EAI Endorsed Transactions on Scalable Information Systems

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index

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: Real-time image information processing with deep neural networks and data mining technologies (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-05-15; Publication of special issue (tentative): 2022-06-15)

Guest Editor: Dr. Prof. Hang Li (Northeastern University, China) Guest Editor: Dr. Prof. Jochen Schiewe (HafenCity Universität Hamburg, Germany)

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)
Hamed Taherdoost (Hamta Business Solution Snd)
Heng Tao Shen (University of Queensland)
Houbing Song (Embry-Riddle Aeronautical University)
José Manuel Machado (University of Minho, Portugal)
Jose Merseguer (Universidad de Zaragoza)
Jie Li (University of Tsukuba)
Lin Yun (Harbin Engineering University)
Phan Cong Vinh (Nguyen Tat Thanh University)
Raj Gururajan (University of Southern Queensland)
Sherman Chow (Chinese University of Hong Kong)
Silva Fábio (University of Minho, Portugal)
Steve Beitzel (Telcordia)
Tuan Phung-Duc (University of Tsukuba, JP)
Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)
Wang-Chien Lee (The Pennsylvania State University)
Weili Wu (The University of Texas at Dallas)
Xueyan Tang (Nanyang Technological University)
Vijayakumar Ponnusamy (SRM University, India)
J Amudhavel (KL University, India)
Yingshu Li (Georgia State University)
Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)
Karolj Skala (Ruđer Bošković Institute, Croatia)
Xiao-Zhi Gao (University of Eastern Finland, Finland)
Thaier Hayajneh (Fordham University, USA)
Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
Nuno M. Garcia (Universidade da Beira Interior, Portugal)
Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)
Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)
Hiep Xuan Huynh (Can Tho University, VietNam)
Vincent A. Cicirello (Stockton University, USA)
Ji Zhang (University of Southern Queensland, Australia)
Xiaohui Tao (University of Southern Queensland, Australia)
Ye Wang (National University of Defense Technology, China)
Nageswara Rao Moparthi (KL University, India)
Shuai Liu (Hunan Normal University, China)

Hua Wang¹

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

Xiaohua Jia²

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

Manik Sharma³

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

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

Recently Published
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RETRACTED: CenterNet-SPP based on multi-feature fusion for basketball posture recognition [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(36): e20

Author:
Zhouxiang Jin

Published:
8th Apr 2022

Abstract:
We, the Publisher, have retracted the following article: Zhouxiang Jin (2022). CenterNet-SPP based on multi-feature fusion for basketball posture recognition. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172780 The authors submitted the article to the Special Issue on “R…We, the Publisher, have retracted the following article:
Zhouxiang Jin (2022). CenterNet-SPP based on multi-feature fusion for basketball posture recognition. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172780

The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.

From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.

Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.

We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(36): e19

Authors:
Dianhuai Shen, Xueying Jiang, Lin Teng

Published:
8th Apr 2022

Abstract:
We, the Publisher, have retracted the following article: Dianhuai Shen, Xueying Jiang, Lin Teng (2022). A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.17-12-2021.172439 The authors sub…We, the Publisher, have retracted the following article:
Dianhuai Shen, Xueying Jiang, Lin Teng (2022). A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.17-12-2021.172439

The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.

From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.

Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.

We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: A multi-keyword parallel ciphertext retrieval scheme based on inverted index under the robot Distributed system [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(36): e18

Authors:
Jiyue Wang, Xi Zhang, Yonggang Zhu

Published:
8th Apr 2022

Abstract:
We, the Publisher, have retracted the following article: Jiyue Wang, Xi Zhang, Yonggang Zhu (2022). A multi-keyword parallel ciphertext retrieval scheme based on inverted index under the robot Distributed system. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.17-12-2021.172438 The au…We, the Publisher, have retracted the following article:
Jiyue Wang, Xi Zhang, Yonggang Zhu (2022). A multi-keyword parallel ciphertext retrieval scheme based on inverted index under the robot Distributed system. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.17-12-2021.172438

The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.

