The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machinelearning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a stro...
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The merger of artificial intelligence (AI) and machinelearning (ML) technologies has drastically changed the field of fraud detection in digital banking in recent years. The increasing frequency of digital transactio...
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This paper proposes a classification prediction model of metabolic fatty liver based on PMAULD-TabNet, which can help doctors realize early screening for people prone to fatty liver disease, help patients realize risk...
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ISBN:
(纸本)9798350351040;9798350351033
This paper proposes a classification prediction model of metabolic fatty liver based on PMAULD-TabNet, which can help doctors realize early screening for people prone to fatty liver disease, help patients realize risk assessment and implement correct and effective treatment as soon as *** physical examination data of 2568 patients in the outpatient department of Infection, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine were selected as the research objects, and the relevant data cohort was *** order to improve the accuracy of the model, decision tree, support vector machine, Bagging, random forest, XGBoost and PMAFLD-TahNet machinelearning algorithms were used to construct the classification prediction models of fatty liver disease, respectively, and to predict the classification of fatty liver disease in 2568 cases of physical examination *** the experimental results, the area under ROC curve of the fatty liver prediction model established by PMAFI,D-TabNet algorithm was 0.7, the accuracy rate was 0.801, and the accuracy rate was 0.872, which were higher than other machinelearning *** can be seen that the model containing PMAULD-TabNet framework has better predictive classification performance.
Early detection of Alzheimer's disease (AD) is crucial for timely intervention and slowing its progression. This research leverages neuroimaging-based machinelearning to classify cognitive impairment levels using...
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Class imbalance is a significant obstacle in machinelearning, causing biased model results and constraining generalization abilities. This systematic survey reviews machinelearning classifier systems designed to mit...
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The scheme aims to develop a predictive model to identify thyroid disorders, leveraging machinelearning techniques. Thyroid diseases, including hypothyroidism and hyperthyroidism, significantly impact metabolic proce...
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Leukaemia is a disease that is related to cancerous cells. It is also lethal, and people of all age groups can be affected by it. WBCs especially affect this, and they are followed by a growth in the amount of lymphoc...
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The emergence of Electric Vehicles (EVs) and related charging infrastructure marks a significant shift toward sustainable transportation solutions. However, the increasing deployment of Electric Vehicle Supply Equipme...
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Air Pollution is the contamination of air due to the presence of substances in the atmosphere that are major issue to human life as well as to the other living organisms. Air quality is the result of the composite int...
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In an era, where diabetes prevalence is on the rise and its associated complications significantly impact overall health, this project addresses diabetic nephropathy, a critical complication linked to diabetes. We pro...
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