Most recent issue published online in the International Journal of System of Systems Engineering.
International Journal of System of Systems Engineering
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International Journal of System of Systems Engineering
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International Journal of System of Systems Engineering
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http://www.inderscience.com/browse/index.php?journalID=184&year=2024&vol=14&issue=2
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Medical data sharing using blockchain with secure patient/doctor interaction
http://www.inderscience.com/link.php?id=137060
In this emerging technological era, digitisation becomes unavoidable and dominates our daily lives. However, in order to ensure the privacy of the data, it must be securely stored and exchanged. Blockchain is commonly employed in the administration of patient health information. In this paper, a health information exchange and secure storage system based on the private blockchain is presented. The network is hosted by individual hospitals to enhance the security of their client's information. The system includes a Central Healthcare Provider (CHP), which manages the patients' healthcare information by utilising this trustworthy system, doctors can access patients' historical data while maintaining patients' privacy. Additionally, a mechanism for symptom-matching is proposed for patients and doctors. It enables two distinct patients/doctors to share their experience with each other. This can be accomplished by performing a mutual handshake between two entities and generating a key to secure communication during the session.
Medical data sharing using blockchain with secure patient/doctor interaction
K. Vivekrabinson; D. Vijayakumar; S. Rajesh Kumar; R. Dhamodharan
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 145 - 158
In this emerging technological era, digitisation becomes unavoidable and dominates our daily lives. However, in order to ensure the privacy of the data, it must be securely stored and exchanged. Blockchain is commonly employed in the administration of patient health information. In this paper, a health information exchange and secure storage system based on the private blockchain is presented. The network is hosted by individual hospitals to enhance the security of their client's information. The system includes a Central Healthcare Provider (CHP), which manages the patients' healthcare information by utilising this trustworthy system, doctors can access patients' historical data while maintaining patients' privacy. Additionally, a mechanism for symptom-matching is proposed for patients and doctors. It enables two distinct patients/doctors to share their experience with each other. This can be accomplished by performing a mutual handshake between two entities and generating a key to secure communication during the session.]]>
10.1504/IJSSE.2024.137060
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 145 - 158
K. Vivekrabinson
D. Vijayakumar
S. Rajesh Kumar
R. Dhamodharan
Department of CSE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, 626117, India ' Department of CSE, National Engineering College, Kovilpatti, Tamilnadu, 628503, India ' Department of CSE, National Engineering College, Kovilpatti, Tamilnadu, 628503, India ' Department of CSE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, 626117, India
blockchain
central health provider
healthcare
symptom-matching
tamper resistance
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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2024-03-01T23:20:50-05:00
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Secure healthcare data transaction using hybrid attribute-based encryption with access control policy on cloud
http://www.inderscience.com/link.php?id=137071
Cloud computing environments are rapidly growing in healthcare, and security and confidentiality of medical records are a major concern. Increasingly, researchers and academics are paying attention to cloud-based medical data exchange. Even if medical data is not misused, it can be compromised or manipulated through the use of untrusted networks. So, in this paper, a hybrid cryptosystem is introduced to protect medical data that overcomes the limitations of existing cryptosystems. The proposed hybrid cryptosystem is called HABE which is a combination of cipher policy attribute-based encryption (CP-ABE) and elliptical curve cryptography (ECC). This method ensures the overall security of the sensor data, which guarantees confidentiality and integrity. Besides, the proposed system gives effective privacy with access control for user access according to trust-based data level access control (TDLA). Based on the TDLA, user access to the cloud environment is restricted. The effectiveness of the presented technique is evaluated based on Encryption time, decryption time, security level, throughput, and Time complexity.
Secure healthcare data transaction using hybrid attribute-based encryption with access control policy on cloud
S. Vinothkumar; J. Amutharaj
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 159 - 173
Cloud computing environments are rapidly growing in healthcare, and security and confidentiality of medical records are a major concern. Increasingly, researchers and academics are paying attention to cloud-based medical data exchange. Even if medical data is not misused, it can be compromised or manipulated through the use of untrusted networks. So, in this paper, a hybrid cryptosystem is introduced to protect medical data that overcomes the limitations of existing cryptosystems. The proposed hybrid cryptosystem is called HABE which is a combination of cipher policy attribute-based encryption (CP-ABE) and elliptical curve cryptography (ECC). This method ensures the overall security of the sensor data, which guarantees confidentiality and integrity. Besides, the proposed system gives effective privacy with access control for user access according to trust-based data level access control (TDLA). Based on the TDLA, user access to the cloud environment is restricted. The effectiveness of the presented technique is evaluated based on Encryption time, decryption time, security level, throughput, and Time complexity.]]>
10.1504/IJSSE.2024.137071
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 159 - 173
S. Vinothkumar
J. Amutharaj
Department of CSE, ACS College of Engineering, Bengaluru, 560074, Karnataka, India ' Department of ISE, Rajarajeswari College of Engineering, Bengaluru, 560074, Karnataka, India
cloud computing
CP-ABEl cipher policy attribute-based encryption
ECC
elliptical curve cryptography
TDLA
trust-based data level access control
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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173
2024-03-01T23:20:50-05:00
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Efficient deep transfer learning based COVID-19 detection and classification using CT images
http://www.inderscience.com/link.php?id=137073
This paper develops an intelligent deep transfer learning-driven COVID-19 detection and classification model using CT images. The major aim of the IDTLD-CDCM model is to identify appropriate class labels for the CT images. The IDTLD-CDCM model undergoes initial pre-processing in two levels namely spline adaptive filtering (SAF) based noise removal and contrast enhancement. In addition, the IDTLD-CDCM model involves SqueezeNet as a feature extractor for deriving a useful set of feature vectors. Furthermore, the hop field neural network (HFNN) model is utilised for the classifier of COVID-19 and Non-COVID-19 images. Furthermore, the parameter tuning of the HFNN model is carried out by the use of root mean square propagation (RMSProp). To investigate the improved outcomes of the IDTLD-CDCM approach, a series of simulations are executed and the outcomes are inspected in several aspects. The simulation outcome demonstrated the improved outcomes of the IDTLD-CDCM approach over the recent approaches.
