This page has only limited features, please log in for full access.

Dr. Arun Kumar Sangaiah
Vellore Institute of Technology (VIT)

Basic Info


Research Keywords & Expertise

0 Internet of Things
0 Software Engineering Practices
0 Sensors & Sensor Networks
0 Sustainable and Socially Responsible Operations
0 Machine Learning & Artificial Intelligence

Fingerprints

Internet of Things
Machine Learning & Artificial Intelligence

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

Dr. Arun Kumar Sangaiah received his Master of Engineering from Anna University and Ph.D. from VIT University, India. He is currently as a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. In 2016, he was a visiting professor at School of Computer Engineering at Nanhai Dongruan Information Technology Institute in China for 6 months. In addition, he has been appointed as a visiting professor in various universities. Further, he has visited many research centres and universities in China, Japan, Singapore and South Korea for join collaboration towards research projects and publications. His areas of research interest include machine learning, software engineering, computational intelligence, IoT. Dr. Sangaiah’s outstanding scientific production spans over 400+ contributions published in high standard ISI journals, such as IEEE-Communication Magazine, IEEE Systems and IEEE IoT. In addition, he has authored/edited 8 books (Elsevier, Springer and others) and edited 50 special issues in reputed ISI journals, such as IEEE-Communication Magazine, IEEE-TII, IEEE-IoT, ACM transaction on Intelligent Systems and Technology etc. Finally, Dr. Sangaiah is responsible for Editorial Board Member and Associate Editor of many reputed ISI journals.Further, he has received many awards that includes, India-Top-10 researcher award, Chinese Academy of Science-PIFI overseas visiting scientist award and etc.

Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Article
Published: 05 May 2021 in Mobile Networks and Applications
Reads 0
Downloads 0

When using the current method to update the index of educational resources, there is no analysis of the hierarchical association structure between educational resources. There are some problems such as too long index construction time, high query time and low query accuracy. Therefore, this paper introduces mobile computing to study the adaptive updating method of education resource index. This paper analyzes the structure of the index system of educational resources. On the basis of mobile computing, the hierarchical association structure between educational resources is analyzed by LDA (Latent Dirichlet Allocation) topic modeling and topic hierarchical clustering. The initial query is expanded by selecting extension words by local co-occurrence method. The weight of the extended query is allocated by genetic algorithm to realize the adaptive updating of the index of educational resources. The experimental results show that when the number of nodes is 25 × 104, the total query time of this method is only 3.5 s, and the average index redundancy rate is only 0.22%, indicating that the resource index update performance of this method is better.

ACS Style

Jian-Wei Liu; Arun Kumar Sangaiah. Research on Adaptive Updating Method of Education Resource Index Based on Mobile Computing. Mobile Networks and Applications 2021, 1 -10.

AMA Style

Jian-Wei Liu, Arun Kumar Sangaiah. Research on Adaptive Updating Method of Education Resource Index Based on Mobile Computing. Mobile Networks and Applications. 2021; ():1-10.

Chicago/Turabian Style

Jian-Wei Liu; Arun Kumar Sangaiah. 2021. "Research on Adaptive Updating Method of Education Resource Index Based on Mobile Computing." Mobile Networks and Applications , no. : 1-10.

Preprint content
Published: 27 April 2021
Reads 0
Downloads 0

VANETs are organized to progress road protection with no specific need for any fixed infrastructure. Subsequently, the movement of all vehicles can be planned in the upcoming future, based on perceived information, Quality of Services Routing (QoSR) algorithms can be pressured on its available options, paths, and links and according to criteria and reliability of the QoSR. Awareness of QoSR to the environmental conditions of the network of vehicles, such as the location of vehicles, direction and speed that can be obtained. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path / link. In this article, A QoSR with Particle Swarm Optimization (QoSR-PSO) for improving QoSs in vehicular ad-hoc networks has been used. The particle swarm optimization algorithm by modeling the behavior of a set of particles looks for the optimal solution of the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop and overload.

ACS Style

Amir Javadpour; Samira Rezaei; Arun Kumar Sangaiah; Adam Slowik; Shadi Mahmoodi Khaniabadi. Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks. 2021, 1 .

