In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To ac...
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As a result of the non-linear in the categorizing technique, the initial section of the study, which sorts EEG data into focal and non-focal, is not suitable for severe evaluation. A combined classification system is ...
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ISBN:
(数字)9798331533663
ISBN:
(纸本)9798331533670
As a result of the non-linear in the categorizing technique, the initial section of the study, which sorts EEG data into focal and non-focal, is not suitable for severe evaluation. A combined classification system is therefore suggested for the purpose of automated epilepsy illness diagnosis and level of severity evaluation. Stages for breakdown, extraction of features, and categorization make up the suggested approach. After applying DTWCT to the EEG signals, we scale them utilizing a signal's scaling coefficient and use the wavelet transform to break them down into their component bands. Then, DTCWT takes these deconstructed sub band correlations and uses them to extract parameters like average, variance, skewing, average deviation, kurtosis, covariance, or period, and coefficients of correlation. The next step is to use a Neural Network (NN) categorization strategy to sort the EEG data into two categories: focal and non-focal. "Mild" and "Severe" are the additional diagnostic categories used for the categorized focused EEG data. A larger quantity of learned signals is needed by NN in order to perform the diagnostic procedure. If fuzzy limitations might improve the NN classifier's diagnostic accuracy, they aren't there. In comparison to Shearlet Transform, Discrete Wavelet Transform (DWT), and Contourlet Transform, the proposed method that makes use of DTCWT maximizes accuracy to 100%. This establishes the hybrid categorization approach as the gold standard for automated seizure detection.
Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class o...
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Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, i...
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The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China...
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The increase in vehicles on the road causes an increase in traffic conditions, which induces difficulty in clearing the path for emergency vehicles such as ambulances, rescue fire engine and emergence vehicles. To avo...
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Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operate...
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The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and wide...
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The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and widely used. In order to explore the detection and classification of apple leaf illnesses, we made use of data sets containing examples of apple grey-spot disease, black star disease, cedar rust disease, and healthy leaves. SVM classifier for image segmentation, ResNet and VGG convolutional neural network models were utilized for comparison and improvement respectively. In our prosed method ResNet-18, which had less layers of the ResNet network, achieved greater recognition effects by obtaining an accuracy rate of 98.5 percent.
Mobile advertising is also closely associated with contemporary digital marketing channels, where the projective targeting is critical in regard to businesses' potential performance. This research makes use of a c...
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Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of ...
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