Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions

Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns about data privacy. Solutions based on client-side encryption have been explored to mitigate privacy concerns. Nonetheless, such solutions hinder data processing, specifically clustering, which is pivotal in dealing with different forms of big data. For instance, clustering is critical to limit the search space and perform real-time search operations on big datasets. To overcome the hindrance in clustering encrypted big data, we propose privacy-preserving clustering schemes for three forms of unstructured encrypted big datasets, namely static, semi-dynamic, and dynamic datasets. To preserve data privacy, the proposed clustering schemes function based on statistical characteristics of the data and determine (A) the suitable number of clusters and (B) appropriate content for each cluster. Experimental results obtained from evaluating the clustering schemes on three different datasets demonstrate between 30% to 60% improvement on the clusters' coherency compared to other clustering schemes for encrypted data. Employing the clustering schemes in a privacy-preserving enterprise search system decreases its search time by up to 78%, while increases the search accuracy by up to 35%.Comment: arXiv admin note: text overlap with arXiv:1908.0496

An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?

Lower intrafamilial transmission rate of hepatitis B in patients with hepatitis D coinfection: A data-mining approach

demographic and viral characteristics of family members affect the transmission rate. Objectives: In this study, we have used data mining techniques to investigate the impact of different variables in intrafamilial transmission of HBV infection. Patients and Methods: demographic information, viral markers, and medical history of 330 patients with chronic hepatitis B and their offspring attending a referral center in Tehran were collected. Data-mining techniques were administered to detect patterns. Results: The overall transmission rate was 15.7 (5.4 and 27.3 for male and female index cases respectively). In female patients, HBe Ag positively affected the transmission rate (49 vs. 23.4). There was a dominant change in transmission rate of female patients with negative results for Hbe Ag with HDV coinfection, where the transmission rate changed from 25 in patients with negative results for HDV Ab to 5 in those with positive results. In Hbe Ag negative male index cases, the transmission rate was 1.3 in cases with positive results for HDV Ab compared to 7 in those with negative findings. The overall transmission rate was statistically different between patients with positive and negative results for HDV Ab (P = 0.016). Conclusions: There is a minor but consistent pattern change in the presence of HDV infection which reduces familial transmission of HBV, especially in female patients with negative results for HBe Ag. © 2013, Kowsar Corp

Hepatitis C virus genotype frequency in Isfahan province of Iran: a descriptive cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Hepatitis C is an infectious disease affecting the liver, caused by the hepatitis C virus (HCV). The hepatitis C virus is a small, enveloped, single-stranded, positive sense RNA virus with a large genetic heterogeneity. Isolates have been classified into at least eleven major genotypes, based on a nucleotide sequence divergence of 30-35%. Genotypes 1, 2 and 3 circulate around the world, while other genotypes are mainly restricted to determined geographical areas. Genotype determination of HCV is clinically valuable as it provides important information which can be used to determine the type and duration of therapy and to predict the outcome of the disease.</p> <p>Results</p> <p>Plasma samples were collected from ninety seven HCV RNA positive patients admitted to two large medical laboratory centers in Isfahan province (Iran) from the years 2007 to 2009. Samples from patients were subjected to HCV genotype determination using a PCR based genotyping kit. The frequency of HCV genotypes was determined as follows: genotype 3a (61.2%), genotype 1a (29.5%), genotype 1b (5.1%), genotype 2 (2%) and mixed genotypes of 1a+3a (2%).</p> <p>Conclusion</p> <p>Genotype 3a is the most frequent followed by the genotype 1a, genotype 1b and genotype 2 in Isfahan province, Iran.</p

Interpretation of uniocular and binocular trials of glaucoma medications: an observational case series

<p>Abstract</p> <p>Background</p> <p>To predict the effectiveness of topical glaucoma medications based on initial uniocular and binocular treatment. To test a traditional hypothesis that effectiveness following a uniocular trial is associated with the change in IOP in the initially treated eye minus the change in the initially untreated eye. To determine whether uniocular or binocular treatment trials are superior.</p> <p>Methods</p> <p>Based on a review of medical records, we identified 168 instances in 154 patients with bilateral primary open angle glaucoma of initial uniocular use of a topical glaucoma medication with well-documented intraocular pressure (IOP) readings at baseline (IOP_A), during the trial (IOP_B), and at follow-up (IOP_C). Abstracted data included demographic data, IOP, and medication use. Predictors of the IOP following the trial (IOP_C) in each eye were identified by multivariable linear regression. In 70 cases, the predictive ability of initial uniocular and binocular treatment could be directly compared.</p> <p>Results</p> <p>In a multivariable analysis, the follow-up pressure in the initially treated eye (IOP_1C) was directly correlated with treated eye IOP during initial uniocular use (IOP_1B, p < 0.001). In a multivariable analysis, the follow-up pressure in the initially untreated eye (IOP_2C) was directly correlated with its baseline IOP_2A(p < 0.001), and also tended to be associated with treated IOP_1B(p = 0.07). The multivariable regression coefficient (b) for the IOP change in the initially untreated eye was generally not close to the value of -1 expected by the classic teaching (for eye 1, b = 0.04, p = 0.35; for eye 2, b = 0.07, p = 0.50). In 70 cases, the uniocular and binocular trials predicted a similar fraction of the variance in follow-up IOP_1C(r²= 0.56 and 0.57, respectively) and IOP_2C(r²= 0.39 and 0.38, respectively).</p> <p>Conclusion</p> <p>1) For uniocular trials, the IOP change in the untreated eye should not be subtracted from that in the treated eye. 2) Uniocular and binocular trials have similar predictive value when interpreted correctly. Either may be selected based on clinical circumstances.</p

Molecular epidemiology of Hepatitis B virus genotypes in Pakistan

<p>Abstract</p> <p>Background</p> <p>Eight genotypes of Hepatitis B virus designated A-H, have been known but in Pakistan, no such data is available on the prevalent HBV genotypes. Therefore, the subject study was conducted to determine HBV genotypes in the indigenous Pakistani population.</p> <p>Methods</p> <p>A total of 690 individuals were enrolled for HBV screening with EIA and nested PCR. Positive samples were further analyzed to determine HBV genotypes (A-F) by multiplex-PCR using type specific primers.</p> <p>Results</p> <p>110 (15.94%) individuals were positive for HBV, including 64% males and 36% females. Out of these, 66 samples (65.34%) were classified into genotype D, 27 (26.73%) were of genotype B while 5(4.95%) had genotype A. In 3 (2.98%) samples, multiple genotypes were detected (genotype A+B; 2(1.99%) and genotypes B+D; 1(0.99%). Nine (8.18%) samples remained untyable.</p> <p>Conclusion</p> <p>In Asia, genotypes B and C are the most prevalent but our study reveals that genotype D is predominant and HBV infection constitutes a significant health problem in Pakistan.</p

Share of afghanistan populace in hepatitis B and hepatitis C infection's pool: is it worthwhile?

There is a notable dearth of data about Hepatitis B Virus (HBV) and Hepatitis C Virus(HCV) prevalence in Afghanistan. Awareness program and research capacity in the field of hepatitis are very limited in Afghanistan. Number of vulnerabilities and patterns of risk behaviors signal the need to take action now