The imbalanced nature of the software datasets leads to the biased learning of prediction model toward the observations of the majority class (non-defective class). The prediction model can produce poor results for the minority class observations. Such misappropriations can prove costly especially in software development where minority class (defective) is the one that has the highest interest from the learning point of view. Various approaches have been used for dealing with class imbalance problem of software defect prediction but no one dominates and hence developing a generalized software defect prediction model for imbalanced datasets remains problematic. This paper surveys existing approaches for handling class imbalance problem of software defect datasets. In this survey, most relevant software defect prediction studies and identified the two main approaches that have been used for handling imbalance issue of software defect datasets. Furthermore, we also provide some comparison of findings in state-of-the-art literature and the guidelines for carrying out future research.
{"title":"A Survey of Different Approaches for the Class Imbalance Problem in Software Defect Prediction","authors":"Abdullah Dar, Sheikh Umar Farooq","doi":"10.4018/ijssci.301268","DOIUrl":"https://doi.org/10.4018/ijssci.301268","url":null,"abstract":"The imbalanced nature of the software datasets leads to the biased learning of prediction model toward the observations of the majority class (non-defective class). The prediction model can produce poor results for the minority class observations. Such misappropriations can prove costly especially in software development where minority class (defective) is the one that has the highest interest from the learning point of view. Various approaches have been used for dealing with class imbalance problem of software defect prediction but no one dominates and hence developing a generalized software defect prediction model for imbalanced datasets remains problematic. This paper surveys existing approaches for handling class imbalance problem of software defect datasets. In this survey, most relevant software defect prediction studies and identified the two main approaches that have been used for handling imbalance issue of software defect datasets. Furthermore, we also provide some comparison of findings in state-of-the-art literature and the guidelines for carrying out future research.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449957","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}
Aiming at the problem of traditional portrait background similarity retrieval methods being low accuracy and time-consuming, a similarity retrieval method based on image background analysis is presented. The proposed method uses a combination of portrait segmentation and retrieval models. Firstly, the portrait segmentation model is used to remove the portraits in the images to eliminate the interference of portraits on background features; secondly, the image retrieval model is used to retrieve images with similar background features; LSH is added to improve the retrieval efficiency; finally, the retrieval results are used to further determine whether the background is similar. The experiment is implemented based on real data from a company. The results showed that the average precision, average map, and recall of this method reached 85%, 90%, and 50%, respectively. The average accuracy and recall are 10% better than the overall image retrieval model.
{"title":"Similarity Retrieval Based on Image Background Analysis","authors":"Chang Zhu, Wenchao Jiang, Weilin Zhou, Hong Xiao","doi":"10.4018/ijssci.309426","DOIUrl":"https://doi.org/10.4018/ijssci.309426","url":null,"abstract":"Aiming at the problem of traditional portrait background similarity retrieval methods being low accuracy and time-consuming, a similarity retrieval method based on image background analysis is presented. The proposed method uses a combination of portrait segmentation and retrieval models. Firstly, the portrait segmentation model is used to remove the portraits in the images to eliminate the interference of portraits on background features; secondly, the image retrieval model is used to retrieve images with similar background features; LSH is added to improve the retrieval efficiency; finally, the retrieval results are used to further determine whether the background is similar. The experiment is implemented based on real data from a company. The results showed that the average precision, average map, and recall of this method reached 85%, 90%, and 50%, respectively. The average accuracy and recall are 10% better than the overall image retrieval model.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130344808","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 goal of this article is to assess the most recent work on Industry 4.0 as well as the present state of science on Industry 4.0 through papers produced between January 2017 and March 2020.A systematic review process with a 5-step approach to article selection was employed, which included the following steps: 1) Selection of database 2) Research of keyword 3) Collection of articles 4) Inclusion/Exclusion criteria 5) Examining Selected Articles. It is noticed that much of the research is philosophical or case-based in character. The prospective study direction described in this paper may be useful to researchers interested in the field of industry 4.0 for research. The paper's future study directions must undoubtedly be beneficial to researchers.
