A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI.

Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in adoption with machine learning techniques are especially useful for pattern recognition of medical imaging to assist the physician in early diagnosis of AD. It is observed that the early abnormal brain atrophy and healthy brain atrophy are same.

Convolution neural network-based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation.

In recent times, accurate and early diagnosis of Alzheimer's disease (AD) plays a vital role in patient care and further treatment. Predicting AD from mild cognitive impairment (MCI) and cognitive normal (CN) has become popular. Neuroimaging and computer-aided diagnosis techniques are used for classification of AD by physicians in the early stage.

Influence of wear parameters on friction performance of A356 aluminum - graphite/ granite particles reinforced metal matrix hybrid composites.

In this work aluminium-silicon alloy (A356) is strengthened with blends of granite and graphite particles using stir casting technique. Granite particulates, an industrial waste generated while cutting granite stones is used along with graphite particulates which has excellent lubricant properties. The reinforcements are mixed in different weight proportions, to base alloy at 2 and 4 weight percentages.