Evaluation of remote sensing techniques-based water quality monitoring for sustainable hydrological applications: an integrated FWZIC-VIKOR modelling approach

Talal, Mohammed; Alamoodi, A. H.; Albahri, O. S.; Albahri, A. S.; Pamučar, Dragan

doi:10.1007/s10668-023-03432-5

Cited by 6 publications

(1 citation statement)

References 85 publications

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“…Feature extraction is one of the essential techniques in computer vision and image processing. This technique is vital to identifying relevant patterns and structures within the image [26]- [28]. So that the extracted features are meaningful in order to reduce the complexity of the data and thus be more amenable to analysis and decision-making tasks.…”

Section: Feature Extractionmentioning

confidence: 99%

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Saher,

Alsaedi,

Al Ibraheemi

2023

Applied Data Science and Analysis

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Breast cancer is the most common type of cancer in the world, affecting both men and women. In 2023, the American Cancer Society's reported that there will be approximately 297,800 new cases of invasive breast cancer in women and 2,850 in men, along with 55,750 cases of ductal carcinoma in situ (DCIS) in women. Further, an estimated 43,750 deaths are expected from breast cancer, of which approximately 43,180 are among women and 570 are among men. In this paper, we propose an automated grading system for breast cancer based on tumor's histopathological images using a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and machine learning algorithms. The proposed system has four main phases: image preprocessing and segmentation, feature extraction, classification, and integration with a website. Grayscale conversion, enhancement, noise and artifact removal methods are used during the image preprocessing stage. Then the image is segment during the segmentation phase to extract regions of interest. And then, features are extracted from the obtained region of interest using the Histogram of Oriented Gradients (HOG) algorithm. The next, the images are classified into three distinct breast cancer grades based on the extracted features using machine learning algorithms. Moreover, the effectiveness of the proposed system was evaluated and reported using vary evaluation methods and the results showed a remarkable accuracy of up to 97% by the SVM classifier. Finally, the machine learning model is integrated into a website to improve the detection and diagnosis of breast cancer disease and facilitate the access and use of patient data. This will make the work easier for physicians to enhance breast cancer detection and treatment

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Section: Feature Extractionmentioning

confidence: 99%

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Saher,

Alsaedi,

Al Ibraheemi

2023

Applied Data Science and Analysis

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Decision through novel ranking of generalized symmetric pentagonal interval-valued fuzzy criteria for sustainable regional selection

Vijayakumar,

Mahapatra,

Dharmalingam

2024

Environ Dev Sustain

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Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

Albahri,

Hamid,

Alzubaidi

et al. 2024

Complex Intell. Syst.

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This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

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Evaluation of remote sensing techniques-based water quality monitoring for sustainable hydrological applications: an integrated FWZIC-VIKOR modelling approach

Cited by 6 publications

References 85 publications

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Decision through novel ranking of generalized symmetric pentagonal interval-valued fuzzy criteria for sustainable regional selection

Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

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