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Artificial Intelligence technologies have elicited a range of policy responses in India, particularly as the Government of India attempts to position and project the country as a global leader in the production of AI technologies. Policy responses have ranged from providing public infrastructure to enable market-led AI production, to nationalising datasets in an effort to enable Big Data analysis through AI. This paper examines the recent history of AI policy in India from a critical political economy perspective, and argues that AI policy and governance in India constructs and legitimises a globally-dominant paradigm of informational capitalism, based on the construction of data as a productive resource for an information-based economic production, and encouraging self-regulation of harmful impacts by firms, even as it attempts to secure a strong hand for the state to determine, both through law and infrastructure, how such a market is structured and to what ends. [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]

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]

Big data analytics involve a systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involves grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It applies feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. The system also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. The overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 82%, and it can be increased as a future work by including deep learning with time-series grape leaf images. [ABSTRACT FROM AUTHOR]

The process of data analytics on large-scale government administrative data — that belong to various domains like education, transport, energy, and health — can be enhanced by retrieving pertinent documents from diverse data sources. Without a supporting framework of metadata, big data analytics can be daunting. Even though statistical algorithms can perform extensive analyses on a variety of data with little help from metadata, applying these techniques to heterogeneous data may not always result in reliable findings. Recently, semantics-aware (or semantic search) search techniques received much attention as they utilize implicit knowledge to enhance the search. Similarly, traditional search engines rely on the inherent linkages within the underlying data model to improve their search quality. In the case of general-purpose information retrieval systems, to gather information from the internet (open access data) or to access open government administrative data, a domain agnostic ontology shall be employed to supply background knowledge. This paper draws on research undertaken by the authors at IIIT Bangalore Center for Open Data Research (CODR) in developing a semantics-aware data lake framework to host and analyze government administrative data. In this study, we present an ontology-based document retrieval solution where an ontology serves as an intermediary to close the gap between what the user seeks and what the search retrieves. Although our study settings are based on the Government of Karnataka (GoK, India), we believe the findings have wider resonance. Our experimental results based on agricultural data from the GoK look promising. [ABSTRACT FROM AUTHOR]

With the growing consumer market, India is one of the most favorable places to open a start-up. Various factors that influence the start-ups' success, change over time. Their effects have to be analyzed properly in order to sustain this ever-evolving fast-changing world of trends. In this paper, we aim to study the most common patterns of funding in Indian start-ups industry and to analyze the current status of these start-ups. Means of interactive graphs have been employed to get an insight of the analysis done on the data set. Various start-ups have been categorized broadly into 8 industry verticals, then the graphs have been presented to show the number of start-ups opened in this category and also about how many of them were unsuccessful or being closed. This paper presents the analysis report to get an insight into present trends of investments in various industries and the success rate of start-ups opened in those industries. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies. Design/methodology/approach: The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics. Findings: Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships. Research limitations/implications: The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed. Originality/value: This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area. [ABSTRACT FROM AUTHOR]

The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users' views, as well as experts'/users' complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users' views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model. [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]

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]

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]

FinTech is the embodiment of technology with finance. The future-savvy firms are now including FinTech for augmentation of the financial structure of their companies. The FinTech ecosystem includes virtual currency, identity verification and authorization, credential security, blockchain application and analytics in big data technologies, which makes the financial system more technologically advance. The Government of India has spent around $19 bn for encouraging FinTech start-ups. However, the main concern is now to adapt a transformative approach rather than additive. In this paper, we have examined the feasibility of the technologies being used in the present generation. We have also scrutinized the future scope of FinTech in India from a post-demonetization perspective. Awareness level of young generation about FinTech was accessed through a structured questionnaire and its future scope predicted. [ABSTRACT FROM AUTHOR]

The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per capita consumption, and electrification at very local levels. However, elasticities between night-lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village—rather than at the district—level. The SHRUG can serve as a model for open high-resolution data in developing countries. [ABSTRACT FROM AUTHOR]

