Most recent issue published online in the International Journal of Grid and Utility Computing.
International Journal of Grid and Utility Computing
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International Journal of Grid and Utility Computing
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International Journal of Grid and Utility Computing
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http://www.inderscience.com/browse/index.php?journalID=108&year=2024&vol=15&issue=1
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A deep learning-inspired IoT-enabled hybrid model for predicting structural changes in CNC machines based on thermal behaviour
http://www.inderscience.com/link.php?id=136746
This research work introduces a hybrid model, BIG-LSTM, designed to enhance the precision of computer numerical control (CNC) machines in the manufacturing industry powered by the Internet of Things (IoT). Traditional models primarily focus on nut temperature's impact on thermal errors, often overlooking factors like bearing and ambient temperatures, and tend to ignore the intercept in the temperature-error relationship. The presented model addresses these gaps by incorporating ambient and bearing temperatures, and considering both intercept and slope for predicting Z-axis thermal deformation. Integration of motor speed and coolant behaviour is also included, acknowledging the rise in temperature with increased speed. BIG-LSTM, combining LSTM, GRU, and Bi-LSTM models, demonstrates efficacy in experiments, achieving Root Mean Square Errors (RMSEs) within 0.9 µm for spindle thermal displacement under varied temperature conditions. These findings highlight the model's potential in significantly improving accuracy and robustness in spindle thermal displacement predictions in the IoT era.
A deep learning-inspired IoT-enabled hybrid model for predicting structural changes in CNC machines based on thermal behaviour
Thompson Stephan; Vinith Anand Thiyagu; Pavan Kumar Shridhar
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 3 - 15
This research work introduces a hybrid model, BIG-LSTM, designed to enhance the precision of computer numerical control (CNC) machines in the manufacturing industry powered by the Internet of Things (IoT). Traditional models primarily focus on nut temperature's impact on thermal errors, often overlooking factors like bearing and ambient temperatures, and tend to ignore the intercept in the temperature-error relationship. The presented model addresses these gaps by incorporating ambient and bearing temperatures, and considering both intercept and slope for predicting Z-axis thermal deformation. Integration of motor speed and coolant behaviour is also included, acknowledging the rise in temperature with increased speed. BIG-LSTM, combining LSTM, GRU, and Bi-LSTM models, demonstrates efficacy in experiments, achieving Root Mean Square Errors (RMSEs) within 0.9 µm for spindle thermal displacement under varied temperature conditions. These findings highlight the model's potential in significantly improving accuracy and robustness in spindle thermal displacement predictions in the IoT era.]]>
10.1504/IJGUC.2024.136746
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 3 - 15
Thompson Stephan
Vinith Anand Thiyagu
Pavan Kumar Shridhar
Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India ' Department of Computer Science Engineering, M.S. Ramaiah University of Applied University, Bengaluru, Karnataka, India ' Department of Computer Science Engineering, M.S. Ramaiah University of Applied University, Bengaluru, Karnataka, India
CNC machine tools
thermal errors
hybrid model
deep learning
LSTM
spindle thermal displacement
prediction accuracy
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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Performance evaluation using throughput and latency of a blockchain-enabled patient centric secure and privacy preserve EHR based on IPFS
http://www.inderscience.com/link.php?id=136720
Every nation needs a better healthcare system and services for general people for digital medical records, which are available on a large scale. However, patients' health data is too sensitive to share and unsecured to store on centralised storage. However, it is required to ensure security and privacy with better storage and retrieval methods for PEHR (Patient Electronic Health Record). Blockchain allows for the secure and effective exchange of PEHR in a decentralised, tamper-proof manner and traceable distributed ledger that stores using Hyperledger Fabric (HLF) framework in encrypted form on the InterPlanetary File System (IPFS). The hyperledger caliper benchmark measures the blockchain network's performance concerning transaction throughput and latency. This paper discusses the performance evaluation of a Blockchain-Enabled Patient Centric Secure (BEPCS) and privacy preserved electronic health record on IPFS. It proposes a strategy that may increase throughput by 5-10% and decrease latency by 5-10% with better security and privacy.