{"title":"Progressive Study and Investigation of Machine Learning Techniques to Enhance the Efficiency and Effectiveness of Industry 4.0","authors":"Kaljot Sharma, D. Anand, K. Mishra, S. Harit","doi":"10.4018/ijssci.300365","DOIUrl":"https://doi.org/10.4018/ijssci.300365","url":null,"abstract":"The goal of this article is to assess the most recent work on Industry 4.0 as well as the present state of science on Industry 4.0 through papers produced between January 2017 and March 2020.A systematic review process with a 5-step approach to article selection was employed, which included the following steps: 1) Selection of database 2) Research of keyword 3) Collection of articles 4) Inclusion/Exclusion criteria 5) Examining Selected Articles. It is noticed that much of the research is philosophical or case-based in character. The prospective study direction described in this paper may be useful to researchers interested in the field of industry 4.0 for research. The paper's future study directions must undoubtedly be beneficial to researchers.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123354083","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}
Computation of an optimal coverage and connectivity aware wake-up schedule of sensor nodes is a fundamental research issue in a 3D-Wireless Sensor Networks. Most of the existing metaheuristic-based wake-up scheduling schemes do not make sure optimal solution and occasionally smacked in local minima. This paper propose a hybrid metaheuristic-based wake-up scheduling scheme (Memtic-Tabu-based-WS) where best feature of memtic algorithm and Tabu Search algorithm is combined. The proposed scheme has considered four parameters such as energy consumption, coverage, connectivity, and optimal size of schedule list. Performance comparison of the proposed Memtic-Tabu-based-WS scheme is performed in different network scenario and compared with three well-known state-of-art schemes in terms of coverage ratio, active sensor nodes and fitness value. The result analysis validate the superiority of the proposed scheme over the existing schemes with better coverage ratio and derivation of the optimal wake-up schedule.
{"title":"Optimization of the Wake-Up Scheduling Using a Hybrid of Memetic and Tabu Search Algorithms for 3D-Wireless Sensor Networks","authors":"V. Chawra, Govind P. Gupta","doi":"10.4018/ijssci.300359","DOIUrl":"https://doi.org/10.4018/ijssci.300359","url":null,"abstract":"Computation of an optimal coverage and connectivity aware wake-up schedule of sensor nodes is a fundamental research issue in a 3D-Wireless Sensor Networks. Most of the existing metaheuristic-based wake-up scheduling schemes do not make sure optimal solution and occasionally smacked in local minima. This paper propose a hybrid metaheuristic-based wake-up scheduling scheme (Memtic-Tabu-based-WS) where best feature of memtic algorithm and Tabu Search algorithm is combined. The proposed scheme has considered four parameters such as energy consumption, coverage, connectivity, and optimal size of schedule list. Performance comparison of the proposed Memtic-Tabu-based-WS scheme is performed in different network scenario and compared with three well-known state-of-art schemes in terms of coverage ratio, active sensor nodes and fitness value. The result analysis validate the superiority of the proposed scheme over the existing schemes with better coverage ratio and derivation of the optimal wake-up schedule.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"47 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124974051","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 : 2021-07-01DOI: 10.4018/IJSSCI.2021070105
D. Pashchenko
The internal transformation and using the fully remote software development under the influence of the pandemic has not only changed the industry, but heralded the construction of a new reality. This article presents the results of a study that covered the experience of transformation of 26 project teams from the world's leading IT corporations, software vendors, and high-tech companies with strong internal development practices: Alphabet, Amazon, BSC Group, Custis, Deutsche Bank, Evernote, Exness, Positive Technologies, PromSvyazBank, Sberbank, VTB, Yandex. Experts determined the results of rapid adaptation to changes, considered the medium-term impact of the pandemic factor on work processes, and made forecasts for 2021. The results of the study are accompanied by brief comments and recommendations of the author, the main idea of which is the need to quickly understand a new trend in software development, hiring specialists, and organizing teams associated with the refusal of high-tech IT companies to return to teamwork in shared offices.
{"title":"Fully Remote Software Development Due to COVID Factor: Results of Industry Research (2020)","authors":"D. Pashchenko","doi":"10.4018/IJSSCI.2021070105","DOIUrl":"https://doi.org/10.4018/IJSSCI.2021070105","url":null,"abstract":"The internal transformation and using the fully remote software development under the influence of the pandemic has not only changed the industry, but heralded the construction of a new reality. This article presents the results of a study that covered the experience of transformation of 26 project teams from the world's leading IT corporations, software vendors, and high-tech companies with strong internal development practices: Alphabet, Amazon, BSC Group, Custis, Deutsche Bank, Evernote, Exness, Positive Technologies, PromSvyazBank, Sberbank, VTB, Yandex. Experts determined the results of rapid adaptation to changes, considered the medium-term impact of the pandemic factor on work processes, and made forecasts for 2021. The results of the study are accompanied by brief comments and recommendations of the author, the main idea of which is the need to quickly understand a new trend in software development, hiring specialists, and organizing teams associated with the refusal of high-tech IT companies to return to teamwork in shared offices.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130003618","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 : 2021-07-01DOI: 10.4018/IJSSCI.2021070102
Sidi Mohammed Kaddour, M. Lehsaini
Nowadays, detecting abnormal power consumption behavior of householders has become a big concern in the smart energy field; overcoming this limitation will help in identifying efficient solutions to reduce power consumption. This paper proposes a new methodology for detecting abnormal energy consumption in residential buildings based on hourly readings of energy consumption and peak energy consumption. The proposition is implemented using three unsupervised outlier detection methods (isolation forest, one-class SVM, and k-means). The authors propose this solution to help residents in reducing operating costs by detecting consumption failures that cannot be detected easily. On the other hand, energy providers will have the access to detailed data about anomalies, faulty appliances, and houses with poor power control strategy in general, which will help in pinpointing overconsumption problems, thus enhancing human awareness and reducing energy consumption.
