Comparison of adaptive filtering scheme for sustainable and efficient communication in smart city

Vimal, Vrince; Khosla, Anita; Prabhakar, Priti; Arora, Sujata; Ashok, Alaknanda

doi:10.1016/j.seta.2021.101472

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Similarly, on the applied testing dataset, the OML-AMPDC algorithm has resulted in accuracy of 90.32%, precision of 91.55%, recall of 90.11%, and F-score of 90.21%. Overall comparative result analysis of the OML-AMPDC with recent methods is carried out in Table 2 [25][26][27]. Figure 13 illustrates the precision and recall analysis of the OML-AMPDC with existing techniques.…”

Section: Resultsmentioning

confidence: 99%

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Optimal Machine Learning Based Automated Malaria Parasite Detection and Classification Model Using Blood Smear Images

Kundu¹,

Anguraj²

2023

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Malaria is a deadly disease which can be spread by the Plasmodium parasites. The existence of malaria can be identified by professional microscopists who examine the microscopic blood smear images. But it remains a challenge owing to the unavailability of experts, poor resolution images, and insufficient diagnostic quality. Therefore, image processing and machine learning (ML) models can be employed to detection of malaria parasites using blood smear images. With this motivation, this study introduces an optimal machine learning based automated malaria parasite detection and classification (OML-AMPDC) model using blood smear images. The proposed OML-AMPDC technique primarily undergoes preprocessing in two stages namely adaptive filtering (AF) based noise removal and contrast enhancement using CLAHE technique. Besides, the feature extraction process was implemented using Local Derivative Radial Patterns (LDRP). In addition, random forest (RF) classifier is applied to allot proper class labels to the blood smear images. Finally, particle swarm optimization (PSO) algorithm was utilized for optimally choose two parameters of the RF model, named maximum number of levels in every decision tree (max_depth) and number of trees in the forest (n_estimators). The design of PSO algorithm helps for enhancing the classification performance of the RF method. A wide-ranging experimental analysis is performed using benchmark dataset and the results reported the betterment of the OML-AMPDC technique over the recent approaches.

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Section: Resultsmentioning

confidence: 99%

“…Vimal et al [26] discuss different adaptive filtering schemes with comparisons between them in their paper. [27].…”

Section: 𝑒(𝑘) = [𝑠(𝑘) + 𝑥 2 (𝑘)] − 𝑦(𝑘)mentioning

confidence: 99%

Optimal Machine Learning Based Automated Malaria Parasite Detection and Classification Model Using Blood Smear Images

Kundu¹,

Anguraj²

2023

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Enhancing Dynamic Abstract Generation for Code Summarization using CODE-OAN Models and DBWTFF Frames

Bhatt,

Sharma,

Sharma

et al. 2024

2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)

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Comparison of adaptive filtering scheme for sustainable and efficient communication in smart city

Cited by 2 publications

References 13 publications

Optimal Machine Learning Based Automated Malaria Parasite Detection and Classification Model Using Blood Smear Images

Optimal Machine Learning Based Automated Malaria Parasite Detection and Classification Model Using Blood Smear Images

Enhancing Dynamic Abstract Generation for Code Summarization using CODE-OAN Models and DBWTFF Frames

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