Cloud computing is a significant technological advancement that finds application in different research domains, facilitating on-demand access to resources. Throughout this process, the cloud encounters many challenge...
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Accurately detecting and tracking drones in real-time poses main challenges due to factors such as varying scales, perspectives, occlusions, and environmental conditions. The proposed implementation helps in the ident...
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Magnetic resonance imaging (MRI) has become a valuable diagnostic assessment means for the detection, segmentation, and characterization of brain tumors. However, low brightness and low contrast in MRI images pose a s...
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ISBN:
(纸本)9789819994410
Magnetic resonance imaging (MRI) has become a valuable diagnostic assessment means for the detection, segmentation, and characterization of brain tumors. However, low brightness and low contrast in MRI images pose a significant challenge for accurate tumor detection, especially in the early stages. Several approaches have been proposed to address this challenge, including image enhancement and filtering techniques. However, these methods often result in loss of image details, making it difficult to discern the tumor regions from the non-tumor ones. To overcome these limitations, deep learning-based approaches have gathered attention in recent years for their capability to automatically learn features from the input images and achieve high accuracy in various medical imaging tasks. The aim of our research is to present a deep learning-based methodology for detecting brain tumors in low-brightness and low-contrast MRI images. We employ a neural network with convolutions’ (CNN) architecture, which has been proven to be effective in acquiring complex image features. Previous studies have used deep learning techniques for brain tumor segmentation and detection (Ramin Ranjbarzadeh et al. in Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images [1]). However, these studies did not specifically address the problem of low brightness and low contrast in MRI images. In contrast, our proposed method is designed to capture the subtle differences between tumor regions and non-tumor regions in such MRI images. Our CNN model has been trained and validated on a larger dataset of MRI images, including both normal and tumor-containing images. Our results demonstrate that our proposed method achieves high accuracy and specificity in detecting brain tumors, even in low-brightness and low-contrast MRI images. Additionally, our method has the potential to aid healthcare professionals in precisely and promptly pronouncing tumors
Stuttering is a speech disorder accompanied by disruptions in the fluency of speaking and is characterized by involuntary repetitions, prolongations, or blocks in speech. The early detection and recommendation for per...
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With the busy lifestyle and lack of time, people are looking for a way for the proactive management of their well-being. A health monitoring app helps users obtain insights about their health and take necessary steps ...
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Internet-of-things is one of the prominent communication technologies in the 21st century. We can connect everyday objects, like baby monitors, thermostats, e-health, etc., Connecting IoT with Software-defined network...
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With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec...
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With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy ***, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is ***, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is ***, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally ***, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other *** the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification
The proposed work introduces an innovative recruitment approach integrating LinkedIn web scraping to compile a comprehensive dataset of job descriptions and essential skills. Utilizing a pioneering Word2Vec-driven mac...
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This study introduces a blockchain-based Internet of Things (IoT)-based healthcare system. By utilizing the immutable and decentralized characteristics of blockchain technology, the suggested system addresses the issu...
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Campus safety is a paramount concern in educational institutions worldwide, especially in combating the prevalence of campus violence. This comprehensive review utilizes advanced image processing and computer vision t...
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