EAI Endorsed Transactions on Scalable Information Systems

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

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

Aims & Scope
Indexing
Editors in Chief
Editorial Board
Special Issues

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

The scope of the journal includes:

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

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: 11/05/2020)

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: 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 editor: Xuan Liu (Southeast University, China)

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

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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Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models
Appears in:
sis 18: e14

Authors:
Kiran Kumar Paidipati, Arjun Banik

Published:
6th Nov 2019

Abstract:
In India, due to the blessing by the outbreak of the National Food Security Mission, the production of cereals such as wheat, rice etc, has increased in an alarming rate. In this Study, forecasting is done with the help Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Ne…In India, due to the blessing by the outbreak of the National Food Security Mission, the production of cereals such as wheat, rice etc, has increased in an alarming rate. In this Study, forecasting is done with the help Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM-NN) models on the basis of the historical data of rice cultivation from the year 1950-51 to 2017-18. The well fitted ARIMA models for the parameters such as Area under Cultivation (0,1,1), Production (0,1,1) and Yielding (2,2,1) are obtained from the significant spikes of their respective Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots. But, the models fitted with a supervised deep learning neural network known as LSTM-NN are found much better time series forecasting model than the ARIMA models. The performances of these models validated with the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. From the study, the LSTM-NN’s are more flexible and able to develop accurate models for predicting the behavior of agricultural parameters than the ARIMA models.
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Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking
Appears in:
sis 18: e13

Authors:
S. Khurana, R. K. Singh

Published:
6th Nov 2019

Abstract:
Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migratio…Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision
problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of
optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.
more »
Machine Learning Based Hybrid Model for Fault Detection in Wireless Sensors Data
Appears in:
sis 18: e12

Authors:
P. Raghu Vamsi, Anjali Chahuan

Published:
5th Nov 2019

Abstract:
Wireless Sensor Networks (WSN) refers to a group of spatially deployed and dedicated sensors for sending, recording, and monitoring the physical conditions of the environment and transmitting the collected data to a central location. The major challenge is to extract high level knowledge from such …Wireless Sensor Networks (WSN) refers to a group of spatially deployed and dedicated sensors for sending, recording, and monitoring the physical conditions of the environment and transmitting the collected data to a central location. The major challenge is to extract high level knowledge from such data. Detecting abnormality in such data can help finding the faulty sensor and also the sensor collecting the most interesting reading from the dataset. This paper proposes a machine learning
based hybrid model for knowledge discovery that works best with multivariate time-series data. The Intel Berkeley Research lab (IBRL) dataset is one of the most trending dataset collected by a WSN is considered for the study. The spatial-temporal correlation was also taken as reference to find anomalies in the dataset using three models - 1) Histogram Based Outlier Score (HBOS), 2) Minimum Covariant Determinant (MCD) and 3) Isolation Forests (IF). Further, the electrical configuration about components of WSN has been used to find faults among the outliers found in the dataset. The results show that the proposed hybrid model with Isolation Forest outperformed with a precision of 94.86%. The
experiment was also able to spot the least trustful or faulty sensors among the deployed sensors in IBRL dataset.
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Optimised Transformation Algorithm For Hadoop Data Loading in Web ETL Framework
Appears in:
sis 18: e11

Authors:
Gaurav Gupta, Neelesh Kumar, Indu Chhabra

Published:
2nd Oct 2019

Abstract:
Web ETL unlike conventional ETL framework requires considerable improvements in all the three layers i.e. Extraction, Transformation and Loading due to the inherent nature of web input data. Websites are huge and are unique source of information, out of such huge information available on the websit…Web ETL unlike conventional ETL framework requires considerable improvements in all the three layers i.e. Extraction, Transformation and Loading due to the inherent nature of web input data. Websites are huge and are unique source of information, out of such huge information available on the websites, finding and analysing the required and relevant data is critical as the data may be foul consisting of redundant data or misspelled. Determining integrated record that stands for identical real world entities in abundant ways is the major problem to be analysed for any database. Hence, Web ETL transformation layer functionality of data transformation becomes mandatory in determining the pertinent information to be examined. Since the data on the web is “very voluminous” hence loading only clean data in data warehouse is necessary for fast processing to achieve accurate result. The present research focuses on data transformation in web ETL framework
and proposes a modified technique to employ token wise sentence sorting to remove redundant records from the patent database along with Levenshtein distance used for string matching. Afterwards the cleaned data is transformed and loaded from this staging area to hadoop environment. The integration of proposed transformation technique with hadoop system
delimits the constraint of data processing, storage and retrieval of large data structure from conventional data warehouse system.
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A Competent and Novel Approach of Designing an Intelligent Image Retrieval System
Appears in:
sis 18: e10

