In today’s healthcare landscape, where participation is widespread, the need for secure and efficient record management is evident. However, many healthcare organizations, particularly in regions like India, still re...
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The technological advancement and mountains of financial data now available, more than ever it is crucial for investors, financial institutions, and decision-makers to make an informed choice by obtaining accurate sto...
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Smart grid (SG) devices depend heavily on accurate load prediction for efficient management, load shedding, and optimal processing dispatch, among other applications. However, achieving precise predictions while minim...
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Information technology is advancing at a record rate in the twenty-first century. It has grown to be an integral component of our daily lives. As the globe develops and more people use ubiquitous computing, cyber-secu...
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Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast ...
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Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the be
With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. W...
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Networking defined by software (SDN) has arisen as an encouraging path for the internet's future expansion, providing enhanced flexibility and transparency to centrally managed networks. However, these advantages ...
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In multi-institutional patient data sharing scenarios, maintaining fine-grained access control while safeguarding privacy and adapting to real-world environments is crucial. Traditional attribute-based encryption (ABE...
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The retina contains millions of photoreceptive cells called cones, rods, and blood vessels that nourish these cells. The optic nerve carries visual information from the retina to the brain. Glaucoma is a condition, wh...
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Detecting malicious URLs by employing the Random Forest algorithm alongside URL parsing techniques. The dataset comprises URLs categorized into phishing, benign, and defacement classes. By parsing these URLs, various ...
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