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 and Xiaohua Jia

Aims & Scope
Indexing
Editorial Board
Special Issues

EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2020, the journal started to publish five issues per year. Authors are not charged for article submission and processing.

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)

Editors-in-Chief
Hua Wang, Victoria University, Australia
Xiaohua Jia, City University of Hong Kong
Editorial board
Manik Sharma, DAV University, India
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)
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)

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Scene Classification of Remotely Sensed Images using Optimized RSISC-16 Net Deep Convolutional Neural Network Model
Appears in:
sis 22(5): e5

Authors:
P. Deepan, L. R. Sudha, K. Kalaivani, J. Ganesh

Published:
12th Sep 2022

Abstract:
Remote Sensing Image (RSI) analysis has seen a massive increase in popularity over the last few decades, due to the advancement of deep learning models. A wide variety of deep learning models have emerged for the task of scene classification in remote sensing image analysis. The majority of these m…Remote Sensing Image (RSI) analysis has seen a massive increase in popularity over the last few decades, due to the advancement of deep learning models. A wide variety of deep learning models have emerged for the task of scene classification in remote sensing image analysis. The majority of these models have shown significant success. However, we found that there is significant variability, in order to improve the system efficiency in characterizing complex patterns in remote sensing imagery. We achieved this goal by expanding the architecture of VGG-16 Net and fine-tuning hyperparameters such as batch size, dropout probabilities, and activation functions to create the optimized Remote Sensing Image Scene Classification (RSISC-16 Net) deep learning model for scene classification. Using the Talos optimization tool, the results are carried out. This will increase efficiency and reduce the risk of over-fitting. Our proposed RSISC-16 Net model outperforms the VGG-16 Net model, according to experimental results.
more »
Web application for data collection in marketing strategies: an approach from the perspective of Digital Humanities
Appears in:
sis 22(5): e10

Authors:
Aldine do Socorro Corrêa Cruz, Edgar Bisset Alvarez, Luciane Paula Vital

Published:
24th Aug 2022

Abstract:
INTRODUCTION: Web applications and information systems are predominantly constituted as platforms for acquiring data and services over the Internet. Such applications integrate a technological context filled with intelligent devices interactive amongst themselves, connected to the network, hardware…INTRODUCTION: Web applications and information systems are predominantly constituted as platforms for acquiring data and services over the Internet. Such applications integrate a technological context filled with intelligent devices interactive amongst themselves, connected to the network, hardware, and software, and accessible to the most varied social segments.
OBJECTIVES: This article aims to present a digital mechanism based on product marketing for the acquisition of personal data used by a company in the cosmetics industry; to characterize privacy and data protection in view of the regulatory acts prevailing in Brazil; as well as to discuss how such scenarios affect the consumer.
METHODS: We performed a bibliographic survey with a qualitative approach to the information collected, and used a digital platform for commercial operation as object to analysis.
RESULTS: We confirmed the use of a digital platform, accessible by different electronic devices, to spread commercial content reaching a considerable volume of users, which then propagated it. We verified an indirect relationship of supply of goods through the transfer of identification, communication and location data. We identified users being directed to the Terms of Promotion and User Privacy Policy, as well as different media resources aiding their understanding.
CONCLUSION: The customer's vulnerability in consumer relations stands out, something increasingly frequent in digital environments, which enables a directly proportional flow of information between market and consumer. Finally, we observed that Digital Humanities constitute a broad field of research under an extensive methodological domain, due to its interdisciplinary character, for the digital study of cultural phenomena, and promote critical reflection on the effects that computational methods have on society.
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RETRACTED: An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 29

Authors:
Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed

Published:
22nd Aug 2022

Abstract:
We, the Publisher, have retracted the following article: Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed (2022). An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst. http://dx…We, the Publisher, have retracted the following article: Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed (2022). An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173163
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 »
An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters
Appears in:
sis 22(4): 17

Authors:
Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed

Published:
22nd Aug 2022

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173796. INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improv…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173796.