Efficient deep transfer learning based COVID-19 detection and classification using CT images
G. Prabakaran; K. Jayanthi
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 174 - 189
This paper develops an intelligent deep transfer learning-driven COVID-19 detection and classification model using CT images. The major aim of the IDTLD-CDCM model is to identify appropriate class labels for the CT images. The IDTLD-CDCM model undergoes initial pre-processing in two levels namely spline adaptive filtering (SAF) based noise removal and contrast enhancement. In addition, the IDTLD-CDCM model involves SqueezeNet as a feature extractor for deriving a useful set of feature vectors. Furthermore, the hop field neural network (HFNN) model is utilised for the classifier of COVID-19 and Non-COVID-19 images. Furthermore, the parameter tuning of the HFNN model is carried out by the use of root mean square propagation (RMSProp). To investigate the improved outcomes of the IDTLD-CDCM approach, a series of simulations are executed and the outcomes are inspected in several aspects. The simulation outcome demonstrated the improved outcomes of the IDTLD-CDCM approach over the recent approaches.]]>
10.1504/IJSSE.2024.137073
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 174 - 189
G. Prabakaran
K. Jayanthi
Department of Computer and Information Science, Faculty of Science, Annamalai University, Tamil Nadu, India ' Department of Computer Application, Government Arts College, C-Mutlur, Chidambaram, Tamil Nadu, India
COVID-19
deep transfer learning
computed tomography images
medical imaging
decision making
machine learning
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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2024-03-01T23:20:50-05:00
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An efficient Rabin-cryptosystem based authentication mechanism for vehicular ad-hoc networks
http://www.inderscience.com/link.php?id=137059
Vehicular ad-hoc network (VANET) is a complex cyber physical system of systems (SOS) having both stand-alone static elements and very sophisticated dynamic elements to provide real time data access. To ensure user's core security concern over crucial data in transit, it essentially demands robust user authentication scheme for accessing desired services from VANET clouds. Recently various schemes have been proposed to address numerous security concerns but very few of them have addressed all major attacks with efficiency. Here we propose an improved and enhanced Rabin-cryptosystem based efficient, dynamic and scalable authentication mechanism to address all known major attacks. We have analysed security of our proposed scheme using AVISVA and Proverif Tools. The analysis has shown that our scheme guarantees positional privacy, user anonymity and mutual authentication to prevent all type of known attacks. The comparison of protocol with available relevant scheme reveals proposed protocol is more efficient with efficacy.
An efficient Rabin-cryptosystem based authentication mechanism for vehicular ad-hoc networks
Md Ismail; Santanu Chatterjee; Jamuna Kanta Sing
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 190 - 211
Vehicular ad-hoc network (VANET) is a complex cyber physical system of systems (SOS) having both stand-alone static elements and very sophisticated dynamic elements to provide real time data access. To ensure user's core security concern over crucial data in transit, it essentially demands robust user authentication scheme for accessing desired services from VANET clouds. Recently various schemes have been proposed to address numerous security concerns but very few of them have addressed all major attacks with efficiency. Here we propose an improved and enhanced Rabin-cryptosystem based efficient, dynamic and scalable authentication mechanism to address all known major attacks. We have analysed security of our proposed scheme using AVISVA and Proverif Tools. The analysis has shown that our scheme guarantees positional privacy, user anonymity and mutual authentication to prevent all type of known attacks. The comparison of protocol with available relevant scheme reveals proposed protocol is more efficient with efficacy.]]>
10.1504/IJSSE.2024.137059
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 190 - 211
Md Ismail
Santanu Chatterjee
Jamuna Kanta Sing
Integrated Test Range, DRDO, Chandipur 756 025, India ' Research Center Imarat, DRDO, Hyderabad 500 069, India ' Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700 032, India
VANET
vehicular ad-hoc network
user authentication
Rabin cryptosystem
AVISPA
security
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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2024-03-01T23:20:50-05:00
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A survey on methods and apparatus of offloading in mobile cloud computing
http://www.inderscience.com/link.php?id=137063
Mobile cloud computing (MCC) is the recent technology that to provide execution of different mobile applications. Mobile devices are flexible and portable. The key disadvantages of mobile devices are limited battery life, lower bandwidth and insufficient storage capabilities. MCC reduces the drawbacks of mobile devices. The resolve the execution delay of mobile devices mobile edge computing (MEC) concept was proposed. Offloading is an effective method to save execution time and energy both. Code or Application Offloading allows executing a task in Cloud instead of Mobile Device itself. This paper presents a comprehensive discussion on different methodologies and the apparatus used for offloading of code or application into cloud. This paper also highlights on merits and demerits of different offloading techniques.
