Evaluating e-Government Services in Kurdistan Institution for Strategic Studies and Scientific Research Using the EGOVSAT Model

Hassan, Bryar A.; Ahmed, Aram Mahmood; Saeed, Soran; Saeed, Awin A.

doi:10.24017/science.2016.1.2.2

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…This study supports previous research by Herlina et al [10], which shows that Customization has no significant effect on user satisfaction. On the other hand, this study does not support previous research conducted by Horan and Abhichandani [3], Alias et al [5] and Ahmed et al [4], who found that Customization had a significant positive effect on user satisfaction.…”

Section: Discussioncontrasting

confidence: 99%

“…It means that the higher the efficiency, the higher the user satisfaction with the Malang City Online Tax Application. This study supports the results of research conducted by Horan and Abhichandani [3], Alias et al [5] and Ahmed et al [4].…”

Section: Discussionsupporting

confidence: 92%

“…The results of this study indicate that users feel that the Malang City Online Regional Tax Application is easy to use and can run well, so users feel satisfied. This study also succeeded in supporting previous research conducted by Horan and Abhichandani [3], Alias et al [5] and Ahmed et al [4].…”

Section: Discussionsupporting

confidence: 89%

“…This study supports Horan and Abhichandani [3] because the flexibility variable does not affect user satisfaction. On the other hand, this study cannot support the research conducted by Alias et al [5] and Ahmed et al [4]. They found that flexibility had a significant positive effect on user satisfaction.…”

Section: Discussioncontrasting

confidence: 88%

“…Reliability is essential to user satisfaction with an e-government service [2]. The research of Ahmed et al [4] shows that reliability affects user satisfaction with e-government services. Efficiency" construct examines the accessibility and organization of the features and information available on the website.…”

Section: Introductionmentioning

confidence: 99%

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Determinants of User Satisfaction with Local Tax Online Applications with Modifications of the EGovsat Approach

Ari,

Arifin

2023

Advances in Economics, Business and Management Research

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The online local tax application is one of the e-government owned by the Malang city government. This online local tax application was created to make it easier for taxpayers to pay local taxes. The Government of Malang City has socialized the use of local tax applications. However, there are still some taxpayers who pay taxes manually. User satisfaction is one of the success factors of an information system. Therefore, this study aimed to determine the determinants of user satisfaction with online tax applications using modifications of the EGovsat approach. The variables used in this study refer to the EGovsat approach. So that the variables in this study are efficiency, utility, flexibility, reliability, customization, and user satisfaction. The EGovsat model was modified in this study by adding a risk variable. Accidental sampling is the technique applied for sampling. The number of samples used is 100 users of online local tax applications in Malang. This study uses an online survey via Google Forms to collect data. The results showed that utility, reliability, and efficiency affected user satisfaction of Online Local Tax Applications. Meanwhile, customization, flexibility, and risk do not affect user satisfaction of Online Local Tax Applications.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 89%

Section: Discussioncontrasting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determinants of User Satisfaction with Local Tax Online Applications with Modifications of the EGovsat Approach

Ari,

Arifin

2023

Advances in Economics, Business and Management Research

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A multidisciplinary ensemble algorithm for clustering heterogeneous datasets

Hassan

Rashid

2021

Neural Comput & Applic

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Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multidisciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECA*) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multiple-featured datasets. The ECA* is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECA* against five conventional approaches: K-means (KM), K-means++ (KM++), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clustering++ (GENCLUST++). That the end, 32 heterogeneous and multiple-featured datasets are used to examine their performance using internal and external and basic statistical performance clustering measures and to measure how their performance is sensitive to five features of these datasets (cluster overlap, the number of clusters, cluster dimensionality, the cluster structure, and the cluster shape) in the form of an operational framework. The results indicate that the ECA* surpasses its counterpart techniques in terms of the ability to find the right clusters. Significantly, compared to its counterpart techniques, the ECA* is less sensitive to the five properties of the datasets mentioned above. Thus, the order of overall performance of these algorithms, from best performing to worst performing, is the ECA*, EM, KM++, KM, LVQ, and the GENCLUST++. Meanwhile, the overall performance rank of the ECA* is 1.1 (where the rank of 1 represents the best performing algorithm and the rank of 6 refers to the worst-performing algorithm) for 32 datasets based on the five dataset features mentioned above.

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An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

Qader

Hassan

Rashid

2022

Multimed Tools Appl

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Automated brain tumor detection is becoming a highly considerable medical diagnosis research.In recent medical diagnoses, detection and classification are highly considered to employ machine learning and deep learning techniques. Nevertheless, the accuracy and performance of current models need to be improved for suitable treatments. In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered. This hybridization features Grey Wolf Optimization (GWO) and HHO to give better results, limiting the convergence rate and enhancing performance.Moreover, Otsu thresholding is adopted to segment the tumor portion that emphasizes brain tumor detection. Experimental studies are conducted to validate the performance of the suggested method on a total number of 2073 augmented MRI images. The technique's performance was ensured by comparing it with the nine existing algorithms on huge augmented MRI images in terms of accuracy, precision, recall, f-measure, execution time, and memory usage. The performance comparison shows that the DCNN-G-HHO is much more successful than existing methods, especially on a scoring accuracy of 97%. Additionally, the statistical performance analysis indicates that the suggested approach is faster and utilizes less memory at identifying and categorizing brain tumor cancers on the MR images. The implementation of this validation is conducted on the Python platform. The relevant codes for the proposed approach are available at: https://github.com/bryarahassan/DCNN-G-HHO.

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Evaluating e-Government Services in Kurdistan Institution for Strategic Studies and Scientific Research Using the EGOVSAT Model

Cited by 14 publications

References 10 publications

Determinants of User Satisfaction with Local Tax Online Applications with Modifications of the EGovsat Approach

Determinants of User Satisfaction with Local Tax Online Applications with Modifications of the EGovsat Approach

A multidisciplinary ensemble algorithm for clustering heterogeneous datasets

An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

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