Sustainable urban mobility is an essential component of sustainable development but requires careful planning in rapidly growing urban areas. This paper investigates the value and limitations of Big Data for evaluating transport policies, plans, and projects in Hubballi‐Dharwad, India. Results show how Big Data can enable the outcomes of transport interventions to be evaluated more readily than conventional transport analysis. However, the analysis also found that this data may be less able to detect the impacts of travel behaviours in informal settlements, and the impact of extreme weather events. These potential shortcomings, as well as a lack of transparency around the methodology and data sources used by sources of Big Data, could generate unintended consequences and biases in transport planning. Reflecting on these challenges, and the wider implications for urban governance, we conclude that there is an urgent need for Big Data and other technical advances in urban modelling to be seen as compliments to, rather than substitutes for, wider methods of knowledge generation in urban areas. [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]

Purpose: This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms. Design/methodology/approach: A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP). Findings: The results showed that "lack of data storage facility", "lack of IT infrastructure", "lack of organisational strategy" and "uncertain about benefits and long terms usage" were most common barriers to adopt BDA practices in all three industries. Practical implications: The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context. Originality/value: The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India. [ABSTRACT FROM AUTHOR]

Big India has achieved high rates of economic growth post after economic liberalization in 1991. A sustained level of high growth was achieved resulting in a large GDP for the country. India is today the 6th largest economy (in terms of nominal GDP) of the world with a GDP of 2.597 trillion USD (Ranking of countries on gross domestic product as on 2017 by World Bank). However this GDP growth has not led to much improvement in social development indicators. One of the key social development indicators pertaining to health is infant mortality rate (IMR). Although, the initiatives of Government of India to bring down the infant mortality rate (IMR) have earned its kudos, they are not enough. Moreover, this improvement in IMR is not uniform across the country. In India, BIMARU (acronym of four Indian states Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh) states are economically poor and having serious deficit in health awareness. Therefore, any improvement in the overall IMR of the country cannot happen without improving the indicator significantly in these BIMARU states. This paper attempts to identify the determinants of IMR for the BIMARU states. A basic regression model (pooled OLS) of IMR has been developed on the basis of data collected from the Annual Health Survey (AHS) for 184 districts of BIMARU states for 3 consecutive years (2011, 2012 and 2013). The analysis presented in this paper lays out the determinants of IMR in BIMARU states in India and argues in favor of developing targeted initiatives to improve IMR. [ABSTRACT FROM AUTHOR]

