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BIG DATA ANALYTICS

  Unit - I Introduction to Big Data Analytics and Data Architecture 1.1 Classification of Data : Structured, Semi-structured and Unstructured 1.2 Introduction :Big Data Definitions, Need of Big Data 1.3 Big Data Characteristics : Volume, Velocity, Variety, Veracity 1.4 Big Data Types 1.5 Big Data Processing Architecture Design 1.6 Big Data Analytics : Data analytics Definitions, Phases in Analytics 1.7 Big Data Analytics Applications : Big Data in Marketing and Sales, Big Data and Healthcare, Big Data in Medicine, Big Data in Advertising Unit - II Introduction to Hadoop and MapReduce 2.1 Introduction to Hadoop 2.2 Hadoop and its Ecosystem : Hadoop Core Components, Features of Hadoop, Hadoop Ecosystem Components 2.3 Hadoop Distributed File System : HDFS data storage, HDFS Commands for interacting with files in HDFS 2.4 MapReduce Framework and Programming Model : Hadoop MapReduce Framework, MapReduce Programming Model 2.5 Hadoop Yarn : Hadoop 2 Execution Model 2.6 MapReduce : Map Tas...

PRINCIPLES OF IMAGE PROCESSING

  Unit - I Introduction to Image Processing 1.1 Image : Pixel, Distance measures, Color fundamentals & models – RGB , HIS, YIQ, Types of an image : Vector and Raster , Types of image processing, Formation of digital image , resolution(Spatial and gray level), Megapixels and Image Quality. Image Acquisition: Cameras and sensors 1.2 Block Diagram: Fundamental Steps in Digital Image Processing, Advantages and disadvantages 1.3 Components of an Image Processing System, Difference between Image Sampling & quantization 1.4 Image file formats : JPEG , PNG, GIF, TIFF, BMP , Image Histogram 1.5 Operations on images : image addition, subtraction, logical operations, scaling, translation, rotation. Applications of Digital Image Processing Unit - II Image Enhancement in Spatial Domain and Frequency Domain 2.1 Basic Gray Level Transformations ( Linear ,Logarithmic, Power – law) , Histogram Processing , Enhancement Using Arithmetic/Logic Operation 2.2 Spatial domain enhancement : Point ...