Cover
List of Contributors
1 Intelligent Sensing and Ubiquitous Systems (ISUS) for Smarter and Safer Home Healthcare
1. 1.1 Introduction to Ubicomp for Home Healthcare
2. 1.2 Processing and Sensing Issues
3. 1.3 Integration and Management Issues
4. 1.4 Communication and Networking Issues
5. 1.5 Intelligence and Reasoning Issues
6. 1.6 Conclusion
7. Bibliography
2 PeMo‐EC: An Intelligent, Pervasive and Mobile Platform for ECG Signal Acquisition, Processing, and Pre‐Diagnostic Extraction
1. 2.1 Electrical System of the Heart
2. 2.2 The Electrocardiogram Signal: A Gold Standard for Monitoring People Suffering from Heart Diseases
3. 2.3 Pervasive and Mobile Computing: Basic Concepts
4. 2.4 Ubiquitous Computing and Healthcare Applications: State of the Art
5. 2.5 PeMo‐EC: Description of the Proposed Framework
6. 2.6 Conclusions
7. Acknowledgements
8. Bibliography
3 The Impact of Implantable Sensors in Biomedical Technology on the Future of Healthcare Systems
1. 3.1 Introduction
2. 3.2 Related Work
3. 3.3 Motivation and Contribution
4. 3.4 Fundamentals of IBANs for Healthcare Monitoring
5. 3.5 Challenges and Future Trends
6. 3.6 Conclusion and Recommendation
7. Bibliography
4 Social Network's Security Related to Healthcare
1. 4.1 The Use of Social Networks in Healthcare
2. 4.2 The Social Media Respond to a Primary Need of Security
3. 4.3 The Type of Medical Data
4. 4.4 Problematic
5. 4.5 Presentation of the Honeypots
6. 4.6 Proposal System for Detecting Malicious Profiles on the Health Sector
7. 4.7 Results and Discussion
8. 4.8 Conclusion
9. Bibliography
5 Multi‐Sensor Fusion for Context‐Aware Applications
1. 5.1 Introduction
2. 5.2 Ambient Methods Used for E‐Health
3. 5.3 Algorithms and Methods
4. 5.4 Intelligent Pervasive Healthcare Applications
5. 5.5 Conclusion
6. Bibliography
6 IoT‐Based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for the Elderly People
1. 6.1 Introduction
2. 6.2 Internet of Things (IoT) and Remote Health Monitoring
3. 6.3 Wearable Health Monitoring
4. 6.4 Related Work
5. 6.5 Architectural Prototype
6. 6.6 Summary
7. Bibliography
7 Pervasive Healthcare System Based on Environmental Monitoring
1. 7.1 Introduction
2. 7.2 Intelligent Pervasive Computing System
3. 7.3 Biosensors for Environmental Monitoring
4. 7.4 IPCS for Healthcare
5. 7.5 Conclusion
6. Bibliography
8 Secure Pervasive Healthcare System and Diabetes Prediction Using Heuristic Algorithm
1. 8.1 Introduction
2. 8.2 Related Work
3. 8.3 System Design
4. 8.4 Implementation
5. 8.5 Results and Discussions
6. 8.6 Conclusion
7. Nomenclatures Used
8. Bibliography
9 Threshold‐Based Energy‐Efficient Routing Protocol for Critical Data Transmission to Increase Lifetime in Heterogeneous Wireless Body Area Sensor Network
1. 9.1 Introduction
2. 9.2 Related Works
3. 9.3 Proposed Protocol: Threshold‐Based Energy‐Efficient Routing Protocol for Critical Data Transmission (EERPCDT)
4. 9.4 System Model
5. 9.5 Analysis of Energy Consumption
6. 9.6 Simulation Results and Discussions
7. 9.7 Conclusion and Future Work
8. Bibliography
10 Privacy and Security Issues on Wireless Body Area and IoT for Remote Healthcare Monitoring
1. 10.1 Introduction
2. 10.2 Healthcare Monitoring System
3. 10.3 Healthcare Monitoring System
4. 10.4 Privacy and Security
5. 10.5 Attacks and Measures
6. 10.6 Internet of Things
7. 10.7 Projects and Related Works in Healthcare Monitoring System
8. 10.8 Summary
9. Bibliography
11 Remote Patient Monitoring: A Key Management and Authentication Framework for Wireless Body Area Networks
1. 11.1 Introduction
2. 11.2 Related Work
3. 11.3 Proposed Framework for Secure Remote Patient Monitoring
4. 11.4 Performance Analysis
5. 11.5 Discussion
6. 11.6 Conclusion
7. Bibliography
12 Image Analysis Using Smartphones for Medical Applications: A Survey
1. 12.1 Introduction
2. 12.2 Pervasive Healthcare Using Image‐Based Smartphone Applications
3. 12.3 Smartphone‐Based Image Diagnosis
4. 12.4 Libraries and Tools for Smartphone‐Based Image Analysis
