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 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.

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 fusion and Intelligent Information Integration (Manuscript submission deadline: 2022-01-30; Notification of acceptance: 2022-03-15; Submission of final revised paper: 2022-04-15; Publication of special issue (tentative): 2022-06-01)

Guest Editor: Shoulin Yin (Shenyang Normal University, China) Guest Editor: Asif Ali Laghari (Sindh Madressatual Islam University, Pakistan)

Call for Papers: Special issue on: Education based Reliable Intelligent Systems (Manuscript submission deadline: 2022-01-31; Notification of acceptance: 2022-03-31; Submission of final revised paper: 2022-04-30; Publication of special issue (tentative): 2022-05-31)

Guest Editor: Shuai Liu (Hunan Normal University, China) Guest Editor: Jerry Chun-Wei Lin (WNUAS, Norway)

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)
Dongming Li (Nanjing University of Posts and Telecommunications (NUPT), China)
Vincent A. Cicirello (Stockton University, USA)
Ji Zhang (University of Southern Queensland, Australia)
Xiaohui Tao (University of Southern Queensland, Australia)
Ye Wang (National University of Defense Technology, China)
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

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Modified dark channel prior based on multi-scale Retinex for power image defogging
Appears in:
sis 18: e36

Authors:
Haiyan Yu, Jihong Wang

Published:
7th Dec 2021

Abstract:
At present, defogging technologies can be roughly divided into two categories: the first category is the method of defogging based on image enhancement non-physical model. This method does not start from the essence of optical imaging, but only improves the …At present, defogging technologies can be roughly divided into two categories: the first category is the method of
defogging based on image enhancement non-physical model. This method does not start from the essence of optical
imaging, but only improves the visual effect of the image by improving the contrast and color of the image, so as to
achieve the purpose of defogging. The commonly used methods include histogram equalization, contrast enhancement and
automatic color levels, Retinex theory and wavelet transform, etc. The second type is based on atmospheric scattering
physical model. This method analyzes the degradation mechanism in the process of imaging, establishes the degradation
model of foggy image, and restores the real scene without fog by using the prior knowledge in the degradation process.
This method needs to obtain prior conditions as model parameters, but the prior conditions are often difficult to obtain. In
this paper, an adaptive power image defogging algorithm based on multi-scale Retinex and modified dark channel is
proposed. Sobel operator is used to detect the edges of luminance components and multi-scale Retinex algorithm is used to
eliminate luminance components. A priori theory of dark channel optimization by guided filtering is used to obtain rough
estimated transmittance. The global atmospheric light value is selected by quadtree subspace search method. In order to
eliminate the phenomenon that the restored image is dark as a whole and cannot display details, the brightness value is
corrected by two-dimensional gamma function, and finally the restored defogging image is obtained. The experimental
results show that the proposed algorithm can effectively restore the details of foggy images, completely remove foggy
images, have good color brightness, and the images are clear and natural.
more »
Encoder-decoder structure based on conditional random field for building extraction in remote sensing images
Appears in:
sis 18: e35

Author:
Yian Xu

Published:
7th Dec 2021

Abstract:
The application of building extraction involves a wide range of fields, including urban planning, land use analysis and change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within the building category. Theref…The application of building extraction involves a wide range of fields, including urban planning, land use analysis and
change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within
the building category. Therefore, automatic building extraction from aerial images is still a challenging research topic.
Although deep convolutional networks have many advantages, the networks used for image-level classification cannot be
directly used for pixel-level building extraction tasks. This is caused by successive steps larger than one in the pooling or
convolution layer. These operations will reduce the spatial resolution of feature maps. Therefore, the spatial resolution of
the output feature map is no longer consistent with that of the input, which cannot meet the task requirements of pixel-
level building extraction. In this paper, we propose a encoder-decoder structure based on conditional random field for
building extraction in remote sensing images. The problem of boundary information lost by unitary potential energy in
traditional conditional random field is solved through multi-scale building information. It also preserves the local structure
information. The network consists of two parts: encoder sub-network and decoder sub-network. The encoder sub-network
compresses the spatial resolution of the input image to complete the feature extraction. The decoder sub-network improves
the spatial resolution from features and completes building extraction. Experimental results show that the proposed
framework is superior to other comparison methods in terms of the accuracy on open data sets, and can extract building
information in complex scenes well.
more »
The use of telehealth during the coronavirus (COVID-19) pandemic in oral and maxillofacial surgery – A qualitative analysis
Appears in:
sis 18: e34

Authors:
Joshua Lee, Joon Soo Park, Kate N. Wang, Boxi Feng, Marc Tennant, Estie Kruger

Published:
2nd Dec 2021

Abstract:
Introduction: Telehealth usage increased especially in the coronavirus pandemic. Objective: To determine whether oral and maxillofacial surgeons (OMS) believe that telehealth is an adequate substitute for in-person consultations. Methods: OMS were interviewed. These were transcribed and…Introduction: Telehealth usage increased especially in the coronavirus pandemic.

Objective: To determine whether oral and maxillofacial surgeons (OMS) believe that telehealth is an adequate substitute for in-person consultations.

Methods: OMS were interviewed. These were transcribed and themes and subthemes coded. Quotes were selected to create narratives about themes and subthemes and a frequency table generated.

Results: 20 OMS were interviewed. There were 200 positive, 215 negative, 9 neutral and 256 unstated comments. Major themes were diagnosis, accessibility, patient-centred care, technology and finances. 34 sub-themes were identified. OMS
were most satisfied with accessibility and most dissatisfied with diagnosis.

Conclusion: OMS had mixed opinions regarding telehealth. While it can improve access, the technology, interventional capacity and diagnostic ability are limited. Face-to-face was preferred. Further studies are required to improve telehealth.
more »
Frequency based Digital Image Forgery Detection Through Optimal Threshold Using SOELTP
Appears in:
sis 18: e33

Authors:
Vikas Srivastava, Sanjay Kumar Yadav

Published:
2nd Dec 2021

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 …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 »
DSSESKN: A depthwise separable squeeze-and-excitation selective kernel network for art image classification
Appears in:
sis 18: e32

Author:
Shaojie Zhang

Published:
26th Nov 2021

Abstract:
Image classification is one of the key technologies of content-based image retrieval, and it is also the focus and hotspot of image content analysis research in recent years. Through the image processing and analysis technology to automatically analyze the image content to comple…Image classification is one of the key technologies of content-based image retrieval, and it is also the focus and hotspot of
image content analysis research in recent years. Through the image processing and analysis technology to automatically
analyze the image content to complete the management and retrieval of images, this process is the main content for image
classification. Faced with massive digital Chinese art works, how to achieve effective management and retrieval for them
has become an urgent problem to be solved. Traditional image retrieval technology is mainly based on image annotation,
which has many problems, such as large workload and not objective enough. In this paper, we propose a depthwise
separable squeeze-and-excitation selective kernel network (DSSESKN) for art image classification. SKNet (Slective
Kernel Network) is used to adaptively adjust the receptive field to extract the global and detailed features of the image. We
use SENet (squeeze-and-excitation network) to enhance the channel features. SKNet and SENet are fused to built the
DSSESKN. The convolution kernel on the branch of DSSESKN module is used to extract the global feature and local
detail features of the input image. The feature maps on the branches are fused, and the fused feature maps are compressed
and activated. The processed feature weights are mapped to the feature maps of different branches and feature fusion is
carried out. Art images are classified by deep separable convolution. Finally, we conduct experiments with other state-of-the-art classification methods, the results show that the effectiveness of the DSSESKN obtains the better effect.
more »
A novel one-stage object detection network for multi-scene vehicle attribute recognition
Appears in:
sis 18: e31

