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Purpose: This paper aims to describe the clinical presentation and demographic distribution of keratoconus (KCN) in India by analyzing the electronic medical records (EMR) of patients presenting at a multitier ophthalmology hospital network. Methods: This cross-sectional hospital-based study included the data of 2,384,523 patients presenting between January 2012 and March 2020. Data were collected from an EMR system. Patients with a clinical diagnosis of KCN in at least one eye were included in this study. Univariate analysis was performed to identify the prevalence of KCN. A multiple logistic regression analysis was performed using R software (version 3.5.1), and the odds ratios are reported. Results: Data were obtained for 14,749 (0.62%) patients with 27,703 eyes diagnosed with KCN and used for the analysis. The median age of the patients was 22 (inter-quartile range (IQR): 17–27). In total, 76.64% of adults (odds ratio = 8.77; P = <0.001) were affected the most. The majority of patients were male (61.25%), and bilateral (87.83%) affliction was the most common presentation. A significant proportion of the patients were students (63.98%). Most eyes had mild or no visual impairment (<20/70; 61.42%). Corneal signs included ectasia (41.35%), Fleischer ring (44.52%), prominent corneal nerves (45.75%), corneal scarring (13.60%), Vogts striae (18.97%), and hydrops (0.71%). Only 7.85% showed an association with allergic conjunctivitis. A contact lens clinic assessment was administered to 47.87% of patients. Overall, 10.23% of the eyes affected with KCN underwent a surgical procedure. the most common surgery was collagen cross-linking (8.05%), followed by deep anterior lamellar keratoplasty (1.13%) and penetrating keratoplasty (0.88%). Conclusion: KCN is usually bilateral and predominantly affects males. It commonly presents in the second and third decade of life, and only a tenth of the affected eyes require surgical treatment. [ABSTRACT FROM AUTHOR]

Farmers submit claims to insurance providers when affected by sowing/planting risk, standing crop risk, post-harvest risk, and localized calamities risk. Decision making for settlement of claims submitted by farmers has been observed to comprise of type-I and type-II errors. The existence of these errors reduces confidence on agri-insurance providers and government in general as it fails to serve the needy farmers (type-I error) and sometimes serve the ineligible farmers (type-II error). The gaps in currently used underlying data, methods and timelines including anomalies in locational data used in crop sampling, inclusion of invalid data points in computation, estimation of crop yield, and determination of the total sown area create barriers in executing the indemnity payments for small and marginal farmers in India. In this paper, we present a satellite big data analytics based case study in a region in India and explain how the anomalies in the legacy processes were addressed to minimize type-I and type-II errors and thereby make ethical decisions while approving farmer claims. Our study demonstrates what big data analytics can offer to increase the ethicality of the decisions and the confidence at which the decision is made, especially when the beneficiaries of the decision are poor and powerless. [ABSTRACT FROM AUTHOR]

Big Data Analytics is considered to be the key disruptive technology of the decade, and is revolutionizing the industries like never before with its ubiquitous applicability. Though the term "Big Data" gained more traction since 2011 after the McKinsey Report [1], some business models have been generating huge volumes of data for several decades. Some prominent examples include the Government [2] and Private [3] Meteorological Departments forecasting weather on the basis of Satellite Imagery [4] and the Logistics industry working with Geospatial Data [5]. There has been a significant boost to the efficiency of operations and the accuracy of results in both sectors after the advent of Big Data Analytics. This paper examined the Geolocation Data provided by Google based on the Location Service API [6], and suggested different ways to preprocess and visualize the data for a business analysis later. The data had been taken from the operational fleet of a leading logistics service provider in India and a small subset has been analyzed to assess the suitability of the application of this paper in a general purpose manner. This study is a part of research[7] on applications of IoT and Big Data Analytics in the supply chain management being conducted by the authors, but can be used independently in other applications as well. [ABSTRACT FROM AUTHOR]

The ability to exploit students' sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students' data using machine learning techniques, and (ii) modelling and predicting students' emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders. [ABSTRACT FROM AUTHOR]

The exponential spread of Covid-19 is not only a serious concern for public health but has also severely affected the global economy. India is not an exception. The banking sector must plan innovatively in a wide range of scenarios focusing upon Covid-19 specific requirements. It becomes essential to examine the impact of Covid-19 on the performance of the Indian banking sector and take focused initiatives at both the tactical and the strategic levels. This paper offers the Covid-19 Impact on Banking Ontology (Covid19-IBO) that provides semantic information about the impact of Covid-19 on the banking sector of India. The developed ontology has been verified and validated and has been made available on the Linked Open Data cloud. It can be utilized to annotate the related data to provide meaningful insights. The Covid-19 ontologies already available have some overlapping information that causes redundancy. Unified integration of these ontologies is required to operate upon them unambiguously. It becomes reasonable to develop a matching approach to link all these ontologies semantically. We, therefore, also provide a schema matching approach with reasonable results to map the Covid-19 ontologies. [ABSTRACT FROM AUTHOR]

