Optimum reactive power dispatch (ORPD) significantly impacts the operation and control of electrical power systems (EPS) due to its undeniable benefit in the economic operation and reliability of the systems. ORPD is a sub-problem of optimal power flow (OPF). The main aim is to reduce/minimize the real power loss. Among the swarm intelligence (SI) metaheuristic algorithms is particle swarm optimization (PSO), which has fast convergence speed and gives the optimum solution to a particular problem by moving the swarm in the intensification (exploitation) search space. Also, the pathfinder algorithm (PFA) mimics the collective movement of the swarms with a leading member. Therefore, combining the fast convergence of PSO with PFA to form a hybrid technique is considered a viable approach in this study to avoid decreasing PFA searchability when the dimension of the problem increases. In this article, a hybrid algorithm based on a particle swarm optimization and pathfinder algorithm (HPSO-PFA) is proposed for the first time to study the combination of the control variables (generator voltage, transformer tap, and sizing of reactive compensation to obtain the objective function (total real power loss). The proposed method is tested on the IEEE 30 and 118 bus systems. The losses were reduced to 16.14262 MW and 107.2913 MW for the IEEE 30 and 118 test systems. Furthermore, the percentage (%) reduction for the IEEE 30 and 118 test systems are 9.8% and 19.25%, respectively. The result demonstrates the performance of HPSO-PFA gives a better solution than the other algorithms.
{"title":"Optimum Reactive Power Dispatch Solution using Hybrid Particle Swarm Optimization and Pathfinder Algorithm","authors":"S. A. Adegoke, Yanxia Sun","doi":"10.47839/ijc.21.4.2775","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2775","url":null,"abstract":"Optimum reactive power dispatch (ORPD) significantly impacts the operation and control of electrical power systems (EPS) due to its undeniable benefit in the economic operation and reliability of the systems. ORPD is a sub-problem of optimal power flow (OPF). The main aim is to reduce/minimize the real power loss. Among the swarm intelligence (SI) metaheuristic algorithms is particle swarm optimization (PSO), which has fast convergence speed and gives the optimum solution to a particular problem by moving the swarm in the intensification (exploitation) search space. Also, the pathfinder algorithm (PFA) mimics the collective movement of the swarms with a leading member. Therefore, combining the fast convergence of PSO with PFA to form a hybrid technique is considered a viable approach in this study to avoid decreasing PFA searchability when the dimension of the problem increases. In this article, a hybrid algorithm based on a particle swarm optimization and pathfinder algorithm (HPSO-PFA) is proposed for the first time to study the combination of the control variables (generator voltage, transformer tap, and sizing of reactive compensation to obtain the objective function (total real power loss). The proposed method is tested on the IEEE 30 and 118 bus systems. The losses were reduced to 16.14262 MW and 107.2913 MW for the IEEE 30 and 118 test systems. Furthermore, the percentage (%) reduction for the IEEE 30 and 118 test systems are 9.8% and 19.25%, respectively. The result demonstrates the performance of HPSO-PFA gives a better solution than the other algorithms.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80216013","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}
Over the last two decades, big data analytics has become a requirement in the research industry. Stream data mining is essential in many areas because data is generated in the form of streams in a wide variety of online applications. Along with the size and speed of the data stream, concept drift is a difficult issue to handle. This paper proposes an Enhanced Boosting-like Online Learning Ensemble Method based on a heuristic modification to the Boosting-like Online Learning Ensemble (BOLE). This algorithm has been improved by implementing a data instance that retains the previous state policy. During the boosting phase of this modified algorithm, the selection and voting strategy for an instance is advanced. Extensive experimental results on a variety of real-world and synthetic datasets show that the proposed method adequately addresses the drift detection problem. It has outperformed several state-of-the-art boosting-based ensembles dedicated to data stream mining (statistically). The proposed method improved overall accuracy by 1.30 percent to 14.45 percent when compared to other boosting-based ensembles on concept drifted datasets.
