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This paper reviews the main perspectives of language acquisition and language comprehension. In language acquisition, we have reviewed the different types of language acquisitions like first language acquisition, second language acquisition, sign language acquisition and skill acquisition. The experimental techniques for neurolinguistic acquisition detection is also discussed. The findings of experiments for acquisition detection is also discussed, it includes the region of brain activated after acquisition. Findings shows that the different types of acquisition involve different regions of the brain. In language comprehension, native language comprehension and bilingual's comprehension has been considered. Comprehension involve different brain regions for different sentence or word comprehension depending upon their semantic and syntax. The different fMRIEEG analysis techniques (statistical or graph theoretical) are also discoursed in our review. Tools for neurolinguistics computations are also discussed.

In recent era prediction of enzyme class from an unknown protein is one of the challenging tasks in bioinformatics. Day to day the number of proteins is increases as result the prediction of enzyme class gives a new opportunity to bioinformatics scholars. The prime objective of this article is to implement the machine learning classification technique for feature selection and predictions also find out an appropriate classification technique for function prediction. In this article the seven different classification technique like CRT, QUEST, CHAID, C5.0, ANN (Artificial Neural Network), SVM and Bayesian has been implemented on 4368 protein data that has been extracted from UniprotKB databank and categories into six different class. The proteins data is high dimensional sequence data and contain a maximum of 48 features.To manipulate the high dimensional sequential protein data with different classification technique, the SPSS has been used as an experimental tool. Different classification techniques give different results for every model and shows that the data are imbalanced for class C4, C5 and C6. The imbalanced data affect the performance of model. In these three classes the precision and recall value is very less or negligible. The experimental results highlight that the C5.0 classification technique accuracy is more suited for protein feature classification and predictions. The C5.0 classification technique gives 95.56% accuracy and also gives high precision and recall value. Finally, we conclude that the features that is selected can be used for function prediction.

In recent years, several Scientometrics and Bibliometrics indicators were proposed to evaluate the scientific impact of individuals, institutions, colleges, universities and research teams. The h-index gives a major breakthrough in the research community to evaluate the scientific impact of an individual. It got a lot of attention due to its simplicity and several other indicators were proposed to extend the properties of h-index as well as to overcome shortcomings of h-index. In this literature review, we have discussed the advantages and limitations of almost all Scientometrics as well as Bibliometrics indicators which have been categorized into seven categories :(i) Complement of h-index, (ii) Based on total number of authors, (iii) Based on publication age, (iv) Combination of two indices, (v) Based on excess citation count, (vi) Based on total publication count, (vii) Based on other variants. The main objective of this article is to study all those indicators which have been proposed to evaluate the scientific impact of an individual researcher or a group of researchers.

Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motivates the need to analyse the sequences for predicting protein functions. Early generation computational methods using BLAST, FASTA, etc. perform function transfer based on sequence similarity with existing databases and are computationally slow. Although machine learning based approaches are fast, they fail to perform well for long protein sequences (i.e., protein sequences with more than 300 amino acid residues). In this paper, we introduce a novel method for construction of two separate feature sets for protein sequences based on analysis of 1) single fixed-sized segments and 2) multi-sized segments, using bi-directional long short-term memory network. Further, model based on proposed feature set is combined with the state of the art Multi-lable Linear Discriminant Analysis (MLDA) features based model to improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate promising results for both single-sized and multi-sized segments based feature sets. While former showed an improvement of +3.37% and +5.48%, the latter produces an improvement of +5.38% and +8.00% respectively for two datasets over the state of the art MLDA based classifier. After combining two models, there is a significant improvement of +7.41% and +9.21% respectively for two datasets compared to MLDA based classifier. Specifically, the proposed approach performed well for the long protein sequences and superior overall performance.

Most of the scientometric indicators use only the total number of citations of an article and produce a single number for scientific assessment of scholars. Although this concept is very simple to compute, it fails to show the scientific productivity and impact of scholars during a time-span or in a year. To overcome this, several time series indicators have been proposed that consider the citations from the entire research career of a scholar. However, these indicators fail to give a comparative assessment of two scholars having same or very similar index value. To overcome this shortcoming, h-index sequence was proposed to assess the impact of scholars during a particular time-span and to compare multiple scholars at a similar stage in their careers. The h-index sequence is based on the h-index formulation. One of the main issues related to the h-index is that it completely ignores the excess citation in scientific assessment; h-index sequence also exhibits a similar behaviour. To overcome these limitations, in this article, we have discussed the EM and $EM^{'}$-index sequence, and performed an empirical study based on yearly citation count earned from all publications of 89 scholars' publication data. The element of the EM and $EM^{'}$-index sequence for a given year shows the impact of a scholar for that year. We conclude that the EM and $EM^{'}$-index sequence could be used as an alternative metrics to asses the impact of scholars.