Multi Query Optimization Algorithm Using Semantic and Heuristic Approaches

Muhammad, L. J.; Zakariyau, Yahaya Bala; Ali, Abdullahi; Mohammed, I. A.

doi:10.14257/ijdta.2016.9.6.22

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…To remove noise from the dataset, the pruning method process is used for the final construction of the tree when fully grown [11,25]. The decision tree algorithm has been used as one of the most effective learning algorithms due to its ability to handle all types of data (continuous types and detailed data), comprehension and simplicity [27].…”

Section: Decision Tree Algorithmmentioning

confidence: 99%

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

et al. 2020

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COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

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Section: Decision Tree Algorithmmentioning

confidence: 99%

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

et al. 2020

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“…These deadline parameters were not explicitly available from the entire traces files. A Monte Carlo distribution method [23,24,26] was used to generate deadline parameters from the traces files. Since all the traces were processed in the real-time systems in a real grid environment, it is therefore to have the assumption that the execution time period should be more than the job completion deadline i.e.…”

Section: Proposed Green Incentive-based Job Scheduling Algorithmmentioning

confidence: 99%

“…The alternative is by thermal-aware monitoring and profiling [30]. Thermal-aware monitoring acts as the thermal eye for the scheduling process and consists of techniques to record and evaluate the heat distribution in DC [26,30]. Thermal profiling is done by keeping a record of the characteristics of heat emission from the servers, the microprocessors, and the computational workload.…”

Section: Introductionmentioning

confidence: 99%

Incentive-Scheduling Algorithms to Provide Green Computational Data Center

Haruna

Jung²,

Arputharaj

et al. 2021

SN COMPUT. SCI.

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The increased computational loads in grid servers are dissipating more heat to eventually amplifies the cooling demand in the data center (DC). This can lead to more submitted jobs missing their job completion deadlines. Unfortunately, the conventional DC job scheduling approaches do not provide compensation to the resource users on their submitted jobs that missed the deadlines. The absence of compensation may dissuade users from submitting jobs to the DCs. While the free aircooling strategy is used elsewhere, it is not generally applicable in tropical countries such as Malaysia. A constant artificial cooling (air conditioning) is needed to sustain the DC long-hour operation. To solve this issue in the tropical region, green incentive-scheduling algorithms were devised to significantly save the cooling electricity consumption cost in DC and to be able to compensate users (as an incentive) for their submitted jobs that missed the job completion deadline(s). However, there is a need to further analyse the performance of the proposed green incentive-scheduling algorithms using a different benchmark traces file and to conduct a cost-saving comparison analysis between the green incentive-scheduling and the nongreen incentive-scheduling algorithms. Therefore, this paper's authors observed and analysed the performance comparison of the proposed green incentive-based job scheduling algorithms using a benchmark traces file and conducted a cost-saving comparison analysis between the green incentive and the non-green incentive-based scheduling algorithms. Ultimately, the green incentive-scheduling algorithms are to show their impact on reducing the DC operation costs (e.g. electricity bills for cooling) and to provide fair compensation to grid users for a win-win situation. Keywords Data center • Green computing • Incentive-based job scheduling algorithm • Server • TemperatureThis article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar.

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“…In Nigeria, CAD is at the moment gaining much popularity following the rising number of health issues related to the disease, including higher death rate, which is mostly due to lack of proper awareness among the common people [38]. According to the latest WHO data published in May 2014 Coronary Artery Disease Deaths in Nigeria reached 53,836 or 2.82% of total deaths.…”

Section: Introductionmentioning

confidence: 99%

Expert system for diagnosis of coronary artery disease: A survey

Atomsa¹,

LJ²,

FS³

et al. 2021

J Clin Images Med Case Rep

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Coronary Artery Disease (CAD) is one of the most dangerous diseases which lead to sudden cardiac death. The diagnosis of CAD is very expensive and time consuming which made computer scientists to use artificial techniques such as expert system to diagnose CAD’ patients. This study presents a state-of-the-art of the methods and techniques used for the development of expert system to diagnose CAD by scholars. The study found fuzzy logic as the most frequently used and successful technique for the development of the expert system to diagnose CAD. While data mining techniques found to be the second and neural network as third most frequently used and successful technique for the development of the expert system to diagnose CAD. The study further found that, 40% of the studies reviewed to use hybrid approach where fuzzy logic and data mining techniques were used in the development of the expert system for diagnosis of CAD. Keywords: expert system; artificial intelligence; fuzzy logic; data mining; coronary artery disease.

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Multi Query Optimization Algorithm Using Semantic and Heuristic Approaches

Abstract: Multi Query Optimization is one of the most important tasks in Relational Database

Cited by 5 publications

References 6 publications

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

Incentive-Scheduling Algorithms to Provide Green Computational Data Center

Expert system for diagnosis of coronary artery disease: A survey

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