AMA Style

Amir Javadpour, Samira Rezaei, Arun Kumar Sangaiah, Adam Slowik, Shadi Mahmoodi Khaniabadi. Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks. . 2021; ():1.

Chicago/Turabian Style

Amir Javadpour; Samira Rezaei; Arun Kumar Sangaiah; Adam Slowik; Shadi Mahmoodi Khaniabadi. 2021. "Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks." , no. : 1.

Journal article
Published: 02 February 2021 in IEEE Internet of Things Journal
Reads 0
Downloads 0

Workforce monitoring is a vital activity in large factories in order to oversee the worker’s concentration on their duty and increase productivity. Workforces are kind of moving targets which can be monitored via Wireless Sensor Networks (WSN). As sensor nodes have a limited source of energy, optimal energy consumption is of crucial importance in these networks. Several protocols for routing are designed in order to consider efficient energy consumption in conjunction with target tracking and coverage. In this paper, a new energy-efficient routing algorithm Geographic Routing Time Transfer (GRTT) is proposed to use topological information of sensor nodes for target tracking and coverage applications. In this paper, a weight called relay ability is defined for each node according to the sensor network topology. These weights are calculated and announced to sensor nodes by Cluster Heads (CH). Once a target enters the area covered by sensor nodes, a signal is sent to the CH through the route having maximum pre-defined weights in the network. Simulations show better results than other tracking routing methods based on the metrics of energy consumption of the Network, Power consumption and Throughput for GRTT (Proposed method), Dynamic Energy Efficient Routing Protocol (DEER), Virtual Force-Based Energy-Hole Mitigation (VFEM), Non-Equal-Probability Multicast Routing Protocol (MRP-NEP) and Trace-Announcing Routing Scheme (TARS) methods.

ACS Style

Arun Kumar Sangaiah; Ali Shokouhi Rostami; Ali Asghar Rahmani Hosseinabadi; Morteza Babazadeh Shareh; Amir Javadpour; Shirin Hatami Bargh; Mohammad Mehedi Hassan. Energy-Aware Geographic Routing for Real-Time Workforce Monitoring in Industrial Informatics. IEEE Internet of Things Journal 2021, 8, 9753 -9762.

AMA Style

Arun Kumar Sangaiah, Ali Shokouhi Rostami, Ali Asghar Rahmani Hosseinabadi, Morteza Babazadeh Shareh, Amir Javadpour, Shirin Hatami Bargh, Mohammad Mehedi Hassan. Energy-Aware Geographic Routing for Real-Time Workforce Monitoring in Industrial Informatics. IEEE Internet of Things Journal. 2021; 8 (12):9753-9762.

Chicago/Turabian Style

Arun Kumar Sangaiah; Ali Shokouhi Rostami; Ali Asghar Rahmani Hosseinabadi; Morteza Babazadeh Shareh; Amir Javadpour; Shirin Hatami Bargh; Mohammad Mehedi Hassan. 2021. "Energy-Aware Geographic Routing for Real-Time Workforce Monitoring in Industrial Informatics." IEEE Internet of Things Journal 8, no. 12: 9753-9762.

Methodologies and application
Published: 15 January 2021 in Soft Computing
Reads 0
Downloads 0

IoT or Internet of Things can improve the possibility of interaction between various smart components in real time. In the infrastructure of IoT, wireless sensors can be used in order to reduce communication costs. Despite having positive effects, using wireless nodes add some challenges to the system. Limited resources, such as energy, CPU power and memory, are the main concerns in this technology. Energy consumption is the most challenging one. Designing an optimized routing pattern through heuristic algorithms is a common way to tackle this problem. Therefore, in the proposed algorithm, a WOA-based method has been proposed to expand the life span of the system. Also, a novel fitness function is defined for reducing the energy consumption of the network, load balancing and node coverage. Clustering is done unequally; it means that cluster heads (CHs) nearer to the base station (BS) have more energy for data relay. In this paper, for reducing the number of messages, a clustering stage is added at the beginning of each metaround. The number of rounds in a metaround is variable. The status of each node is analyzed by BS before each round. Low energy level causes a new metaround. Moreover, the CH–BS interaction is implemented through multi-hop pattern. Results suggest that there is an enhancement instability, energy-saving, throughput and lifespan.