Author:
Jiefei Zhang

Published:
22nd Nov 2021

Abstract:
In recent years, with the continuous development of computer vision technology, computer vision has been widely used in many scientific research fields and civil applications. As one of the basic tasks of many advanced visual tasks, object detection has important research signific…In recent years, with the continuous development of computer vision technology, computer vision has been widely used in many scientific research fields and civil applications. As one of the basic tasks of many advanced visual tasks, object detection has important research significance in the field of computer vision and practical applications. At present, with the joint efforts of many scholars, the research on object detection based on deep learning has made remarkable progress. However, in some special weather, such as rainy days, foggy days, nights and the lack of visible light source, the visual distance and visibility are very poor, and the obtained images cannot be used normally, thus affecting the result of object detection. To solve the above problem, this paper proposes a novel one-stage object detection network for multi-scene
vehicle attribute recognition, which mainly contains vehicle type and color attributes. The one-stage object detection network YOLOv3 is used as the basic network, and GIOU loss function is used to replace MSE loss function. Finally, experimental results show that the accuracy of the proposed algorithm is improved significantly on public data sets.
more »
Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion
Appears in:
sis 18: e30

Authors:
Jihong Wang, Haiyan Yu

Published:
22nd Nov 2021

Abstract:
At present, visible light imaging sensor and infrared imaging sensor are two commonly used sensors, which are widely used in aviation, navigation and other military fields of detection, monitoring and tracking. Due to their different working principles, their performance is d…At present, visible light imaging sensor and infrared imaging sensor are two commonly used sensors, which are widely used in aviation, navigation and other military fields of detection, monitoring and tracking. Due to their different working
principles, their performance is different. The infrared imaging sensor records the infrared radiation information of the target itself by acquiring the infrared radiation of the ground target. It identifies the target by detecting the thermal radiation difference between the target and the background, so it has special recognition and camouflage ability, such as finding people, vehicles and artillery hidden in the woods and grass. Although the infrared imaging sensor has a good
detection performance for thermal targets, it is insensitive to the brightness changes of the scene and has low imaging resolution, which is not conducive to human eyes interpretation. Visible light imaging sensor is sensitive to the reflection of the target scene and has nothing to do with the thermal contrast of the target scene. The obtained image has high clarity and can provide the details of the target scene. Therefore, the fusion of infrared and visible images will be beneficial to the combination of infrared image's better target indication characteristics and visible image's scene clearing information. In this paper, we propose a double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion. The experimental results show that the fusion image not only retains the target information of the infrared image, but also retains more details of the visible image, and achieves better performance in both subjective and objective evaluation
more »
Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition
Appears in:
sis 18: e29

Author:
Yubo Geng

Published:
22nd Nov 2021

Abstract:
Implicit discourse relation recognition is a sub-task of discourse relation recognition, which is challenging because it is difficult to learn the argument representation with rich semantic information and interactive information. To solve this problem, this pap…Implicit discourse relation recognition is a sub-task of discourse relation recognition, which is challenging because it is
difficult to learn the argument representation with rich semantic information and interactive information. To solve this
problem, this paper proposes a self-organizing incremental and graph convolution neural network for English implicit
discourse relation recognition. The method adopts the preliminary training language model BERT (Bidirectional Encoder
Representation from Transformers) coding argument for argument. A classification model based on self-organizing
incremental and graph convolutional neural network is constructed to obtain the argument representation which is helpful
for English implicit discourse relation recognition. The experimental results show that the proposed method is superior to
the benchmark model in terms of contingency relations and expansion relations.
more »
Convolutional block attention module based on visual mechanism for robot image edge detection
Appears in:
sis 18: e28

Authors:
Aiyun Ju, Zhongli Wang

Published:
19th Nov 2021

Abstract:
In recent years, with the continuous development of computer vision, digital image and other information technology, its application in robot image has attracted many domestic and foreign scholars to conduct researches. Edge detection technology based on traditional deep learnin…In recent years, with the continuous development of computer vision, digital image and other information technology, its application in robot image has attracted many domestic and foreign scholars to conduct researches. Edge detection technology based on traditional deep learning produces messy and fuzzy edge lines. Therefore, we present a new convolutional block attention module (CBAM) based on visual mechanism for robot image edge detection. CBAM is added into the trunk network, and a down-sampling technique with translation invariance is adopted. Some down-sampling operations in the trunk network are removed to retain the details of the image. Meanwhile, the extended convolution technique is used to increase the model's receptive field. Training is carried out on BSDS500 and PASCAL VOL Context datasets. We use the image pyramid technique to enhance the edges quality during testing. Experimental results show that the proposed model can extract image contour more clearly than other networks, and can solve the problem of edge blur.
more »
Correlation temporal feature extraction network via residual network for English relation extraction
Appears in:
sis 18: e27

Author:
Ping Li

Published:
19th Nov 2021

Abstract:
In relation extraction, a major challenge is the absence of annotated samples. Relation extraction aims to extract the relationships between entity pairs from a large amount of unstructured data. To solve the above problems, this paper presents a new method for Engl…In relation extraction, a major challenge is the absence of annotated samples. Relation extraction aims to extract the relationships between entity pairs from a large amount of unstructured data. To solve the above problems, this paper presents a new method for English relation extraction based on correlation temporal feature extraction network via residual network. Firstly, the attention mechanism and recurrent neural network are used to obtain the temporal features of English word correlation. Secondly, a multi-branch feature sensing convolutional neural network is constructed to obtain global and local temporal correlation features respectively. Residual network can dynamically reduce the influence of noise data and better extract the deep information of English text. Finally, the relation extraction is realized with Softmax classifier.
Experimental results show that the proposed method can extract English relation effectively than other methods.
more »
LRSDSFD: low-rank sparse decomposition and symmetrical frame difference method for moving video foreground-background separation
Appears in:
sis 18: e26

Author:
Hongqiao Gao

Published:
16th Nov 2021

Abstract:
In scenes with dynamic background or measurement noise, the low-rank sparse decomposition background modeling algorithm based on kernel norm constraint is easy to separate the moving background or noise as part of the foreground and the foreground at the same time, a…In scenes with dynamic background or measurement noise, the low-rank sparse decomposition background modeling
algorithm based on kernel norm constraint is easy to separate the moving background or noise as part of the foreground
and the foreground at the same time, and it has poor modeling performance for complex background. In order to solve this
problem, this paper proposes a low-rank sparse decomposition and symmetrical frame difference method for moving video
foreground-background separation. Firstly, low-rank sparse decomposition is used to constrain the background matrix.
Secondly, the moving objects in the region of interest (ROI) are extracted by symmetrical frame difference method, and
the background image is obtained by block background modeling. Numerical experiments show that compared with other
five main algorithms, the proposed algorithm can separate moving objects more accurately in the scene with dynamic
background.
more »
A novel image clustering method based on coupled convolutional and graph convolutional network
Appears in:
sis 18: e25

Author:
Rangjun Li

Published:
16th Nov 2021

Abstract:
Image clustering is a key and challenging task in the field of machine learning and computer vision. Technically, image clustering is the process of grouping images without the use of any supervisory information in order to retain similar images within the same…Image clustering is a key and challenging task in the field of machine learning and computer vision. Technically, image
clustering is the process of grouping images without the use of any supervisory information in order to retain similar
images within the same cluster. This paper proposes a novel image clustering method based on coupled convolutional and
graph convolutional network. It solves the problem that the deep clustering method usually only focuses on the useful
features extracted from the sample itself, and seldom considers the structural information behind the sample. Experimental
results show that the proposed algorithm can effectively extract more discriminative deep features, and the model achieves
good clustering effect due to the combination of attribute information and structure information of samples in GCN.
more »
Multiscale fuzzy entropy based on local mean decomposition and Fisher rule for EEG feature extraction in human motion analysis
Appears in:
sis 18: e24

Author:
Huili He

Published:
12th Nov 2021

Abstract:
Electroencephalogram (EEG) is a nonlinear, non-stationary, and random weak signal generated by a large number of neurons. It has great research value and practical significance in artificial intelligence, biomedical engineering and other fields. EEG feature extraction…Electroencephalogram (EEG) is a nonlinear, non-stationary, and random weak signal generated by a large number of
neurons. It has great research value and practical significance in artificial intelligence, biomedical engineering and other
fields. EEG feature extraction is an important step which directly affects the processing results. Currently, the commonly
used methods for EEG feature extraction include frequency domain or time domain analysis and time-frequency
combination. Due to the nonlinearity of EEG, the above methods have certain limitations. Therefore, this paper proposes a
multiscale fuzzy entropy based on local mean decomposition and Fisher rule for EEG feature extraction in human motion
analysis. Firstly, the EEG signal is decomposed adaptively into a series of product function (PF) components. Then the
effective PF component is selected and the multiscale fuzzy entropy is calculated. Multi-scale fuzzy entropy is used for
feature extraction. Fisher rule is used to rank the feature classification ability of fuzzy entropy at different scales, and the
multi-scale fuzzy entropy with the highest ranking is selected to form the optimal feature vector to achieve feature
dimension reduction. Experimental results show that this proposed method can extract the features of EEG signal
effectively, which verifies the validity and feasibility of the new method.
more »
Hybrid neural network model based on multi-head attention for English text emotion analysis
Appears in:
sis 18: e23

