Automatic sound synthesis using the fly algorithm

Abbood, Zainab Ali; Alshawi, Imad S.; Alhijaj, Asaad A.; Vidal, Franck

doi:10.12928/telkomnika.v18i5.15665

Cited by 2 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…Such models are called classifiers which predict categorical (discrete, unordered) class labels [6], [7]. Many classification methods have been proposed by researchers in ML, like the DT classifier [8], [9] and Neural Network classifier [10], [11]. In this paper, the researchers propose to create e-government from three government departments (Military, Social Welfare, and Statistics_ planning) by applying ML for five classifiers (SVM, DT, KNN, RF, and NB), using the ECR data, and choosing the algorithm with the highest accuracy for all government departments.…”

Section: Introductionmentioning

confidence: 99%

Big Data in E-government: Classification and Prediction using Machine Learning Algorithms

Altamimi¹

2022

IJICI

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Many countries have used big data to develop their institutions, such as Estonia in policing, India in health care, and the development of agriculture in the United Kingdom, etc. Data is very important as it is no longer oil that is the most valuable resource in the world, but data. This research examines ways to develop the Iraqi state institutions by using the big data of one of its institutions (electronic civil registry) (ECR) using the mining and analysis of this data. The pre-processing and analysis of this data are carried out depending on the needs of each institution and then using Machine Learning (ML) techniques. Its use has shown remarkable results in many areas, especially in data analysis, classification and forecasting. We applied five ML algorithms that are Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Naive Bayes (NB) carried out in the Orange Data Mining tool. According to the simulation results of the proposed system, the accuracy of the classifications was around 100%, 99%, and 100% for the military department by the SVM classifier, the social welfare department by the RF classifier, and the statistics-planning department by the SVM classifier, respectively.

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Section: Introductionmentioning

confidence: 99%

Big Data in E-government: Classification and Prediction using Machine Learning Algorithms

Altamimi¹

2022

IJICI

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“…Such models are called classifiers which predict categorical (discrete, unordered) class labels [6,7]. Many classification methods have been proposed by researchers in ML, like the Decision Tree (DT) classifier [8,9] and Neural Network (NN) classifier [10,11]. In this paper, the researchers propose to create an e-government project to join the National ID with three government departments (Military, Social Welfare, and Statistics_ Planning).…”

Section: Introductionmentioning

confidence: 99%

Big Data Framework Classification for Public E-Governance Using Machine Learning Techniques

Altamimi

Aljabery

Alshawi

2022

Basrah Researches Sciences

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Using Machine Learning (ML) in many fields has shown remarkable results, especially in government data analysis, classification, and prediction. This technology has been applied to the National ID data (Electronic Civil Registry) (ECR). It is used in analyzing this data and creating an e-government project to join the National ID with three government departments (Military, Social Welfare, and Statistics_ Planning). The proposed system works in two parts: Online and Offline at the same time; based on five (ML) algorithms: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Naive Bayes (NB). The system offline part applies the stages of pre-processing and classification to the ECR and then predicts what government departments need in the online part. The system chooses the best classification algorithm, which shows perfect results for each government department when online communication is made between the department and the national ID. According to the simulation results of the proposed system, the accuracy of the classifications is around 100%, 99%, and 100% for the military department by the SVM classifier, the social welfare department by the RF classifier, and the statistics-planning department by the SVM classifier, respectively.

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Automatic sound synthesis using the fly algorithm

Cited by 2 publications

References 14 publications

Big Data in E-government: Classification and Prediction using Machine Learning Algorithms

Big Data in E-government: Classification and Prediction using Machine Learning Algorithms

Big Data Framework Classification for Public E-Governance Using Machine Learning Techniques

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