Purpose: In spite of the fact that literature shows that big data analytics (BDA) pass on a distinct corporate ability, little is thought about their performance impacts, specifically logical conditions. Establishing this research in the dynamic capability view (DCV) and corporate culture and dependent on an sample of 310 Indian production industries, the purpose of this paper is to experimentally study the impacts of BDA on corporate social performance (CSP) and corporate green performance (CGP) using variance-based structural equation modeling (for example, PLS). Design/methodology/approach: A questionnaire was used to accumulate data sets to examine research hypothesis. The authors pre-examined the survey with six scholastics and six directors from production firms in India. With the help of their sources of data, the authors have adjusted their wordings to improve the transparency and guarantee that length of the survey is accurate. Finally, the questionnaire was prepared for definite data collection. Findings: The authors conclude that BDA has noteworthy effect on CSP/CGP. Notwithstanding, the authors did not find proof for directing role of flexible direction and control direction in the connections among BDA and CSP/CGP. This research offers a more nuanced comprehension of the performance ramifications of BDA, and in this way, it is tending to the critical inquiries of how and when BDA can improve in supply chains. Originality/value: This investigation makes helpful commitments to the BDA research and its effect on CSP/CGP. To the authors' best of information, this is the first hypothesis-focused approach to clarify the effect of BDA on ecological and social supportability. Second, this investigation likewise gives empirical proof that BDA impact on CSP/CGP and is free of flexible or control direction of the industry. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to provide an insight about factors affecting Big Data Analytics (BDA) utilization and adoption in Indian firms. Research studies have so far focused on BDA adoption in developed economies. This study examines the factors that influence BDA usage and adoption in the context of emerging economies.Design/methodology/approach This study proposed a theoretical model of factors influencing BDA utilization and adoption. Two independent research streams – first, the top managers’ perceived strategic value (PSV) in BDA and second, the factors that influence the adoption of BDA theoretically – have been integrated with the technology-organization-environment (TOE) framework. In the BDA context, there was a theoretical necessity to identify the driver and barriers of BDA from the TOE framework on PSV and adoption of BDA. A qualitative exploratory study using face-to-face semi-structured interviews was carried out to collect data from 22 different enterprises and service providers in India. India was selected as the context as it is one of the fastest growing large economies of the world with huge potential of BDA to improve the business landscape.Findings The results showed that the major reason behind BDA non-adoption is that the organizations did not realize the strategic value (SV) of BDA, and they were not ready to make the changes because of technological, organizational and environmental difficulties. The findings corroborate previous results about significant factors affecting IT adoption and implementation and provide new and interesting insights. The main factors identified as playing a significant role in organizations’ adoption of BDA were SV of BDA, complexity, compatibility, IT assets, top management support, organization data environment, perceived costs, external pressure and industry type.Research limitations/implications The main limitation related to this study is the difficulty in generalizing the findings to a larger population of enterprises. To overcome this, a statistical survey has been planned to be conducted in the future.Practical implications The BDA adoption model in this study will have both managerial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BDA adoption in developing counties, in terms of formulating better strategies for BDA adoption. For managers, using the research model of this study could assist in increasing their understanding of why some organizations choose to adopt BDA, while similar ones facing similar conditions do not. Also, the understanding of the strategic utilization of BDA in different business processes may improve the adoption of BDA in organizations.Originality/value This paper contributes in exploring and enhancing the understanding of the factors affecting the utilization and adoption of BDA in organizations from an Indian perspective. This study is an attempt to develop and explore a BDA adoption model by the fusion of PSV and TOE framework. The effect of the three contexts of this framework (technological, organizational and environmental) on the strategic utilization of BDA has been studied for the first time. [ABSTRACT FROM AUTHOR]

Background: The use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery models could facilitate healthcare delivery into rural areas where there is limited access to high-quality access care. Mobile technologies, Internet of Things and 5G connectivity may hold the key to supporting increased velocity, variety and volume of healthcare data., Objective: The purpose of this study is to identify and analyse challenges related to the current status of India's healthcare system-with a specific focus on mHealth and big-data analytics technologies. To address these challenges, a framework is proposed for integrating the generated mHealth big-data and applying the results in India's healthcare., Method: A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards., Main Outcome: This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage., Conclusion: Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

With its high importance in the Indian economy, roadways are one of the major forms of transportation in India that cannot be avoided. There is a public safety problem with roadways and a significant number of injuries in our minds. Due to lack of proper real-time monitoring of the accident details that could be transferred to the nearest police station, hospitals, the percentage of incidents in the country is rising to such a high level that leads to loss of human life. Paper helps to provide a solution to this issue by providing information on the location of accident and the severity of the victim during the accident, which will be instantly shared with the police station, hospitals with details such as images, location, video and impact of the accident for quicker decision making, will improve the chances of saving the lives of the sufferers. It is a system which is fully automated. Therefore, protecting the lives of individuals will be a huge contribution to humanity. [ABSTRACT FROM AUTHOR]

Government of India launched in June 2015, the Smart Cities Mission, amidst much fun and fare with aim to develop 100 smart cities in India that would have state of the art infra structural facilities with extensive use of big data and information technology and with people's participation in governance. It was termed as a pilot project which could later be extended to other cities also. Five years have gone by and project has not yielded its desired result as postulated at the time of launch of project. This paper evaluates the 'smart cities mission' critically and tries to find if the project is worth the money spent on it or there could be better approaches to give better life to its citizens for a country like India. [ABSTRACT FROM AUTHOR]

