Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a...
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Nowadays, electronic medical records are increasing rapidly. Electronic medical records consist of sensitive and confidential information. These pieces of information need to be protected from attackers during the exc...
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Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analy...
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The concept of a medical intelligent system has steadily garnered attention as modern technology advances. An intelligent medical system is a medical system that develops a certain amount of intelligence and performs ...
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In the recent era of technology, the internet of things (IoT) plays a tremendous role in enhancing the quality of human life through smart devices and sensing the real-world environment. IoT aims to interconnect anyth...
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Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while...
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In this paper, machine learning based method for the estimation of solar radiation in earth surface is presented. To design the machine learning model, multispectral (visible and infrared) satellite images of the very...
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Conversation between both the deaf and the general population is becoming exceedingly challenging, and there is no reputable translator accessible in society to aid. This program enables real-time voice and signal lan...
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Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much ...
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This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditio...
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This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditional methods based on color, shape, or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features, reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet) model for Region-of-Interest(ROI) segmentation and classification, with enhanced image preprocessing. The study examines various loss functions, finding that a hybrid loss function yields superior results, and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.
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