Author:
Ping Li

Published:
12th Nov 2021

Abstract:
Traditional Convolutional Neural Network (CNN) ignores the contextual semantics information when performing emotion analysis tasks. And CNN will lose a lot of feature information during the maximum pooling operation, which will limit the text classification performance. CNN cannot extract the e…Traditional Convolutional Neural Network (CNN) ignores the contextual semantics information when performing emotion
analysis tasks. And CNN will lose a lot of feature information during the maximum pooling operation, which will limit the
text classification performance. CNN cannot extract the emotion features of English text more comprehensively, and relies
heavily on a large number of language knowledge and emotion resources. In this paper, we propose a hybrid neural
network model based on multi-head attention for English text emotion analysis. Firstly, the new model uses multi-head
attention to learn the dependence between words and capture the emotion words in the English text. Secondly, the
improved bidirectional gated recurrent unit is used to extract different granularity emotion features of English text.
According to each emotion category and attention mechanism, feature vectors are generated to construct the emotion
feature vector set. Finally, the text emotion categories are judged according to the model attributes. The model is tested on
MR, IMDB and SST-5 data sets, the results show that the proposed method has better classification effect compared with
other models.
more »
Smart Mirror Based Home Automation Using Voice Command and Mobile Application
Appears in:
sis 18: e22

Authors:
Muhammad Shakir, Shahid Karim, Zaheen Fatima, Vishal Kumar, Mamoon Mehmood, Shahzor Memon, Halar Mustafa

Published:
12th Nov 2021

Abstract:
The smart mirror helps humans to use their time effectively. It serves as the simple mirror and allows the user to see important updates and control electrical appliances simultaneously. Smart mirror is developed to let the user do multi-tasks while he was leaving from ho…The smart mirror helps humans to use their time effectively. It serves as the simple mirror and allows the user to see
important updates and control electrical appliances simultaneously. Smart mirror is developed to let the user do multi-tasks
while he was leaving from home. This system consists of two applications one is the smart mirror application developed
using python and Tkinter library. The second one is smart mirror mobile application developed using the IONIC mobile
development framework. Firebase firestore real-time database is exploited to show the user the latest news, weather
updates, the current date, and time. It also allows the user to add his to-do list items. The user can control home appliances
using voice commands and mobile applications. There is also a security module that captures the image of an intruder and
sends the message to the mobile application. Smart mirror is developed to reduce human efforts and to allow the users to
use their time effectively.
more »
Secure Digital Transactions in The Education Sector Using Blockchain
Appears in:
sis 18: e21

Authors:
Muhammad Nouman, Kareem Ullah, Muhammad Azam

Published:
3rd Nov 2021

Abstract:
A blockchain is a decentralized, distributed peer-to-peer network that allows one node to communicate with other nodes. When using blockchain technology in education, data or records can be stored permanently without fear of hacking du e to the secure hashing algorithms in blockchain tech…A blockchain is a decentralized, distributed peer-to-peer network that allows one node to communicate with other nodes.
When using blockchain technology in education, data or records can be stored permanently without fear of hacking du e to
the secure hashing algorithms in blockchain technology. The goal is to create a new blockchain technology that has the
potential to form a decentralized application in education, with the help of different components, such as Meta Mask,
IPFS, Ganache, and other test networks such as Rinke by and Rostand, as well as Web3 JS and Ethereum cryptocurrency.
This research was applied in the following ways: (a) analysing the three most well-known blockchain cryptocurrencies
(Bitcoin, Litecoin, and Ethereum); (b) investigating the features and issues surrounding the Bitcoin cryptocurrency, and (c)
creating a graphical interface for the IPFS bandwidth analysis for storing files on the network using Web3 JS and Smart
Contracts. As a result, this research paper provides a proper demonstration of data storage without the use of a centralized
system (decentralized and distributed P2P network) using IPFS to aid in data segmentation for storage purposes.
more »
Energy efficient hybrid routing protocol for Wireless Sensor Networks Using AI Technique
Appears in:
sis 18: e20

Authors:
Amit Kumar Gautam, Ravi Yadav

Published:
3rd Nov 2021

Abstract:
Wireless sensor networks (WSNs) are frequently used in various critical sectors such as monitoring of nuclear power plants, habitat monitoring, precision agriculture, internet of things (IoT), industrial management, etc. Various challenges such as the limited resources in sensor nodes can bound t…Wireless sensor networks (WSNs) are frequently used in various critical sectors such as monitoring of nuclear power plants, habitat monitoring, precision agriculture, internet of things (IoT), industrial management, etc. Various challenges such as
the limited resources in sensor nodes can bound their usefulness. In the critical application of WSN, energy conservation is a major problem in the routing process to extend network life. Clustering is one of the promising techniques for efficient utilization of energy in the network. Our proposed method is using a hybrid routing protocol for efficient utilization of energy in the network. In our hybrid clustering mechanism, Fuzzy Logics is used for the selection of cluster heads based on distance, noise, and residual energy as parameters of sensor nodes. The simulation results show that our proposed method has a 43.57% better stability period than the SEP protocol. The simulation results also show that the hybrid routing protocol has 15 % better network lifetime than SEP Protocol.
more »
Clustering based Contact Tracing Analysis and Prediction of SARS-CoV-2 Infections
Appears in:
sis 18: e19

Authors:
Meenu Gupta, Rakesh Kumar, Sunil Kumar Chawla, Sunny Mishra, Sourabh Dhiman

Published:
3rd Nov 2021

Abstract:
INTRODUCTION: Contact tracing is a method to track the victims, which have been infected from the host with any particular disease. Therefore, clustering based machine learning techniques can be employed for contact tracing. Contact tracing can be automated by using technolo…INTRODUCTION: Contact tracing is a method to track the victims, which have been infected from the host with any particular disease. Therefore, clustering based machine learning techniques can be employed for contact tracing. Contact tracing can be automated by using technology and thus helps us in producing much more accurate and efficient results.

OBJECTIVES: This work aims at finding usefulness of clustering techniques for contact tracing. Two different clustering techniques namely density-based clustering and partitioning-based clustering have been used to analyse corresponding results for COVID-19 infected cases. The dataset is generated from a mock data generator with
certain assumptions.

RESULTS: The paper compares DBSCAN and K-means for contact tracing for COVID-19 Pandemic. The comparative analysis of two algorithms is presented.

CONCLUSION: The effectiveness of certain clustering algorithms in COVID-19 contact tracing is analysed. DBSCAN performs well for clustering tasks. This work only focuses on possible techniques useful for contact tracing and does not
claim any medical accuracy.
more »
Hybrid CNN and RNN-based shilling attack framework in social recommender networks
Appears in:
sis 18: e18

Authors:
Praveena Narayanan, Vivekanandan. K

Published:
2nd Nov 2021

Abstract:
INTRODUCTION: Recommender system is considered to be widely utilized in diversified domain for the purpose of effectively handling information overload. But, recommender systems are prone to vulnerabilities that are significantly exploited by malicious attacks. In part…INTRODUCTION: Recommender system is considered to be widely utilized in diversified domain for the purpose of effectively handling information overload. But, recommender systems are prone to vulnerabilities that are significantly
exploited by malicious attacks. In particular, shilling attack is determined to crucial in the recommender system due to its openness characteristics and data dependence.

OBJECTIVES: Authors focused on detecting shilling attack by using hybrid deep learning techniques.

METHODS: Hybrid CNN and RNNs-based shilling attack framework is proposed for shilling attack detection based on the selection of dynamic features for attaining maximized detection accuracy.