{"title":"An Enhanced Online Boosting Ensemble Classification Technique to Deal with Data Drift","authors":"R. Samant, S. Patil","doi":"10.47839/ijc.21.4.2778","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2778","url":null,"abstract":"Over the last two decades, big data analytics has become a requirement in the research industry. Stream data mining is essential in many areas because data is generated in the form of streams in a wide variety of online applications. Along with the size and speed of the data stream, concept drift is a difficult issue to handle. This paper proposes an Enhanced Boosting-like Online Learning Ensemble Method based on a heuristic modification to the Boosting-like Online Learning Ensemble (BOLE). This algorithm has been improved by implementing a data instance that retains the previous state policy. During the boosting phase of this modified algorithm, the selection and voting strategy for an instance is advanced. Extensive experimental results on a variety of real-world and synthetic datasets show that the proposed method adequately addresses the drift detection problem. It has outperformed several state-of-the-art boosting-based ensembles dedicated to data stream mining (statistically). The proposed method improved overall accuracy by 1.30 percent to 14.45 percent when compared to other boosting-based ensembles on concept drifted datasets.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82808649","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}
Ikenna Rene Chiadighikaobi, N. Katuk, Baharudin Osman
The Internet of Things (IoT) has become the fundamental infrastructure of many intelligent applications, such as smart homes. IoT applications store distributes various information, including user authentication information, over a public channel that exposes it to security threats and attacks. Therefore, this study intends to protect authentication data communication through a decentralised multi-factor user authentication scheme for secure IoT applications (DMUAS-IoT). The scheme is secure and enables efficient user registration, login and authentication, and the user profile updating process where legitimate users can access the IoT system resources. DMUAS-IoT adopted PRESENT for face image encryption and elliptic curve cryptography for data exchange. The scheme security was verified using ProVerif and AVISPA, and mutual authentication was checked with BAN-Logic. The results show that the scheme is secure against man-in-the-middle and impersonation attacks, provides mutual authentication and has a low computation cost. Hence, the outcomes of this study could help secure user authentication data from attacks on applications involved with IoT and resource constraint environments.
{"title":"DMUAS-IoT: A Decentralised Multi-Factor User Authentication Scheme for IoT Systems","authors":"Ikenna Rene Chiadighikaobi, N. Katuk, Baharudin Osman","doi":"10.47839/ijc.21.4.2777","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2777","url":null,"abstract":"The Internet of Things (IoT) has become the fundamental infrastructure of many intelligent applications, such as smart homes. IoT applications store distributes various information, including user authentication information, over a public channel that exposes it to security threats and attacks. Therefore, this study intends to protect authentication data communication through a decentralised multi-factor user authentication scheme for secure IoT applications (DMUAS-IoT). The scheme is secure and enables efficient user registration, login and authentication, and the user profile updating process where legitimate users can access the IoT system resources. DMUAS-IoT adopted PRESENT for face image encryption and elliptic curve cryptography for data exchange. The scheme security was verified using ProVerif and AVISPA, and mutual authentication was checked with BAN-Logic. The results show that the scheme is secure against man-in-the-middle and impersonation attacks, provides mutual authentication and has a low computation cost. Hence, the outcomes of this study could help secure user authentication data from attacks on applications involved with IoT and resource constraint environments.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89666747","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}
In this paper, a method of decreasing the redundancy of information flow by using recurrent properties of Galois code sequences is proposed. For this purpose, the service information is compiled and the priority compression is identified. The method is based on applying one of the adaptive algorithms (prediction first-order, interpolation zero-order, interpolation first-order) by comparing the efficiency of its use when applied to the selected fragments of a signal. It is shown that the developed method is effective for the quick-change signals when the structure and behavior of a signal change drastically. The efficiency of redundancy decreasing at the different sampling rate and the number of the significant samples is evaluated. This makes it possible to establish the limits of the positive effect for redundancy of information flows for the existing and developed methods. Experimental research is carried out for various permissible deviations with obtaining the number of the significant readings. A comparison of the obtained data with results of applying the existing methods in deep pumping installations proved that the proposed method is in 1.3 times more effective than existing ones.