Performance evaluation using throughput and latency of a blockchain-enabled patient centric secure and privacy preserve EHR based on IPFS
Vishal Sharma; Niranjan Lal; Anand Sharma
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 16 - 30
Every nation needs a better healthcare system and services for general people for digital medical records, which are available on a large scale. However, patients' health data is too sensitive to share and unsecured to store on centralised storage. However, it is required to ensure security and privacy with better storage and retrieval methods for PEHR (Patient Electronic Health Record). Blockchain allows for the secure and effective exchange of PEHR in a decentralised, tamper-proof manner and traceable distributed ledger that stores using Hyperledger Fabric (HLF) framework in encrypted form on the InterPlanetary File System (IPFS). The hyperledger caliper benchmark measures the blockchain network's performance concerning transaction throughput and latency. This paper discusses the performance evaluation of a Blockchain-Enabled Patient Centric Secure (BEPCS) and privacy preserved electronic health record on IPFS. It proposes a strategy that may increase throughput by 5-10% and decrease latency by 5-10% with better security and privacy.]]>
10.1504/IJGUC.2024.136720
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 16 - 30
Vishal Sharma
Niranjan Lal
Anand Sharma
CSE Department (SET), Mody University of Science and Technology, Lakshmangarh, Sikar, Rajasthan, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology (Delhi-NCR Campus), Modinagar, Ghaziabad, Uttar Pradesh, India ' CSE Department (SET), Mody University of Science and Technology, Lakshmangarh, Sikar, Rajasthan, India
medical data security
patient electronic healthcare records
consortium blockchain
inter planetary file system
medical data privacy preservation
chaincode
proxy re-encryption
patient-directed healthcare system
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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30
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Target imaging technology of wireless orbital communication radar
http://www.inderscience.com/link.php?id=136722
With the continuous advancement of technology, non-communication radar imaging is increasingly used in aerial observation and ground observation fields. This article conducts a systematic study on wireless orbit communication radar imaging technology and compensates for deviations in image quality accuracy through motion autofocus. The results show that the video-based backward projection algorithm proposed in this article is better than the RD algorithm and CBP algorithm in identifying large and small corner points. The feature fusion difference test of the rotated convolution unit was conducted on the ResNet18 and VGG16 neural networks and it was found that the recognition rate of the network architecture using the rotated convolution unit was significantly improved. The designed lightweight network based on rotational convolution units has an average recognition rate of 99.48% in the MSTAR data set.
Target imaging technology of wireless orbital communication radar
Xin Tan; Chaoqi Wang; Mingwei Wang; Wenyuan Liu; Xianghui Wang
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 31 - 43
With the continuous advancement of technology, non-communication radar imaging is increasingly used in aerial observation and ground observation fields. This article conducts a systematic study on wireless orbit communication radar imaging technology and compensates for deviations in image quality accuracy through motion autofocus. The results show that the video-based backward projection algorithm proposed in this article is better than the RD algorithm and CBP algorithm in identifying large and small corner points. The feature fusion difference test of the rotated convolution unit was conducted on the ResNet18 and VGG16 neural networks and it was found that the recognition rate of the network architecture using the rotated convolution unit was significantly improved. The designed lightweight network based on rotational convolution units has an average recognition rate of 99.48% in the MSTAR data set.]]>
10.1504/IJGUC.2024.136722
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 31 - 43
Xin Tan
Chaoqi Wang
Mingwei Wang
Wenyuan Liu
Xianghui Wang
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian, Shaanxi, China; Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian, Shaanxi, China ' School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian, Shaanxi, China; Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian, Shaanxi, China ' School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian, Shaanxi, China; Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian, Shaanxi, China ' School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian, Shaanxi, China; Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian, Shaanxi, China ' School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian, Shaanxi, China; Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian, Shaanxi, China
wireless communication radar
radar imaging technology
target recognition imaging
SAR imaging
Bayesian imaging
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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43
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Developing software predictive model for examining the software bugs using machine learning
http://www.inderscience.com/link.php?id=136726
Software faults prediction is an emerging research area in the software engineering. It is an important issue for IT industry and professionals. We need prior information of an application for faults or faulty modules in traditional approach to determine software faults. If we use machine leaching techniques then we can easily automate the models enabling application software to knowingly predict and recover the application software faults. This capability type features helps in developing the application software to execute more productively and minimise faults, cost and time. In the scenario of this research, we are considering the software appropriate models that predicted development models using subsets of artificial intelligence-based approaches. Besides, we utilise noticeable benchmark techniques for evaluation of performance for software predictive models. However, researchers and software exponents can accomplish independent perception from this research and can pick out automated tasks for their deliberated application.
