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COVID19 is a contagious ailment, which is emanated from the city of Wuhan (Hebei district), China in late of 2019, genesis from newly come across corona virus. Humans are contaminated with the COVI...

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.

Big data analytics is becoming tremendously popular in every field today. Everyday lots of data are being generated and analyzed using big data analytics tools and technique. Here the technology used is apache spark and language used is Scala. So, in this paper study is being done on the behalf of research done in stock market data using apache spark technique. Here the nifty-50 data is taken to analyze the impact due to covid-19. As it is being seen that Covid-19 has affected almost everything around the globe, so the purpose is to analyze its effect on stock market. Thereafter comparison is done between the techniques used to analyze that massive volume of stock exchange data. Here the comparative analysis between Hadoop maps-reduce and apache spark on the behalf of some important parameter is being done. That concludes which technique is better for the analysis of the stock exchange data.

Data is generated by humans every day via various sources such as Instagram, Facebook, Twitter, Google, etc at a rate of 2.5 quintillion bytes with high volume, high speed and high variety. When this huge volume of data with high velocity is handled by the traditional approaches, it becomes inefficient and time-consuming. Apache Spark technology has been used that is an open-source in-memory clusters computing system for fast processing. This paper introduces a brief study of Big Data Analytics and Apache Spark which consists of characteristics (7V’s) of big data, tools and application areas for big data analytics, as well as Apache Spark Ecosystem including components, libraries and cluster managers that is deployment modes of Apache spark. Furthermore, this also presents a comparative study of Python and Scala programming languages with many parameters in the aspect of Apache Spark. This comparative study will help to identify which programming language like Python and Scala are suitable for Apache Spark technology. As result, both Python and Scala programming languages are suitable for Apache Spark, however language choice for programming in Apache Spark is depending on the features that best suited the needs of the project since each one has its own advantages and disadvantages. The main purpose of this paper is to make things easier for the programmer in the selection of programming languages in Apache Spark-based on their project.

Big data analytics is the one which acquire, organise and analyse the huge volume of data with high velocity to find some patterns and useful information. The data sets are so large that it can’t be handled by traditional databases to manage and process the structure and unstructured data. Hence, big data tools i.e. Hadoop, is required due to its high scalability, availability and cluster environment mechanism for analysing large volume of data. MapReduce is one of the important components of Hadoop which is able to handle the unstructured data. But to use MapReduce, high programming skills are needed. Therefore, due to the reason of programming, users are moving towards some other tools i.e. Apache Pig or Apache Cassandra. In these tools, the data is simply analysed by executing the queries or commands. This paper will discuss about the architectural of Apache Pig and Apache Cassandra and afterwards both the technologies regarding some factors are compared to find out which one is better.

Big data refers to the accumulation of sundry kinds of data in a plethora. In healthcare science, it refers to some radiologistic medical images datasets. This paper converses about medical imaging modalities such as CT scan, MRI, USG, and X-ray images to extract the hidden information in the form of pixels contained in these images. Pixels are the most prominent addressable elements in an image, and are quantified in dpi. The number of pixels in an image refers to the amount of information in that particular image. Analysis of medical images is very paramount aspect as they have so much consequential information, and leaving that information hidden and undetected can cause quandaries in near future. In medical science, radiologists face many quandaries as the images have very less number of pixels and the information is not feasibly extractable. With the avail of some implements and technologies used to analyze immensely colossal data, we can extract this hidden information from medical images and count the number of pixels of several images at the same period of time. In this article, we will perform pixel count on medical images in distributed environment. There are sundry implements for this purport, but here we have utilized Hadoop Image Processing Interface (HIPI) to perform this task in expeditious speed utilizing Hadoop Distributed File System (HDFS) and clusters of commodity hardware.

In medical science, Big Data refers to the accumulation of sundry kinds of clinical datasets i.e. medical imaging data that grow rapidly day to day in our life, so it is very arduous for the radiologist to store and process this massive volume of data in a period of time, they will take more time, sometimes it is seems as impractical, because most of the data are unstructured, so we proposed a system that avail the radiologist to store and process the plethora of medical imaging data in clusters of commodity hardware and facilitate the patient to get the test reports more efficiently and efficaciously within a period of time. In this paper, we are going to store and process the massive volume of amassed non-invasive medical imaging datasets such as CT-Scan, MRI, USG, and X-Ray in distributed environment to extract the subsidiary or hidden information from the images that avail the radiologist for early detection. There are sundry kinds of algorithms for this purport, but here we proposed CannyEdgeDetection MapReduce programming model using java that runs in a Hadoop Image Processing Interface (HIPI) environment to perform this task efficaciously in a cluster of commodity hardware.

in the current era IT enabled world, term "Big Data" is a new concept used to define the collection of data set which is large and complex. There are different sources defined which generates huge amount of data on daily basis like social media, stock exchange, medical science or clinical healthcare data which are elaborated in distributed fashion around the world. In this paper change on data by size may require new tools and storage methods to overcome the problem of storing and processing big data. Hadoop is an open source framework to analyze massive volume of data. One of the precious components of Hadoop is Apache Pig. Pig processes the data in the form of Latin script. We attempt to inspect stock exchange data of banking sector using Pig. In this observation, we worked on yearly data to come across 52 week low, high, average data and perceive the mapper and reducer time with the help of pig latin script for the banking data. All the observed result presented in the graphical form. In the research, we considered data of 6 years of different banks such as AXIS, HDFC, ICICI, and SBI

A In the medical field, the Image dispensation techniques are extensively employed for image amelioration in finding and treatment of lung disease in the big data environment, where the point in time feature is very paramount to determine the idiosyncrasy issues in intention images, particularly in lung disease such as cancer, pneumonia, COVID-19 etc, for early detection and treatment stages of lungs disease, Image processing technique are widely used for identification of genetic as well as environmental factors are very important in developing a novel method of lung disease prevention The core factors of this research are quality, time, and precision of the dataset The modification and evaluation of image quality depend on the segmentation techniques, an improved area of the object that is utilized as a rudimentary substructure of feature extraction is obtained and comparison is made on relying feature Medical images are analyzed by different segmentation techniques of image processing The segmentation techniques are used dataset to find patterns and retrieve information from the dataset for processing The goal of this study is discussed various image processing techniques and big data analytics tools for lung disease has been given in the tabular form and provides comparative study This study provides minutiae of big data analytics tools and image processing techniques, specifically discussed in the context of lung disease images © 2021 Institute of Physics Publishing All rights reserved

Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations; overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy.

Big Data refers to the collection of various kinds of data in a huge amount. Big Data in medical field refers to some clinical datasets and medical images. This paper discusses about medical images and their enhancement. Analysis of medical images is very important in the area of health care. With the help of the systems used to analyze Big Data, we can analyze medical images. In this paper, we are going to perform image enhancement on medical images for better understanding of these images. There are various tools for this purpose. This paper presents Big Data analytical tool to perform image enhancement on these images in a parallel distributed environment.