From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.

Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.

We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Parallax information fusion-based for dance moving image posture extraction [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(36): e17

Authors:
Yin Lyu, Lin Teng

Published:
8th Apr 2022

Abstract:
We, the Publisher, have retracted the following article: Yin Lyu, Lin Teng (2022). Parallax information fusion-based for dance moving image posture extraction. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.16-12-2021.172437 The authors submitted the article to the Special Issue on “…We, the Publisher, have retracted the following article:
Yin Lyu, Lin Teng (2022). Parallax information fusion-based for dance moving image posture extraction. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.16-12-2021.172437

The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.

From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.

Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.

We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Modified dark channel prior based on multi-scale Retinex for power image defogging [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(36): e16

Authors:
Haiyan Yu, Jihong Wang

Published:
8th Apr 2022

Abstract:
We, the Publisher, have retracted the following article: Haiyan Yu, Jihong Wang (2022). Modified dark channel prior based on multi-scale Retinex for power image defogging. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.7-12-2021.172363 The authors submitted the article to the Special…We, the Publisher, have retracted the following article:
Haiyan Yu, Jihong Wang (2022). Modified dark channel prior based on multi-scale Retinex for power image defogging. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.7-12-2021.172363

The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.

From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.

Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.

We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Encoder-decoder structure based on conditional random field for building extraction in remote sensing images [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(36): e15

Author:
Yian Xu

Published:
8th Apr 2022

Abstract:
We, the Publisher, have retracted the following article: Yian Xu (2022). Encoder-decoder structure based on conditional random field for building extraction in remote sensing images. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.7-12-2021.172362 The authors submitted the article to …We, the Publisher, have retracted the following article:
Yian Xu (2022). Encoder-decoder structure based on conditional random field for building extraction in remote sensing images. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.7-12-2021.172362

The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.

From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.

Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.

We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
CenterNet-SPP based on multi-feature fusion for basketball posture recognition
Appears in:
sis 22(36): e14

Author:
Zhouxiang Jin

Published:
5th Jan 2022

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173788. Aiming at the problem that the existing posture recognition algorithms can not fully reflect the dynamic characteristics of athletes' posture, this paper proposes a CenterNe…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173788. Aiming at the problem that the existing posture recognition algorithms can not fully reflect the dynamic characteristics of
athletes' posture, this paper proposes a CenterNet-SPP model based on multi-feature fusion algorithm for basketball
posture recognition. Firstly, motion posture images are collected by optical image collector, and then gray scale
transformation is performed to improve the image quality. Furthermore, body contour and motion posture region are
obtained based on shadow elimination technology and inter-frame difference method. Finally, radon transform and discrete
wavelet transform are used to extract the motion posture region and body contour, and the two complementary features are
fused and then input into the CenterNet-SPP network to realize the final posture recognition. Experimental results show
that the recognition accuracy of the proposed method is higher than that of other new methods.
more »
A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement
Appears in:
sis 22(36): e13

Authors:
Dianhuai Shen, Xueying Jiang, Lin Teng

Published:
17th Dec 2021

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173797. Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolut…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173797. Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a
novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale
convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and
combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge
detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator
and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and
strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional
methods to verify the effectiveness of the proposed method.
more »
A multi-keyword parallel ciphertext retrieval scheme based on inverted index under the robot distributed system
Appears in:
sis 22(36): e12

