This research aims to build a system that automatically categorizes good and bad fruits, for the top six popular Indian varieties of Fruits using deep learning. By adapting pre-trained deep learning models (transfer l...
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This paper presents a novel color image encryption technique using a 5D piecewise chaotic map. In recent years, image encryption has become crucial for securing multimedia data in various applications, especially in c...
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In this paper, we propose a text summarization method for low-compute devices with limited hardware capabilities like smartphones, tablets and IOT devices. Traditional methods for text summarization face significant c...
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Epileptic seizures are unpredictable and pose significant risks to individuals affected by epilepsy. Electroencephalogram (EEG) signals offer a promising avenue for early seizure prediction, enabling timely interventi...
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Phishing attacks are a form of cyberattack that involves sending malicious emails, texts, or websites with the goal of obtaining confidential information or gaining access to a user's accounts and systems. These a...
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In-context learning (ICL) refers to a remarkable capability of pretrained large language models, which can learn a new task given a few examples during inference. However, theoretical understanding of ICL is largely u...
In computational biology, it is still quite difficult to predict the tertiary structure of proteins based on their amino acid sequence. In order to accurately predict the tertiary level, this paper proposes a new meth...
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Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** ...
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Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.
作者:
Khadse, ShrikantGourshettiwar, PalashPawar, Adesh
Faculty of Engineering and Technology Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India
Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India
Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamental...
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作者:
Mahesh, R.T.
Department of Computer Science and Engineering Karnataka Bangalore India
This study investigates the application of deep learning models, specifically EfficientNet-B7, combined with advanced image augmentation strategies to improve the diagnostic accuracy of breast ultrasound image classif...
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