The menace of insurance frauds has assumed gargantuan proportions leading to losses for the insurance industry. Fraudsters are using sophisticated tools and techniques to commit frauds. This paper highlights the increase in the number of fraudulent claims in India and their deleterious impact on sustainability of the insurance business model. The growth in the occurrence of hard frauds in rural India is a matter of concern too. Indian insurance sector has a long way to go in their attempts to fight frauds using sophisticated tools like the Western world. Automation and digitization has only added to the vulnerabilities of the insurance sector. The conventional methods using statistical data to detect frauds are no longer enough. The insurance industry needs to deploy analytics solutions to manage frauds effectively. Analytics offers a plethora of advantages for insurers. Integration of structured as well as unstructured data is essential for taking the right decisions. The challenge here is to explore the various options and choose the right solution for the business. There is also a need to set up dedicated fraud fighting teams with the right skill-sets and competencies. Dealing with insurance frauds has to be a collective effort with the support from all stakeholders. [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 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]

Purpose: Significant advances have been made in the field of healthcare service delivery across the world; however, health coverage particular for the poor and disadvantaged still remains a distant dream in developing world. In large developing countries like India, disparities in access to healthcare are pervasive. Despite recent progress in ensuring improved access to health care in past decade or so, disparities across gender, geography and socioeconomic status continue to persist. Fragmented and scattered health records and lack of integration are some of the primary causes leading to uneven healthcare service delivery. The devised framework is intended to address these challenges. The paper aims to discuss these issues. Design/methodology/approach: In view of such challenges, in this research a Big Data and blockchain anchored integrative healthcare framework is proposed focusing upon providing timely and appropriate healthcare services to every citizen of the country. The framework uses unique identification number (UID) system as formalized and implemented by the Government of India for identification of the patients, their specific case histories and so forth. Findings: The key characteristic of our proposed framework is that it provides easy access to secure, immutable and comprehensive medical records of patients across all treatment centers within the country. The model also ensures security and privacy of the medical records based upon the incorporation of biometric authentication by the patients for access of their records to healthcare providers. Originality/value: A key component of our evolved framework is the Big Data analytics-based framework that seeks to provide structured health data to concerned stakeholders in healthcare services. The model entails all pertinent stakeholders starting from patients to healthcare service providers. [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]

Purpose: Technological developments have made it possible for organizations to use enterprise resource planning (ERP) services without indulging in heavy investments like IT infrastructure, trained manpower for implementation and maintenance and updating the systems regularly to maintain business competitiveness. Plug and play model offered by cloud ERP has led to a constant creation of large data sets which are structured, semi-structured and unstructured by nature. Thus, there has been a need to analyze such complex data sets and the purpose of this paper is to focus on how cloud ERP and big data predictive analytics (BDPA) will impact the performance of a firm. Design/methodology/approach: A dynamic capability view (DCV) theory-based model was developed and the authors have collected data by using an online questionnaire from India. Thereafter, the authors have analyzed it by employing structural equation modeling. Findings: SEM analysis of 231 respondents showcases that the use of DCV theory to define the relationships of cloud ERP and BDPA has been the right move. Out of the 13 hypotheses empirically tested, only 7 hypotheses were supported by the data. Research limitations/implications: The study showcases cross-sectional data from India. It would be interesting for this study to see if the country-level differences would influence these relationships between cloud ERP and financial performance, BDPA and financial performance and cloud ERP and BDPA. Originality/value: This study empirically tests the relationship of cloud ERP and BDPA through a model based on DCV theory. [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]