Authors:
Jiyue Wang, Xi Zhang, Yonggang Zhu

Published:
17th Dec 2021

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173798. The traditional ciphertext retrieval scheme has some problems, such as low retrieval performance, lack of single keyword retrieval mode and limitation of single machine reso…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173798. The traditional ciphertext retrieval scheme has some problems, such as low retrieval performance, lack of single keyword
retrieval mode and limitation of single machine resources in traditional single server architecture. At the same time, for
searchable encryption, it needs to balance the data security and retrieval efficiency. In this paper, a multi-keyword parallel
ciphertext retrieval system based on inverted index is proposed. The system adopts different index encryption methods to
improve the performance of ciphertext retrieval. Through the segmentation of ciphertext inverted index, the block retrieval
of inverted index is realized, which overcomes the limitation of single machine resources and improves the retrieval
efficiency. By combining the characteristics of distribution, the traditional single-machine retrieval architecture is
extended and multi-keyword parallel retrieval is realized. The experimental results show that compared with SSE-1
scheme, the proposed scheme can improve the efficiency of retrieval, update and other operations on the premise of
ensuring the security of ciphertext data, achieve multi-keyword retrieval, and dynamically expand the distributed
architecture of the system. Finally, it can improve the system load capacity.
more »
Parallax information fusion-based for dance moving image posture extraction
Appears in:
sis 22(36): e11

Authors:
Yin Lyu, Lin Teng

Published:
16th Dec 2021

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173799. The existing motion image posture contour extraction results have low definition and serious detail loss. To solve this problem, we propose a novel dance moving image postur…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173799. The existing motion image posture contour extraction results have low definition and serious detail loss. To solve this
problem, we propose a novel dance moving image posture extraction method based on parallax information fusion. Firstly,
the image with motion information is statistically analyzed by using the information fusion process to determine the
position of the motion region. After the noise is reduced by morphological processing, the initial motion posture profile is
obtained. The parallax between different control points and the center is used as the active contour model to shape the
contraction force and expansion force, which can effectively assist the initial edge contour curve to gradually approach the
real edge contour. Finally, the contour of the current moving image is extracted from the sequence image contour to obtain
the attitude contour of the moving image. The experimental results show that the proposed method can extract the contour
of the moving image clearly with less detail loss, which proves that the proposed method has strong practical performance
and can effectively find the contour of the moving object.
more »

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

Author:
Abdulla Jaafar Desmal

Downloads:
6672

Abstract:
The internet and social media provide students with a range of academic benefits and opportunities to enhance their learning process. The main goal of this research is to examine the impact of using the social media on the academic performance. The new social networks, such as Instagram, Facebook, …The internet and social media provide students with a range of academic benefits and opportunities to enhance their learning process. The main goal of this research is to examine the impact of using the social media on the academic performance. The new social networks, such as Instagram, Facebook, Twitter, etc., can affect the behavior and academic performance of the universities' students; therefore the selected universities were Ahlia University, Applied Science University and University of Bahrain. The sample was (150) students distributed equally among the three universities. The research questions will answer (1) what is the evolution of ICTs and the Internet in the World; (2) what is the impact caused by ICT in education; (3) what are the effects of social media on the academic performance of students at Bahrain Universities; (4) what are the social networking sites that are more popular among students at Bahrain Universities. The results show that the social media has a positive impact on academic performance and 57% of students prefer the mobile application WhatsApp as a social media for their academic purpose.
more »
Preventing DDoS using Bloom Filter: A Survey
Appears in:
sis 18(19): e3

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
4037

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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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:
3798

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 »
Topic Modeling: A Comprehensive Review
Appears in:
sis 20(24): e2

Authors:
Pooja Kherwa, Poonam Bansal

Downloads:
3557

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

Author:
K.M. Rahman

Downloads:
3542

Abstract:
In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To…In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show less-focused research areas in addition to future directions.
more »
A Hybrid Approach for Breast Cancer Classification and Diagnosis
Appears in:
sis 19(20): e2

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

Downloads:
3156

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

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

Downloads:
3154

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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Stock Price Prediction using Artificial Neural Model: An Application of Big Data
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sis 19(20): e1

Authors:
Malav Shastri, Sudipta Roy, Mamta Mittal

Downloads:
2974

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

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
2879

Abstract:
The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The…The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite
comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is
taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.
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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:
2754

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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Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 9

Last Published: 2022-04-28

EAI Endorsed Transactions on Scalable Information Systems

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³