RESULTS: The proposed CNN-RNNs-based shilling attack framework was determined to improve the recall with different filler size under Netflix dataset by 4.48% and 6.14%, better than the benchmarked HDLM and RMRA frameworks. The proposed CNN-RNNs-based shilling attack framework was determined to minimize the false positive rate by 4.82% and 5.94%, better than the benchmarked HDLM and RMRA frameworks.

CONCLUSION: This framework integrated user popularity and rating-based indicators in order to consider the deviations that happens, when the users select items. It also included information entropy for dynamically choosing the detection
indicators in order to improve the reliability in attack detection. It was proposed with three different attack detection models that contextually handles different shilling attacks.
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Airport localization based on contextual knowledge complementarity in large scale remote sensing images
Appears in:
sis 18: e17

Author:
Mengshi Guo

Published:
2nd Nov 2021

Abstract:
Localization of airport object region is an important task of object detection, which also is a significant application in remote sensing image processing. Because a single feature cannot fully describe the object, some non-airport images with flat line characteristics (such …Localization of airport object region is an important task of object detection, which also is a significant application in remote sensing image processing. Because a single feature cannot fully describe the object, some non-airport images with
flat line characteristics (such as road) or similar texture characteristics with the airport can be detected as the existing airport regions, which brings difficulties to subsequent object recognition and change detection. To address the above
problems, a new airport localization method based on contextual knowledge complementarity in large scale remote sensing images is proposed. This new method first makes the utmost of the contextual information of the airport region in remote sensing image to construct a feature dictionary base. This dictionary base contains shallow vision knowledge and high semantic knowledge. Then the saliency maps of extracted knowledge are obtained and fused. A simple Ostu segmentation method is adopted to remove the false alarms and obtain the final airport region. It also gives the relative position coordinate and acreage of the detected airport. Compared with two state-of-the-art airport extraction methods through abundant experiments, the results show that the proposed airport localization method can fast and accurate locate the airport region, which has better effectiveness and robustness.
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Improved Channel Equalization using Deep Reinforcement Learning and Optimization
Appears in:
sis 18: e16

Authors:
Swati Katwal, Vinay Bhatia

Published:
28th Oct 2021

Abstract:
INTRODUCTION: Data transmission through channels observe large distortions arising due to the channel's dispersive nature challenged with inter-symbol interference. OBJECTIVES: The paper serves twin tasks, firstly addresses the challenges of signal interference using RL based model and second…INTRODUCTION: Data transmission through channels observe large distortions arising due to the channel's dispersive nature challenged with inter-symbol interference.

OBJECTIVES: The paper serves twin tasks, firstly addresses the challenges of signal interference using RL based model and secondly evaluates its effectiveness using different communication channels.

METHODS: The author proposes an improvement in channel equalization with the implementation of Whale Optimization Algorithm (WOA) followed by the Q-Learning model for Reinforcement Learning (RL) to identify the most suitable bit streams that will offer least interference.

RESULTS: Simulation analysis is performed against four existing works in terms of Bit Error Rate (BER), reflecting 20 to 30% improvement. The performance evaluation is executed using AWGN, Rician, Rayleigh, and Nakagami channels to evaluate BER against SNR, Eb/No, and Es/No.

CONCLUSION: Overall, the proposed work offers high-speed data transfer through a reliable communication channel with least BER under different scenarios.
more »
Intuitionistic Fuzzy Set Similarity Degree Based on Modified Genetic Algorithm for Solving Heterogenous Multi-dimension Targeted Poverty Alleviation Data Scheduling Problem
Appears in:
sis 18: e15

Authors:
Yang Shi, Qingwu Shi, Xiaofeng Mou

Published:
22nd Oct 2021

Abstract:
Targeted poverty alleviation is a proposed concept in comparison with extensive poverty alleviation. It mainly aims at the poverty situation of different rural areas and farmers in China and adopts scientific and reasonable methods to carry out targeted assistance policies. It …Targeted poverty alleviation is a proposed concept in comparison with extensive poverty alleviation. It mainly aims at the poverty situation of different rural areas and farmers in China and adopts scientific and reasonable methods to carry out targeted assistance policies. It executes accurate management for the targeted poverty alleviation. This way for poverty alleviation is more precise. In the research of heterogenous multi-dimension targeted poverty alleviation data scheduling, the multi-dimension processing is very important. In this paper, we propose an intuitionistic fuzzy set similarity degree based on modified genetic algorithm for solving heterogenous multi-dimension targeted poverty alleviation data scheduling problem. In the proposed algorithm, the reference solution and Pareto solution are mapped to the reference solution intuitive fuzzy set and Pareto solution intuitive fuzzy set respectively. The intuitionistic fuzzy similarity between two sets is calculated to judge the quality of Pareto solution. The similarity value of intuitionistic fuzzy sets is used to guide the evolution of multi--dimension genetic algorithm. The results show that the proposed algorithm can effectively solve the problem of heterogenous multi-dimension targeted poverty alleviation data scheduling, especially, in large scale problems.
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Combining GLCM with LBP features for knee osteoarthritis prediction: Data from the Osteoarthritis initiative
Appears in:
sis 18: e14

Authors:
Khaled Harrar, Khadidja Messaoudene, Mohammed Ammar

Published:
20th Oct 2021

Abstract:
INTRODUCTION: Knee osteoarthritis is a chronic disease that can make a person more susceptible to develop health complications. It is a significant cause of disability among adults. In advanced stages, people can die from these complications. OBJECTIVES: This…INTRODUCTION: Knee osteoarthritis is a chronic disease that can make a person more susceptible to develop health complications. It is a significant cause of disability among adults. In advanced stages, people can die from these
complications.

OBJECTIVES: This paper introduces a quick and effective approach to classify knee X-ray images using LogitBoost and wavelet-based Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) to increase image classification accuracy and minimize training and testing time.

METHODS: The proposed technique involves image enhancement followed by Haar wavelet transformation. GLCM and LBP were extracted from the transformed image and these attributes were used to differentiate the radiographs into two
groups of patients composed of 100 normal subjects (KL 0) and 100 pathological cases with osteoarthritis (KL 2). The validation of the classification was carried out using the K-fold cross-validation technique with k = 10.

RESULTS: The results revealed that the GLCM provided an accuracy of 77 % and the LBP approach achieved an accuracy of 82.5 %. Moreover, the combination of the two techniques LBP-GLCM improved the accuracy of the
prediction with the LogitBoost model (91.16 %). Compared to other classifiers (SVM, logistic regression, and decision tree), the LogitBoost provided a low root mean square error (RMSE) of 27.5 %.

CONCLUSION: In addition, the proposed method was compared to the state-of-the-art and revealed the highest accuracy in the prediction of KOA, outperforming the methods existing in the literature.
more »
Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images
Appears in:
sis 18: e13

Authors:
F.L. Gambo, G.M. Wajiga, L. Shuib, E.J. Garba, A.A. Abdullahi, D.B. Bisandu

Published:
20th Oct 2021

Abstract:
Improving the accuracy of learning style detection models is a primary concern in the area of automatic detection of learning style, which can be achieved either through, attribute/feature selection or classification algorithm. However, the role of facial expressio…Improving the accuracy of learning style detection models is a primary concern in the area of automatic detection of learning style, which can be achieved either through, attribute/feature selection or classification algorithm. However, the role of facial expression in improving accuracy has not been fully explored in the research domain. On the other hand, deep learning solutions have become a new approach for solving complex problems using Deep Neural networks (DNNs);
these DNNs have deep architectures that are capable of decomposing problems into multiple processing layers, enabling and devising multiple mapping of complex problems functions. In this paper, we investigate and compare the performance of Convolutional Neural Network (CNN) and MultiClass Neural Network (MCNN) for classification of learners into VARK learning-style dimensions (i.e Visual, Aural, Reading Kinaesthetic, including Neutral class) based on facial images.
The performances of the two networks were evaluated and compared using square mean error MSE for training and accuracy metric for testing. The results show that MCNN offers better and robust classification performance of VARK
learning style based on facial images. Finally, this paper has demonstrated a potential of a new method for automatic classification of VARK LS based on Facial Expressions (FEs). Based on the experimental results of the models, this approach can benefit both researchers and users of adaptive e-learning systems to uncover the potential of using FEs as identifier learning styles for recommendations and personalization of learning environments.
more »
Feature Extraction using CNN for Peripheral Blood Cells Recognition
Appears in:
sis 18: e12

Authors:
Mohammed Ammar, Mostafa El Habib Daho, Khaled Harrar, Amel Laidi

Published:
20th Oct 2021

Abstract:
INTRODUCTION: The diagnosis of hematological diseases is based on the morphological differentiation of the peripheral blood cell types. OBJECTIVES: In this work, a hybrid model based on CNN features extraction and machine learning classifiers were proposed to improve peripheral blood cell imag…INTRODUCTION: The diagnosis of hematological diseases is based on the morphological differentiation of the peripheral blood cell types.

