Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques. Traditional detection approaches are usually based on ...
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Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverles...
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A Brain Tumors are highly dangerous illnesses that significantly reduce the life expectancy of patients. The classification of brain tumors plays a crucial role in clinical diagnosis and effective treatment. The misdi...
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A Brain Tumors are highly dangerous illnesses that significantly reduce the life expectancy of patients. The classification of brain tumors plays a crucial role in clinical diagnosis and effective treatment. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients Precisely diagnosing brain tumors is of utmost importance for devising suitable treatment plans that can effectively cure and improve the quality of life for patients afflicted with this condition. To tackle this challenge, present a framework that harnesses deep convolutional layers to automatically extract crucial and resilient features from the input data. Systems that use computers and with the help of convolutional neural networks have provided huge success stories in early detection of tumors. In our framework, utilize VGG19 model combined with fuzzy logic type-2 where used fuzzy logic type-2 that applied to enhancement the images brain where Type-2 fuzzy logic better handles uncertainty in medical images, improving the interpretability of image enhancement by managing noise and subtle differences with greater precision than Type-1 fuzzy logic for MRI images often contain ambiguous or low-contrast areas where noise, lighting conditions different and greatly improve accuracy. while used the VGG19 architecture to feature extraction and classify Tumor and non- Tumor. This approach enhances the accuracy of tumors classification, aiding in the development of targeted treatment strategies for patients. The method is trained on the Br35H dataset, resulting in a training accuracy of 0.9983 % and Train loss of 0.2118 while the validation accuracy of 0.9953 % validation loss of 0.2264. This demonstrates effective pattern learning and generalization capabilities. The model achieves outstanding accuracy, with a best accuracy for the model of 0.9983 %, While the test accuracy of the model reached of 99 %, and both of sensitivity and specificity at 0.9967
Optical wireless communication (OWC) techniques offer ultra-broad spectrum to mitigate the bandwidth-hungry in radio frequency for wireless connections. However, security risk is a critical issue that should receive s...
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Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based ...
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The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)*** study introduces Dynamic GradNet,a novel deep learning model design...
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The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)*** study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD ***,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair *** these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 *** a result,EfficientNetwas selected as the foundation for implementing Dynamic *** GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia *** adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and *** model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD *** proposed model outperformed the baseline architectures,achieving remarkable generalizability across all *** findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification *** findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based *** model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early det
Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minor...
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Cancer remains a leading cause of mortality worldwide, with early detection and accurate diagnosis critical to improving patient outcomes. While computer-aided diagnosis systems powered by deep learning have shown con...
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"The Siri Bhoovalaya is a seminal work of literature, believed to have been composed approximately a millennium ago, which encompasses diverse information encrypted using numerals of the Kannada language—a predo...
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With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promisi...
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