A survey on methods and apparatus of offloading in mobile cloud computing
Priyajit Sen; Rajat Pandit; Debabrata Sarddar
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 212 - 225
Mobile cloud computing (MCC) is the recent technology that to provide execution of different mobile applications. Mobile devices are flexible and portable. The key disadvantages of mobile devices are limited battery life, lower bandwidth and insufficient storage capabilities. MCC reduces the drawbacks of mobile devices. The resolve the execution delay of mobile devices mobile edge computing (MEC) concept was proposed. Offloading is an effective method to save execution time and energy both. Code or Application Offloading allows executing a task in Cloud instead of Mobile Device itself. This paper presents a comprehensive discussion on different methodologies and the apparatus used for offloading of code or application into cloud. This paper also highlights on merits and demerits of different offloading techniques.]]>
10.1504/IJSSE.2024.137063
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 212 - 225
Priyajit Sen
Rajat Pandit
Debabrata Sarddar
Department of Computer Science, West Bengal State University, Kolkata-700126, West Bengal, India ' Department of Computer Science, West Bengal State University, Kolkata-700126, West Bengal, India ' Department of Computer Science and Engineering, University of Kalyani, Kalyani, 741235, West Bengal, India
MCC
mobile cloud computing
MEC
mobile edge computing
execution time
Femtolet
QoE
quality of experience
QoS
quality of service
delay
energy consumption
data offloading
computation offloading
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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225
2024-03-01T23:20:50-05:00
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Hyper-heuristic glowworm swarm optimised support vector machines for heart and thyroid disease classification
http://www.inderscience.com/link.php?id=137067
In order to improve illness detection accuracy and reduce complexity, this study seeks to construct sophisticated machine learning (ML) classifiers and effective feature selection (FS) methods. The proposed model includes two stages: classification using Hyper-heuristic Glow Worm Swarm Optimised Support Vector Machines and FS utilising Information Gain (IG) and Spotted Hyena Optimiser (IG-SHO) (HHGWSO-SVM). By removing the irrelevant characteristics with the IG metric, the dimensionality of attributes is decreased in the IG-SHO technique. By combining the hybrid optimisation approach of HHGWSO with the SVM, the suggested HHGWSO-SVM classifier has been created. Its configuration has been improved by optimally setting the margin parameter, kernel type, and kernel parameters. The Hyperheuristic algorithm and the Glowworm Swarm Optimisation (HHGWSO) have been combined to create a method for fine-tuning SVM parameters based on accuracy and model complexity. The proposed HHGWSO-SVM model is tested in experiments on benchmark datasets to predict thyroid and heart illnesses. According to the results, the suggested categorisation model has improved precision and accuracy while reducing model complexity.
Hyper-heuristic glowworm swarm optimised support vector machines for heart and thyroid disease classification
G. Kiruthiga; S.Mary Vennila
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 226 - 246
In order to improve illness detection accuracy and reduce complexity, this study seeks to construct sophisticated machine learning (ML) classifiers and effective feature selection (FS) methods. The proposed model includes two stages: classification using Hyper-heuristic Glow Worm Swarm Optimised Support Vector Machines and FS utilising Information Gain (IG) and Spotted Hyena Optimiser (IG-SHO) (HHGWSO-SVM). By removing the irrelevant characteristics with the IG metric, the dimensionality of attributes is decreased in the IG-SHO technique. By combining the hybrid optimisation approach of HHGWSO with the SVM, the suggested HHGWSO-SVM classifier has been created. Its configuration has been improved by optimally setting the margin parameter, kernel type, and kernel parameters. The Hyperheuristic algorithm and the Glowworm Swarm Optimisation (HHGWSO) have been combined to create a method for fine-tuning SVM parameters based on accuracy and model complexity. The proposed HHGWSO-SVM model is tested in experiments on benchmark datasets to predict thyroid and heart illnesses. According to the results, the suggested categorisation model has improved precision and accuracy while reducing model complexity.]]>
10.1504/IJSSE.2024.137067
International Journal of System of Systems Engineering, Vol. 14, No. 2 (2024) pp. 226 - 246
G. Kiruthiga
S.Mary Vennila
Department of Computer Applications, Guru Nanak College, Chennai, 600 042, Tamil Nadu, India ' Department of Computer Science, Presidency College, Chennai, 600 005, Tamil Nadu, India
heart disease
thyroid disease
machine learning
FS
information gain
spotted hyena optimiser
hyper-heuristic glowworm swarm optimisation
support vector machines
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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246
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