5. 12.5 Challenges and Future Perspectives
6. 12.6 Conclusion
7. Bibliography
13 Bounds of Spreading Rate of Virus for a Network Through an Intuitionistic Fuzzy Graph
1. 13.1 Intuitionistic Fuzzy Matrices Using Incoming and Outgoing Links
2. 13.2 Virus Spreading Rate Between Outgoing and Incoming Links
3. 13.3 Numerical Examples
4. Bibliography
14 Data Mining Techniques for the Detection of the Risk in Cardiovascular Diseases
1. 14.1 Introduction
2. 14.2 PPG Signal Analysis
3. 14.3 Related Works
4. 14.4 Methodology
5. 14.5 Preprocessing in PPG Signal
6. 14.6 Results and Discussion
7. 14.7 Conclusion
8. Bibliography
15 Smart Sensing System for Cardio Pulmonary Sound Signals
1. 15.1 Introduction
2. 15.2 Background Theory
3. 15.3 Heart Sound Detection
4. 15.4 Polyvinylidene Fluoride (PVDF)
5. 15.5 Hardware Implementation
6. 15.6 LabVIEW Design
7. 15.7 Heart Sound Segmentation
8. 15.8 Conclusion
9. Bibliography
16 Anomaly Detection and Pattern Matching Algorithm for Healthcare Application: Identifying Ambulance Siren in Traffic
1. 16.1 Introduction
2. 16.2 Related Work
3. 16.3 Pattern Matching Algorithm for Ambulance Siren Detection
4. 16.4 Results and Conclusion
5. Bibliography
17 Detecting Diabetic Retinopathy from Retinal Images Using CUDA Deep Neural Network
1. 17.1 Introduction
2. 17.2 Proposed Method
3. 17.3 Experimental Results
4. 17.4 Conclusion and Future Work
5. Bibliography
18 An Energy‐Efficient Wireless Body Area Network Design in Health Monitoring Scenarios
1. 18.1 Wireless Body Area Network
2. 18.2 Proposed Opportunistic Scheduling
3. 18.3 Performance Analysis Environment and Metrics
4. 18.4 Summary
5. Bibliography
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Max and min VAI values for each activity.
Chapter 3
1. Table 3.1 Wireless technologies used in healthcare applications as desc...
Chapter 4
1. Table 4.1 Moroccan population in healthcare.
Chapter 5
1. Table 5.1 Characteristics of context awareness through three levels.
2. Table 5.2 Common bio‐signals and their metrics.
3. Table 5.3 Wireless communication technologies for pervasive health syst...
Chapter 6
1. Table 6.1 Research gaps in remote health monitoring for elderly.
Chapter 7
1. Table 7.1 Pervasive hardware.
2. Table 7.2 Challenges overwhelmed by pervasive computing.
3. Table 7.3 Biosensors used in environmental pollution monitoring.
4. Table 7.4 Air pollutants: sources and impacts.
5. Table 7.5 Healthcare system: An empirical view.
6. Table 7.6 Sensors used in healthcare systems.
Chapter 8
1. Table 8.1 Comparison of HPSO‐K and K‐means.
2. Table 8.2 Performance of PRA.
3. Table 8.3 Performance of HPSO‐K.
4. Table 8.4 Computation of time complexity.
Chapter 9
1. Table 9.1 Energy parameters of transceivers used in WBAN.
2. Table 9.2 Simulation parameters for proposed work.
Chapter 10
1. Table 10.1 Security threats, requirement, and solutions.
2. Table 10.2 Security collections.
3. Table 10.3 Threat categories.
Chapter 11
1. Table 11.1 Number of logical operations in key generation, encryption a...
2. Table 11.2 Number of logical operations in NetAMS based on assumed valu...
3. Table 11.3 Number of logical operations in key generation, encryption a...
4. Table 11.4 Number of logical operations in NetSHA based on assumed valu...
Chapter 12
1. Table 12.1 Smartphone camera‐based diagnosis.
2. Table 12.2 External sensors or device‐based diagnosis using smartphones...
3. Table 12.3 Libraries and tools useful for smartphone‐based image analys...
Chapter 13
1. Table 13.1 Comparison of energy and
2. Table 13.2 Spreading rate of virus and epidemic threshold of and
Chapter 14
1. Table 14.1 The accuracy of different classifiers.
Chapter 15
1. Table 15.1 Characteristic of heart sounds.
2. Table 15.2 Advantages and disadvantages of PVDF.
3. Table 15.3 Separation coefficients for normal heart sounds.
Chapter 17
1. Table 17.1 Class distribution.
2. Table 17.2 Architecture.
3. Table 17.3 Classification stats.
4. Table 17.4 Confusion matrix.
5. Table 17.5 Comparison with existing models.

