electronic Health Records (EHR) is a significant hurdle while outsourcing private health data in the cloud because there is a higher peril of leaking health information to unauthorized parties. Several existing techniques are able to analyse the security and privacy issues associated with e-healthcare services. These methods are designed for single database, or databases, with an authentication centre and thus cannot adequately protect the data from
insider attacks. Therefore, this research study mainly focusses on how to ensure the patient privacy while
sharing the sensitive data between same or different organisations as well as healthcare providers in a cloud
environment. This paper proposes a multi-layer access control mechanism named MLAC Model to construct a
secure and privacy-preserving EHR system that enables patients to share their data with stakeholders. In this paper, we use a Dual layer access control model named Pseudo-Role Attribute based access control (PR-ABAC) mechanism that integrates attributes with roles for the secure sharing of EHR between multiple collaborators. The proposed framework also uses the concept of Provenance to ensure the Integrity of patient data. This work is expected to provide a foundation for developing security solutions against cyber-attacks, and thus contribute to the robustness of healthcare information sharing environments.
more »
Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams
Appears in:
sis 19(22): e5

Authors:
Najam us Sahar, Muhammad Sohail Irshad, Muhammad Adnan Khan

Published:
4th Jul 2019

Abstract:
Today the computational study of people’s opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part…Today the computational study of people’s opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part of importance to organizations because of online commerce is included in their operational strategy. Due to excess of user’s comments, feedback on web there is a need to analyze the user generated text. This research focuses on aspect level sentiment analysis in which identification of aspects and their related sentiments
is being done. Opinion analysis helps to identify the polarity of the text and feature extraction. This study is being done to provide an effective and efficient framework to calculate the sentiments of written text by using Naïve Bayes approach. For sentiment analysis dataset of 1060 reviews of different restaurants from online website TripAdvisor.com is being used. The
outcome achieved good accuracy 80.833 percent.
more »
Cloud Services Ranking by measuring Multiple Parameters using AFIS
Appears in:
sis 19(22): e4

Authors:
Sagheer Abbas, Tahir Alyas, Atifa Athar, Muhammad Adnan Khan, Areej Fatima, Waseem Ahmad Khan

Published:
4th Jul 2019

Abstract:
Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly used these days. Similarly, in cloud standardization, ranking the web services …Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many
situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly
used these days. Similarly, in cloud standardization, ranking the web services is a principal concern, as it is a relatively new approach, assigning ranks to cloud facilities has gained significant attention from researchers across the globe. Furthermore, cloud services standardization is an important idea as it is necessary if it is required to assign ranking for cloud services. There are few limitations in cloud standardization as there is no technique to check valid services and its classifications, wherein, the standardization of cloud services will play a major role in controlling the redundancy of cloud services. In this article, a new cloud service ranking method is proposed using an Adaptive Fuzzy Inference System (AFIS).
more »
Scalable Source Code Similarity Detection in Large Code Repositories
Appears in:
sis 19(22): e3

Authors:
Firas Alomari, Muhammed Harbi

Published:
4th Jul 2019

Abstract:
Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations. Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy. Other maintenance changes, such as …Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations. Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy. Other maintenance changes, such as extensions or patches, must be applied multiple times. Furthermore, the diversity of coding styles and flexibility of modern languages makes it difficult and cost ineffective to manually inspect large code repositories. Therefore, detection is only feasible by automatic techniques. We present an efficient and scalable approach for similar code fragment identification based on source code control flow graphs fingerprinting. The source code is processed to generate control flow graphs that are then hashed to create a unique fingerprint of the code capturing semantics as well as syntax similarity. The fingerprints can then be efficiently stored and retrieved to perform similarity search between code fragments. Experimental results from our prototype implementation supports the validity of our approach and show its effectiveness and efficiency in comparison with other solutions.
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Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models
Appears in:
sis 19(22): e2

Author:
Sarat Chandra Nayak

Published:
28th Jun 2019

Abstract:
Precise and proficient modelling and forecasting financial time series has been paying attention of researchers, which leads to the development of various statistical and machine learning based models. Accuracy of a particular method is problem and domain specific, hence identifying best method i…Precise and proficient modelling and forecasting financial time series has been paying attention of researchers,
which leads to the development of various statistical and machine learning based models. Accuracy of a particular
method is problem and domain specific, hence identifying best method is controversial. To boost up overall
accuracies and minimizing risk of model selection, combination of outputs of different models has been
recommended in the literature. This work presents a linear combiner of five predictive models i.e. ARIMA,
RBFNN, MLP, SVM, and FLANN for improving prediction accuracy. Four statistical methods i.e. trimmed mean,
simple average, median, and an error based method are used for suitable choice of combining weights. The
individual forecasts and the linear combiner are used separately to predict closing price of five stock markets and
exchange rate of five global markets. Extensive simulation work demonstrates the feasibility and supremacy of the
linear combiner.
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An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval
Appears in:
sis 19(22): e1

