The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases t...
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The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.
Data confidentiality is a critical concern in corporate settings, particularly as data analysts seek to uncover trends and insights from sensitive datasets. Traditional approaches often require plaintext data, posing ...
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Data encryption is a fundamental aspect of ensuring the confidentiality and security of sensitive information in various applications. In this paper, we present an implementation of the Data Encryption Standard (DES) ...
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The mental health of individuals has a major influence on society. Mental disorders such as depression and anxiety are related to issues and distress to function in work, social, or family gatherings. Motivated by hel...
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This paper presents the development of an interactive virtual reality (VR) web application for exploring Wat Pho, a UNESCO Memory of the World heritage site in Bangkok, Thailand. Aimed at enhancing cultural tourism an...
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Digital speech processing applications including automatic speech recognition (ASR), speaker recognition, speech translation, and others, essentially require large volumes of speech data for training and testing purpo...
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Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information ...
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Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.
Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and...
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Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data,...
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In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solv...
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In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.
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