Pub Date : 2021-11-16DOI: 10.15837/ijccc.2021.6.4305
S. Imavathy, M. Chinnadurai
Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.
{"title":"Threshold based Support Vector Machine Learning Algorithm for Sequential Patterns","authors":"S. Imavathy, M. Chinnadurai","doi":"10.15837/ijccc.2021.6.4305","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4305","url":null,"abstract":"Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127692465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-16DOI: 10.15837/ijccc.2021.6.4291
M. Kamaladevi, V. Venkatraman
In recent years, imbalanced data classification are utilized in several domains including, detecting fraudulent activities in banking sector, disease prediction in healthcare sector and so on. To solve the Imbalanced classification problem at data level, strategy such as undersampling or oversampling are widely used. Sampling technique pose a challenge of significant information loss. The proposed method involves two processes namely, undersampling and classification. First, undersampling is performed by means of Tversky Similarity Indexive Regression model. Here, regression along with the Tversky similarity index is used in analyzing the relationship between two instances from the dataset. Next, Gaussian Kernelized Decision stump AdaBoosting is used for classifying the instances into two classes. Here, the root node in the Decision Stump takes a decision on the basis of the Gaussian Kernel function, considering average of neighboring points accordingly the results is obtained at the leaf node. Weights are also adjusted to minimizing the training errors occurring during classification to find the best classifier. Experimental assessment is performed with two different imbalanced dataset (Pima Indian diabetes and Hepatitis dataset). Various performance metrics such as precision, recall, AUC under ROC score and F1-score are compared with the existing undersampling methods. Experimental results showed that prediction accuracy of minority class has improved and therefore minimizing false positive and false negative.
{"title":"Tversky Similarity based UnderSampling with Gaussian Kernelized Decision Stump Adaboost Algorithm for Imbalanced Medical Data Classification","authors":"M. Kamaladevi, V. Venkatraman","doi":"10.15837/ijccc.2021.6.4291","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4291","url":null,"abstract":"In recent years, imbalanced data classification are utilized in several domains including, detecting fraudulent activities in banking sector, disease prediction in healthcare sector and so on. To solve the Imbalanced classification problem at data level, strategy such as undersampling or oversampling are widely used. Sampling technique pose a challenge of significant information loss. The proposed method involves two processes namely, undersampling and classification. First, undersampling is performed by means of Tversky Similarity Indexive Regression model. Here, regression along with the Tversky similarity index is used in analyzing the relationship between two instances from the dataset. Next, Gaussian Kernelized Decision stump AdaBoosting is used for classifying the instances into two classes. Here, the root node in the Decision Stump takes a decision on the basis of the Gaussian Kernel function, considering average of neighboring points accordingly the results is obtained at the leaf node. Weights are also adjusted to minimizing the training errors occurring during classification to find the best classifier. Experimental assessment is performed with two different imbalanced dataset (Pima Indian diabetes and Hepatitis dataset). Various performance metrics such as precision, recall, AUC under ROC score and F1-score are compared with the existing undersampling methods. Experimental results showed that prediction accuracy of minority class has improved and therefore minimizing false positive and false negative.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129918621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-09DOI: 10.1016/j.ifacol.2019.11.311
I. Dumitrache, S. Caramihai, D. C. Popescu, M. Moisescu, I. Sacala
{"title":"Neuro-inspired Framework for Cognitive Manufacturing Control","authors":"I. Dumitrache, S. Caramihai, D. C. Popescu, M. Moisescu, I. Sacala","doi":"10.1016/j.ifacol.2019.11.311","DOIUrl":"https://doi.org/10.1016/j.ifacol.2019.11.311","url":null,"abstract":"","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-09DOI: 10.15837/ijccc.2021.6.4251
Francisco José Pérez, Alberto García, Victor Garrido, M. Esteve, Marcelo Zambrano
Nowadays, the free movement of people and goods within the European Union is one of the topical issues. Each member state and border practitioner exploits its own set of assets in their goal of border surveillance and control. States have invested significantly in these assets and infrastructures necessary to manage and control the transit in the border areas. As new capabilities and assets become available and as current Command and Control (C2) systems become older, border control practitioners are faced with the increasing challenge of how to integrate new assets, command and control all of them in a coordinated and coherent way without having to invest in a completely new C2 systems built from the ground up. Therefore, and bearing in mind that the systems already developed up to date are very old and are not framed in a global standard data model, it has been identified, on one side the need to define a platform that allows to interact with multiple UxVs (land, sea and air), and on the other, unify all data models so that it can globalize and generate a much more concise analysis of what happens in places of conflict.
