Millions of people worldwide, especially in low- and middle-income nations, suffer from anemia, a common health problem. The disease is characterized by a lack of hemoglobin or red blood cells, which can result in sym...
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ISBN:
(纸本)9798331508845
Millions of people worldwide, especially in low- and middle-income nations, suffer from anemia, a common health problem. The disease is characterized by a lack of hemoglobin or red blood cells, which can result in symptoms including weakness and exhaustion as well as serious consequences if ignored. Conventional techniques for identifying anemia usually entail intrusive blood tests, which can be expensive, time-consuming, and sometimes unavailable to a large number of people. In order to overcome these obstacles, this study uses MATLAB to provide an automated, non-invasive method for detecting anemia via fingernail image analysis. This project's main goal is to develop an effective and precise anemia screening tool by utilizing machine learning and imageprocessing methods. The fingernail photos are taken by the system and pre-processed to improve quality and guarantee consistency. The technology uses sophisticated imageprocessing techniques to extract pertinent elements from the fingernail photos, including texture and color characteristics that indicate hemoglobin levels. Machine learning algorithms use these traits to categorize the photos as either anemic or non-anemic. MATLAB is used because of its strong skills in machine learning and imageprocessing. From picture capture and pre-processing to feature extraction and classification, the program streamlines the whole procedure. MATLAB's extensive toolkit makes it possible to manipulate photos effectively and has strong machine learning capabilities, which makes it the perfect platform for creating an automatic detection system. The system is guaranteed to be scalable, simple to install, and accurate through the use of MATLAB. Clinical data is used to thoroughly validate the system's functionality. The machine learning models are trained and tested using images from both non-anemic individuals and patients with clinically confirmed anemia. The system's sensitivity, specificity, and accuracy are assessed to guar
Traditional motion capture systems are prone to environmental interference, resulting in noise and errors in the captured data. This article proposes an artificial intelligence oriented intelligent processing algorith...
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Faults in electrical power transmission systems can cause system failures and even may cause explosions. It is desired to remove a faulty component immediately to prevent further damage to the system and the environme...
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ISBN:
(纸本)9798350344004
Faults in electrical power transmission systems can cause system failures and even may cause explosions. It is desired to remove a faulty component immediately to prevent further damage to the system and the environment. To address this, a typical technique is devised based on thermal imaging and various artificial neural network algorithms. The faulty element or component will radiate or emit higher energy when compared to normal or healthy conditions, because of the higher current flow rates. The thermal image taken from such defective part of the power system will be more highlighted in the image in contrast with the normal cool background. This drastic change in the grey level values in contrast with the healthy power system's picture hints or predicts a fault in that region. To support this methodology, an exhaustive simulation is implemented and demonstrated using thermal imageprocessing and self-learning neural network algorithms and the simulation results are compared. This analysis is performed through various types of ANN techniques, and comparisons are established between them to report the network with best prediction results on a typical 'Step' dataset and 'Realistic' dataset. A similar low score mean square error MSE is exhibited with these models, and the R-square values are closer to the best score of one in all algorithms discussed. A better graph is obtained in Levenberg-Marquadrdt training Algorithm where most of the predictions fall alongside the target;however, Bayesian regularization gives a better plot than LM with the best fit is being obtained at lesser number of iterations that is in less time.
For the last few decades satellite imaging technology has taken massive strides towards higher spatial resolution, larger swath coverage and almost real-time data delivery. Satellite imaging or remote sensing is exten...
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Age-related macular degeneration (AMD) is a leading cause of irreversible vision impairment among the elderly population worldwide, affecting over 196 million people globally. This study delves into innovative approac...
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ISBN:
(纸本)9798350350661;9798350350654
Age-related macular degeneration (AMD) is a leading cause of irreversible vision impairment among the elderly population worldwide, affecting over 196 million people globally. This study delves into innovative approaches for improving the detection and management of AMD through advanced technological solutions. To investigate the impact of ultraviolet (UV) and blue light exposure on AMD, the research analyzes their contribution to oxidative stress and retinal damage and evaluates potential protective measures or interventions to mitigate these effects. By leveraging state-of-the-art machine learning algorithms and advanced imageprocessing techniques, the research aims to enhance the precision and efficiency of AMD diagnosis. The statistical burden of AMD underscores its significant impact on global health, with projections indicating a rising prevalence due to aging populations, lifestyle factors, and increasing digital screen use among younger generations. Effective management hinges on early detection and accurate monitoring of AMD biomarkers, which these methodologies seek to facilitate. Experimental evaluations demonstrate promising outcomes in diagnostic accuracy and scalability, highlighting the potential for widespread adoption in clinical practice. Furthermore, these advancements contribute to broader efforts in global eye health by offering scalable, AI-driven solutions that can improve patient outcomes and streamline healthcare workflows. By addressing the complexities of AMD diagnosis, this research supports healthcare providers in delivering timely interventions and personalized care, ultimately reducing the burden of AMD-related vision loss on individuals and healthcare systems.
Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unrel...
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ISBN:
(纸本)9798350318920;9798350318937
Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the-essentially hand-crafted-non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration;this makes it parallelizable with no hyperparameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The code for all our experiments is publicly available(1).
We present a novel waveguide-integrated eye tracking system for smart glasses, offering a compact and aesthetically pleasing solution by embedding the eye tracking camera within the temple of the eyewear. The system f...
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ISBN:
(纸本)9781510684980;9781510684997
We present a novel waveguide-integrated eye tracking system for smart glasses, offering a compact and aesthetically pleasing solution by embedding the eye tracking camera within the temple of the eyewear. The system features a front-view capturing capability, enabled by the waveguide, which minimize distortion and occlusion while ensuring consistent imaging conditions. Waveguide systems typically face challenges such as low light efficiency, which introduces white noise, degrading image quality and tracking performance. To address this, we employed lightweight imageprocessingalgorithms, including a neural network-based pupil segmentation model to extract elliptical parameters, center positions, shape, and orientation, from noisy, low-resolution images, enabling accurate and robust gaze estimation. In controlled benchtop experiments using an eyeball robot, the system demonstrated promising performance, achieving a mean gaze error of approximately 1 degree, validating its capability to maintain precision despite the challenges associated with waveguide optics. Variability in feature extraction was observed near the edge region of the target object space, emphasizing the need for refinements in waveguide design and feature extraction algorithms. Overall, the proposed waveguide-integrated eye tracking system represents a significant advancement by seamlessly incorporating eye tracking technology into everyday eyewear. Its design maintains user comfort and aesthetics while achieving the precision required for AR/VR applications, enabling intuitive and reliable user interaction.
Lung cancer, a significant global health concern, continues to take many lives, especially as a result of late-stage diagnoses. Early detection continues to have the most significant impact on patient outcomes and mor...
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This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsisten...
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This paper presents a novel approach for detecting possible faults in underground cables at the manufacturing industry using imageprocessing techniques. With the increasing adoption of underground cables to minimize ...
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