Big data refers to data collected and consolidated in huge volumes by organisations. In recent years, use of big data has become vitally important in various industries; the insurance sector being no exception. India's insurance industry is going through a rough patch due to the redundant practices of the companies and lack of personalization in handling customer needs. This paper attempts to explore use of big data analytics to revive the insurance industry from its current state and aid in an exponential pan-industry growth. With the advent of social media, data availability has increased manifold. This presents an opportunity to the insurance companies to not only collate structured data readily available with them, but also gain access to various kinds of unstructured information present on social media. Here, big data can be effectively used for predictive analytics and fraud analytics. Big data ensures faster data processing thus helping insurance companies in faster processing of applications and claims, leading to higher levels of customer satisfaction. Insurance companies have to adopt a marketing approach to sustainably grow in the market, and big data is the key to this growth. However, sufficient caution has to be exercised in the implementation of big data analytics as companies have to ensure smooth transition from the age-old data management systems to the new big data system. An unplanned approach could pose greater risks for an enterprise. However, in this era of technology and speedy transactions, Indian insurance companies must adapt to newer technologies such as big data for continued survival. [ABSTRACT FROM AUTHOR]

With Insurance penetration at 4-5 per cent of GDP in India, there is immense scope for growth in the Insurance sector in the economy. With recent advances in Digitalization technology, Insurance companies are increasingly using digitalization processes in insurance industry. A number of digitalized distribution processes such as Common Service Centres, restructured corporate agency system for banks, Web Aggregators, Point of Sale persons and have come into existence to expand the reach of insurance delivery mechanisms. Policy holders as consumers have grown to expect 'best in class 'experiences from their online and mobile interactions, as well as traditional agents. Policy holders and potential customers have to cross - subsidize other policy holders leading to birth of hybrid instruments exposing new supply factors with demand forces making a market between them. This paper begins with the importance of the insurance sector the growth of the economy. Second part narrates the factors which have been responsible for low penetration of insurance in the economy. Third part discusses recent attempts of some leading insurers, in product development and customization of the needs of consumers. The role of digitalization in the expanding the scope of insurance business would depend upon efficacy of the distribution networks and the customer segments are examined. Insurers are now competing in new areas of digitalization / big data which lead to blur the boundaries between traditional and new insurance players. Emerging horizons present the 'new scenarios' and the role of IRDA as regulator to harmonize the insurance industry the insurance players in 'redefining the competitive landscape in India'. [ABSTRACT FROM AUTHOR]

Accident prediction is an important safety issue to raise alarms before accidents happen. In this paper we formulate relevant questions to anticipate the occurrence of accidents and process the available information using Hadoop. We examine the execution time on Hadoop when compared with other methods, and propose 2 algorithms to use Congestion Control Machine Framework (CCMF) and Traffic Congestion Analyzer using Map Reduce (TCAMP) to effectively analyze the available data and assess road accident reasons and advise authorities to take appropriate actions to increase road safety. We apply information mining to examine recorded road attributes to reduce road accidents specifically in India, and formulate a set of standards that can be utilized by the National Highway Authority of India to improve safety. [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]

Data is not a new term in the field of computer science, but Big Data is essentially a new word. When data grows beyond the capacity of currently existing database tools, it begins to be referred as Big Data. Big Data posses a grand challenge for both data analytics and database. It has been only in 2013 to 2015 that we humans have created 90 percent of data existing on the planet earth since existence of humans on this planet. The huge technological up gradation in social network, in retail industry, in health sector, in engineering disciplines, in the field of wireless sensors, in stock market, in public and private sector, all has collectively amassed enormous data. This data is very huge in volume, it gets created at very high speed, it may be structured, unstructured, semi-structured or may be in text, audio or video format and most important that it is not totally precise and can be messy or misleading. The central theme of our research work is concerned with handling huge amount of data that is concerned with different formats of elections that are been contested in India. The framework used in this research work is Apache Hadoop. Apache Hadoop framework makes use of Map-Reduce technology which operates in three steps: mapping, shuffling and reduction. Map-Reduce is the same technique which Facebook use for handling its section of "People you may know". Research paper also discusses the working of Map-Reduce technology with competent examples. [ABSTRACT FROM AUTHOR]