Developing software predictive model for examining the software bugs using machine learning
Swati Singh; Monica Mehrotra; Taran Singh Bharati
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 44 - 52
Software faults prediction is an emerging research area in the software engineering. It is an important issue for IT industry and professionals. We need prior information of an application for faults or faulty modules in traditional approach to determine software faults. If we use machine leaching techniques then we can easily automate the models enabling application software to knowingly predict and recover the application software faults. This capability type features helps in developing the application software to execute more productively and minimise faults, cost and time. In the scenario of this research, we are considering the software appropriate models that predicted development models using subsets of artificial intelligence-based approaches. Besides, we utilise noticeable benchmark techniques for evaluation of performance for software predictive models. However, researchers and software exponents can accomplish independent perception from this research and can pick out automated tasks for their deliberated application.]]>
10.1504/IJGUC.2024.136726
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 44 - 52
Swati Singh
Monica Mehrotra
Taran Singh Bharati
Jamia Millia Islamia, Delhi, India ' Jamia Millia Islamia, Delhi, India ' Jamia Millia Islamia, Delhi, India
machine learning
software predictive model
software faults
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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Optimisation of the hybrid grey wolf method in cluster-based wireless sensor network using edge computing
http://www.inderscience.com/link.php?id=136723
Wireless Sensor Networks (WSNs) cover most of the secure data transfer applications and play a significant role in the IoT for primary data collection, which needs energy-efficient data transfer and improved network lifetime. The major challenge for these protocols is setting up optimum clusters and Cluster Head (CH) formation for efficient operation. WSNs have a critical role in parallel computation in which resources can be assigned to the sub-task and equalise the load, which improves the network lifetime. This paper uses the Grey Wolf Optimisation (GWO) algorithm in the proposed work by observing two variables, i.e., Residual Energy (RE) and node distance (DS) from Base Station (BS) that visualised and analysed the GWO under variable parameters in WSN. This approach identifies the most suitable node from all normal nodes for the selection of CH. The outcome demonstrates that using GWO improved the performance of the proposed model.
Optimisation of the hybrid grey wolf method in cluster-based wireless sensor network using edge computing
Ashok Kumar Rai; Rakesh Kumar
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 53 - 64
Wireless Sensor Networks (WSNs) cover most of the secure data transfer applications and play a significant role in the IoT for primary data collection, which needs energy-efficient data transfer and improved network lifetime. The major challenge for these protocols is setting up optimum clusters and Cluster Head (CH) formation for efficient operation. WSNs have a critical role in parallel computation in which resources can be assigned to the sub-task and equalise the load, which improves the network lifetime. This paper uses the Grey Wolf Optimisation (GWO) algorithm in the proposed work by observing two variables, i.e., Residual Energy (RE) and node distance (DS) from Base Station (BS) that visualised and analysed the GWO under variable parameters in WSN. This approach identifies the most suitable node from all normal nodes for the selection of CH. The outcome demonstrates that using GWO improved the performance of the proposed model.]]>
10.1504/IJGUC.2024.136723
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 53 - 64
Ashok Kumar Rai
Rakesh Kumar
Computer Science and Engineering Department, M.M.M University of Technology Gorakhpur (UP), Gorakhpur, Uttar Pradesh, India ' Computer Science and Engineering Department, M.M.M University of Technology Gorakhpur (UP), Gorakhpur, Uttar Pradesh, India
base station
cluster head
energy efficiency
grey wolf optimisation
wireless sensor network
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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64
2024-02-19T23:20:50-05:00
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Detection of crop disorder using deep learning
http://www.inderscience.com/link.php?id=136725
An estimated 14% of global yield is lost to plant diseases each year, causing suffering to billions of people. Plant pathology studies diseases, microbes and climatic conditions that lead to plant death. Temperature, pH, humidity and moisture can cause plant diseases. Chemical misuse, environmental imbalance and drug resistance can result from misdiagnosis. Diseases can be diagnosed by human scouting. Image analysis of plant leaves can help diagnose diseases automatically. Automated disease detection involves image selection, pre-processing, segmentation, augmented features and model prediction. Crop diseases can be detected and classified accurately by Deep Convolutional-Networks since a few years ago. This paper compares deep learning approaches for predicting healthy and diseased leaves from Mendley database. We suggest variations that improve classification accuracy. In this work for disease, Deep CNNs are implemented including ResNet-50, Mobilenet, Densenet121, EfficientnetB0 and the proposed approach. Over 99% accuracy was achieved in detecting various crop diseases.