List of Illustrations

Chapter 1
1. Figure 1.1 Corporal device. (a) XBee pro radio, (b) accelerometer, (c) mic...
2. Figure 1.2 Walking accelerometer raw data.
3. Figure 1.3 Fall accelerometer raw data.
4. Figure 1.4 Distance estimation layered architecture.
5. Figure 1.5 RSSI readings for two LG phones on the same conditions.
6. Figure 1.6 Calibrated vs. uncalibrated distance calculation.
7. Figure 1.7 Moving average algorithm.
8. Figure 1.8 Moving average algorithm results.
9. Figure 1.9 Improving error variance with moving average algorithm.
10. Figure 1.10 (a) Cloud services for integration of COTS systems (b) DD prot...
11. Figure 1.11 Graphs and media: SHC test graphs.
12. Figure 1.12 Graphs and media: shared medium user awareness.
13. Figure 1.13 SHC SNMP architecture (a) and SHC‐MIB tree (b).
14. Figure 1.14 Wireless sensor network architecture.
15. Figure 1.15 Environmental module: (a) Arduino Diecimila + XBee shield, (b)...
16. Figure 1.16 XBee‐GPRS gateway: (a) GPRS Hilo SAGEM module, (b) Arduino Meg...
17. Figure 1.17 System architecture.
18. Figure 1.18 Taxonomy expressed in OWL.
19. Figure 1.19 Deployment architecture.
20. Figure 1.20 Bayesian network model.
Chapter 2
1. Figure 2.1 ECG signal. Characteristic waves and some basic parameters for...
2. Figure 2.2 Electrocardiogram vs. anatomy of the conduction system. Relati...
3. Figure 2.3 Proposed framework. Framework for the pervasive and mobile pla...
4. Figure 2.4 General architecture. General architecture for acquisition sta...
5. Figure 2.5 Block diagram. Block diagram of a typical ECG data recording s...
6. Figure 2.6 ECG signal processing module. ECG signal processing module: pr...
7. Figure 2.7 QRS detection. Enhancement and detection of QRS complexes.
8. Figure 2.8 Physician's smartphone application. Physician's smartphone...
9. Figure 2.9 Collaborative database. Proposed collaborative and integrated ...
Chapter 3
1. Figure 3.1 Retina implants.
2. Figure 3.2 Research methodology.
3. Figure 3.3 Conception of the implantable device. (Sadik et al. 2009).
4. Figure 3.4 Block diagram of the implantable device. (Sadik et al. 2009 )....
5. Figure 3.5 Prototype of the implantable device. (Sadik et al. 2009 ).
6. Figure 3.6 Implantable wireless pressure sensor based on the RFID standard...
7. Figure 3.7 Implantable system for blood monitoring. (Theodor et al. 2014 )...
8. Figure 3.8 Sensor mounted on an artery in a rabbit using elastic silicone ...
9. Figure 3.9 The implantable accelerometer system is mounted to the carotid ...
Chapter 4
1. Figure 4.1 Health Pyramid.
2. Figure 4.2 The relationship between patients, practitioners, and social ne...
3. Figure 4.3 The features of a social network.
4. Figure 4.4 Data collection.
5. Figure 4.5 Confusion matrix example.
6. Figure 4.6 Classification of users.
7. Figure 4.7 Estimates of the trees predictive performance
8. Figure 4.8 Confusion matrix.
9. Figure 4.9 Threshold curve for the prediction.
10. Figure 4.10 Malicious user's detector performance with different feature s...
Chapter 5
1. Figure 5.1 The compact view of context life cycle.
2. Figure 5.2 Interconnected world of intelligent pervasive healthcare.
3. Figure 5.3 Health information system.
4. Figure 5.4 Intra‐hospital intelligent pervasive systems.
5. Figure 5.5 Remote patient monitoring.
Chapter 6
1. Figure 6.1 Architecture of IITMPHMS.
Chapter 7
1. Figure 7.1 Computer evolution over time period.
2. Figure 7.2 IPCS heterogeneity environment.
3. Figure 7.3 IPCS characteristics paradigm.
4. Figure 7.4 IPCS services in healthcare.
5. Figure 7.5 Healthcare system architecture based on environmental monitorin...
Chapter 8
1. Figure 8.1 Major components of SMDAP system.
2. Figure 8.2 SMDAP model.
3. Figure 8.3 Proxy re‐encryption algorithm (PRA).
4. Figure 8.4 Flow of PSO.
5. Figure 8.5 HMM model for diabetes prediction.
6. Figure 8.6 Elgamal encryption.
7. Figure 8.7 HPSO‐K cluster.
8. Figure 8.8 HMM‐VA predictor.
9. Figure 8.9 Original medical data.
10. Figure 8.10 Re‐encrypted medical data.
11. Figure 8.11 Time taken in PRA. (a) Encryption. (b) Decryption.
12. Figure 8.12 Storage space in PRA.
13. Figure 8.13 Rate of convergence. (a) Number of patients = 500. (b) Number ...
14. Figure 8.14 Performance of HPSO‐K. (a) Intra‐cluster distance. (b) Inter‐c...
15. Figure 8.15 Patients in each cluster.
16. Figure 8.16 Time complexity of HPSO‐K and K‐means.
17. Figure 8.17 HMM‐VA forecasting diabetes.
18. Figure 8.18 Performance of HMM‐VA.
Chapter 9
1. Figure 9.1 Sensor nodes placement on the human body.
