ANTI-ANXIETY EVALUATION OF EXTRACTS OF STIGMA MAYDIS (CORN SILK)

Objective: The anxiolytic activity of petroleum ether, chloroform and ethyl acetate extracts of Stigma maydis was investigated by Elevated Plus Maze, Hole Board and Mirror Chamber Test.Methods: The study was conducted using elevated plus maze, whole board and mirror chamber test. Female Laca/Balb c was used to carry out the studies. In each experiment, animals were equally divided into five groups; control, given saline solution and Tween 80, standard given diazepam (2 mg/kg i. p.) and test groups were given 250, 500, 750 and 1000 mg/kg of petroleum ether, chloroform and ethyl acetate extracts of Stigma maydis. The data were subjected to analysis of variance by taking mean and standard error to the mean using Tukey's post-hoc test.Results: In Elevated Plus Maze chloroform extract (750 and 1000 mg/kg) of Stigma maydis revealed increase in time spent in open arm, frequency and preference to open arm as compared to control, which was almost comparable to diazepam. In Hole Board test decrease in number of head dips as compared to control was observed. In Mirror Chamber Test, the decrease in latency, increase in time spent in the mirror chamber and frequency as compared to control was observed. All of the changes were statistically highly significant.Conclusion: From our results it can be concluded that the chloroform extract of Stigma maydis showed anxiolytic activity at the dose of 750 and 1000 mg/kg.Â

Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture

Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively

Text Summarization Technique for Punjabi Language Using Neural Networks

In the contemporary world, utilization of digital content has risen exponentially. For example, newspaper and web articles, status updates, advertisements etc. have become an integral part of our daily routine. Thus, there is a need to build an automated system to summarize such large documents of text in order to save time and effort. Although, there are summarizers for languages such as English since the work has started in the 1950s and at present has led it up to a matured stage but there are several languages that still need special attention such as Punjabi language. The Punjabi language is highly rich in morphological structure as compared to English and other foreign languages. In this work, we provide three phase extractive summarization methodology using neural networks. It induces compendious summary of Punjabi single text document. The methodology incorporates pre-processing phase that cleans the text; processing phase that extracts statistical and linguistic features; and classification phase. The classification based neural network applies an activation function- sigmoid and weighted error reduction-gradient descent optimization to generate the resultant output summary. The proposed summarization system is applied over monolingual Punjabi text corpus from Indian languages corpora initiative phase-II. The precision, recall and F-measure are achieved as 90.0%, 89.28% an 89.65% respectively which is reasonably good in comparison to the performance of other existing Indian languages" summarizers.This research is partially funded by the Ministry of Economy, Industry and Competitiveness, Spain (CSO2017-86747-R)

Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm

In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the ever-growing volume of text data available online to discover relevant information faster. In this research, the ATS methodology is proposed for the Hindi language using Real Coded Genetic Algorithm (RCGA) over the health corpus, available in the Kaggle dataset. The methodology comprises five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Rigorous experimentation on varied feature sets is performed where distinguishing features, namely- sentence similarity and named entity features are combined with others for computing the evaluation metrics. The top 14 feature combinations are evaluated through Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure. RCGA computes appropriate feature weights through strings of features, chromosomes selection, and reproduction operators: Simulating Binary Crossover and Polynomial Mutation. To extract the highest scored sentences as the corpus summary, different compression rates are tested. In comparison with existing summarization tools, the ATS extractive method gives a summary reduction of 65%

Pairwise Reviews Ranking and Classification for Medicine E-Commerce Application

E-Commerce applications provide an added advantage to customer to buy product with added suggestions in the form of reviews. Obviously, reviews are useful and impactful for customers those are going to a buy product. But these enormous amount of reviews create problem also for customers as they are not able to segregate useful ones. Therefore, there is a need for an approach which will showcase only relevant reviews to the customers. This same problem has been attempted in this research paper as this is a less explored area. Pairwise Review relevance ranking method is proposed in this research paper. This approach will sort reviews based on their relevance with the product and avoid showing irrelevant reviews. This work has been done in three phases- feature extraction, pairwise review ranking, and classification. The outcome is sorted list of reviews, review ranking accuracy and classification accuracy. Four classifiers- SVM, Random forest, Neural network, and logistic regression have been applied to validate ranking accuracy. Out of all four applied classification models, Random forest gives the best result. our proposed system is able to achieve 99.76% classification accuracy and 99.56% ranking accuracy for a complete dataset using random forest.</p

Cross-domain based Event Recommendation using Tensor Factorization

Context in the form of meta-data has been accredited as an important component in cross-domain collaborative filtering (CDCF). In this research paper CDCF concept is used to exploit event information (context) from two UI matrices to allow the recommendation performance of one domain (Facebook- User-Event Matrix) to benefit from the information from another domain (Bookmyshow- Event-Tag Matrix). The model based collaborative filtering approach Tensor Factorization(TF) has been used to integrate Facebook provided User-Event context information with Bookmyshow Event-Tag context information to recommend events. In contrast to the standard collaborative tag recommendation, our CDCF approach uses one User-Event matrix of Facebook that takes another Bookmyshow Event-Tag matrix as additional informant. The proposed cross-domain based Event Recommendation approach is divided into three modules- i) data collection which extracts the unstructured dataset from the two domains Bookmyshow and social networking site Facebook using API’s; ii) data mapping module which is basically used to integrate the common knowledge/ data that can be shared between considered different domains (Facebook & Bookmyshow). This module integrates and reduces the data into structured events’ instances. As the dataset was collected from two different sites, an intersection of both was taken out. Therefore this module is carefully designed according to reliability of information that is common between two domains; iii) 3 order tensor factorization and Latent Dirichlet Allocation (LDA) used for most preferable recommendation by less pertinent result reduction. The proposed 3 order tensor factorization is designed for maximizing the mutual benefit from both the considered domains (organizer and user). Therefore providing three recommendations: For organizers: 1) system recommends places to conduct specific event according to maximum of attendees of a particular type of event at a specific location; 2) recommending target audience to organizer: those who are interested to attend event on the basis of past data for promotion purposes. For users: 3) recommending events to users of their interest on the basis of past record. Our result shows significant improvement in reduction of less relevant data and result effectiveness is measured through recall and precision. Reduction of less relevant recommendation is 64%, 72% and 63% for place recommendation to organizer, target audience recommendation to organizer and event recommendation to user respectively. The proposed tensor factorization approach achieved 68% precision, 15.5% recall in recommending attendees to organizer and 62% precision, 13.4% recall for event recommendation to user