Microplastics (MP) have become a major concern, given the threat they pose to marine-derived food and human health. One way to investigate this threat is to quantify MP found in marine organisms, for instance making u...
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Computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular sect...
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Computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.
Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CN...
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Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.
Computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based d...
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Computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.
We show that f(Q) cosmology with a non-trivial connection, namely the Connection II of the literature, is dynamically equivalent with a quintom-like model. In particular, we show that the scalar field arising from the...
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XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such ...
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By using both the neutron monitor data and the Global Muon Detector Network (GMDN) data recorded in global networks monitoring a wide rigidity range of primary cosmic rays, we analyze two long-lasting cosmic ray inten...
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XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such ...
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The integration of immunotoxicity data into chemical risk assessment paradigms is essential for comprehensively evaluating the potential health hazards posed by chemical exposures. This review provides a comprehensive...
The integration of immunotoxicity data into chemical risk assessment paradigms is essential for comprehensively evaluating the potential health hazards posed by chemical exposures. This review provides a comprehensive overview of the methodologies, challenges, and future directions for integrating immunotoxicity data into risk assessment frameworks. It discusses the fundamental principles of immunotoxicology and its relevance to chemical risk assessment, highlighting the critical roles of the immune system in health and defense against harmful agents. Next, we explore traditional chemical risk assessment frameworks, including exposure assessment, hazard identification, dose–response assessment, and risk characterization, highlighting the need for incorporating immunotoxicity endpoints to enhance hazard characterization and risk estimation. Subsequently, we delve into dose–response modelling for immunotoxicity, elucidating principles, methods, and case studies illustrating dose–response relationships and extrapolation of data from animal studies to humans. Furthermore, we discuss hazard characterization of immunotoxicity, focusing on the identification of immunotoxic hazards, evaluation of immunotoxicity endpoints, and utilization of immunological biomarkers in risk assessment. We then examine cumulative risk assessment strategies, presenting a conceptual framework for assessing cumulative risks of immunotoxicity from multiple chemical exposures and methods for integrating exposure and hazard data from different chemicals. Lastly, we explore emerging trends and future directions in immunotoxicity risk assessment, including high-throughput screening assays, omics technologies, computational modelling, and alternative testing methods, along with potential regulatory implications and future research needs. This review provides valuable insights for researchers, regulators, and stakeholders involved in chemical risk assessment and public health protection, facilitating t
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use di...
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