Modern electric power systems with high levels of penetration of renewable energy sources (RES) often present frequency security problems. The lack of inertia due to the reduced number of synchronous generators in the...
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The Cancer is the second-leading cause of death for women aged 20-59 worldwide and very few men. Compared to other cancers, breast cancer kills more people. According to ***, 13% of American women are at risk of havin...
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The Exploration-Exploitation dilemma in Reinforcement Learning (RL) algorithms is about deciding whether to select a sub-optimal path to the outcome and acquire a more varied learning of the environment or to select t...
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People usually express their emotions, views, or sentiment in textual form. The textual sentiment analysis (TSA) identifies or classifies opinions or feelings from texts in a predefined class. The TSA is complicated o...
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In recent years, energy transfer technology provides a new opportunity for recharging the sensors using the mobile charger. Most of the studies proposed recharging algorithms using a single charger but overlooked the ...
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Nowadays the handmade tribal art products attract the people due to their smooth finishing. Currently many platforms are available for them but, they are fetching result with irrelevant list of the product and support...
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Kidney stones represent a significant global health concern, frequently causing individuals to seek immediate medical care due to intense pain. Radiological imaging modalities are one of the most common modes of diagn...
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ISBN:
(数字)9798331529765
ISBN:
(纸本)9798331529772
Kidney stones represent a significant global health concern, frequently causing individuals to seek immediate medical care due to intense pain. Radiological imaging modalities are one of the most common modes of diagnosis, but specialist interpretation is always important for accuracy. In this paper, we present computer vision in an automated kidney stone detection system that involves five stages: loading the dataset, preprocessing data, loading the InceptionV3 model, predicting, and evaluating performance. Various methods of preprocessing and data augmentation were utilized to make the model less biased and more robust. This study uses the InceptionV3 architecture to classify kidney stones in CT scan images of KUB (Kidney, Ureter, and Bladder). We utilize a total dataset of 433 participants, either diagnosed with kidney stones or in a normal state. 278 cases are diagnosed with kidney stones, and 165 are normal cases. The proposed approach achieved an impressive 98.55% accuracy across performance metrics when evaluated on an independent test dataset.
Physical impairment is and will always be an impediment to daily progression. It is a cruel reality that many tend to ignore. This paper deals with how technology can be used to help people with certain physical/senso...
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Many analyses show that Artificial Neural Network (ANN) based final solutions are more likely to trapped in local minimums, provide poor generalization and extrapolation behaviors, and are not unique due to the inappr...
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Despite Machine Learning (ML) and Deep Learning (DL) models in specific have achieved significant success in various applications of computer vision in recent years, they remain vulnerable to carefully crafted, human-...
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