Database Under Pressure - Scaling Database Performance Tests in Microsoft Azure Public Cloud

Mrozek, Dariusz; Paliga, Anna; Małysiak-Mrozek, Bożena; Kozielski, Stanisław

doi:10.1007/978-3-319-18422-7_6

Cited by 2 publications

(5 citation statements)

References 8 publications

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“…Figure 13 compares WCA, PSO, and GA in terms of accuracy rate with other methods. • According to Table 7, the dataset in [36] shows the proposed method gives high accuracy in WCA and PSO, but in GA, the result was close to the algorithm in [11]; the average WCA, PSO, and GA accuracy rates are 94.1%, 93.1%, and 91.9%, respectively. True Positive(TP), True Negative(TN), False Positive(FP), and False Negative(FN).…”

Section: Comparisons Of the Proposed Methodsmentioning

confidence: 87%

“…In this paper, the proposed method is tested using the dataset of lip prints, which consists of 148 lip print images, and the two websites that provide images of lip prints [35] and [36]. Table 4 provides details about the datasets.…”

Section: Datasetsmentioning

confidence: 99%

“…of lip prints [35] There are 15 individuals, each with four lip print images, with no indication of gender. 60 [36] There are 22 individuals, each with four lip print images, with 9 males and 13 females. 88…”

Section: Datasets Detailsmentioning

confidence: 99%

“…The SVM classifier uses WCA, PSO, and GA analysis for accuracy, error, sensitivity, and precision for dataset in [36]. 96.8% FS (GA) 95% Statistical method [37] 96 % The algorithm in [38] 92.7 % Bifurcation analysis [39] 77.0 % Section Comparison [40] 85.1 % DTW + Voting system [41] 88.5 % DTW [42] 78.8 % ROI + cross − correlation [43] 93%…”

Section: Figurementioning

confidence: 99%

“…Comparison the result of the proposed method and algorithm in[11] for dataset[36].𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐫𝐚𝐭𝐞 𝐄𝐫𝐫𝐨𝐫 𝐫𝐚𝐭𝐞 𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐲 𝐫𝐚𝐭𝐞 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐫𝐚𝐭𝐞 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐓𝐢𝐦𝐞. 𝐬𝐞𝐜.…”

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The Effect of Meta-heuristic Methods on the Performance of ImageClassification

Anai,

Mersal,

Mostafa

2024

Iraqi Journal of Science

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Classification accuracy is strongly affected by the quality of the input features. In recent years, datasets have increased in size and number of features. Analysis of huge datasets can be challenging due to redundant, noisy, and irrelevant features that mayreduce the classifier's performance. Feature selection is a vital process in which the best subset of features from the original dataset is chosen. The feature selection strategy is critical for increasing classification accuracy while decreasing computational costs. This research proposed a method for classifying lip print images by exploiting meta-heuristic methods and optimization-based feature selection methods. It involves four main phases: pre-processing, feature extraction, feature selection, and classification. After pre-processing, the features are extracted from the enhanced image. Meta-heuristic methods such as Genetic Algorithm (GA), ParticleSwarm Optimization (PSO), and Water Cycle Algorithm (WCA) are studied for feature selection using the mean function as the objective function. Finally, the lip print images are classified using a support vector machine (SVM). In this research, the experimental results are compared in terms of accuracy, error, sensitivity, and precision rate between three meta-heuristic methods and the accuracy rate of the proposed method with other algorithms that do not use meta-heuristic methods. The accuracy reached 97.9%, 96.8%, and 95% using WCA, PSO, and GA, respectively.

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Section: Comparisons Of the Proposed Methodsmentioning

confidence: 87%

Section: Datasetsmentioning

confidence: 99%