Caste and Gender: A Study of Dalit Feminism

Indian Social systems are characterized and operated through Caste which marks the graded inequality on a vertical ladder resulting out of the Hindu Social Framework with Brahmins being positioned at Top and Shudras, Untouchables at the bottom. Brahmins enjoyed their dominion over knowledge structure which gave them a superior position in the society, while the ones at the bottom (broadly Dalits) are the most suffered since they did not have access to education. In the post-independence with the constitutional frameworks being in place Dalits started acquiring Education. Thus their articulations have come in to being, at first through prose and poetry, later emerged in to a strong base for what is broadly termed as Dalit Literature

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming

Cross sectional study evaluating the correlation of thyroid dysfunction with severity of disease in rheumatoid arthritis

Background: The present study was conducted to evaluate the correlation of disease severity in RA and thyroid dysfunction.Methods: The present cross-sectional descriptive study enrolled 164 participants aged 12 years and above diagnosed as having RA. Use of drugs causing thyroid dysfunction, malignancy, diabetes mellitus, systemic hypertension, pregnancy and prior thyroidectomy were the criteria for exclusion. Data was analyzed using R and tests of significance were Chi square test and independent sample t-test and Pearson correlation. Institutional ethics committee approved the study and written informed consent was obtained from all study participants.Results: Serum TSH positively correlated with DAS 28 (r=0.2, p=0.005), ESR (r=0.2, p=0.03), CRP (r=0.2, p=0.006), RA factor (r=0.2, p=0.003), subjective assessment (r=0.3, p= 0.001) and anti TPO antibodies (r=0.7, p=0.001). Free T4 negatively correlated with DAS28 (r=-0.2, p=0.006), ESR (r=-0.2, p=0.02), CRP (r=-0.2, p=0.01). RA factor (r=-0.2, p=0.01), subjective assessment (r=-0.2, p= 0.01), anti TPO (r=-0.6, p=0.001) and Free T3 negatively correlated with DAS28 score (r=-0.2, p=0.02) , ESR (r=-0.2, p=0.03), RA factor (r=-0.3, p=0.001) and anti TPO antibodies (r=- 0.3, p=0.001).Conclusions: Hypothyroidism was significantly associated with disease severity of RA with linear positive correlation of TSH with DAS28 score, ESR, CRP, RA factor, subjective assessment and anti TPO antibodies, linear negative correlation of serum free T4 with DAS 28 score, ESR, CRP, RA factor, subjective assessment and anti TPO antibody and linear negative correlation of free T3 with DAS28 score, ESR, RA factor and anti TPO antibody was observed

Business intelligence analytics using sentiment analysis-a survey

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique

Careers and Trends of Engineering Graduates – a Case for Study

This paper provides a template for preparing papers for electronic production of the EduLearn. A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety. The Abstract should be informative and completely self-explanatory, provide a clear statement of the problem, the proposed approach or solution, and point out major findings and conclusions. The Abstract should be 100 to 150 words in length. The abstract should be written in the past tense. Standard nomenclature should be used and abbreviations should be avoided. No literature should be cited. The keyword list provides the opportunity to add keywords, used by the indexing and abstracting services, in addition to those already present in the title. Judicious use of keywords may increase the ease with which interested parties can locate our article

Business recommendation based on collaborative filtering and feature engineering – aproposed approach

Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle.  The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained

Real Time Monitoring and Neuro-Fuzzy Based Fault Diagnosis of Flow Process in Hybrid System

Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as open global standard to address the low-cost, low-power wireless sensor networks. The goal is to monitor the parameters and to classify the parameters in normal and abnormal conditions to detect fault in the process as early as possible by using artificial intelligent techniques. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. Several techniques are being investigated as an extension to the traditional fault detection and diagnosis. Computational intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis methods. This paper proposes ANFIS (Adaptive Neural Fuzzy Inference System) for fault detection and diagnosis. In ANFIS, the fuzzy logic will create the rules and membership functions whereas the neural network trains the membership function to get the best output. The output of ANFIS is compared with Back Propagation Algorithm (BPN) algorithm of neural network. The training and testing data required to develop the ANFIS model were generated at different operating conditions by running the process and by creating various faults in real time in a laboratory experimental model

Bayesian Inference for Median of the Lognormal Distribution

Lognormal distribution has many applications. The past research papers concentrated on the estimation of the mean of this distribution. This paper develops credible interval for the median of the lognormal distribution. The estimated coverage probability and average length of the credible interval is compared with the confidence interval using Monte Carlo simulation