{"title":"Electricity Consumption Data Analysis Using Various Outlier Detection Methods","authors":"Sidi Mohammed Kaddour, M. Lehsaini","doi":"10.4018/IJSSCI.2021070102","DOIUrl":"https://doi.org/10.4018/IJSSCI.2021070102","url":null,"abstract":"Nowadays, detecting abnormal power consumption behavior of householders has become a big concern in the smart energy field; overcoming this limitation will help in identifying efficient solutions to reduce power consumption. This paper proposes a new methodology for detecting abnormal energy consumption in residential buildings based on hourly readings of energy consumption and peak energy consumption. The proposition is implemented using three unsupervised outlier detection methods (isolation forest, one-class SVM, and k-means). The authors propose this solution to help residents in reducing operating costs by detecting consumption failures that cannot be detected easily. On the other hand, energy providers will have the access to detailed data about anomalies, faulty appliances, and houses with poor power control strategy in general, which will help in pinpointing overconsumption problems, thus enhancing human awareness and reducing energy consumption.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130924503","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 : 2021-07-01DOI: 10.4018/IJSSCI.2021070101
Hadj Ahmed Bouarara
A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behaviour in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia. The authors have adapted the recurrent neural network (RNN) in order to prevent the situations of threats, suicide, loneliness, or any other form of psychological problem through the analysis of tweets. The obtained results were validated by different experimental measures such as f-measure, recall, precision, entropy, accuracy. The RNN gives best results with 85% of accuracy compared to other techniques in literature such as social cockroaches, decision tree, and naïve Bayes.
{"title":"Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media","authors":"Hadj Ahmed Bouarara","doi":"10.4018/IJSSCI.2021070101","DOIUrl":"https://doi.org/10.4018/IJSSCI.2021070101","url":null,"abstract":"A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behaviour in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia. The authors have adapted the recurrent neural network (RNN) in order to prevent the situations of threats, suicide, loneliness, or any other form of psychological problem through the analysis of tweets. The obtained results were validated by different experimental measures such as f-measure, recall, precision, entropy, accuracy. The RNN gives best results with 85% of accuracy compared to other techniques in literature such as social cockroaches, decision tree, and naïve Bayes.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904818","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 : 2021-07-01DOI: 10.4018/IJSSCI.2021070104
Houcine Matallah, Ghalem Belalem, K. Bouamrane
NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.
{"title":"Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database","authors":"Houcine Matallah, Ghalem Belalem, K. Bouamrane","doi":"10.4018/IJSSCI.2021070104","DOIUrl":"https://doi.org/10.4018/IJSSCI.2021070104","url":null,"abstract":"NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133453277","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 : 2021-07-01DOI: 10.4018/IJSSCI.2021070103
Wasiur Rhmann
Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-based modeling (FRSBM-R), symbolic fuzzy learning based on genetic programming (GFS-GP-R), symbolic fuzzy learning based on genetic programming grammar operators and simulated annealing (GFS_GSP_R), and least mean squares linear regression (LinearLMS_R)—are used to create an ensemble (EHSBA). The EHSBA is compared with machine learning-based ensemble bagging, vote, and stacking on datasets obtained from PROMISE repository. Obtained results reported that EHSBA outperformed all other techniques.
{"title":"An Ensemble of Hybrid Search-Based Algorithms for Software Effort Prediction","authors":"Wasiur Rhmann","doi":"10.4018/IJSSCI.2021070103","DOIUrl":"https://doi.org/10.4018/IJSSCI.2021070103","url":null,"abstract":"Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-based modeling (FRSBM-R), symbolic fuzzy learning based on genetic programming (GFS-GP-R), symbolic fuzzy learning based on genetic programming grammar operators and simulated annealing (GFS_GSP_R), and least mean squares linear regression (LinearLMS_R)—are used to create an ensemble (EHSBA). The EHSBA is compared with machine learning-based ensemble bagging, vote, and stacking on datasets obtained from PROMISE repository. Obtained results reported that EHSBA outperformed all other techniques.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114389644","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 : 2021-01-01DOI: 10.4018/ijssci.2021010103
Archana Singh, R. Kumar
Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.
{"title":"A Two-Phase Load Balancing Algorithm for Cloud Environment","authors":"Archana Singh, R. Kumar","doi":"10.4018/ijssci.2021010103","DOIUrl":"https://doi.org/10.4018/ijssci.2021010103","url":null,"abstract":"Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129231558","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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