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)

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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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: e24

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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Internet-of-Video Things Based Real-Time Traffic Flow Characterization
Appears in:
sis 18: e23

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 »
Combining GLCM with LBP features for knee osteoarthritis prediction: Data from the Osteoarthritis initiative
Appears in:
sis 18: e22

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: e21

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.
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Feature Extraction using CNN for Peripheral Blood Cells Recognition
Appears in:
sis 18: e20

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.
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New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP
Appears in:
sis 18: e19

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.
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A Novel Eye-Tracking Device Designed with a Head Gesture Control Module
Appears in:
sis 18: e18

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: e17

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 »
A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection
Appears in:
sis 18: e16

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 18: e15

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.
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: e14

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.
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Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection
Appears in:
sis 18: e13

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: e12

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: e11

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: e10

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: e9

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.
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Channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition
Appears in:
sis 18: e8

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.
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A spatio-temporal attention fusion model for students behaviour recognition
Appears in:
sis 18: e7

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%.
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Progress in the use of tools for the selection of employees at the time of digitizing
Appears in:
sis 18: 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 18: 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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Predicting the least air polluted path using the neural network approach
Appears in:
sis 18: 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 18: 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 18: 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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Word Embedding and String-Matching Techniques for Automobile Entity Name Identification from Web Reviews
Appears in:
sis 18: e1

Authors:
Satanu Maity, Nilanjana Das, Mukta Majumder, Dibya Ranjan Dasadhikary

Published:
14th May 2021

Abstract:
With the huge popularity of Internet, various types of information on a wide range of domains are floating over different social media platforms. To extract this information for using in diverse natural language processing applications, identifying the names is prerequisite. A study …With the huge popularity of Internet, various types of information on a wide range of domains are floating over different social media platforms. To extract this information for using in diverse natural language processing applications, identifying the names is prerequisite. A study is presented here, to identify automobile names from noisy web reviews by exploring two widely used machine learning algorithms, Conditional Random Field and Support Vector Machine. The accuracy of machine learning classifiers radically rely on size and quality of training data which has been prepared manually by extracting discussion forum corpus; the task is time consuming and laborious; hence to leverage this word embedding is adopted. Though it enhances the system’s performance but is unable to spot noisy names which occur in web reviews. Next, a gazetteer based string matching technique is proposed, it recognizes a new set of noisy automobile entities, resulting considerable improvement in accuracy.
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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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Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO
Appears in:
sis 21(32): e9

Authors:
Shiva Shankar Reddy, Gadiraju Mahesh

Published:
3rd May 2021

Abstract:
Risk Assessment of Diabetes Type-II is crucial in preventing it and reducing the risk of various comorbidities. There are many existing machine learning models for predicting iType-II diabetics in short term future or in unspecified future. But obtaining a model having …Risk Assessment of Diabetes Type-II is crucial in preventing it and reducing the risk of various comorbidities. There are many existing machine learning models for predicting iType-II diabetics in short term future or in unspecified future. But obtaining a model having optimal performance and predicting diabetes risk in long term future are the main problems. These problems are handled in this work by proposing a stacking based integrated KELM imodel to predict the risk of diabetes Type-II for a person within five years after assessment. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. A Min-Max normalization is used to pre-process the noisy datasets. The HAFPSO algorithm used in this work explores the best combination of Base learners by increasing the Classification Accuracy (CA) and decreasing the kernel complexity of the optimal learners. Finally, the model is integrated by utilizing the KELM as a meta-classifier that combines the predictions of the twenty Base Learners. The proposed method is assessed with different measures such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics. The proposed KELM-HAFPSO approach has got better values of the considered metrics confirming its effectiveness in identifying type-II diabetes. The proposed method helps the clinicians to predict the patients who are at a high risk of Type-II diabetes in the future with the highest accuracy of 98.5%. The results obtained show that the KELM-HAFPSO approach is a promising new tool for identifying type-II diabetes.
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Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms
Appears in:
sis 21(32): e8

Authors:
R. Regin, S. Suman Rajest, Bhopendra Singh

Published:
3rd May 2021

Abstract:
This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted …This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted as the number of nodes increases. Second, we proposed a Naïve Bayes classifier and convolution neural network (CNN) to improve the convergence performance and find the node faults. Finally, we analyze convex hull, Naïve bayes and CNN algorithms using real-world datasets to identify and organize the faults. Simulation and experimental outcomes retain feasibility and efficiency and show that the CNN algorithm has better-identified faults than the convex hull algorithm based on performance metrics.
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A Framework for Grammatical Error Detection and Correction System for Punjabi Language Using Stochastic Approach
Appears in:
sis 21(32): e7