The increase in the usage of internet and social media has enabled people exchange views, opinions and thoughts as never before. This exchange of data has paved the way for sentiment analysis. The basic task of sentiment analysis is to classify the data into positive, negative and neutral. In this paper an effective MapReduce-Based Hybrid NBC-TFIDF (Naive Bayes Classifier -Term Frequency Inverse Document Frequency) algorithm is proposed to mine people sentiment. A Map Reduce-Based Hybrid NBC is employed to classify the data based on the polarity score of each sentence in social media data. The polarity score is calculated using the emotion corpus and the Diabetic corpus is created using food Glycemic Index and physical activity index. This study analyses the correlation of food habits, physical activity and diabetic risk factors among Indian population using social network data. Around two million data has been identified for the study and the study is restricted to India. The experimental result shows that MapReduce-Based Hybrid NBC–TFIDF performs efficiently in multimode cluster. The results reveal that no individual factor is associated with diabetic risk and also a group of common factors contribute to diabetes mellitus. It is found that 60% of the social media data had positive polarity about the food items that are high in Glycemic Index which is the main root cause for type – 2 Diabetes. This Big-Data analysis reveals that the young generations of India are unaware of risk factors of Diabetes mellitus. [ABSTRACT FROM AUTHOR]

Coal mining is negatively impacting the environment as a whole. On the fragile world, the chaotic human race is continually utilizing a range of tools for everyday life. Coal has been known as India's primary source of electricity for many decades and leads to about 27 per cent of the world's industrial energy requirements. Coal is primarily extracted using two types, surface or opencast, and underground mines. The process of mining is dictated by the geological environment. Coal mining is generally correlated with the depletion of environmental wealth and ecosystem loss. This allows invasive species to inhabit the region, posing a danger to biodiversity. A variety of mining operations in the coal mining area create large amounts of waste material. Mining can harm the natural ecosystem if due consideration is not taken for the handling of waste. The waste management system impacts ground, water and climate and, in effect, the quality of life of residents in surrounding regions. This paper focuses on the pressing problems of coal mining and their effect on the ecosystem [ABSTRACT FROM AUTHOR]

Purpose: With the emergence of big data analytics and the importance of analytics-driven decisions, the travel industry is swiftly jumping on and adopting the bandwagon. However, research in this domain is limited. Accordingly, the present research seeks to understand how big data analytics capabilities (BDAC) add value to tourism supply chains (TSCs) and can dynamic capabilities (DC) improve the triple bottom line. Design/methodology/approach: Data from 218 valid responses were collected from different Indian tourism industry units. Confirmatory factor analysis (CFA) was applied to confirm the constructs, followed by partial least squares structural equation modelling (PLS-SEM) to check the mediating effect of DC on TSCs performance. Findings: The findings show that BDAC significantly influence the performance of TSCs and that DC plays a critical role in strengthening the impact of BDAC on TSCs' economic performance. These results corroborate that DC plays a key moderating role. Research limitations/implications: This study contributes significantly to the tourism sector in India, where tourism is a key contributor to the country's gross domestic product. Theoretically, this study contributes to the resource-based view (RBV) and practically encourages professionals in the tourism sector to promote the use of BDAC to enhance the performance of TSCs. Originality/value: The originality of the study is that it has tried to comprehend the moderating role of dynamic capabilities which impact BDAC to improve TSC performance. [ABSTRACT FROM AUTHOR]

This paper gives an overall introduction on big data and has tried to introduce Big Data in Tamil. It discusses the potential opportunities, benefits and likely challenges from a very Tamil and Tamil Nadu perspective. The paper has also made original contribution by proposing the 'big data's' terminology in Tamil. The paper further suggests a few areas to explore using big data Tamil on the lines of the Tamil Nadu Government 'vision 2023'. Whilst, big data has something to offer everyone, it is argued that Tamil language proficiency and favourable policy decision is key to the success of Big data in Tamil and more specifically in Tamil Nadu. [ABSTRACT FROM AUTHOR]

This paper applies different approaches to modelling sources of economic growth from time series and panel data sets for 10 Asian countries over the period 1970-2010. After being subjected to fragility tests, the cross-country estimates indicate that investment, together with policy variables and openness to trade, explains about 90 per cent of the estimated 3.2 per cent steady-state growth rate for the region. Regional growth policy points to expanding trade, supporting financial development, and maintaining sound investment environments. Although country-specific growth effects vary, the results imply that different estimation methods, combined with fragility tests, can help establish stronger links between growth theory and policy advice. [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]