Authors:
Shefali Dhingra, Poonam Bansal

Published:
30th Sep 2019

Abstract:
A prominent technique used to search imperceptible images from any vast repository is denoted by Content based image retrieval (CBIR) system. The most eminent features of CBIR system are Texture, Color and Shape and in this paper these features are extracted using Local binary pattern, Color moment…A prominent technique used to search imperceptible images from any vast repository is denoted by Content based image retrieval (CBIR) system. The most eminent features of CBIR system are Texture, Color and Shape and in this paper these features are extracted using Local binary pattern, Color moment and Automatic segmentation process respectively. The
features of these descriptors are combined for the formation of a hybrid feature vector by utilizing the process of normalization. Then, for enhancing the retrieval accuracy of the proposed system, Extreme learning machine (ELM) has been utilized as a classifier. The proposed hybrid CBIR system with ELM leads to an evolution of an intelligent system. Various
evaluation metrics like Precision, Recall, Accuracy, etc. have been calculated on the proposed system on standard datasets. Average precision of 90%, 78%, 72% and 88.70% has been obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM datasets which is significantly higher than the related techniques.
more »
Effect of Packet Loss and Reorder on Quality of Audio Streaming
Appears in:
sis 18: e9

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

Published:
24th Sep 2019

Abstract:
In this paper, Audio QoE assessment experiments were conducted by using network parameters such as packet loss and reorder. The network model was created by using three computers, which includes sender, receiver, and middle between Linux based router machines was configured. NetEm tool was used to …In this paper, Audio QoE assessment experiments were conducted by using network parameters such as packet loss and reorder. The network model was created by using three computers, which includes sender, receiver, and middle between Linux based router machines was configured. NetEm tool was used to disturb the on-going network traffic to real-time packet loss and reorder in the broadcasting environment. VLC player was used to broadcasting and recording audio from
sender to receiver machines and audios were presented to users to listen, and assign ratings according to their level of satisfaction. The results show the user’s satisfaction level is decreased when packet loss and reorder level is increased in audio streams. The user accepted a certain level of network traffic disturbance, however, the increment of disturbance in
network traffic damage the audio quality below the then acceptable level. This research work will help to cloud service providers to provide QoE to users according to singed in service level agreements.
more »
Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters
Appears in:
sis 18: e8

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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Prediction of Cross Project Defects using Ensemble based Multinomial Classifier
Appears in:
sis 18: e7

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

Published:
9th Sep 2019

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

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

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

RESULTS & CONCLUSION: Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data.
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A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique
Appears in:
sis 18: e6

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 »
Improving Semi-Supervised Classification using Clustering
Appears in:
sis 18: e5

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: e4

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.
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Topic Modeling: A Comprehensive Review
Appears in:
sis 18: e3

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.
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A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
Appears in:
sis 18: e2

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.
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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 »

Optimization of Governance Factors for Smart City Through Hierarchical Mamdani Type-1 Fuzzy Expert System Empowered with Intelligent Data Ingestion Techniques
Appears in:
sis 19(23): e8

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

Published:
9th Sep 2019

Abstract:
A Smart City is an urban area that uses the Internet of things (IoT) sensors to collect data and information to enhance the operational aptitude, in a way to manage assets and resources efficiently. Smart governance is a factor of a smart city for intelligent utilization of ICT to enhance the basic…A Smart City is an urban area that uses the Internet of things (IoT) sensors to collect data and information to enhance the operational aptitude, in a way to manage assets and resources efficiently. Smart governance is a factor of a smart city for intelligent utilization of ICT to enhance the basic leadership. The smart government may be considered as a reason for creating smart governance, through the application of rising information and communication technology for administering. Smart Governance is totally dependent on the information that is being recorderded. Smart consists of multiple factors that an essential role in smart city activities, which require complex collaborations between governments, citizens and different partners. In this article, a new computational method is proposed for the evaluation of the Governance factors of the smart city using Hierarchical Mamdani Type-1 Fuzzy Expert System and empowered with fuzzy based data ingestion techniques.
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Detection and mitigation of DDoS attack in cloud computing using machine learning algorithm
Appears in:
sis 19(23): e7

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 19(23): e6

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.
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Simulation and Security Calibration of Weather Management System for the Least Rainy Areas of Pakistan through Quantum Key Distribution
Appears in:
sis 19(23): e5

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 »
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 »
The impact of Planned Special Events (PSEs) on urban traffic congestion
Appears in:
sis 19(23): e4

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 »
Empirical Analysis of Recent Advances, Characteristics and Challenges of Big Data
Appears in:
sis 19(23): e3

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 19(23): e2

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.
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Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system
Appears in:
sis 19(23): e1

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.
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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.
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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:
3694

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:
1274

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

Author:
K.M. Rahman

Downloads:
1247

Abstract:
In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To…In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show less-focused research areas in addition to future directions.
more »
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:
1094

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

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
1001

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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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:
987

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 »
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:
924

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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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:
865

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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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:
825

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.
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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:
770

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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Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 6

Last Published: 18th Oct 2019

EAI Endorsed Transactions on Scalable Information Systems

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³