Authors:
L. Jindal, H. Singh, S. K. Sharma

Published:
27th Apr 2021

Abstract:
INTRODUCTION: In this modern era of internet and technology natural language processing task has emerged as one of the major research area in computer science. Grammatical error detection and correction system assists to detect and correct syntactic errors present in written text. OBJECTIVES: In th…INTRODUCTION: In this modern era of internet and technology natural language processing task has emerged as one of the major research area in computer science. Grammatical error detection and correction system assists to detect and correct syntactic errors present in written text. OBJECTIVES: In this research article, author investigate the applicability of stochastic approach for the development of grammatical error detection and correction system for Punjabi language. METHOD: Author used corpus based stochastic approach to developed the system. The corpus used was taken from Indian language corpora initiative. RESULTS: On testing, the developed system shows a precision as 82.5%, recall as 89% .and f-measure as 85%. The results of the proposed system outperform the existing rule based system that shows precision of 76.79%, recall of 87.08%, and F-measure of 81.61%. CONCLUSION: author concluded that for syntax analysis stochastic approach can perform better than rule based approach.
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Prediction Based Dynamic Controller Placement in SDN
Appears in:
sis 21(32): e6

Authors:
Ramya G, Manoharan R

Published:
27th Apr 2021

Abstract:
The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and …The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and control planes from the network devices and enables programmability of network devices. The major challenge in SDN is identification of number of controllers to be placed and its optimal placements in the network. To address this issue, this work proposes a Traffic Engineering mechanism that leverages the performance of Machine Learning to predict controller numbers by analysing and predicting the controller’s traffic. The optimal locations of controllers are identified by using the K-Means++ algorithm. The proposed method is simulated using Mininet and the results depict that the proposed methodology outperforms the existing methodologies in terms of performance parameters.
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Performance Analysis of Fractional Earthworm Optimization Algorithm for Optimal Routing in Wireless Sensor Networks
Appears in:
sis 21(32): e5

Authors:
Venkat Rao Pasupuleti, Ch. Balaswamy

Published:
21st Apr 2021

Abstract:
In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent stud…In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent studies show that the bio-inspired meta-heuristic algorithms for solving engineering problems such as energy reduction in autonomous networks in the multidisciplinary areas of WSN, Internet of Things (IoT) and Machine learning models. Hence to increase Network lifetime, optimized clustering and energy-efficient routing techniques are required. In all applications of WSN, not only energy-efficient but also delay and throughput of the network are important for efficient transmission of data to the destination. This paper analyses optimized clustering by selecting cluster heads based on fractional calculus earthworm optimization algorithm (FEWA). The route from cluster heads to sink node is selected based on the fit factor. This paper's main intention is to provide an extensive comparative study of the FEWA with all standard optimization-based clustering and routing techniques. This method's performance is compared with existing optimized clustering methods like GA, PSO, ACO, DE and EWO in terms of the number of energy, delay, and throughput. At the end of 1000 iterations, the analysis shows that the FEWA outperforms existing methods with maximum average remaining energy of the nodes as 0.216J, the minimum average delay of 0.208 sec and maximum average throughput of 88.57% for 100 nodes.
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Machine Learning in Computer Vision: A Review
Appears in:
sis 21(32): e4

Authors:
Abdullah Ayub Khan, Asif Ali Laghari, Shafique Ahmed Awan

Published:
21st Apr 2021

Abstract:
INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The s…INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification.

OBJECTIVES: In Computer Vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation.

CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and image processing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process.

METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of image processing and the results based on the impact; neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.
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Improved ECG based Stress Prediction using Optimization and Machine Learning Techniques
Appears in:
sis 21(32): e3

Authors:
Vikas Malhotra, Mandeep Kaur Sandhu

Published:
6th Apr 2021

Abstract:
INTRODUCTION: ECG have emerged as the most acceptable and widely used technique to infer mental health status using cardiac signals thereby resolving major challenge of Mental Health Assessment protocols. OBJECTIVES: Authors mainly aimed at identification of stressed signal…INTRODUCTION: ECG have emerged as the most acceptable and widely used technique to infer mental health status using cardiac signals thereby resolving major challenge of Mental Health Assessment protocols.

OBJECTIVES: Authors mainly aimed at identification of stressed signals to distinguish subjects exhibiting stress ECG signals.

METHODS: Authors have taken advantage of three optimization techniques namely, Genetic Algorithm (GA), Artificial Bee Colony (ABC) and improved Particle Swarm Optimization (PSO) that further improves the classification accuracy of Multi-kernel SVM.

RESULTS: The simulation analysis confer that the proposed work outperforms the existing works while demonstrating an average accuracy, precision, recall and specificity of 98.93%, 96.83%, 96.83% and 96.72%, respectively when evaluated against dataset comprising of 1000 ECG samples.