{"title":"A Generalized Method of Decreasing Data Redundancy","authors":"Y. Iliash","doi":"10.47839/ijc.21.4.2786","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2786","url":null,"abstract":"In this paper, a method of decreasing the redundancy of information flow by using recurrent properties of Galois code sequences is proposed. For this purpose, the service information is compiled and the priority compression is identified. The method is based on applying one of the adaptive algorithms (prediction first-order, interpolation zero-order, interpolation first-order) by comparing the efficiency of its use when applied to the selected fragments of a signal. It is shown that the developed method is effective for the quick-change signals when the structure and behavior of a signal change drastically. The efficiency of redundancy decreasing at the different sampling rate and the number of the significant samples is evaluated. This makes it possible to establish the limits of the positive effect for redundancy of information flows for the existing and developed methods. Experimental research is carried out for various permissible deviations with obtaining the number of the significant readings. A comparison of the obtained data with results of applying the existing methods in deep pumping installations proved that the proposed method is in 1.3 times more effective than existing ones.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86915795","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}
This article presents a computational method for monitoring the energy consumption of technological systems with the assessment of their hidden energy losses caused by erroneous actions of personnel or equipment failures. Herewith, energy losses are calculated as the difference between the actual energy consumed and the minimum energy required to conduct the process in all operating modes. The minimum required energy is determined by the machine learning method based on stationary consumption precedents. Two approaches to the implementation of energy consumption monitoring with the assessment of hidden energy losses are considered – hardware and software. The hardware approach is based on the preliminary definition of normative, or minimum specific energy consumption in each technological mode. The software approach is based on the modeling of stationary areas of energy consumption in the form of precedents and their further analysis in the space of influential technological parameters. The paper notes the advantages and disadvantages of the proposed monitoring method, it is emphasized that the method is able to work with both linear and non-linear functions of energy dependence on the parameters of the technological process. It is noted in the paper that the advantage of the proposed method is the automated construction of the minimum energy function.
{"title":"Energy Consumption Monitoring with Evaluation of Hidden Energy Losses","authors":"B. Pleskach","doi":"10.47839/ijc.21.4.2784","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2784","url":null,"abstract":"This article presents a computational method for monitoring the energy consumption of technological systems with the assessment of their hidden energy losses caused by erroneous actions of personnel or equipment failures. Herewith, energy losses are calculated as the difference between the actual energy consumed and the minimum energy required to conduct the process in all operating modes. The minimum required energy is determined by the machine learning method based on stationary consumption precedents. Two approaches to the implementation of energy consumption monitoring with the assessment of hidden energy losses are considered – hardware and software. The hardware approach is based on the preliminary definition of normative, or minimum specific energy consumption in each technological mode. The software approach is based on the modeling of stationary areas of energy consumption in the form of precedents and their further analysis in the space of influential technological parameters. The paper notes the advantages and disadvantages of the proposed monitoring method, it is emphasized that the method is able to work with both linear and non-linear functions of energy dependence on the parameters of the technological process. It is noted in the paper that the advantage of the proposed method is the automated construction of the minimum energy function.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80531803","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}
IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.
{"title":"Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization","authors":"S. Balaji, S. Sankara Narayanan","doi":"10.47839/ijc.21.4.2781","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2781","url":null,"abstract":"IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89226732","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}
Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients
{"title":"Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image","authors":"Mino George, Anita Hadadi Bhimasena","doi":"10.47839/ijc.21.4.2780","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2780","url":null,"abstract":"Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85388489","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}
The problem of substantiating decisions made in the field of information security through estimates of the complexity of detecting types of DDOS attacks is considered. Estimates are a quantitative measure of a particular type of attack relative to normal network operation traffic data in its own feature space. Own space is represented by a set of informative features. To assess the complexity of detecting types of DDOS attacks, a measure of compactness by latent features on the numerical axis was used. The values of this measure were calculated as the product of intraclass similarity and interclass difference. It is shown that compactness in terms of latent features in its own space is higher than in the entire space. The values of latent features were calculated using the method of generalized estimates. According to this method, objects of normal traffic and a specific type of attack are considered as opposition to each other. An informative feature set is the result of an algorithm that uses the rules of hierarchical agglomerative grouping. At the first step, the feature with the maximum weight value is included in the set. The grouping rules apply the feature invariance property to the scales of their measurements. An analysis of the complexity of detection for 12 types of DDOS attacks is given. The power of sets of informative features ranged from 3 to 16.