ACS Style

Seyed Mostafa Bozorgi; Mahdi Rohani Hajiabadi; Ali Asghar Rahmani Hosseinabadi; Arun Kumar Sangaiah. Clustering based on whale optimization algorithm for IoT over wireless nodes. Soft Computing 2021, 25, 5663 -5682.

AMA Style

Seyed Mostafa Bozorgi, Mahdi Rohani Hajiabadi, Ali Asghar Rahmani Hosseinabadi, Arun Kumar Sangaiah. Clustering based on whale optimization algorithm for IoT over wireless nodes. Soft Computing. 2021; 25 (7):5663-5682.

Chicago/Turabian Style

Seyed Mostafa Bozorgi; Mahdi Rohani Hajiabadi; Ali Asghar Rahmani Hosseinabadi; Arun Kumar Sangaiah. 2021. "Clustering based on whale optimization algorithm for IoT over wireless nodes." Soft Computing 25, no. 7: 5663-5682.

Journal article
Published: 01 January 2021 in Intelligent Automation & Soft Computing
Reads 0
Downloads 0
ACS Style

Hao Wu; Arun Kumar Sangaiah. Oral English Speech Recognition Based on Enhanced Temporal Convolutional Network. Intelligent Automation & Soft Computing 2021, 28, 121 -132.

AMA Style

Hao Wu, Arun Kumar Sangaiah. Oral English Speech Recognition Based on Enhanced Temporal Convolutional Network. Intelligent Automation & Soft Computing. 2021; 28 (1):121-132.

Chicago/Turabian Style

Hao Wu; Arun Kumar Sangaiah. 2021. "Oral English Speech Recognition Based on Enhanced Temporal Convolutional Network." Intelligent Automation & Soft Computing 28, no. 1: 121-132.

Research article
Published: 21 December 2020 in Security and Communication Networks
Reads 0
Downloads 0

Seam carving has been widely used in image resizing due to its superior performance in avoiding image distortion and deformation, which can maliciously be used on purpose, such as tampering contents of an image. As a result, seam-carving detection is becoming crucially important to recognize the image authenticity. However, existing methods do not perform well in the accuracy of seam-carving detection especially when the scaling ratio is low. In this paper, we propose an image forensic approach based on the cooccurrence of adjacent local binary patterns (LBPs), which employs LBP to better display texture information. Specifically, a total of 24 energy-based, seam-based, half-seam-based, and noise-based features in the LBP domain are applied to the seam-carving detection. Moreover, the cooccurrence features of adjacent LBPs are combined to highlight the local relationship between LBPs. Besides, SVM after training is adopted for feature classification to determine whether an image is seam-carved or not. Experimental results demonstrate the effectiveness in improving the detection accuracy with respect to different scaling ratios, especially under low scaling ratios.

ACS Style

Dengyong Zhang; Xiao Chen; Feng Li; Arun Kumar Sangaiah; Xiangling Ding. Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs. Security and Communication Networks 2020, 2020, 1 -12.

AMA Style

Dengyong Zhang, Xiao Chen, Feng Li, Arun Kumar Sangaiah, Xiangling Ding. Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs. Security and Communication Networks. 2020; 2020 ():1-12.

Chicago/Turabian Style

Dengyong Zhang; Xiao Chen; Feng Li; Arun Kumar Sangaiah; Xiangling Ding. 2020. "Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs." Security and Communication Networks 2020, no. : 1-12.

Journal article
Published: 17 December 2020 in IEEE Transactions on Intelligent Transportation Systems
Reads 0
Downloads 0

Vehicle-to-vehicle communication assists road-side information exchange granting ease of access and sharing between users. The communication between the vehicles is short-lived due to interference and data congestion in the resource constraint medium. This manuscript introduces a linear adaptive congestion control (LACC) augmenting the benefits of greedy routing and data dissemination model (DDM). LACC focuses on selecting beneficiary vehicle by assessing its end-to-end service capacity and link stability preference. Different from the conventional greedy approach, routing is aided by a linear integer programming module for smart decisions on neighbor selection. The interrupts in data transmission and forwarding due to non-localized vehicles, congested routing paths and paused transmissions are addressed using LACC as a series of linear optimization. This helps to improve the performance of vehicular communication estimated using delay, message delivery, outage, and beacon messages.

