Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS ...
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In order to meet the pressing demand for early diagnosis in healthcare, we proposed a unique hybrid architecture in this study that is intended for the categorization of gastrointestinal (GI) illnesses. With fewer tra...
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Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are...
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The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medic...
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The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for *** a result,there is a critical necessity for more accurate computeraided methods for early tumor *** remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s *** publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this *** proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 *** results indicate that our proposed technique outperforms the best results associated with the existing methods.
The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not...
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This research aims to break communication barriers for the deaf and hard-of-hearing by pioneering a real-time, dynamic sign language translator. Unlike existing apps, it uses a CNN to translate simultaneously between ...
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Learner diversity is a matter for universities enrolling international students. Hence, learner engagement then becomes a major concern for instructors. This study uses gamification techniques to determine its impact ...
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Nowadays, coronary heart disease is one of the most fatal disease globally. Many researchers and medical technicians have developed and designed various computer-aided diagnosis systems using various machine learning ...
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A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, ...
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With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is *** routine real-time reverse transcription-polymerase chain reacti...
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With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is *** routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening *** the good choices for an accurate and fast test to screen COVID-19 are deep learning *** this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is *** EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan *** addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan *** proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is *** proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and *** proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.
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