{"title":"Estimates of the Complexity of Detecting Types of DDOS Attacks","authors":"N. Ignatev, E. Navruzov","doi":"10.47839/ijc.21.4.2779","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2779","url":null,"abstract":"The problem of substantiating decisions made in the field of information security through estimates of the complexity of detecting types of DDOS attacks is considered. Estimates are a quantitative measure of a particular type of attack relative to normal network operation traffic data in its own feature space. Own space is represented by a set of informative features. To assess the complexity of detecting types of DDOS attacks, a measure of compactness by latent features on the numerical axis was used. The values of this measure were calculated as the product of intraclass similarity and interclass difference. It is shown that compactness in terms of latent features in its own space is higher than in the entire space. The values of latent features were calculated using the method of generalized estimates. According to this method, objects of normal traffic and a specific type of attack are considered as opposition to each other. An informative feature set is the result of an algorithm that uses the rules of hierarchical agglomerative grouping. At the first step, the feature with the maximum weight value is included in the set. The grouping rules apply the feature invariance property to the scales of their measurements. An analysis of the complexity of detection for 12 types of DDOS attacks is given. The power of sets of informative features ranged from 3 to 16.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86156677","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}
Type 2 diabetes is a well-known lifelong condition disease that reduces the human body’s ability to produce insulin. This causes high blood sugar levels, which leads to different complications, including stroke, eye, cardiovascular, kidney, and nerve damage. Although diabetes has attained the attention of huge research, the classification performance of such medical problems utilizing techniques of machine learning is quite low, primarily due to the class imbalance and the presence of missing values in data. In this work, we proposed a model using binary Grey wolf optimization (GWO) and a Decision tree. The proposed model is composed of preprocessing, feature selection, and classification. In preprocessing, that is responsible for minority class oversampling and handling missing values. In the second step, binary GWO are used to select the most significant features. In the third step, the proposed model is trained using the Decision tree algorithm. The model achieved an accuracy of 83.11% when it was applied on the Pima Indian`s dataset.
{"title":"Diabetes Prediction Using Binary Grey Wolf Optimization and Decision Tree","authors":"Layla AL.hak","doi":"10.47839/ijc.21.4.2785","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2785","url":null,"abstract":"Type 2 diabetes is a well-known lifelong condition disease that reduces the human body’s ability to produce insulin. This causes high blood sugar levels, which leads to different complications, including stroke, eye, cardiovascular, kidney, and nerve damage. Although diabetes has attained the attention of huge research, the classification performance of such medical problems utilizing techniques of machine learning is quite low, primarily due to the class imbalance and the presence of missing values in data. In this work, we proposed a model using binary Grey wolf optimization (GWO) and a Decision tree. The proposed model is composed of preprocessing, feature selection, and classification. In preprocessing, that is responsible for minority class oversampling and handling missing values. In the second step, binary GWO are used to select the most significant features. In the third step, the proposed model is trained using the Decision tree algorithm. The model achieved an accuracy of 83.11% when it was applied on the Pima Indian`s dataset.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83071912","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}
In order to keep accessible, the patient care information recorded by a dental provider, a software architecture must be designed to allow availability among the different providers. In this research, a software architecture based on the Microservices approach is designed to enable the availability of dental medical records. The quality attributes and functional requirements were identified to design the architecture, determining that it should be composed of 4 Microservices, Patient, Dental Medical Record, Odontogram and Dental Service Provider; each microservice implements its database, the secure communication between the microservices and the clients is done through an API Gateway of HTTP resources and an authentication token. To evaluate the software architecture, a prototype was developed in which each component was deployed in containers using the Microsoft Azure App Service. On this prototype load tests were performed to evaluate Availability and Performance determining that up to 21 dental records per second can be available with 100% availability, and if the demand of requests increases the architecture scales automatically.
{"title":"A Microservice-based Software Architecture for Improving the Availability of Dental Health Records","authors":"Juan Arcila-Diaz, Carlos Valdivia","doi":"10.47839/ijc.21.4.2783","DOIUrl":"https://doi.org/10.47839/ijc.21.4.2783","url":null,"abstract":"In order to keep accessible, the patient care information recorded by a dental provider, a software architecture must be designed to allow availability among the different providers. \u0000In this research, a software architecture based on the Microservices approach is designed to enable the availability of dental medical records. The quality attributes and functional requirements were identified to design the architecture, determining that it should be composed of 4 Microservices, Patient, Dental Medical Record, Odontogram and Dental Service Provider; each microservice implements its database, the secure communication between the microservices and the clients is done through an API Gateway of HTTP resources and an authentication token. \u0000To evaluate the software architecture, a prototype was developed in which each component was deployed in containers using the Microsoft Azure App Service. On this prototype load tests were performed to evaluate Availability and Performance determining that up to 21 dental records per second can be available with 100% availability, and if the demand of requests increases the architecture scales automatically.","PeriodicalId":37669,"journal":{"name":"International Journal of Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81680050","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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