ACS Style

Arun Kumar Sangaiah; Jaya Subalakshmi Ramamoorthi; Joel J. P. C. Rodrigues; Abdur Rahman; Ghulam Muhammad; Mubarak Alrashoud. LACCVoV: Linear Adaptive Congestion Control With Optimization of Data Dissemination Model in Vehicle-to-Vehicle Communication. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 5319 -5328.

AMA Style

Arun Kumar Sangaiah, Jaya Subalakshmi Ramamoorthi, Joel J. P. C. Rodrigues, Abdur Rahman, Ghulam Muhammad, Mubarak Alrashoud. LACCVoV: Linear Adaptive Congestion Control With Optimization of Data Dissemination Model in Vehicle-to-Vehicle Communication. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (8):5319-5328.

Chicago/Turabian Style

Arun Kumar Sangaiah; Jaya Subalakshmi Ramamoorthi; Joel J. P. C. Rodrigues; Abdur Rahman; Ghulam Muhammad; Mubarak Alrashoud. 2020. "LACCVoV: Linear Adaptive Congestion Control With Optimization of Data Dissemination Model in Vehicle-to-Vehicle Communication." IEEE Transactions on Intelligent Transportation Systems 22, no. 8: 5319-5328.

Journal article
Published: 01 December 2020 in Sustainable Computing: Informatics and Systems
Reads 0
Downloads 0

The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations.

ACS Style

L. Minh Dang; Syed Ibrahim Hassan; Im Suhyeon; Arun Kumar Sangaiah; Irfan Mehmood; Seungmin Rho; Sanghyun Seo; Hyeonjoon Moon. UAV based wilt detection system via convolutional neural networks. Sustainable Computing: Informatics and Systems 2020, 28, 100250 .

AMA Style

L. Minh Dang, Syed Ibrahim Hassan, Im Suhyeon, Arun Kumar Sangaiah, Irfan Mehmood, Seungmin Rho, Sanghyun Seo, Hyeonjoon Moon. UAV based wilt detection system via convolutional neural networks. Sustainable Computing: Informatics and Systems. 2020; 28 ():100250.

Chicago/Turabian Style

L. Minh Dang; Syed Ibrahim Hassan; Im Suhyeon; Arun Kumar Sangaiah; Irfan Mehmood; Seungmin Rho; Sanghyun Seo; Hyeonjoon Moon. 2020. "UAV based wilt detection system via convolutional neural networks." Sustainable Computing: Informatics and Systems 28, no. : 100250.

Journal article
Published: 01 December 2020 in Sustainable Computing: Informatics and Systems
Reads 0
Downloads 0
ACS Style

Wenying Zheng; Dengzhi Liu; Xiong Li; Arun Kumar Sangaiah. Secure sustainable storage auditing protocol (SSSAP) with efficient key updates for cloud computing. Sustainable Computing: Informatics and Systems 2020, 28, 100237 .

AMA Style

Wenying Zheng, Dengzhi Liu, Xiong Li, Arun Kumar Sangaiah. Secure sustainable storage auditing protocol (SSSAP) with efficient key updates for cloud computing. Sustainable Computing: Informatics and Systems. 2020; 28 ():100237.

Chicago/Turabian Style

Wenying Zheng; Dengzhi Liu; Xiong Li; Arun Kumar Sangaiah. 2020. "Secure sustainable storage auditing protocol (SSSAP) with efficient key updates for cloud computing." Sustainable Computing: Informatics and Systems 28, no. : 100237.