Authors:
Shikha Bhardwaj, Gitanjali Pandove, Pawan Kumar Dahiya

Published:
27th Jun 2019

Abstract:
Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet tran…Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.
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EODVGA: An Enhanced ODV Based Genetic Algorithm for Multi-Depot Vehicle Routing Problem
Appears in:
sis 19(21): e8

Authors:
Prabu U, Ravisasthiri P, Sriram R, Malarvizhi N, Amudhavel J

Published:
10th Jun 2019

Abstract:
Multi-Depot Vehicle Routing Problem (MDVRP) is a familiar combinative optimization problem that simultaneously determines the direction for different vehicles from over one depot to a collection of consumers. Researchers have suggested variety of meta-heuristic and heuristic algorithms to elucidate…Multi-Depot Vehicle Routing Problem (MDVRP) is a familiar combinative optimization problem that simultaneously determines the direction for different vehicles from over one depot to a collection of consumers. Researchers have suggested variety of meta-heuristic and heuristic algorithms to elucidate MDVRP, but none of the existing technique has
improved the fitness of the solution at the time of initial population generation. This motivates to propose an enhanced ODV based population initialization for Genetic Algorithm (GA) to solve MDVRP effectively. The Ordered Distance Vector (ODV) based population seeding method is a current and effective population initialization method for Genetic Algorithm to produce an early population with quality, individual diversity and randomness. In the proposed model, the customers are first grouped based on distance to their nearest depots and then routes are scheduled and optimized using
enhanced ODV based GA. The experiments are performed based on different types of instances of Cordeau. From the experimental results, it is very clear that the proposed technique outperforms the existing techniques in terms of convergence rate, error rate and convergence diversity.
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Load Balancing Policies of Web Servers: Research Analysis, Classification and Perspectives
Appears in:
sis 19(21): e7

Authors:
Prabu U, Malarvizhi N, Amudhavel J, Sriram R, Ravisasthiri P

Published:
10th Jun 2019

Abstract:
The usage of the internet has increased rapidly from the past few years. Thus it automatically increases the internet traffic. In the internet, a web server always responds to the client or web browser's requests. The major feature of a web server is to be available, scalable and predictable. So th…The usage of the internet has increased rapidly from the past few years. Thus it automatically increases the internet traffic. In the internet, a web server always responds to the client or web browser's requests. The major feature of a web server is to be available, scalable and predictable. So the core part to be concentrated is web server load balancing. Load balancing plays a vital role in allocating the jobs to the web server based on its status. There are various policies available for web server load balancing. Each of these policies came into existence based on the certain needs. In this paper, we have examined the latest policies of web server load balancing.
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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:
3291

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.
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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:
1134

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.
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A Narrative Literature Review and E-Commerce Website Research
Appears in:
sis 18(17): e1

Author:
K.M. Rahman

Downloads:
1114

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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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:
1036

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.
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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:
903

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.
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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:
869

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.
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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:
827

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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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:
765

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.
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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:
716

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.
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Software-Defined Network Testbed Using ZodiacFX a Hardware Switch for OpenFlow
Appears in:
sis 17(14): e5

Authors:
Fahad Nazir, Qazi Humayun, R Badlishah Ahmad, Shamsul Jamel Elias

Downloads:
711

Abstract:
Software-defined network (SDN) is a new programmable networking designed to perform tasks easier by enabling network administrators to add network control via a centralized control platform and open interfaces. Common network use procedures such as traffic shifts, troubleshooting and various types …Software-defined network (SDN) is a new programmable networking designed to perform tasks easier by enabling network administrators to add network control via a centralized control platform and open interfaces. Common network use procedures such as traffic shifts, troubleshooting and various types of device configuration. Thus devices are needed to reconfigure with the network, in order to create a reaction with events. In this paper, we have developed an SDN testbed using Zodiac FX a hardware switch for OpenFlow experiment. This research is utilized Zodiac FX a hardware switch to test the usage and discuss about the SDN controllers. Ryu controller is configured for testbed development. The main contribution of this paper in threefold as follows: first it provides the configuration of Ryu controller, second, it provides the configuration of Zodiac FX switch and lastly it develops a simple SDN testbed for OpenFlow.
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Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 6

Last Published: 29th Jul 2019

EAI Endorsed Transactions on Scalable Information Systems

An open access journal abstracted/indexed in WoS (ESCI), DOAJ, DBLP, CrossRef, EBSCO Discovery Services, OCLC Discovery Services, EuroPub, among others. It focuses on scalable distributed information system, scalable, data mining, grid information systems and more, with no publishing fees.

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³