OBJECTIVES: In this work, a hybrid model based on CNN features extraction and machine learning classifiers were proposed to improve peripheral blood cell image classification.

METHODS: At first, a CNN model composed of four convolution layers and three fully connected layers was proposed. Second, the features from the deeper layers of the CNN classifier were extracted. Third, several models were trained and tested on the data. Moreover, a combination of CNN with traditional machine learning classifiers was carried out. This includes CNN_KNN, CNN_SVM (Linear), CNN_SVM (RBF), and CNN_AdaboostM1. The proposed methods were validated on two datasets. We have used a public dataset containing 12444 images with four types of leukocytes to find the best optimizer function(eosinophil, lymphocyte, monocyte, and neutrophil images). The second dataset contains 17,092 images divided into eight groups: lymphocytes, neutrophils, monocytes). the second public dataset was used to find the best combination of CNN and the machine learning algorithms. the dataset containing 17,092 images: lymphocytes, neutrophils, monocytes, eosinophils, basophils, immature granulocytes, erythroblasts, and platelets.

RESULTS: The results reveal that CNN combined with AdaBoost decision tree classifier provided the best
performance in terms of cells recognition with an accuracy of 88.8%, demonstrating the performance of the proposed approach.

CONCLUSION: The obtained results show that the proposed system can be used in clinical practice.
more »
New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP
Appears in:
sis 18: e11

Authors:
Abdessalam Hattab, Ali Behloul

Published:
20th Oct 2021

Abstract:
Face recognition is one of the most important topics in biometrics, where it achieved great success under controlled scenarios. Still, its accuracy degraded significantly in unconstrained conditions. To meet this challenge, we proposed a handcraft method based on extracting impor…Face recognition is one of the most important topics in biometrics, where it achieved great success under controlled scenarios.
Still, its accuracy degraded significantly in unconstrained conditions. To meet this challenge, we proposed a handcraft
method based on extracting important regions from the face image. We have been using Scale-Invariant Feature Transform
(SIFT) besides the Adaptive Local Ternary Patterns (ALTP). We have achieved an accuracy of 99.75% on the ORL database
and 94.70% on the FERET database. Then, we proposed a second method based on deep learning to achieve more accurate
face recognition. The deep learning models failed to achieve a high accuracy rate because they require a large amount of
training data. We used firstly Data Augmentation to solve this failure. However, this solution does not show high
performance. Secondly, our proposed ImageNet pre-trained AlexNet-v2 and VGG16 models with LinearSVC increased the
accuracy rate to 100% for the both databases.
more »
A Novel Eye-Tracking Device Designed with a Head Gesture Control Module
Appears in:
sis 18: e10

Authors:
Shahid Karim, Muhammad Shakir, Ali Sheikh, Shahzor Memon, Halar Mustafa, Vishal Kumar

Published:
19th Oct 2021

Abstract:
In this paper a novel eye-tracking device designed which uses tiny CCD cameras to capture the eye image from the screen with the help of a video capture card. In the head gesture control mode, a light source projector is turned on and the CCD camera detects the position of the light source. The…In this paper a novel eye-tracking device designed which uses tiny CCD cameras to capture the eye image from the screen
with the help of a video capture card. In the head gesture control mode, a light source projector is turned on and the CCD
camera detects the position of the light source. The locations of the spots on the screen and on the image pupil of the eye
image are calculated, compared with the previous point and are subsequently mapped to the point on the screen. The
movement increment-coordinate control is also discussed which could improve the ease of use of the computer. We investigate the use of non-rigid head fixation using a helmet that constrains only general head orientation and allows some
freedom of movement. Device results simulated with the help of software which achieves excellent timing performance due to the use of live data streaming, instead of the traditionally employed data storage mode for processing analogous eye
position data.
more »
Adaptive and ADRC information fusion method for high speed train braking system
Appears in:
sis 18: e9

Authors:
Xiaojun Ma, Yuhua Qin, Dequan Kong, Desheng Liu, Chaoyang Wang

Published:
6th Oct 2021

Abstract:
Aiming at the problem of poor adaptability and lag of traditional braking control methods of high-speed train, a high-speed train braking information fusion method based on adaptive linear auto disturbance rejection is proposed to arrange the transition process for…Aiming at the problem of poor adaptability and lag of traditional braking control methods of high-speed train, a high-speed train braking information fusion method based on adaptive linear auto disturbance rejection is proposed to arrange the transition process for accurate braking and stable operation of the train, and an extended state observer is designed to estimate and compensate the internal disturbance and external disturbance, so as to enhance the anti-interference ability of the system,
By introducing adaptive control into linear ADRC, the real-time adaptive self-tuning of parameters is realized, the efficiency of parameter tuning is improved, and the problem that too many parameters have a direct impact on the control effect in ADRC is solved. The simulation results show that the control method can estimate and compensate the disturbance well, shows good robustness, and can track the ideal parking curve quickly and accurately.
more »
Dynamic constitutive analysis of aluminum alloy materials commonly used in railway vehicles big data and its application in LS-DYNA
Appears in:
sis 18: e8

Authors:
Juxing Liang, Jia Sun, Wei Wei, Rashid Ali Laghari

Published:
28th Sep 2021

Abstract:
The mechanical test for the three commonly used aluminum alloy materials (6005A-T6, 6008-T6 and 6082-T6) for rail vehicle big data are carried at different temperatures and different strain rates in this study. It can be seen that there are significant differ…The mechanical test for the three commonly used aluminum alloy materials (6005A-T6, 6008-T6 and 6082-T6) for rail vehicle big data are carried at different temperatures and different strain rates in this study. It can be seen that there are significant difference mechanical properties for these aluminum alloy. The main differences between them are the sensitivity of the strain rate and temperature. 6008-T6 and 6005A-T6 alloys have lower sensitivity to strain rate, while 6082-T6 alloy has higher sensitivity to strain rate and temperature. Based on the experimental data, the two commonly used dynamic material models of LS-DYNA, the Cowper-Symonds constitutive model and the Johnson-Cook constitutive model, are fitted. And then the two constitutive models are verified and compared by the dropping hammer impact test of the anti-climbing device. The results show that the Cowper-Symonds constitutive model has higher accuracy.
more »
Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection
Appears in:
sis 18: e7

Author:
Yuhang Li

Published:
21st Sep 2021

Abstract:
In complex background, the accuracy of moving object detection can be affected by some factors such as illumination change, short occlusion and background movement. This paper proposes a new multi-scale information fusion based on convolution kernel pyramid and dil…In complex background, the accuracy of moving object detection can be affected by some factors such as illumination change, short occlusion and background movement. This paper proposes a new multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection. The proposed model uses a variety of ways to fuse the feature information. First, the multi-layer feature map information with different sizes is fused by the per-pixel addition method. Then the feature map of different stages is splicing in the channel dimension to form the information fusion feature layer with rich semantic information and detail information as the prediction layer of the model. In this model, convolution kernel pyramid structure is introduced into the anchor frame mechanism to solve the multi-scale
problem of detecting objects. The number of parameters increased by large convolution kernel is reduced by using dilated convolution to reduce the number of anchor frames reasonably. Experimental results show that the proposed fusion algorithm has certain anti-interference ability and high precision for moving object detection in complex environment compared the state-of-the-art methods.
more »
Spatio-temporal weight Tai Chi motion feature extraction based on deep network cross-layer feature fusion
Appears in:
sis 18: e6