Editorial
Published: 25 November 2020 in Enterprise Information Systems
Reads 0
Downloads 0
ACS Style

Arun Kumar Sangaiah; Ankit Chaudhary; Chun-Wei Tsai; Jin Wang; Francesco Mercaldo. Cognitive computing for big data systems over internet of things for enterprise information systems. Enterprise Information Systems 2020, 14, 1233 -1237.

AMA Style

Arun Kumar Sangaiah, Ankit Chaudhary, Chun-Wei Tsai, Jin Wang, Francesco Mercaldo. Cognitive computing for big data systems over internet of things for enterprise information systems. Enterprise Information Systems. 2020; 14 (9-10):1233-1237.

Chicago/Turabian Style

Arun Kumar Sangaiah; Ankit Chaudhary; Chun-Wei Tsai; Jin Wang; Francesco Mercaldo. 2020. "Cognitive computing for big data systems over internet of things for enterprise information systems." Enterprise Information Systems 14, no. 9-10: 1233-1237.

Article
Published: 31 October 2020 in Mobile Networks and Applications
Reads 0
Downloads 0

In order to improve the search performance of rich text content, a cloud search engine system based on rich text content is designed. On the basis of traditional search engine hardware system, several hardware devices such as Solr index server, collector, Chinese word segmentation device and searcher are installed, and the data interface is adjusted. On the basis of hardware equipment and database support, this paper uses the open source Apache Tika framework to obtain the metadata of rich text documents, implements word segmentation according to the rich text content and semantics, and calculates the weight of each keyword. Input search keywords, establish a text index, use BM25 algorithm to calculate the similarity between keywords and text, and output the search results of rich text according to the similarity calculation results. The experimental results show that the design system has high recall rate, high throughput, and the construction time of each data item index in different files is short, which improves the search efficiency and search accuracy.

ACS Style

Hao-Peng Chan; Liang Xu; Hui-Hui Liu; Run-Tian Zhang; Arun Kumar Sangaiah. System Design of Cloud Search Engine Based on Rich Text Content. Mobile Networks and Applications 2020, 26, 459 -472.

AMA Style

Hao-Peng Chan, Liang Xu, Hui-Hui Liu, Run-Tian Zhang, Arun Kumar Sangaiah. System Design of Cloud Search Engine Based on Rich Text Content. Mobile Networks and Applications. 2020; 26 (1):459-472.

Chicago/Turabian Style

Hao-Peng Chan; Liang Xu; Hui-Hui Liu; Run-Tian Zhang; Arun Kumar Sangaiah. 2020. "System Design of Cloud Search Engine Based on Rich Text Content." Mobile Networks and Applications 26, no. 1: 459-472.

Journal article
Published: 16 October 2020 in Symmetry
Reads 0
Downloads 0

The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

ACS Style

Mojisola Grace Asogbon; Oluwarotimi Williams Samuel; Yanbing Jiang; Lin Wang; Yanjuan Geng; Arun Kumar Sangaiah; Shixiong Chen; Peng Fang; Guanglin Li. Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System. Symmetry 2020, 12, 1710 .

AMA Style

Mojisola Grace Asogbon, Oluwarotimi Williams Samuel, Yanbing Jiang, Lin Wang, Yanjuan Geng, Arun Kumar Sangaiah, Shixiong Chen, Peng Fang, Guanglin Li. Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System. Symmetry. 2020; 12 (10):1710.

Chicago/Turabian Style

Mojisola Grace Asogbon; Oluwarotimi Williams Samuel; Yanbing Jiang; Lin Wang; Yanjuan Geng; Arun Kumar Sangaiah; Shixiong Chen; Peng Fang; Guanglin Li. 2020. "Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System." Symmetry 12, no. 10: 1710.

Journal article
Published: 30 September 2020 in Symmetry
Reads 0
Downloads 0

Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.

ACS Style

You-Shyang Chen; Arun Kumar Sangaiah; Su-Fen Chen; Hsiu-Chen Huang. Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model. Symmetry 2020, 12, 1620 .

AMA Style

You-Shyang Chen, Arun Kumar Sangaiah, Su-Fen Chen, Hsiu-Chen Huang. Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model. Symmetry. 2020; 12 (10):1620.

