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The Internet revolution has resulted in abundant data from various sources, including social media, traditional media, etcetera. Although the availability of data is no longer an issue, data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts. The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning. More specifically, this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels. To achieve this objective, different experiments have been performed on the publicly available dataset. In first set of experiments, we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set. In the second set of experiments, we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set. The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3% on sentence level datasets for auto labelling. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a vital role in image processing according to the widespread development of human interactive applications. In the past few years, various researchers have focused on FER for implementing it in different applications. The existing system suffers from various complexities such as low accuracy, computational cost, and poor recognition performances. In this article, we proposed a novel concept to recognize facial expressions. The proposed work comprises three sections that are pre-processing, feature extraction, and classification. The pre-processing techniques remove the unwanted data from the original image and enhance the crucial details for further processing. The Convolutional Neural Network (CNN) is used for feature extraction. But, it yields lower performance in terms of feature extraction due to the shortage of hyperparameter tuning. Hence, the Multi-strategy Gravitational Search Algorithm (M-GSA) is utilized to extract the facial expression features from the eyebrow movement, nose, chin, and lip corner of the facial images. The facial expressions are classified via the Support Vector Machine (SVM) classifier. In this work, the top five facial expressions such as surprise, sad, happy, fear, and angry with three facial expression datasets such as FER-2013 dataset, CK+ dataset, and JAFFE dataset. Ultimately, the proposed method demonstrates better classification accuracy and recognition rates than different kinds of state-of-art methods. [ABSTRACT FROM AUTHOR]

Background: During the last 25 years, vitamin D -being a member of the superfamily of nuclear steroid transcription regulators - has emerged as a serious candidate in nervous system development and function and a therapeutic tool in a number of neurological pathologies. More recently, experimental and pre-clinical data suggest a link between vitamin D status and cognitive function. Human studies strongly support a correlation between low levels of circulating 25-hydroxyvitamin D (25(OH)D) and cognitive impairment or dementia in aging populations. In parallel, animal studies show that supplementation with vitamin D is protective against biological processes associated with Alzheimer's disease (AD) and enhances learning and memory performance in various animal models of aging and AD. More recently epidemiological associations have been made between low vitamin D and psychiatric disorders not typically associated with abnormalities in brain development such as depression and Alzheimer's disease Aim of the Study: was to review the recent literature to investigate the correlation between vitamin D status and neurocognitive function. Methods: We searched the medical literature to retrieve studies for the review to 30 November 2017. electronic search in the scientific database from 1965 to 2017- (Medline, Embase, AMED, Psych INFO, the Cochrane Library websites were searched for English Publications were obtained from both reprint requests and by searching the database. Data extracted included authors, country, year of publication, characteristics of patients, pathophysiology, risk factors, clinical manifestations, different diagnostic approaches and treatment modalities. Conclusion: Accumulating evidence in the current literature indicate that vitamin D deficiency is highly prevalent among patients with cognitive impairment, and that low vitamin D status may negatively impact cognition and that hypovitaminosis D is usually associated with increased risk of developing AD and dementia. Nevertheless, while vitamin D supplementation is safe for those with cognitive impairment, there is no solid proof that they will see significant improvement in their symptoms. [ABSTRACT FROM AUTHOR]