{"title":"C2 Advanced Multi-domain Environment and Live Observation Technologies","authors":"Francisco José Pérez, Alberto García, Victor Garrido, M. Esteve, Marcelo Zambrano","doi":"10.15837/ijccc.2021.6.4251","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4251","url":null,"abstract":"\u0000 \u0000 \u0000Nowadays, the free movement of people and goods within the European Union is one of the topical issues. Each member state and border practitioner exploits its own set of assets in their goal of border surveillance and control. States have invested significantly in these assets and infrastructures necessary to manage and control the transit in the border areas. As new capabilities and assets become available and as current Command and Control (C2) systems become older, border control practitioners are faced with the increasing challenge of how to integrate new assets, command and control all of them in a coordinated and coherent way without having to invest in a completely new C2 systems built from the ground up. Therefore, and bearing in mind that the systems already developed up to date are very old and are not framed in a global standard data model, it has been identified, on one side the need to define a platform that allows to interact with multiple UxVs (land, sea and air), and on the other, unify all data models so that it can globalize and generate a much more concise analysis of what happens in places of conflict. \u0000 \u0000 \u0000","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"35 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130729853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-04DOI: 10.15837/ijccc.2021.5.4308
A. K. Alazzawi, H. Rais, S. Basri, Y. A. Alsariera, Luiz Fernando Capretz, A. Balogun, A. A. Imam
Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution.
{"title":"HABCSm: A Hamming Based t-way Strategy Based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation","authors":"A. K. Alazzawi, H. Rais, S. Basri, Y. A. Alsariera, Luiz Fernando Capretz, A. Balogun, A. A. Imam","doi":"10.15837/ijccc.2021.5.4308","DOIUrl":"https://doi.org/10.15837/ijccc.2021.5.4308","url":null,"abstract":"Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129900171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-04DOI: 10.15837/ijccc.2021.5.4087
Mohammed Yousuf Uddin, H. A. Abdeljaber, T. Ahanger
Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.
{"title":"Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence","authors":"Mohammed Yousuf Uddin, H. A. Abdeljaber, T. Ahanger","doi":"10.15837/ijccc.2021.5.4087","DOIUrl":"https://doi.org/10.15837/ijccc.2021.5.4087","url":null,"abstract":"Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133982526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.15837/ijccc.2021.5.4256
Monia Hamdi, H. Bouhamed, A. Algarni, H. Elmannai, S. Meshoul
For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs.
{"title":"Deep Learning and Uniform LBP Histograms for Position Recognition of Elderly People with Privacy Preservation","authors":"Monia Hamdi, H. Bouhamed, A. Algarni, H. Elmannai, S. Meshoul","doi":"10.15837/ijccc.2021.5.4256","DOIUrl":"https://doi.org/10.15837/ijccc.2021.5.4256","url":null,"abstract":"For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124789662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.15837/ijccc.2021.5.4260
I. Muntean, G. Mois, S. Folea
The accelerated pace of urbanization is having a major impact over the world’s environment. Although urban dwellers have higher living standards and can access better public services as compared to their rural counterparts, they are usually exposed to poor environmental conditions such as air pollution and noise. In order for municipalities and citizens to mitigate the negative effects of pollution, the monitoring of certain parameters, such as air quality and ambient sound levels, both in indoor and outdoor locations, has to be performed. The current paper presents a complete solution that allows the monitoring of ambient parameters such as Volatile Organic Compounds, temperature, relative humidity, pressure, and sound intensity levels both in indoor and outdoor spaces. The presented solution comprises of low-cost, easy to deploy, wireless sensors and a cloud application for their management and for storing and visualizing the recorded data.