Chicago/Turabian Style

You-Shyang Chen; Arun Kumar Sangaiah; Su-Fen Chen; Hsiu-Chen Huang. 2020. "Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model." Symmetry 12, no. 10: 1620.

Journal article
Published: 21 September 2020 in Symmetry
Reads 0
Downloads 0

The ontology sparse vector learning algorithm is essentially a dimensionality reduction trick, i.e., the key components in the p-dimensional vector are taken out, and the remaining components are set to zero, so as to obtain the key information in a certain ontology application background. In the early stage of ontology data processing, the goal of the algorithm is to find the location of key components through the learning of some ontology sample points, if the relevant concepts and structure information of each ontology vertex with p-dimensional vectors are expressed. The ontology sparse vector itself contains a certain structure, such as the symmetry between components and the binding relationship between certain components, and the algorithm can also be used to dig out the correlation and decisive components between the components. In this paper, the graph structure is used to express these components and their interrelationships, and the optimal solution is obtained by using spectral graph theory and graph optimization techniques. The essence of the proposed ontology learning algorithm is to find the decisive vertices in the graph Gβ. Finally, two experiments show that the given ontology learning algorithm is effective in similarity calculation and ontology mapping in some specific engineering fields.

ACS Style

Jianzhang Wu; Arun Kumar Sangaiah; Wei Gao. Graph Learning-Based Ontology Sparse Vector Computing. Symmetry 2020, 12, 1562 .

AMA Style

Jianzhang Wu, Arun Kumar Sangaiah, Wei Gao. Graph Learning-Based Ontology Sparse Vector Computing. Symmetry. 2020; 12 (9):1562.

Chicago/Turabian Style

Jianzhang Wu; Arun Kumar Sangaiah; Wei Gao. 2020. "Graph Learning-Based Ontology Sparse Vector Computing." Symmetry 12, no. 9: 1562.

Journal article
Published: 18 September 2020 in IEEE Transactions on Industrial Informatics
Reads 0
Downloads 0

The new frontier research era and convergence of cognitive data science methods and models with reference to the Internet of Things (IoT) and big data systems have brought about various challenges in industrial systems that need to be addressed in the current scenario. Cognitive science will lead to a high level of fluidity to analytics. This special section aims to explore the domain knowledge and reasoning of data science technologies and cognitive methods with the IoT over the big data systems. Data science techniques have been adopted to improve the IoT in terms of data throughput, optimization, and management, and to have a major impact on the future of IoT networking systems. The main focus is the design of best cognitive embedded data science technologies to process and analyze the large amount of data collected through industrial IoT systems and help for good decision making.

ACS Style

Patrick Siarry; Arun Kumar Sangaiah; Yi-Bing Lin; Shiwen Mao; Marek R. Ogiela. Guest Editorial: Special Section on Cognitive Big Data Science Over Intelligent IoT Networking Systems in Industrial Informatics. IEEE Transactions on Industrial Informatics 2020, 17, 2112 -2115.

AMA Style

Patrick Siarry, Arun Kumar Sangaiah, Yi-Bing Lin, Shiwen Mao, Marek R. Ogiela. Guest Editorial: Special Section on Cognitive Big Data Science Over Intelligent IoT Networking Systems in Industrial Informatics. IEEE Transactions on Industrial Informatics. 2020; 17 (3):2112-2115.

Chicago/Turabian Style

Patrick Siarry; Arun Kumar Sangaiah; Yi-Bing Lin; Shiwen Mao; Marek R. Ogiela. 2020. "Guest Editorial: Special Section on Cognitive Big Data Science Over Intelligent IoT Networking Systems in Industrial Informatics." IEEE Transactions on Industrial Informatics 17, no. 3: 2112-2115.

Research article
Published: 09 September 2020 in Enterprise Information Systems
Reads 0
Downloads 0

In recent times, location recommendation has received significant attention from the researchers due to emerging utilisation of Location Based Social Networks in the prediction process. In this paper, we present a new multi-agent based framework to generate better-personalised location recommendations. We address the personalisation problem through the dynamic user profile that incorporates the user’s long-term and short-term cognitive behaviour. The better adaptation of user cognitive behaviour enhances the prediction process and improves overall user experience with better recommendations. A detailed user study is conducted to reveal the improved performance of proposed approach through enhanced recommendations in comparison with other approaches.