CONCLUSION: It is observed that the proposed stress prediction based on improved VMD and Improved SVM outperformed the existing work that comprised of traditional VMD and SVM.
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g-Sum: A Graph Summarization Approach for a Single Large Social Network
Appears in:
sis 21(32): e2

Authors:
Saif ur Rehman, Asif Nawaz, Tariq Ali, Naveed Amin

Published:
23rd Mar 2021

Abstract:
With the advances in computing resources, the processing of huge amount of data becomes possible. However, the human ability to identify patterns from such data has not scaled accordingly. Consequently, efficient computational approaches for condensing and simplifying data are b…With the advances in computing resources, the processing of huge amount of data becomes possible. However, the human ability to identify patterns from such data has not scaled accordingly. Consequently, efficient computational approaches for condensing and simplifying data are becoming essential for uncovering actionable insights, especially the big social networks. Many large datasets analyzed today are represented with the help of graphs. In many real-world applications, summarizing large graphs is beneficial to achieve a number of advantages, including 1) significant speedup for graph algorithms, 2) graph storage space reduction, 3) faster network transmission, 4) improved data privacy, 5) more effective graph visualization, etc. All these benefits can be obtained from the summarized graph, if it is summarized accurately. The quality of the summary graph is mostly measured using the Reconstruction Error (RE). In the existing literature, RE is a very challenging task. Most of the approaches investigated so farending up with high value of RE. Hence, the summarized graph does not express the original graph completely due to the high value of the RE. Therefore, in this work we have examined how can we summarize a big graph structure into a compact summary by reducing its RE and ensuring its usefulness. For this task, we have proposed a novel graph summarization approach, called g-Sum, to calculate the graph structure summaries while minimizing RE and compare to the existing work in the domain. The functionality of the g-Sum is decomposed into three different, but interlinked phases; (1)- graph dataset pre-processing; (2)- graph partitioning; and (3)- graph summarization. We have performed an extensive series of experiments on different real-world social graph dataset, including Ego-Facebook, Enron Email and Web-Stanford graph datasets. The computed results show the effectiveness of the g-Sum and also compared with the state-of-the-art graph summarization approaches, S2L and GraSS.
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Big Data Management of Hospital Data using Deep Learning and Block-chain Technology: A Systematic Review
Appears in:
sis 21(32): e1

Authors:
Nawaz Ejaz, Raza Ramzan, Tooba Maryam, Shazia Saqib

Published:
23rd Mar 2021

Abstract:
The main recompenses of remote and healthcare are sensor-based medical information gathering and remote access to medical data for real-time advice. The large volume of data coming from sensors requires to be handled by implementing deep learning and machine learning algorithms to i…The main recompenses of remote and healthcare are sensor-based medical information gathering and remote access to medical data for real-time advice. The large volume of data coming from sensors requires to be handled by implementing deep learning and machine learning algorithms to improve an intelligent knowledge base for providing suitable solutions as and when needed. Electronic medical records (EMR) are mostly stored in a client-server database and are supported by enabling technologies like Internet of Things (IoT), Sensors, cloud, big data, Deep Learning, etc. It is accessed by several users involved like doctors, hospitals, labs, insurance providers, patients, etc. Therefore, data security from illegal access is crucial especially to manage the integrity of data. In this paper, we describe all the basic concepts involved in management and security of such data and proposed a novel system to securely manage the hospital’s big data using Deep Learning and Block-Chain technology.
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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:
5604

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

Authors:
Ripon Patgiri, Sabuzima Nayak, Samir Kumar Borgohain

Downloads:
2840

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:
2478

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:
2383

Abstract:
In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To…In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show less-focused research areas in addition to future directions.
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An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
Appears in:
sis 18(18): e5

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

Downloads:
2193

Abstract:
The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solu…The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.
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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:
2143

Abstract:
Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It …Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It can be cured if the disease identified at early stage. Accurate classification of benign tumours can avoid patients undergoing unnecessary treatments. Data Analytics and machine learning methods provides framework for prognostic studies by errorless classification of data instances into relevant based on the cancer severity. In this study we have purposed a prediction model by combining artificial intelligent based learning technique with multivariate statistical method. For automation of the diagnosis process data mining plays an significant role. The data sets available in different repositories are noisy in nature. This study suggests a hybrid feature selection method to be used with PCA (Principal Component Analysis) and Artificial Neural Network (ANN). Preprocessing of data and extracting the most relevant features done by PCA. The proposed algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases. In classification phase 10 fold cross validation was used. The suggested algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved better accuracy with sensitivity and F measure comparison with others and by enhancing this concept we can provide a future scope to produce sophisticated learning models for diagnosis.
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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:
1997

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:
1827

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

Authors:
Satyam Gangwar, Vikram Bali, Ajay Kumar

Downloads:
1778

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:
1677

Abstract:
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented i…Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its
applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges
of topic modelling, which will definitely give researchers an insight for good research.
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Publisher: EAI
ISSN: 2032-9407
Number of Volumes: 8

Last Published: 2021-08-25

EAI Endorsed Transactions on Scalable Information Systems

Hua Wang1

Xiaohua Jia2

Manik Sharma3

Hua Wang¹

Xiaohua Jia²

Manik Sharma³