Authors:
Naiqiu Wu, Yang Shi

Published:
21st Sep 2021

Abstract:
Tai Chi is a valuable exercise for human health. The research on Tai Chi is helpful to improve people's exercise level. There is a problem with low efficiency in traditional Tai Chi motion feature extraction. Therefore, we propose a spatio-temporal weight Ta…Tai Chi is a valuable exercise for human health. The research on Tai Chi is helpful to improve people's exercise level. There is a problem with low efficiency in traditional Tai Chi motion feature extraction. Therefore, we propose a spatio-temporal weight Tai Chi motion feature extraction based on deep network cross-layer feature fusion. According to the selected motion spatio-temporal sample, the corresponding spatio-temporal motion key frame is extracted and output in the form of static image. The initial motion image is preprocessed by motion object detection and image enhancement. Traditional convolutional neural network extracts features from the shallow to the deep and builds a classifier for image classification, which is easy to ignore the shallow features. Based on the AlexNet network, a CL-AlexNet network is proposed. Batch normalization (BN) is used for data normalization. The cross-connection structure is introduced and the sensitivity analysis is performed. The Inception module is embedded for multi-scale depth feature extraction. It integrates deep features and shallow features. The spatio-temporal weight adaptive interpolation method is used to reduce the error of
edge detection. From the edge features and the motion spatio-temporal features, it realizes motion features extraction, and outputs the extraction results. Compared with the state-of-the-art feature extraction algorithms, the experiment results show that the proposed algorithm can extract more effective features. The recognition rate exceeds 90%. It can be used as guidance and evidence for Tai Chi training.
more »
Multi-feature data fusion based on common space model and recurrent convolutional neural networks for EEG tristimania recognition used in upper limb rehabilitation exercises
Appears in:
sis 18: e5

Author:
Hudun Sun

Published:
14th Sep 2021

Abstract:
Traditional tristimania recognition methods cannot accurately recognize the mood of patients, which cannot provide effective adjuvant therapy for rehabilitation. Therefore, this paper proposes a new multi-feature data fusion method for Electroencephalography (EEG) tri…Traditional tristimania recognition methods cannot accurately recognize the mood of patients, which cannot provide effective adjuvant therapy for rehabilitation. Therefore, this paper proposes a new multi-feature data fusion method for
Electroencephalography (EEG) tristimania recognition. It combines common space model and recurrent convolutional neural networks to classify the tristimania group and control group. According to the phase lock value, the phase
synchronization functional network between electrode channels is constructed, and the functional connection modes of two kinds under different frequency bands are analyzed. The Xception network and LSTM are used as two non-interfering parts to extract two feature matrices from EEG tristimania signals. They are fused into a single feature matrix by merge algorithm. The single feature matrix is input into the recurrent convolutional neural networks (RCNN) for feature extraction and pooling. L2 regularized Softmax function is used as the classifier to complete the training and testing of RCNN. Finally, combining the Fisher score feature selection method and the classifier dependency structure, a low dimensional and efficient feature subset is obtained. Experimental results on public tristimania data sets validate that the proposed method has better effect in terms of accuracy and PLV compared with other strategies.
more »
A novel A* method fusing bio-inspired algorithm for mobile robot path planning
Appears in:
sis 18: e4

Authors:
Yang Sun, Haipeng Wang

Published:
14th Sep 2021

Abstract:
The path planning of mobile robot is to find an optimal collision-free path in time distance or space from the starting point to the target point in a given environment. With the popularization and application of mobile robots, if the efficiency of mobile robots path is not high,…The path planning of mobile robot is to find an optimal collision-free path in time distance or space from the starting point to the target point in a given environment. With the popularization and application of mobile robots, if the efficiency of mobile robots path is not high, the working quality will be seriously affected. How to quickly plan an effective safe path is of great research significance and practical application value. Therefore, we propose a novel A* algorithm based on Bio-inspired algorithm for mobile robot path planning. Firstly, the synchronous bidirectional A* algorithm is used to optimize
the pheromone of ant colony algorithm, and the transition probability and pheromone update mechanism of ant colony algorithm are improved, so that the global optimization speed of the algorithm is faster and the path length of mobile robot is shortened. Furthermore, the static path is used to initialize the pigeon algorithm. Then, the improved pigeon algorithm is utilized to plan the local path of the mobile robot, and the simulated annealing criterion is introduced to solve the local optimal problem. The logarithmic S-type transfer function is adopted to optimize the step size of the pigeon number, so that the collision with the dynamic obstacles can be better avoided. Finally, a modified B-spline curve is used to smooth
and re-plan the path. The simulation results show that the proposed method can realize path planning more effectively in complex dynamic environment.
more »
Secure Data Fusion Analysis on Certificateless Short Signature Scheme Based on Integrated Neural Networks and Elliptic Curve Cryptography
Appears in:
sis 18: e3

Authors:
Lina Zou, Xueying Wang, Lifeng Deng

Published:
14th Sep 2021

Abstract:
In the traditional public key cryptosystem based on certificates, the issuance and management of user certificates are realized through the authoritative certificate center, but amount of time is spent in the transmission and verification of user public key certificates. After a malicious user ob…In the traditional public key cryptosystem based on certificates, the issuance and management of user
certificates are realized through the authoritative certificate center, but amount of time is spent in the
transmission and verification of user public key certificates. After a malicious user obtaining legitimate users’
private keys, he can select a secret value and signature process to generate the final private key, public key and signature. And he will announce that he is the legal user, while others are unable to distinguish this process. This is the defect of traditional digital signature scheme without certificate. Therefore, this paper proposes a certificateless short signature scheme based on integrated neural networks and elliptic curve cryptography for secure data fusion analysis. The security of the solution is based on Inv-CDH problem. The complete security
proof is given under the stochastic predictor model. It is proved that the new model can resist existence forgery in adaptive selective message attack with new adversary. Experiment results show that the calculation amount of our proposed certificateless short signature scheme is small and the efficiency is high compared with other state-of-the-art schemes.
more »
Channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition
Appears in:
sis 18: e2

Author:
Wenhao Yang

Published:
3rd Sep 2021

Abstract:
In order to solve the problems of weak generalization ability and low classification accuracy in motor imagery EEG signal classification, this paper proposes a channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition. First, the sho…In order to solve the problems of weak generalization ability and low classification accuracy in motor imagery EEG signal classification, this paper proposes a channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition. First, the short-time Fourier transform is used to obtain the EEG time-frequency map. Then, it builds a new feature pyramid network(FPN). The attention mechanism module is integrated into the FPN module, and the channel spatial weighted fusion-oriented feature pyramid network is proposed. This new structure can not only learn the weight of important channel features in the feature map, but also learn the representation of important feature areas in the network layers. Meanwhile, Skip-FPN module is added into the network structure, which fuses more details of EEG signals through short connections. The Dropout layer is added to prevent network training from over-fitting. In the classification model, we improve the AdaBoost algorithm to automatically update the base learner according to the classification error rate. Finally, the proposed model is used to classify the test data and the Kappa value is used as the evaluation index. Compared with the state-of-the-art motor image EEG signal recognition methods, the proposed method achieves better performance on the BCI Competition IV 2b data set. It has good generalization ability and can improve the classification effect.
more »
A spatio-temporal attention fusion model for students behaviour recognition
Appears in:
sis 18: e1