Abstract The state-of-practice in planning, designing, and conducting freight surveys leave much to be desired, even in the era of big data analytics. This paper addresses this issue by providing a comprehensive, yet, inexpensive integrated data collection framework for conducting establishment-based freight survey (EBFS). The paper demonstrates the application of the proposed framework by implementing it in eight cities across two geographically dissimilar states of India. This is the first freight survey of its kind in terms of scale and scope in developing countries, where there is no established practice in freight data collection. Guidelines are suggested for overcoming challenges in EBFS such as: (a) efficient allocation of survey resources within budgetary constraints; (b) effective survey instrument design for reducing the respondent burden; (c) determination of sample size requirements and the expected number of sampling units to be contacted (d) development of sampling strategies using sampling frames with limited auxiliary information; and (e) data collection strategies to improve response rates. The heuristics for allocation of survey resources are mathematically formulated and predicted using the web-based survey responses obtained from planners of past surveys. Trade-off scenarios between different components of survey resources (money, time, and manpower) are presented to enable the planners to arrive at a suitable EBFS design for meeting research requirements within resource constraints. Analysis of results suggest that the response rates for EBFS are largely linked to the physical characteristics of commodities and city demography. The discussions provided on resource allocation, survey instrument design, sampling design, pilot surveys, interviewer training, response rate improvement strategies, and data processing are expected to guide for best practices in future surveys. In sum, the literature synthesis, case-study illustrations, and the proposed framework for EBFS design are expected to strengthen the state-of-practice of EBFS by making the rigorous random sample surveys less expensive, more systematic, and in turn, replacing the need to opt for convenience samples. Highlights • A structured synthesis of establishment freight surveys (EBFS) is presented and insights are drawn. • An integrated comprehensive framework is proposed for planning, designing, and conducting EBFS. • Guidelines and tools provided for resource allocation, survey instrument design, sampling and data collection strategies. • Survey design framework is implemented, and findings are reported for two dissimilar states in India. • Survey response rates found to be influenced by physical characteristics of commodities and city demography. [ABSTRACT FROM AUTHOR]

The timeliness dimension of data quality has not received sufficient scholarly attention even though the publication of official statistics of various countries are often delayed. This paper examines the growing delays in the release of census data in India amidst the technocratisation of policy-making, public professions of faith in evidence based policy-making and growing fascination with big data. We show that the growing delay in the release of census data of India is a fact, rather than a mere allegation, and contextualise the problem by comparing seven decennial censuses conducted between 1951 and 2011. We suggest that delays can be measured vis-à-vis usual, desirable, declared and feasible schedules of publication. Further, delays can be understood from the perspectives of the unwillingness of the government statistical system to face public scrutiny, decline in the quality of public goods, political interference and, in case of data on identity, communalisation of politics. [ABSTRACT FROM AUTHOR]

Instead of exploring 'smart cities' as future utopias, this paper concentrates on historically constructed, yet actively contested socio-spatial inequalities. Drawing upon Chandigarh's master-planning experience, it explores epistemic, material, and civic dimensions of Chandigarh's Smart City Proposal to ask whether vernacular reinterpretations of 'smart citizenry' help the subaltern reclaim their 'right to the city'. Thus, following a critical genealogy that shifts attention from 'smart cities' towards 'citizen centeredness', this research focuses on the construction and contestation of 'smart citizenship'. Overall, technocratic and city-branding discourses, which legitimate restricting funds to a 'smart enclave' at the cost of evictions and banning 'encroachers', are confronted by housing rights activists. This motivates scholars to theorize a subversive identity, in which 'smartness' gains new meaning. However, epistemic contestations are not enough to create recognition for the needs and rights of the working poor, who work for but cannot reside in Chandigarh. Further alliances and political will are required. [ABSTRACT FROM AUTHOR]

In cloud environment, big data analytics is a very innovative idea;Big data is defined as a very huge quantity of data. To extract meaning full value from it we need different technologies for analysis of the data. Big data Analytic is used to study pattern of historical data and the effect of changes of different parameters. Rainfall Forecasting has been a standout amongst the most intriguing and captivating space and it assumes a noteworthy part in meteorology and daily human life. Rainfall forecasting is used to estimate the probability of whether it will rain or not. Rainfall situation is the state of atmosphere at a given time in terms of different parameter like temperature, humidity and wind speed. Forecasting of rainfall is important to facilitate preparing for the finest and the nastiest of the climate. India is agrarian country so it is essential to know in advance about climate. This paper presents big data analytics for Rainfall forecasting and studies the advantage of using it. The algorithm which is used in this project is Time Series. The intention of time series analysis is generally in two ways, one is to recognize or representation the stochastic mechanisms that gives rise to an observed series and second is to predict or forecast the future value of an arrangement in light of the historical backdrop of that arrangement. Forecasts are prepared for new data when the actual result may not be known until some future date. What's to come is being anticipated, yet all earlier perceptions are quite often treated similarly. [ABSTRACT FROM AUTHOR]

While plans to develop “smart cities” are gathering pace across the world, we know little about the ways in which the discourses of datafication, smartness, and big data play out in material contexts of urban development, including utility and resource management. In this paper, we explore this intersection in the case of Bangalore’s water supply, where IBM in alliance with the Bangalore Water Supply and Sewerage Board (BWSSB) is implementing a water-flow sensor network and geographic database system under the label of “big data for water supply.” We illustrate how the BWSSB-IBM approach narrows down the complex field of water provision to a question of water in- and out-flow measurements and the monitoring of BWSSB ground personnel. In theoretical terms, we discuss the ways in which these processes constitute both particular claims to knowledge, and the redefinition of citizenship as consumption. [ABSTRACT FROM AUTHOR]

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach. [ABSTRACT FROM AUTHOR]

All other cited papers address etiological diagnosis.[[3]],[[4]],[[5]] If an EMR is not updated immediately, as soon as new information is gathered - such as test results - anyone viewing that EMR could be receiving incorrect or incomplete information. Electronic medical records (EMRs) have a great impact in clinical practice. However, in materials and in results they have reviewed only the medical records of secondary-center and the tertiary-center patients, probably because they assumed that all uveitis patients of the 176 vision centers were referred to tertiary centers. [Extracted from the article]