2. Figure 9.2 Analysis of network lifetime and stability period.
3. Figure 9.3 Analysis of averaged residual energy.
4. Figure 9.4 Analysis of averaged throughput.
Chapter 10
1. Figure 10.1 Patient monitoring in a WBAN.
2. Figure 10.2 Wireless sensor network.
3. Figure 10.3 Value of the global market for wireless sensor devices.
4. Figure 10.4 WBAN technologies data rate.
5. Figure 10.5 Hospital environment.
6. Figure 10.6 Denial of service attacks in OSI layers.
Chapter 11
1. Figure 11.1 WBAN‐based remote patient monitoring system.
2. Figure 11.2 Structure of initiation frame in NetAMS.
3. Figure 11.3 Structure of acknowledgment frame from HMS.
4. Figure 11.4 Structure of data frame from WSH.
5. Figure 11.5 Key establishment and data transmission in NetAMS.
6. Figure 11.6 Structure of initiation frame from WSN.
7. Figure 11.7 Structure of acknowledgment frame from HPA.
8. Figure 11.8 Key establishment and data transmission in NetSHA.
9. Figure 11.9 Correlation coefficient of 1000 sequences of length 1024 with ...
Chapter 12
1. Figure 12.1 Stages in smartphone‐based medical image diagnosis.
2. Figure 12.2 Examples of diagnosis using smartphone's camera: (a) detection...
3. Figure 12.3 Examples of diagnosis using sensors/devices attached to smartp...
Chapter 13
1. Figure 13.1 Intuitionistic fuzzy graph.
2. Figure 13.2 Intuitionistic fuzzy graph for period I.
3. Figure 13.3 Intuitionistic fuzzy graph for period II.
4. Figure 13.4 Intuitionistic fuzzy graph for period III.
5. Figure 13.5 Intuitionistic fuzzy graph for period IV.
6. Figure 13.6 Spreading rate of virus between incoming and outgoing links.
Chapter 14
1. Figure 14.1 A typical waveform of the PPG and its characteristic parameter...
2. Figure 14.2 Original fingertip photoplethysmograph. A1 and A2 are the area...
3. Figure 14.3 Two consecutive PPG waves represent pulse interval and peak‐to...
4. Figure 14.4 Typical waveform of the PPG and its feature.
5. Figure 14.5 Typical PPG waveforms show the parameters change with age.
6. Figure 14.6 PPG signal detection block diagram.
7. Figure 14.7 Sensor placements for transmittance mode.
8. Figure 14.8 Accuracy of classifiers using data mining techniques.
Chapter 15
1. Figure 15.1 Human heart.
2. Figure 15.2 Poly‐1,1‐difluoroethene.
3. Figure 15.3 Structure of the PVDF material.
4. Figure 15.4 Location of heart sounds and valves.
5. Figure 15.5 Block diagram of the PVDF‐based smart sensing system.
6. Figure 15.6 PVDF material.
7. Figure 15.7 The PVDF‐based sensing system.
8. Figure 15.8 Charge and voltage circuits for the device.
9. Figure 15.9 Charge amplifier.
10. Figure 15.10 Frequency response of the band‐rejection filter.
11. Figure 15.11 Band‐stop filter circuit diagram.
12. Figure 15.12 Low‐pass filter circuit diagram.
13. Figure 15.13 Block diagram of data acquisition.
14. Figure 15.14 USB‐6221 DAQ device pin layout.
15. Figure 15.15 USB‐6221 DAQ setup.
16. Figure 15.16 Signal acquisition.
17. Figure 15.17 Fixing of threshold value.
18. Figure 15.18 Threshold on ‐axis.
19. Figure 15.19 Separations of peaks.
20. Figure 15.20 Separations of peaks.
21. Figure 15.21 Results of keeping threshold on ‐axis.
22. Figure 15.22 Flat sequence used for separation.
23. Figure 15.23 Segmented S1 from normal heart sound.
24. Figure 15.24 Segmented S2 from normal heart sound.
Chapter 16
1. Figure 16.1 The general structure of integrated traffic system for ambulan...
2. Figure 16.2 Traffic signal.
3. Figure 16.3 Sound detecting sensor.
Chapter 17
1. Figure 17.1 Non‐proliferative diabetic retinopathy.
2. Figure 17.2 Before initial preprocessing.
3. Figure 17.3 After initial preprocessing.
4. Figure 17.4 Digital artifact.
5. Figure 17.5 Normalized “nomi,” for (a, b), for actual label b, predicted a...
6. Figure 17.6 Normalized “denomi,” for (a, b), for actual label b, predicted...
7. Figure 17.7 Graph for confusion matrix.
8. Figure 17.8 Comparison of sensitivity values of the proposed method with t...
9. Figure 17.9 Comparison of specificity values of the proposed method with t...
Chapter 18
1. Figure 18.1 WBAN‐based healthcare monitoring architecture.
2. Figure 18.2 Inter‐WBAN communication: infrastructure‐based architecture.
3. Figure 18.3 Inter‐WBAN communication: ad hoc‐based architecture.
4. Figure 18.4 Illustration of utility function calculation.
5. Figure 18.5 Effects of the number of packets in static length of the frame...
6. Figure 18.6 Effects of the length of the super frame.
7. Figure 18.7 Effects of the number of packets in dynamic length.