Author:
Xiaoli Wang

Published:
3rd Sep 2021

Abstract:
Student behavior analysis can reflect students' learning situation in real time, which provides an important basis for optimizing classroom teaching strategies and improving teaching methods. It is an important task for smart classroom to explore how to use big dat…Student behavior analysis can reflect students' learning situation in real time, which provides an important basis for optimizing classroom teaching strategies and improving teaching methods. It is an important task for smart classroom to
explore how to use big data to detect and recognize students behavior. Traditional recognition methods have some defects, such as low efficiency, edge blur, time-consuming, etc. In this paper, we propose a new students behaviour recognition
method based on spatio-temporal attention fusion model. It makes full use of key spatio-temporal information of video, the problem of spatio-temporal information redundancy is solved. Firstly, the channel attention mechanism is introduced into the spatio-temporal network, and the channel information is calibrated by modeling the dependency relationship between feature channels. It can improve the expression ability of features. Secondly, a time attention model based on convolutional neural network (CNN) is proposed, which uses fewer parameters to learn the attention score of each frame,
focusing on the frames with obvious behaviour amplitude. Meanwhile, a multi-spatial attention model is presented to calculate the attention score of each position in each frame from different angles, extract several saliency areas of behaviour, and fuse the spatio-temporal features to further enhance the feature representation of video. Finally, the fused features are input into the classification network, and the behaviour recognition results are obtained by combining the two output streams according to different weights. Experiment results on HMDB51, UCF101 datasets and eight typical classroom behaviors of students show that the proposed method can effectively recognize the behaviours in videos. The accuracy of HMDB51 is higher than 90%, that of UCF101 and real data are higher than 90%.
more »

When Artificial Intelligence Meets Printing The Evidence of Black Generation
Appears in:
sis 21(33): e10

Authors:
Xu Tongkai, Li Manjiang, Li Shanmin, Xie Jiliang, Yuan Zhende

Published:
29th Oct 2021

Abstract:
Recent years wit a rapid development of new technologies. As a significant part, artificial intelligence leads to a great change of our life. While the color printing technique which is struggled with obstacles such as color reproducing now finds a revolutionary approach to overcome the difficultie…Recent years wit a rapid development of new technologies. As a significant part, artificial intelligence leads to a great change of our life. While the color printing technique which is struggled with obstacles such as color reproducing now finds a revolutionary approach to overcome the difficulties. The new method significantly improved the accuracy although it requires the parallel computing process to deal with the complexity of computing. This paper firstly introduces the brief histories and the basics of black generation technique of the printing process and artificial intelligence. Then comes to the traditional techniques of black generation. In the next part we suggest an artificial intelligence approach to black generation. At last we make a comparison
between various methods and point out the advantages of the new approach.
more »
Internet-of-Video Things Based Real-Time Traffic Flow Characterization
Appears in:
sis 21(33): e9

Authors:
Ali Khan, Khurram S. Khattak, Zawar H. Khan, T. A. Gulliver, Waheed Imran, Nasru Minallah

Published:
21st Oct 2021

Abstract:
Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome …Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and
congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to
stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management.
more »
A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection
Appears in:
sis 21(33): e8

Authors:
Shoulin Yin, Hang Li, Asif Ali Laghari, Shahid Karim, Awais Khan Jumani

Published:
6th Oct 2021

Abstract:
Network intrusion can enter the network through informal channels. Some illegal users utilize Trojans and self-programmed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information. Network i…Network intrusion can enter the network through informal channels. Some illegal users utilize Trojans and self-programmed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information. Network intrusion seriously harms the security of network information and the legitimate rights of users. Therefore, a bagging strategy-based kernel extreme learning machine for complex network intrusion detection is presented in this paper. This method adopts a bagging strategy to train several sub-kernel extreme learning machines independently. Then the integrated gain of above machines is measured based on the margin distance minimization (MDM) criterion. Selected machines with high gain degree are selected for selective integration to obtain selective integrated learners with strong generalization ability and high efficiency. Then an improved universal gravitation search algorithm is used to optimize the kernel parameters. Meanwhile, a sub-kernel extreme learning machine online update strategy based on incremental learning of batch samples is introduced to realize the online update of intrusion detection model, so that the proposed detection method can effectively be adapted to the changes of complex network environment. Finally, experiments illustrate that the proposed method has better effect on network intrusion detection in terms of detection accuracy and speed, especially for unknown network intrusion connection events, the response speed is
fast, the false alarm rate is low.
more »
AIoT Enabled Traffic Congestion Control System Using Deep Neural Network
Appears in:
sis 21(33): e7

Authors:
Shahan Yamin Siddiqui, Inzmam Ahmad, Muhammad Adnan Khan, Bilal Shoaib Khan, Muhammad Nadeem Ali, Iftikhar Naseer, Kausar Parveen, Hafiz Muhammad Usama

Published:
28th Sep 2021

Abstract:
With rapid population growth in cities, to allow full use of modern technology, transportation networks need to be developed efficiently and sustainability. A significant problem in the traffic motion barrier is dynamic traffic flow. To manage traffic congestion problems, this paper provides a meth…With rapid population growth in cities, to allow full use of modern technology, transportation networks need to be developed efficiently and sustainability. A significant problem in the traffic motion barrier is dynamic traffic flow. To manage traffic congestion problems, this paper provides a method for forecasting traffic congestion with the aid of a Deep neural network that minimizes blockage and plays a vital role in traffic smoothing. In the proposed model, data is collected and received by using smart Internet of things enabled devices. With the help of this model, data of the previous junction of signals will send to another junction and update after that next layer named as intelligence prediction for the congestion layer will receive data from sensors and the cloud which is used to find out the congestion point. The proposed TC2S- DNN model achieved
the accuracy of 98.03 percent and miss rate of 1.97 percent which is better then previous published approaches.
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Progress in the use of tools for the selection of employees at the time of digitizing
Appears in:
sis 21(33): e6

Authors:
Katarína Stachová, Zdenko Stacho, Dagmar Cagáňová, Alexandra Baroková

Published:
25th Aug 2021

Abstract:
INTRODUCTION: Employee selection is a process in which qualified employees of the organization, most often HR employees, should identify the most suitable candidates based on predetermined soft and hard criteria and then in cooperation with future direct superior or future colleague…INTRODUCTION: Employee selection is a process in which qualified employees of the organization, most often HR employees, should identify the most suitable candidates based on predetermined soft and hard criteria and then in cooperation with future direct superior or future colleagues, select the most suitable candidate in the context of both qualification and
cultural and relational aspects. The employee selection tools used by the organization with the upcoming the fourth industrial revolution provide HR staff, but also candidates, with more comprehensive and in-depth information about the candidate's
personality and the characteristics and life of the organization.

OBJECTIVES: In our paper, we will focus on finding out which selection tools organizations use in practice, which selection tools they experience from the past and what new tools are being promoted. We will also focus on how the choice of tools is influenced by the size and location of the organization and whether organizations focus on revising and changing them.

METHODS: We surveyed a relevant sample of Slovak organizations in the period 2014-2018. Based on the results of the questionnaire survey, the authors carried out several partial activities such as evaluation of the current state by descriptive statistics, statistical evaluation of formulated hypotheses and evaluation of the overall change of analyzed attributes in the observed period using the basic index of change.

RESULTS: The part of the survey focused on the selection of employees showed that the most important methods in the selection of employees are long-term one-to-one interviews and references. Although organizations are still expanding their
portfolio of employee selection tools, the two tools are the most widely used. So far, organizations are hesitant to use the tools coming with the Fourth Industrial Revolution. And they use them on a much smaller scale than traditional selection tools.