{"title":"Development and Analysis of Low-Cost IoT Sensors for Urban Environmental Monitoring","authors":"I. Muntean, G. Mois, S. Folea","doi":"10.15837/ijccc.2021.5.4260","DOIUrl":"https://doi.org/10.15837/ijccc.2021.5.4260","url":null,"abstract":"The accelerated pace of urbanization is having a major impact over the world’s environment. Although urban dwellers have higher living standards and can access better public services as compared to their rural counterparts, they are usually exposed to poor environmental conditions such as air pollution and noise. In order for municipalities and citizens to mitigate the negative effects of pollution, the monitoring of certain parameters, such as air quality and ambient sound levels, both in indoor and outdoor locations, has to be performed. The current paper presents a complete solution that allows the monitoring of ambient parameters such as Volatile Organic Compounds, temperature, relative humidity, pressure, and sound intensity levels both in indoor and outdoor spaces. The presented solution comprises of low-cost, easy to deploy, wireless sensors and a cloud application for their management and for storing and visualizing the recorded data.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129418929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-14DOI: 10.15837/ijccc.2021.5.4274
M. Marufuzzaman, Muneed Anjum Timu, Jubayer Sarkar, Aminul Islam, L. F. Rahman, L. Sidek
High-level architecture (HLA) and Distributed Interactive Simulation (DIS) are commonly used for the distributed system. However, HLA suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. Efficient load balancing can minimize the simulation time of HLA and this optimization can be done using the multi-objective evolutionary algorithms (MOEA). Multi-Objective Particle Swarm Optimization (MOPSO) based on crowding distance (CD) is a popular MOEA method used to balance HLA load. In this research, the efficiency of MOPSO-CD is further improved by introducing the passive congregation (PC) method. Several simulation tests are done on this improved MOPSO-CD-PC method and the results showed that in terms of Coverage, Spacing, Non-dominated solutions and Inverted generational distance metrics, the MOPSO-CD-PC performed better than the previous MOPSO-CD algorithm. Hence, it can be a useful tool to optimize the load balancing problem in HLA.
{"title":"Multi Objective PSO with Passive Congregation for Load Balancing Problem","authors":"M. Marufuzzaman, Muneed Anjum Timu, Jubayer Sarkar, Aminul Islam, L. F. Rahman, L. Sidek","doi":"10.15837/ijccc.2021.5.4274","DOIUrl":"https://doi.org/10.15837/ijccc.2021.5.4274","url":null,"abstract":"High-level architecture (HLA) and Distributed Interactive Simulation (DIS) are commonly used for the distributed system. However, HLA suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. Efficient load balancing can minimize the simulation time of HLA and this optimization can be done using the multi-objective evolutionary algorithms (MOEA). Multi-Objective Particle Swarm Optimization (MOPSO) based on crowding distance (CD) is a popular MOEA method used to balance HLA load. In this research, the efficiency of MOPSO-CD is further improved by introducing the passive congregation (PC) method. Several simulation tests are done on this improved MOPSO-CD-PC method and the results showed that in terms of Coverage, Spacing, Non-dominated solutions and Inverted generational distance metrics, the MOPSO-CD-PC performed better than the previous MOPSO-CD algorithm. Hence, it can be a useful tool to optimize the load balancing problem in HLA.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114465627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-03DOI: 10.15837/ijccc.2021.5.4266
Y. Shakan, B. Kumalakov, G. Mutanov, Z. Mamykova, Yerlan Kistaubayev
Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.
{"title":"Verification of University Student and Graduate Data using Blockchain Technology","authors":"Y. Shakan, B. Kumalakov, G. Mutanov, Z. Mamykova, Yerlan Kistaubayev","doi":"10.15837/ijccc.2021.5.4266","DOIUrl":"https://doi.org/10.15837/ijccc.2021.5.4266","url":null,"abstract":"Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127508334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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