As emerging powers forge ahead with big data initiatives, questions arise regarding the implications of these programs for governance in the Global South more broadly. One understudied aspect deals with how actors attribute legitimacy to governments' big data activities. We explore actors' agency in one crucial case: the world's largest demographic and biometric data program, India's Aadhaar. Analyzing roughly 250,000 tweets collected in the first 10 years of Aadhaar's operation, we find that both normative acceptance and cost–benefit calculations are crucial for legitimacy attribution. This finding challenges mainstream theoretical approaches, which prioritize normative factors and often fail to examine how normative and material factors interact during legitimacy attribution. In addition, our study demonstrates a new, mixed-methods approach to measuring legitimacy attribution using Twitter data, which overcomes traditional challenges. As such, we underline the viability of Twitter data as a tool for social measurement. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the impacts of adopting big data analytics (BDA) on firm sustainability performance (FSP) mediated through firm financial performance (FIP) and operational performance (OPP). Design/methodology/approach: A theoretical model is based on ideas from existing literature on BDA, sustainability, FIP, dynamic capability view theory and resource capability view theory. The model is then validated using the partial least squares–structural equation modeling technique with consideration of 312 responses from 24 Indian firms. Findings: The study provides three important findings. First, there is a significant and positive impact of BDA on firms' financial and OPP. Second, BDA significantly and positively impacts firm business process performance (BPP) and dynamic capabilities (DYC), which, in turn, significantly impacts the firm's financial and OPP. Finally, both the financial and OPP of the firm significantly and positively impact sustainability performance. Research limitations/implications: This theoretical model is unique in showing the impacts of BDA on BPP, firm DYC, financial and OPP. The study also shows how BDA can enhance FSP by mediating through financial as well as the OPP of the firms. The study uses data only from India and thus the proposed model cannot be generalizable. Originality/value: This study provides valuable input to researchers, academicians and industry practitioners on the importance of BDA for FSP. The study also adds value to the body of knowledge on sustainability, FIP and technology adoption. The proposed unique theoretical model has an explanative power of 70%, which is quite high and can be used across different industries. [ABSTRACT FROM AUTHOR]

The use of digital technologies such as 'internet of things' and 'big data analytics' have transformed the traditional retail supply chains into data-driven retail supply chains referred to as 'Retail 4.0.' These big data-driven retail supply chains have the advantage of providing superior products and services and enhance the customers shopping experience. The retailing industry in India is highly competitive and eager to transform into the environment of retail 4.0. The literature on big data in the supply chain has mainly focused on the applications in manufacturing industries and therefore needs to be further investigated on how the big data-driven retail supply chains influence the supply chain performance. Therefore, this study investigates how the retailing 4.0 context in India is influencing the existing supply chain performance measures and what effect it has on the organisational performance. The findings of the study provide valuable insights for retail supply chain practitioners on planning BDA investments. Based on a survey of 380 respondents selected from retail organisations in India, this study uses governance structure as the moderating variable. Implications for managers and future research possibilities are presented. [ABSTRACT FROM AUTHOR]

The present world data is most important in public because without data cannot live in the world. This data is big data and that data daily increases through Social Media like "Twitter, Facebook, Youtube, WhatsApp, Instagram, LinkedIn", etc.., because this media only share public opinion very fast through the net. From this media, more people used especially Twitter. Thus media are used to analyze the public opinion of the tweets and predict the future through Machine Learning Algorithm. This analyzed data should make it polarity score. This score data has very little security because, this score can change the score and affected the future, so apply the existing security algorithms is Salsa and ChaCha. The Salsa algorithm is diagonal values moved to the first row. The ChaCha algorithm is diagonal values moved to the first column. The existing algorithms do not have good security because they focused only on performance, not security. So, the novel security algorithm is RPBB31. This algorithm has seven stages. The 1st stage is to find the secret key N, n, and p values from the matrix. The 2nd stage is to apply the secret key in PN(n) operation. The 3rd stage operation of PN(n) up to n = 1. The 4th stage is all PN(n) operations make their single line. The 5th stage is to pair the values and swap the values in the matrix. The 6th stage is column operations in the matrix. The 7th stage is again Step 4 values used to swap but "0th" cell value start from reverse in the matrix. In world cricket is the most famous game. In all social media Millions of people are following in different manner, especially in indian cricketer are more famous in India and world. In some point of time it may lead to match fixing, betting. To overcome this kind of issue the four datasets chosen. The proposed algorithm has provide good security and performance while compare to existing algorithms. [ABSTRACT FROM AUTHOR]