CONCLUSION: Organizations in the selection process innovate only to a small extent the used tools of employee selection. Which can be described as a significant competitive disadvantage, as modern technologies not only speed up and improve their work, but also increase the attractiveness of the organization and the validity of the outputs.
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GLOS: a global and local features oriented link prediction technique in social network
Appears in:
sis 21(33): e5

Authors:
Mamoona Qadir, Abdul Samad, Hafeez Ur Rehman Siddiqui

Published:
13th Aug 2021

Abstract:
The link prediction has attracted majority of researchers from various domains since the beginning of behavioral science. For instance, online social networks such as Twitter, LinkedIn and Facebook change rapidly as new users appear in the graph. For all these networks, the more challenging task …The link prediction has attracted majority of researchers from various domains since the beginning of
behavioral science. For instance, online social networks such as Twitter, LinkedIn and Facebook change rapidly
as new users appear in the graph. For all these networks, the more challenging task is to find and recommend
friends to the users. In case of social graph, the foremost objective of link prediction is to predict which new
links are likely to be appearing from the actual state of the graph. Varieties of methods have been developed
such as probabilistic, maximum likelihood and similarity-based techniques where similarity-based techniques
are considered as the best prediction methods. Similarity-based methods uses a strategy, where each pair of
nodes assigned a similarity score such that more similar nodes have more chances to connect in a future.
Similarity estimation works on the global and local features i.e. path, random walk and neighbors. Local
features are those features of node that consider at node level i.e. adjacent neighbors nodes. On the other
hand, global features are those type of features that considers at graph level i.e. path between two nodes.
Our hypothesis is that the combination of both local and global features is more powerful predictor for link
formation. Here in this study, we have evaluated global, local and hybrid similarity measures. Moreover, we
also proposed a hybrid approach GLOS. We performed experiments on five different dataset (Astor, CondMat,
GrQc, HepPh and HepTh). After the result evaluation, it is found that, hybrid approach GLOS obtained
the highest accuracy by 1 on all the dataset, while, global approaches could not produced lowest accuracy
on all dataset. On the other hand, HP from local similarity outperformed than rest of the local and global
approaches.
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A Fuzzy Logic Model for the Analysis of Social Corporate Responsibility
Appears in:
sis 21(32): e10

Authors:
A. C. Golzio, M. Puerta-Díaz, D. Martínez-Ávila

Published:
23rd Jul 2021

Abstract:
INTRODUCTION: Critical public opinion, based on information that is made available to the public through different systems, has led companies that operate in the environment to continually improve their social, environmental, and ethical performance. OBJECTIVES: This paper ai…INTRODUCTION: Critical public opinion, based on information that is made available to the public through
different systems, has led companies that operate in the environment to continually improve their social,
environmental, and ethical performance.

OBJECTIVES: This paper aims to propose a fuzzy-logic-based model for the analysis of social corporate
responsibility in cases of environmental accidents.

METHODS: Our study employs techniques derived from social network analysis. The data was collected from the
online database of The New York Times for the timespan from March 24, 1989, to September 1, 2017.

RESULTS: The results show that the proposed model can be replicated, after some adjustments.

CONCLUSION: We conclude that, despite the complexity of an analysis of this kind in which the model is applied
considering isolated words in the text and not the semantic aspects, the proposed model based on fuzzy logic is
adequate for the analysis of social corporate responsibility.
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Predicting the least air polluted path using the neural network approach
Appears in:
sis 21(33): e4

Authors:
K. Krishna Rani Samal, Korra Sathya Babu, Santos Kumar Das

Published:
29th Jun 2021

Abstract:
Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for t…Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for the current time might not be helpful for real-time applications. Therefore, we develop a routing algorithm based on a neural network-based CNN-LSTM-EBK (CLE), a temporal-spatial interpolation model. The proposed model predicts pollution levels at high temporal granularity. This paper introduces a weight function to compute air pollution concentration at the road network. It also predicts the least air polluted path among all possible paths from a source to a destination at different time granularity. The results show that the predicted path may be longer than the shortest route but minimize pollution exposure risk all the time, which proves its effectiveness during daily transportation.
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Experimental Comparison of Classification Methods under Class Imbalance
Appears in:
sis 21(33): e3

Authors:
Hui Chen, Mengru Ji

Published:
11th Jun 2021

Abstract:
The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc. We perform a systematic experimental comparison of different imbalance classification algorithms — ranging from sampling, distance …The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc. We perform a systematic experimental comparison of different imbalance classification algorithms — ranging from sampling, distance metric learning, cost-sensitive learning to ensemble learning approaches — on several datasets from UCI, KEEL and OpenML. The algorithms included DDAE, MWMOTE, SMOTE, RUSBoost, AdaBoost, cost-sensitive decision tree (csDCT), self-paced Ensemble Classifier, MetaCost, CAdaMEC and Iterative Metric Learning (IML). As the substantial bias potentially caused by imbalance classification can be harmful for underrepresented classes which are of critical social and economic values and even lives, the main objective of our study is thus to understand the impact of imbalance ratio and the size of the utilized datasets on the performance of the above-mentioned algorithms. Our experiments show that 1) Sampling methods perform the worst and cannot be used directly for imbalanced classification, since they lack of consideration of neighborhoods based on distance. However, some classifiers can be improved after the balance of class distribution. 2) Cost-sensitive learning models should be utilized when the dataset is less imbalanced, because it is difficult to set an appropriate cost matrix for a specific dataset, which can cause performance fluctuations. 3) IML consistently shows good performance (in terms of F1 and AUCPRC), is resilient to different imbalance ratios but sensitive to the data distribution of the dataset. 4) Ensemble learning techniques generally perform better over other approaches due to theircombined intelligence of multiple basic classifiers. 5) In terms of system performance, self-paced Ensemble Classifier performs fairly well with regards to learning time, while IML and DDAE yield the longest learningtime; AdaBoost and self-paced Ensemble Classifier are two algorithms require lowest memory usage. Wealso provide our empirical recommendation for algorithm selection under different requirements and usagescenarios based on our analysis.
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An Improved Approach for Stress Detection Using Physiological Signals
Appears in:
sis 21(33): e2

Authors:
Kushagra Nigam, Kirti Godani, Deepshi Sharma, Shikha Jain

Published:
14th May 2021

Abstract:
Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiologica…Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiological signals. In this paper, WESAD (Wearable and Stress Affect Detection) dataset is used which consists of physiological data recorded from both the chest and wrist. Further, a Long Short-Term Memory (LSTM) based model is used to detect stress. The simulation results indicate that, indeed, Electrocardiograph (ECG), Electromyogram (EMG), and Respiration (RESP) signals may not be necessary for identifying stress. A three-way validation is carried out with an accuracy of 98%. The novelty of the paper is the way time-series data is handled to make it closer to real-time data captured from sensors. The work can be used widely in clinical practices to detect stress at an early stage.
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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:
5686

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:
2955

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:
2566

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:
2498

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:
2264

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 »
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:
2223

Abstract:
Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It …Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It can be cured if the disease identified at early stage. Accurate classification of benign tumours can avoid patients undergoing unnecessary treatments. Data Analytics and machine learning methods provides framework for prognostic studies by errorless classification of data instances into relevant based on the cancer severity. In this study we have purposed a prediction model by combining artificial intelligent based learning technique with multivariate statistical method. For automation of the diagnosis process data mining plays an significant role. The data sets available in different repositories are noisy in nature. This study suggests a hybrid feature selection method to be used with PCA (Principal Component Analysis) and Artificial Neural Network (ANN). Preprocessing of data and extracting the most relevant features done by PCA. The proposed algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases. In classification phase 10 fold cross validation was used. The suggested algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved better accuracy with sensitivity and F measure comparison with others and by enhancing this concept we can provide a future scope to produce sophisticated learning models for diagnosis.
more »
Stock Price Prediction using Artificial Neural Model: An Application of Big Data
Appears in:
sis 19(20): e1

Authors:
Malav Shastri, Sudipta Roy, Mamta Mittal

Downloads:
2074

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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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:
1896

Abstract:
In economies that are dependent on fossil fuel revenues, Realization of long-term plans, mid-term and annual budgeting requires a fairly accurate estimation of the amount of consumption and its price fluctuations. Accordingly, the present study is using machine learning techniques to predict the us…In economies that are dependent on fossil fuel revenues, Realization of long-term plans, mid-term and annual budgeting requires a fairly accurate estimation of the amount of consumption and its price fluctuations. Accordingly, the present study is using machine learning techniques to predict the usage of fossil fuels (Diesel, Black oil, Heating oil, and Petrol) in mid-term. Exponential Smoothing, a model of time series and the Neural Network model have been applied on the actual usage data obtained from Shahroud area from 2010 to 2015. For estimation of predictive value by Neural Network method, the training and testing samples, the highest and lowest errors with a range of 41% -0.89% and 88% -3% for the Mean Absolute Percent Deviation are the most appropriate predictions for Petrol consumption. And in the Single Exponential Smoothing, the forecast rate for each product is estimated on a
quarterly as well as monthly basis.
more »
Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM
Appears in:
sis 20(25): e1

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
1880

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

Authors:
Pooja Kherwa, Poonam Bansal

Downloads:
1867

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 »

Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 8

Last Published: 2021-10-29

EAI Endorsed Transactions on Scalable Information Systems

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³