Big Data Analytics Using Neural networks

Machine learning is a branch of artificial intelligence in which the system is made to learn from data which can be used to make predictions, real world simulations, pattern recognitions and classifications of the input data. Among the various machine learning approaches in the sub-field of data classification, neural-network methods have been found to be an useful alternatives to the statistical techniques. An artificial neural network is a mathematical model, inspired by biological neural networks, are used for modeling complex relationships between inputs and outputs or to find patterns in data. The goal of the project is to construct a system capable of analyzing and predicting the output for the evaluation dataset provided by the IBM Watson: The Great Mind Challenge organized by IBM Research and InnoCentive INSTINCT (Investigating Novel Statistical Techniques to Identify Neurophysiological Correlates of Trustworthiness) : The IARPA Trustworthiness Challenge organized by the office of The Director Of National Intelligence. The objective of this paper is to understand the machine learning using neural networks. At the end of the paper, the comparison between different learning strategies have been shown which are used to increase the accuracy of the predictions. From the trained neural network up to a satisfactory level, we will be able to classify any generalized input, process often termed as generalization capability of the learning system

Status of Textile Engineering College Libraries in Haryana, India

The purpose of the study is to estimate the performance of the textile engineering institute libraries of Haryana, India, and determine what collections and services are provided. A survey was conducted through a questionnaire circulated among the librarians of textile engineering institutes. The paper discusses the collection and worth of various services rendered by these libraries

Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

Comparison between intrathecal bupivacaine and combination of bupivacaine and midazolam for postoperative pain relief after infraumbilical elective surgeries

Background: Objective of the study was to identify the efficacy of midazolam as an adjuvant to intrathecal hyperbaric bupivacaine 0.5%, a prospective, randomized, double blind study was conducted to compare the onset, duration of sensory and motor block, postoperative analgesia, hemodynamic changes and complications.Methods: Sixty patients, ASA I/II, Age 18-60 year, scheduled for infraumbilical surgeries, were randomly allocated to group BNS(n=30) to receive intrathecally 2.5 ml of 0.5% hyperbaric bupivacaine with 0.4 ml normal saline; and group BM (n=30) to receive 2.5 ml of 0.5% hyperbaric bupivacaine +2 mg preservative free midazolam 0.4 ml (5mg/ml). We observed onset, duration and regression of sensory and motor block, degree of sedation and pain scores, hemodynamic changes and adverse effects. (PS) version 3.0.0.34 was used for power and sample size calculation. Statistical analysis was performed using Microsoft (MS) office excel software with the student’s t-test and chi-square test (P=0.05)Results: Highest level of sensory blockade (p<.05), motor block duration (179.67±14.94 vs 151.83±10.96 min), sensory block duration (222±16.5 vs 174±12.53 min) and time to first requirement of i.v. analgesia were significantly higher in group BM. Postoperative VAS score was significantly less in group BM. Both groups were comparable in demographic data and hemodynamic changes.Conclusions: Intrathecal 2 mg midazolam found as an attractive adjuvant to 0.5% hyperbaric bupivacaine in spinal anesthesia in infraumbilical surgeries by producing significantly longer duration of motor and sensory block, good quality of intraoperative and postoperative analgesia with less incidence of nausea vomiting as compared to bupivacaine alone.

A comparative study of cloud services use by prospective IT professionals in five countries

Individuals and organizations utilise the cloud technology and its services in various ways. Cloud-based services are becoming increasingly popular, while there is no adequate knowledge offered for their secure use in the education for future IT professionals. It is important to understand how security and privacy issues are perceived and handled by male/female users and IT professionals of different cultures. The authors aim at presenting and scrutinizing information about cloud services’ use by prospective IT professionals in five countries, namely China, Finland, Greece, Nepal, and the UK. In particular the authors, wanting to find out what are the future IT professionals’ conceptualisations and awareness, collected data from male and female IT students in higher education, who use (or not) cloud services. The authors further illustrate the research findings by proceeding to a comparative analysis considering different perspectives such as: gender, education background, national culture (values and culture), and IT-related knowledge. The final research outcomes reveal attention-grabbing information for future IT professionals’ skills, knowledge, and digital competencies. For the IT professionals and software quality engineering communities the latter comprise a body of realistic knowledge, worthy of note when designing curricula for security technology by accommodating practical and accessible solutions (e.g., cryptography-based cloud security) for developing and enhancing the IT professionals’ role