Detection of crop disorder using deep learning
Vinita; Suma Dawn
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 65 - 74
An estimated 14% of global yield is lost to plant diseases each year, causing suffering to billions of people. Plant pathology studies diseases, microbes and climatic conditions that lead to plant death. Temperature, pH, humidity and moisture can cause plant diseases. Chemical misuse, environmental imbalance and drug resistance can result from misdiagnosis. Diseases can be diagnosed by human scouting. Image analysis of plant leaves can help diagnose diseases automatically. Automated disease detection involves image selection, pre-processing, segmentation, augmented features and model prediction. Crop diseases can be detected and classified accurately by Deep Convolutional-Networks since a few years ago. This paper compares deep learning approaches for predicting healthy and diseased leaves from Mendley database. We suggest variations that improve classification accuracy. In this work for disease, Deep CNNs are implemented including ResNet-50, Mobilenet, Densenet121, EfficientnetB0 and the proposed approach. Over 99% accuracy was achieved in detecting various crop diseases.]]>
10.1504/IJGUC.2024.136725
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 65 - 74
Vinita
Suma Dawn
Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India ' Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
deep learning
crop disease detection
ResNet-50
Mobilenet
Densenet121
EfficientnetB0
image processing
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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A page weight-based replacement algorithm to enhance the performance of buffer management in flash memory
http://www.inderscience.com/link.php?id=136727
Flash memory is used in various electronic handheld devices such as laptops and PDAs as secondary storage because of its excellent performance, low energy consumption, compact size, high-access speed and resistance to shock with growing density and lowering prices. However, the intrinsic properties, such as no in-place update and asymmetric I/O operations, provide challenges to designing buffer replacement strategies. This paper suggests an improved buffer management strategy called the Page Weight Buffer Replacement (PWBR) algorithm for flash memory, which considers buffers' page weight. An eviction approach is applied which tries to minimise the number of write counts and maintain a higher buffer hit rate by integrating recency, operational cost and temporal locality. Our finding shows PWBR is superior to existing buffers' management policies in terms of increasing the hit ratio of LRU-WSR, CF-LRU, CCF-LRU and AD-LRU by 9.3%, 6.4%, 3.7% and 2.5% higher, respectively.
A page weight-based replacement algorithm to enhance the performance of buffer management in flash memory
Shweta; P.K. Singh
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 75 - 83
Flash memory is used in various electronic handheld devices such as laptops and PDAs as secondary storage because of its excellent performance, low energy consumption, compact size, high-access speed and resistance to shock with growing density and lowering prices. However, the intrinsic properties, such as no in-place update and asymmetric I/O operations, provide challenges to designing buffer replacement strategies. This paper suggests an improved buffer management strategy called the Page Weight Buffer Replacement (PWBR) algorithm for flash memory, which considers buffers' page weight. An eviction approach is applied which tries to minimise the number of write counts and maintain a higher buffer hit rate by integrating recency, operational cost and temporal locality. Our finding shows PWBR is superior to existing buffers' management policies in terms of increasing the hit ratio of LRU-WSR, CF-LRU, CCF-LRU and AD-LRU by 9.3%, 6.4%, 3.7% and 2.5% higher, respectively.]]>
10.1504/IJGUC.2024.136727
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 75 - 83
Shweta
P.K. Singh
Computer Science and Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India ' Computer Science and Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India
buffer replacement algorithm
frequency
recency
page migration
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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83
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Performance comparison of various machine learning classifiers using fusion of LBP, intensity and GLCM feature extraction techniques for thyroid nodules classification
http://www.inderscience.com/link.php?id=136708
Machine Learning (ML) and feature extraction techniques have shown a great potential in medical imaging field. This work presents an effective approach for the identification and classification of thyroid nodules. In the proposed model, various features are extracted using Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and intensity-based matrix. These features are fed to various ML classifiers like K-Nearest Neighbour (KNN), Decision-Tree (DT), Artificial Neural Network (ANN), Naïve Bayes, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Linear Regression (LR) and Support Vector Machine (SVM). From the result analysis, it can be observed that proposed Model-4 has performed better in comparison with the rest of seven proposed models with the reported literature. An improvement of 4% to 5% is seen in performance evaluation of model in comparison with reported literature.