ACS Style

Logesh Ravi; Malathi Devarajan; Vijayakumar V; Arun Kumar Sangaiah; Lipo Wang; Sasikumar A; V Subramaniyaswamy. An intelligent location recommender system utilising multi-agent induced cognitive behavioural model. Enterprise Information Systems 2020, 1 -19.

AMA Style

Logesh Ravi, Malathi Devarajan, Vijayakumar V, Arun Kumar Sangaiah, Lipo Wang, Sasikumar A, V Subramaniyaswamy. An intelligent location recommender system utilising multi-agent induced cognitive behavioural model. Enterprise Information Systems. 2020; ():1-19.

Chicago/Turabian Style

Logesh Ravi; Malathi Devarajan; Vijayakumar V; Arun Kumar Sangaiah; Lipo Wang; Sasikumar A; V Subramaniyaswamy. 2020. "An intelligent location recommender system utilising multi-agent induced cognitive behavioural model." Enterprise Information Systems , no. : 1-19.

Journal article
Published: 19 August 2020 in Computer Communications
Reads 0
Downloads 0

Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

ACS Style

Omer Deperlioglu; Utku Kose; Deepak Gupta; Ashish Khanna; Arun Kumar Sangaiah. Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network. Computer Communications 2020, 162, 31 -50.

AMA Style

Omer Deperlioglu, Utku Kose, Deepak Gupta, Ashish Khanna, Arun Kumar Sangaiah. Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network. Computer Communications. 2020; 162 ():31-50.

Chicago/Turabian Style

Omer Deperlioglu; Utku Kose; Deepak Gupta; Ashish Khanna; Arun Kumar Sangaiah. 2020. "Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network." Computer Communications 162, no. : 31-50.

Article
Published: 17 July 2020 in Mobile Networks and Applications
Reads 0
Downloads 0

White blood cells (Leukocytes) are considered to be an essential part of the human body’s immune system. The count of WBCs is considered to be a parameter for the indication of disease. Over time several methods have been proposed to classify these WBCs into their subtypes namely Neutrophils, Eosinophils, Basophils, Lymphocytes, and Monocytes which helps in the estimation of the body’s WBC count. These methods range from various morphological image processing-based methodologies to advanced deep neural systems. Due to the superior ability of neural systems to achieve the state of the art results more research is been carried out in this field. However, most of the such previously proposed methods have concentrated only in establishing and explaining the overall methodology for achieving high accuracy scores and less emphasis has been made in discussing the impact of modular changes in such methodologies like the impact of various activation functions, optimizers and data pre-processing methods very explicitly for this problem. This has led to a deficiency of work to be carried out with very recently developed activation functions and more essentially optimization algorithms other than backpropagation. It is extremely essential to explore and analyse different modules of the methodology to accelerate future research work further which might possibly help the research community in achieving a much better and efficient solution. This paper compares various architectures and discusses the behaviour and impact of different hyperparameters and proposes a novel methodology by incorporating recently developed swish activation to enhance the results. Unlike previously proposed methods of proposing single better neural network model this paper suggests a good choice of modular changes that could be incorporated in future works to enhance their results.

ACS Style

B. A. Harshanand; Arun Kumar Sangaiah. Comprehensive Analysis of Deep Learning Methodology in Classification of Leukocytes and Enhancement Using Swish Activation Units. Mobile Networks and Applications 2020, 25, 2302 -2320.

AMA Style

B. A. Harshanand, Arun Kumar Sangaiah. Comprehensive Analysis of Deep Learning Methodology in Classification of Leukocytes and Enhancement Using Swish Activation Units. Mobile Networks and Applications. 2020; 25 (6):2302-2320.

Chicago/Turabian Style

B. A. Harshanand; Arun Kumar Sangaiah. 2020. "Comprehensive Analysis of Deep Learning Methodology in Classification of Leukocytes and Enhancement Using Swish Activation Units." Mobile Networks and Applications 25, no. 6: 2302-2320.

