The finding of a hyperfrontality in unmedicated and never medicated psychotic schizophrenic patients is observed when there is a predominance of positive symptoms. There could be a possible disruption of cortico-striato-thalamic feedback loops causing hyperfrontality as seen in experimentally induced models of psychosis .

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The activity and the ability of brain to maintain the state of calmness in individuals practicing meditation has been a subject of research from long time. The aim of the study here is to prove that the meditation aids in retaining the state of calmness of brain. A MATLAB based multifaceted framework is developed for analyzing the dataset of brain EEG of people practicing meditation. The proposed method performs the processing of 32 electrode EEG data and denoises the signal in time series. The plotting of data followed by PSD analysis and FFT transform of the signal to analyze the data in frequency domain for examining each frequency band. The comparison is done using the L2 norm. The ICWT is later found to analyze the data and calculate for Modulus and angle of the EEG signal. The statistical analysis in time and frequency domain is use to study the effect of meditation on focused attention and retaining of same in meditating and non-meditating brains.

Opinion mining has gained increasing attention and shown great practical value in recent years. Extracting opinion words and targets is a main task in opinion mining. For the purpose of customer and business perspective, the task of scanning these reviews manually is computational burden. Hence, to process reviews automatically and summarizing them in suitable form is more efficient. The distinguished problem of producing opinion summary addresses is how to determine the mood, and opinion expressed in the review with respect to a numerical feature value. This paper proposes a novel approach with a hybrid algorithm which combines Expectation Maximation (EM) algorithm. It focus on the main task of opinion mining called as opinion summarization. The extraction of product feature, technical feature value and opinion are critical for opinion summarization as they affect the performance significantly. The proposed approach consists of a software system in which mining of product feature, technical feature value and opinion is performed. The main motto of this software system is to recognize the technical feature value depending on review, which the reviews are summarized. This software is helpful for humans to understand the technical values expressed in the reviews. It represent relations between opinion words and targets, which is employed to measure the confidence of each candidate from opinion words and targets datasets. The words or targets with high confidence are kept in their respective datasets and the rest are removed as false results which are used to refine extraction rules. k-nearest neighbor classifier-used for classify the extracted data's in a opinion mining. Experimental results Shows the effectiveness of proposed method and finally, candidates with higher confidence are extracted as opinion targets or opinion words.