Language of smell: Tracing some cross-cultural insights from past and present

It is assumed that the human sense of smell is inferior to vision. This framework of underdeveloped human olfaction can be mainly attributed previously to its historical demotion of it by the Western, quasi-scientific ideas, and evolutionary narratives or differences in reference to other animals, such as rats or mice. However, this notion of a comparatively poor sense of smell may have derived from the narrowly focused pseudoscientific practices of the past and needs to be revisited under more recent findings. Similarly, the generalization of language as poorly connected with the olfactory system needs more cross-cultural references to support or abolish this hypothesis. Humans’ olfactory system is not inferior, but certainly different from other species and humans have excellent olfactory abilities. Humans are exceptional in detection and discrimination; in fact, they are more sensitive than rodents and dogs to some odors. Language does not constrain the naming of odors due to cognitive architecture, and the mapping of language on the senses is culturally related. Jahai hunter-gatherers were found naming odors as easy as colors. Plasticity and culture were found to have a huge influence on odor naming. No universal hierarchy of senses, such as if vision is more codable than odor, was found imposing on languages

Antimicrobial Activity of Ethanolic Extracts of Syzygium aromaticum and Allium sativum Against Food Associated Bacteria and Fungi

The successful control of food spoilage microorganisms require the use of indigenous antimicrobials in foods including certain botanical compounds that have been historically used for flavour enhancement as well as preservation. The present study was designed to evaluate the in vitro antimicrobial activity of ethanolic extracts of Syzygium aromaticum (clove) and Allium sativum (garlic) against Gram-positive and Gram-negative food associated bacteria (Bacillus subtilis, B. megaterium, B. polymyxa, B. sphaericus, Staphylococcus aureus and Escherichia coli) and molds (Penicillium oxalicum, Aspergillus flavus, A. luchuensis, Rhizopus stolonifer, Scopulariopsis sp. and Mucor sp.) assayed by agar well diffusion method and poisoned food technique, respectively. Clove extract showed better antimicrobial activity than the garlic extract. The zone of inhibition in clove ethanolic extract against all the food associated bacteria was in the range of 25mm to 32mm and in molds the percent mycelial growth inhibition ranged from 70% to 100%. The growth inhibition zone in garlic ethanolic extract against bacteria was in the range of 20mm to 31mm and in molds the percent mycelial growth inhibition ranged between 20% and 50%. The clove ethanolic extract exhibited the maximum zone of inhibition against E. coli whereas garlic ethanolic extract showed maximum activity against B. subtilis. Both the extracts exhibited maximum percent mycelial growth inhibition against R. stolonifer. However garlic extract was not effective against P. oxalicum. The MIC values of clove ethanolic extract for different bacterial isolates ranged from 5.0mg/ml to 20mg/ml and 10 mg/ml to 20mg/ml against molds. The MIC values of garlic ethanolic extract for different bacterial and fungal isolates ranged from 10 mg/ml to 20mg/ml. The value of MBC and MFC equaled the MIC. Based on this finding, it may be suggested that these extracts may be used as natural antimicrobial additives to reclaim the shelf-life of foods

Role and Impact of E-Journals: A Case Study of University Institute of Engineering and Technology (UIET), Kurukshetra University

Present study aims to determine the use and awareness of e-journals among teachers and research scholars of University Institute of Engineering and Technology (UIET). Study also targets to define the purpose of e-journals’ use, access place, preferred format and frequency to use. Data was collected from 60 users of different departments of UIET. Study indicates that majority of the respondents use e-journals for their research assignments, they prefer to retrieve e-resources mainly through simple search techniques such as key word, author and title

In Vitro Antimicrobial Activity of Sapindus mukorossi and Emblica officinalis Against Dental Caries Pathogens

The in vitro antimicrobial activity of Sapindus mukorossi and Emblica officinalis fruit extracts were studied against Streptococcus mutans, Staphylococcus aureus, Lactobacillus acidophilus, Candida albicans and Saccharomyces cerevisiae. The acetone, ethanol, methanol, hot water and cold water extracts of S.mukorossi exhibited antimicrobial activity against one of the tested microorganisms i.e. S.cerevisiae. All the five extracts of E.officinalis showed inhibitory activity against S.mutans while the acetonic, hot and cold aqueous extracts showed inhibitory activity against S.aureus also. The largest zone of inhibition was obtained with the acetonic extract of S.mukorossi against S.cerevisiae (29.65mm) and hot water extract of E.officinalis against S.aureus (40.32mm). Minimum inhibitory concentrations (MIC) of the extracts were also determined against the selected microorganisms showing zones of inhibition ≥8mm. This study depicts that the fruits of Sapindus mukorossi and Emblica officinalis possess very good antifungal and antibacterial activities respectively and can be used as a potential source of novel antimicrobial agents used to cure dental caries

Use of E-Shodhsindhu Consortium by Research Scholars: A Comparative Study of Kurukshetra University and Maharshi Dayanand University

E-ShodhSindhu consortium is backbone of research in higher education system in India. E-ShodhSindhu consortium provides full text and bibliographical journal databases to higher education institutions at subscription cost. The current study is an effort to find out the use of E-ShodhSindhu consortium by the Research Scholars of Kurukshetra University and Maharshi Dayanand University. Study indicates that more than 55% Research Scholars including both the universities are aware about E-ShodhSindhu consortium and they use it mainly for research work