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Library of Congress Cataloging‐in‐Publication Data

Names: Sangaiah, Arun Kumar, 1981‐ editor. | Shantharajah, S. P., 1972‐

editor. | Theagarajan, Padma, 1968‐ editor.

Title: Intelligent pervasive computing systems for smarter healthcare / Arun

Kumar Sangaiah, VIT University, Vellore, India, S.P. Shantharajah, VIT

University, Vellore, India, Padma Theagarajan, Sona College of Technology,

Salem, India.

Description: First edition. | Hoboken, NJ : John Wiley & Sons, Inc., [2019] |

Includes bibliographical references and index. |

Identifiers: LCCN 2019009752 (print) | LCCN 2019012900 (ebook) | ISBN

9781119438991 (Adobe PDF) | ISBN 9781119439011 (ePub) | ISBN 9781119438960

(hardcover)

Subjects: LCSH: Medical care–Data processing. | Ubiquitous computing. |

Medical electronics.

Classification: LCC R859.7.U27 (ebook) | LCC R859.7.U27 I58 2019 (print) |

DDC 610.285–dc23

LC record available at https://lccn.loc.gov/2019009752

Cover design by Wiley

Cover image: © ktsimage/Getty Images

List of Contributors

Amudha Thangavel

Department of Computer Applications

Bharathiar University

Coimbatore

India

Ponnuraman Balakrishnan

Department of Analytics, SCOPE

VIT Deemed University

Vellore

India

Angelo Brayner

Computing Science Department

Federal University of Ceará

Fortaleza

Brazil

Chandrasekaran Vellankoil Marappan

School of Advanced Sciences

VIT University

Vellore

India

Habiba Chaoui

Systems Engineering Laboratory

National School of Applied Sciences

Ibn Tofail University

Kenitra

Morocco

Ashraf Darwish

Faculty of Science

Helwan University

Cairo

Egypt

Deepa Ganesan

School of Advanced Sciences

VIT University

Vellore

India

Dinakaran Karunakaran

Department of Information Technology

Saveetha Engineering College

Chennai

India

Sumathi Doraikannan

CSE

Malla Reddy Engineering College

JNTUH

Hyderabad

India

Younès El Bouzekri El Idrissi

Systems Engineering Laboratory

National School of Applied Sciences

Ibn Tofail University

Kenitra

Morocco

Fatna Elmendili

Systems Engineering Laboratory

National School of Applied Sciences

Ibn Tofail University

Kenitra

Morocco

Gowthambabu Karthikeyan

School of Computer Science and Engineering

VIT University

Vellore

India

Aboul Ella Hassanien

Faculty of Computers and Information

Cairo University

Cairo

Egypt

Jothilakshmi Rajendiran

Department of Physics

Veltech University

Chennai

India

Ramanathan Lakshmanan

School of Computer Science and Engineering

Vellore Institute of Technology

Vellore

India

João Paulo Madeiro

Institute for Engineering and Sustainable Development

University for the International Integration of the Afro‐Brazilian Lusophony

Redenção

Brazil

Mary Mekala

School of Information Technology and Engineering

VIT University

Vellore

India

José Maria Monteiro

Computing Science Department

Federal University of Ceará

Fortaleza

Brazil

Rui Silva Moreira

ISUS unit at FCT

University Fernando Pessoa

Porto, Portugal