Journal article
Published: 11 July 2020 in Digital Communications and Networks
Reads 0
Downloads 0

Modern information technology has been utilized progressively for the storage and distribution of the large amount of healthcare data to reduce cost and improve the medical facilities. In this context, the emergence of e-Health clouds offers novel opportunities, like easy and remote accessibility of medical data. However, this achievement produces plenty of new risks and challenges like how to provide integrity, security and confidentiality to the highly susceptible e-Health data. Among these challenges, authentication is a major issue which ensures that the susceptible medical data in clouds is not available to illegal participants. The smart card, password and biometrics are three factors of authentication which fulfill the requirement of giving high security. Numerous three factor ECC-based authentication protocols on e-Health clouds have been presented so far. However, most of the protocols have serious security flaws and produce high computation and communication overheads. Therefore, we introduce a novel protocol for e-Health cloud, which thwarts some major attacks, such as user anonymity, offline password guessing, impersonation and stolen smart card attacks. Moreover, we evaluate our protocol through formal security analysis using the Random Oracle Model (ROM). The analysis shows that our proposed protocol is more efficient than many existing protocols in terms of computation and communication costs. Thus, our proposed protocol is proved to be more efficient, robust and secure.

ACS Style

Minahil; Muhammad Faizan Ayub; Khalid Mahmood; Saru Kumari; Arun Kumar Sangaiah. Lightweight authentication protocol for e-health clouds in IoT-based applications through 5G technology. Digital Communications and Networks 2020, 7, 235 -244.

AMA Style

Minahil, Muhammad Faizan Ayub, Khalid Mahmood, Saru Kumari, Arun Kumar Sangaiah. Lightweight authentication protocol for e-health clouds in IoT-based applications through 5G technology. Digital Communications and Networks. 2020; 7 (2):235-244.

Chicago/Turabian Style

Minahil; Muhammad Faizan Ayub; Khalid Mahmood; Saru Kumari; Arun Kumar Sangaiah. 2020. "Lightweight authentication protocol for e-health clouds in IoT-based applications through 5G technology." Digital Communications and Networks 7, no. 2: 235-244.

Journal article
Published: 19 June 2020 in IEEE Transactions on Intelligent Transportation Systems
Reads 0
Downloads 0

Smart cities can manage assets and resources efficiently by using different types of electronic data collection sensors, devices and vehicles. However, growing complexity of systems and heterogeneous networking also enlarge the destructive effect of compromised or malicious sensor nodes. In this paper, we introduce electric vehicles to conduct trust evaluation for heterogeneous vehicle network in smart cities. Compared with traditional trust evaluation mechanism, mobility-based trust evaluation owns the advantages of low energy consumption and high evaluation accuracy. Meanwhile, we investigate the problem of minimizing transmission hops of trust evaluation and refers to this as the mobile trust evaluation problem (MTEP). We first formalize the MTEP into an optimization problem and present a heuristic moving strategy of single electric vehicle. Then, we consider the MTEP with multiple electric vehicles. By scheduling the electric vehicles to access the nodes on spanning tree with maximum neighbor distance ratio, the algorithm can improve the efficiency of trust evaluation. In experiments, we compare moving strategy of single electric vehicle and multiple electric vehicles with existing methods respectively. The results demonstrate that the proposed algorithms are able to effectively reduce the entire transmission hops of trust evaluation and thus prolong the life of the network.

ACS Style

Tian Wang; Hao Luo; Xiangxiang Zeng; Zhiyong Yu; Anfeng Liu; Arun Kumar Sangaiah. Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 1797 -1806.

AMA Style

Tian Wang, Hao Luo, Xiangxiang Zeng, Zhiyong Yu, Anfeng Liu, Arun Kumar Sangaiah. Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (3):1797-1806.

Chicago/Turabian Style

Tian Wang; Hao Luo; Xiangxiang Zeng; Zhiyong Yu; Anfeng Liu; Arun Kumar Sangaiah. 2020. "Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities." IEEE Transactions on Intelligent Transportation Systems 22, no. 3: 1797-1806.