Performance comparison of various machine learning classifiers using fusion of LBP, intensity and GLCM feature extraction techniques for thyroid nodules classification
Rajshree Srivastava; Pardeep Kumar
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 84 - 96
Machine Learning (ML) and feature extraction techniques have shown a great potential in medical imaging field. This work presents an effective approach for the identification and classification of thyroid nodules. In the proposed model, various features are extracted using Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and intensity-based matrix. These features are fed to various ML classifiers like K-Nearest Neighbour (KNN), Decision-Tree (DT), Artificial Neural Network (ANN), Naïve Bayes, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Linear Regression (LR) and Support Vector Machine (SVM). From the result analysis, it can be observed that proposed Model-4 has performed better in comparison with the rest of seven proposed models with the reported literature. An improvement of 4% to 5% is seen in performance evaluation of model in comparison with reported literature.]]>
10.1504/IJGUC.2024.136708
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 84 - 96
Rajshree Srivastava
Pardeep Kumar
Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India ' Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India
machine learning
LBP
GLCM
intensity
noise removal
feature extraction
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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96
2024-02-19T23:20:50-05:00
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Complex networks applied to the analysis of the dynamics of social systems
http://www.inderscience.com/link.php?id=136738
Social systems' inherent structure has an impact on how they act. The system's internal structure is determined by the parts that make it up and the connections that link them. The system's behaviour dynamics through time are determined by interactions among its components, and determining the internal structure of a social system in order to analyse its behaviour over time is a very complex process. The analysis of the system is complicated due to the numerous variables whose behaviour is challenging to quantify through functional relationships, which also contributes to the complexity of the network or graph that must be constructed to represent the system. The best data structure for modelling complex systems is a network or a directed graph, which can be used to analyse and study the dynamics of the system in order to make decisions.
Complex networks applied to the analysis of the dynamics of social systems
Abdulsattar A. Hamad; Muayyad Mahmood Khalil; Ahmed S. Al-Obeidi; Saad Fawzi Al-Azzawi; Lellis Thivagar
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 97 - 103
Social systems' inherent structure has an impact on how they act. The system's internal structure is determined by the parts that make it up and the connections that link them. The system's behaviour dynamics through time are determined by interactions among its components, and determining the internal structure of a social system in order to analyse its behaviour over time is a very complex process. The analysis of the system is complicated due to the numerous variables whose behaviour is challenging to quantify through functional relationships, which also contributes to the complexity of the network or graph that must be constructed to represent the system. The best data structure for modelling complex systems is a network or a directed graph, which can be used to analyse and study the dynamics of the system in order to make decisions.]]>
10.1504/IJGUC.2024.136738
International Journal of Grid and Utility Computing, Vol. 15, No. 1 (2024) pp. 97 - 103
Abdulsattar A. Hamad
Muayyad Mahmood Khalil
Ahmed S. Al-Obeidi
Saad Fawzi Al-Azzawi
Lellis Thivagar
College of Education, University of Samarra, Samarra, Iraq ' Department of Mathematics, College of Education for Pure Sciences, Tikrit University, Tikrit, Iraq ' Speciality of Mathematics, Gifted School of Nineveh, Directorate of Education (Mosul), Mosul, Iraq ' Department of Mathematics, College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq ' School of Mathematics, Madurai Kamaraj University, Madurai, Tamil Nadu, India
complex networks
social dynamics
Vensim PLE
decision making
2024-02-19T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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