Purpose: To describe the demographics and clinical profile of pseudoexfoliation syndrome (PXF or PES) in patients presenting to a multi-tier ophthalmology hospital network in India. Methods: This cross-sectional hospital-based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of PXF in at least one eye were included as cases. The data were collected using an electronic medical record system. Results: Overall, 23,223 (0.75%) patients were diagnosed with PXF. The majority of the patients were male (67.08%) and had unilateral (60.96%) affliction. The most common age group at presentation was during the seventh decade of life with 9,495 (40.89%) patients. The overall prevalence was higher in patients from a lower socio-economic status (1.48%) presenting from the urban geography (0.84%) and in retired individuals (3.61%). The most common location of the PXF material was the pupillary margin (81.01%) followed by the iris (19.15%). The majority of the eyes had mild or no visual impairment (<20/70) in 12,962 (40.14%) eyes. PXF glaucoma was documented in 7,954 (24.63%) eyes. Krukenberg's spindle was found in 64 (0.20%) eyes, phacodonesis in 328 (1.02%) eyes, and lens subluxation in 299 (0.93%) eyes. Among the surgical interventions, cataract surgery was performed in 8,363 (25.9%) eyes, trabeculectomy was performed in 966 (2.99%) eyes, and a combined procedure in 822 (2.55%) eyes. Conclusion: PXF more commonly affects males presenting during the seventh decade of life from lower socio-economic status and is predominantly unilateral. A quarter of the affected eyes are associated with glaucoma and the majority of the eyes have mild or no visual impairment. [ABSTRACT FROM AUTHOR]

How are citizens experiencing the increase in Air pollution? How can the Social media data be explored and used to develop deeper insights to study citizen’s observations, opinions and experiences? In current research paper, by utilizing micro-blog social media application – Twitter data, these questions are examined. A suitable framework, He a l th P ractice - “ HELP ”, is emerged based on computational analysis and visualizations of the content of the Tweets. [ABSTRACT FROM AUTHOR]

Environmental sustainability is the rate of renewable resource harvesting, pollution control, and non-renewable resource exhaustion. Air pollution is a significant issue confronted by the environment particularly by highly populated countries like India. Due to increased population, the number of vehicles also continues to increase. Each vehicle has its individual emission rate; however, the issue arises when the emission rate crosses the standard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop prediction approaches to monitor and control pollution using real time data. With the development of the Internet of Things (IoT) and Big Data Analytics (BDA), there is a huge paradigm shift in how environmental data are employed for sustainable cities and societies, especially by applying intelligent algorithms. In this view, this study develops an optimal AI based air quality prediction and classification (OAI-AQPC) model in big data environment. For handling big data from environmental monitoring, Hadoop MapReduce tool is employed. In addition, a predictive model is built using the hybridization of ARIMA and neural network (NN) called ARIMA-NN to predict the pollution level. For improving the performance of the ARIMA-NN algorithm, the parameter tuning process takes place using oppositional swallow swarm optimization (OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS) classifier is used to classify the air quality into pollutant and non-pollutant. A detailed experimental analysis is performed for highlighting the better prediction performance of the proposed ARIMA-NN method. The obtained outcomes pointed out the enhanced outcomes of the proposed OAI-AQPC technique over the recent state of art techniques. [ABSTRACT FROM AUTHOR]