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF).
OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc.
METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels.
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Frequency based Digital Image Forgery Detection Through Optimal Threshold Using SOELTP
Appears in:
sis 22(4): 1

Authors:
Vikas Srivastava, Sanjay Kumar Yadav

Published:
22nd Aug 2022

Abstract:
INTRODUCTION: Image forgery detection is a very challenging task now a day. Latest tools and applications make it easy. Artefact change our thought and perceptions. OBJECTIVES: A forgery detection system is a need of time to detect image forgery. METHODS: We proposed a blind image forgery detecti…INTRODUCTION: Image forgery detection is a very challenging task now a day. Latest tools and applications make it easy. Artefact change our thought and perceptions.
OBJECTIVES: A forgery detection system is a need of time to detect image forgery.
METHODS: We proposed a blind image forgery detection technique. Optimal threshold-based Enhanced Local Ternary Pattern (OELTP) technique implemented on smoothed image. Features are extracted in the form of frequency to implement Discrete Wavelet Transform (DWT) on the chrominance component of the image. Support Vector Machine is used for classification.
RESULTS: The accuracy of the forgery detection on the proposed technique is better than some of the previous states of work.
CONCLUSION: Image forgery detection system performance has been improved by better localization of the forgery. Performance of the global threshold improved by using the latest technique, and reducing the operational complexity.
more »
Bibliometric Mapping of Trends, Applications and Challenges of Artificial Intelligence in Smart Cities
Appears in:
sis 22(4): 8

Authors:
Shilpi Harnal, Gaurav Sharma, Swati Malik, Gagandeep Kaur, Savita Khurana, Prabhjot Kaur, Sarita Simaiya, Deepak Bagga

Published:
28th Jun 2022

Abstract:
INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affor…INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affordable cost. Smart Cities are part of the solution to the growing challenges of urbanization. The adoption of new technologies like artificial intelligence (AI) is transforming cities, making them smarter, faster, and predicting opportunities for improvement.
OBJECTIVES: This study is conducting a detailed bibliometric survey to investigate the applications and trends of Artificial Intelligence research for different areas of smart cities and emphasizing the potential effects and challenges of AI adaptation in smart cities over the past 30.5 years.
METHODS: For this study, the Scopus database was used to collect a total of 1925 documents published between 1991-2021 (July). The bibliometric analysis includes document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of most cited documents.
RESULTS: The analyzed findings of this research study reflect not only the significance of AI technology for various applications within numerous sectors in the smart city but also major obstacles in AI research for various sectors of smart cities.
CONCLUSION: The research demonstrates that AI has the ability to construct today’s and tomorrow’s smart cities, but that each region’s potentials, conditions, and circumstances must be addressed in order to achieve a smooth internet city development.
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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 »

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:
6807

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

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
4192

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

Authors:
Pooja Kherwa, Poonam Bansal

Downloads:
4025

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 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:
3959

Abstract:
The number of vehicles in cities has increased dramatically due to rapid economic development. However, the infrastructure for accommodating these vehicles has grown relatively slow. Alleviating the pressure on the urban transport system and solving the ‘parking difficulty’ problem have thus becom…The number of vehicles in cities has increased dramatically due to rapid economic development. However, the infrastructure for accommodating these vehicles has grown
relatively slow. Alleviating the pressure on the urban transport system and solving the ‘parking difficulty’ problem have thus become hot topics recently. In this paper, an intelligent parking system based on geomagnetic field variations is presented to solve
this problem. An algorithm which detects the presence of vehicles in parking spaces in a parking lot is designed and field test results are presented. Our results show that this system has an acceptably high accuracy with low cost, high feasibility, high efficiency and hence is recommended for wide use.
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A Narrative Literature Review and E-Commerce Website Research
Appears in:
sis 18(17): e1

Author:
K.M. Rahman

Downloads:
3692

Abstract:
In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To…In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show less-focused research areas in addition to future directions.
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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:
3296

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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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:
3294

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
Appears in:
sis 19(20): e1

Authors:
Malav Shastri, Sudipta Roy, Mamta Mittal

Downloads:
3107

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
Appears in:
sis 20(25): e1

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
3039

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:
2884

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: 2022-08-24