INESC TEC and LIACC at FEUP

University of Porto

Porto

Portugal

Jayashree Nair

AIMS Institutes

Bangalore

India

Patitha Parameswaran

Department of Computer Technology

MIT Campus, Anna University

Chennai

India

Padma T.

Sona College of Technology

Salem

India

Ricky Parmar

Dell EMC

Bangaluru

India

Deepalakshmi Perumalsamy

Department of CSE

Kalasalingam Academy of Research and Education

Krishnankoil

India

Praba Bashyam

Department of Mathematics

SSN College of Engineering

Affiliated to Anna University

Chennai

India

Swarnalatha Purushotham

School of Computer Science and Engineering

Vellore Institute of Technology

Vellore

India

Pethru Raj

Site Reliability Engineering (SRE) Division

Reliance Jio Infocomm. Ltd. (RJIL)

Bangalore

India

Rajakumar Krishnan

School of Computer Science and Engineering

VIT

Vellore

India

Rajeswari Kurubarahalli Chinnasamy

Department of Computer Science

Sona College of Technology

Salem

India

Rajeswari Rajendran

Department of Computer Applications

Bharathiar University

Coimbatore

India

Nersisson Ruban

School of Electrical Engineering

VIT University

Vellore

India

Sangeetha Archunan

Department of Computer Applications

Bharathiar University

Coimbatore

India

Sasikala Ramasamy

School of Computer Science and Engineering

VIT University

Vellore

India

Gehad Ismail Sayed

Faculty of Computers and Information

Cairo University

Cairo

Egypt

Sathiyabhama Balasubramaniam

Department of Computer Science

Sona College of Technology

Salem

India

Prabha Selvaraj

CSE

Malla Reddy Institute of Engineering and Technology

JNTUH

Secunderabad

India

Kannan Shanmugam

Department of Computer Science and Engineering

Malla Reddy Engineering College

Hyderabad

India

Rajalakshmi Shenbaga Moorthy

Department of Computer Science and Engineering

St. Joseph's Institute of Technology

Anna University

Chennai

India

Christophe Soares

ISUS unit at FCT

University Fernando Pessoa

Porto

Portugal

Pedro Sobral

ISUS unit at FCT

University Fernando Pessoa

Porto

Portugal

Rajkumar Soundrapandiyan

School of Computer Science and Engineering

Vellore Institute of Technology

Vellore

India

Karthik Subburathinam

Department of Computer Science and Engineering

SNS College of Technology

Chennai

India

Suresh Kumar Nagarajan

School of Computer Science and Engineering

VIT University

Vellore

India

José Torres

ISUS unit at FCT

University Fernando Pessoa

Porto

Portugal

Valarmathie Palanisamy

Department of Computer Science and Engineering

Saveetha Engineering College

Anna University

Chennai

India

Navya Venkatamari

Department of ECE

Kalasalingam Academy of Research and Education

Krishnankoil

India

Krishnamoorthy Venkatesan

Department of Mathematics

College of Natural Sciences

Arba Minch University

Arba Minch

Ethiopia

Veeramuthu Venkatesh

School of Computing

SASTRA Deemed University

Thanjavur

India

Vishnu Priya

Department of Computer Science and Engineering

P.M.R. Engineering College

Anna University

Chennai

India