Publications

Books

Learn Computer Vision using OpenCV

ISBN: 9781484242612, 1484242610

Pages: 151

Publisher: apress

Format: Paperback, Kindle

Source Code: https://github.com/Apress/learn-computer-vision-using-opencv

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Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with C…

Practical machine learning

ISBN: 9781784394011, 1784394017

Pages: 468

Publisher: Packt

Format: Paperback, Kindle

Source Code: https://github.com/PacktCode/Practical-Machine-Learning

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Packt Authors (@PacktAuthors) | TwitterO'Reilly Announces Certification Guides for the Most In-Demand IT ...

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This BookFully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and SparkComprehensive practical solutions taking you into the future of machine learningGo a step further and integrate your machine learning projects with HadoopWho This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling …


getting started with greenplum for big data analytics

ISBN: 9781782177050, 1782177051

Pages: 172

Publisher: Packt

Format: Paperback, Kindle

Source Code: N/A

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Packt Authors (@PacktAuthors) | TwitterO'Reilly Announces Certification Guides for the Most In-Demand IT ...

“Getting Started with Greenplum for Big Data” Analytics is great for data scientists and data analysts with a basic knowledge of Data Warehousing and Business Intelligence platforms who are new to Big Data and who are looking to get a good grounding in how to use the Greenplum Platform. It’s assumed that you will have some experience with database design and programming as well as be familiar with analytics tools like R and Weka…


Papers

Paper TitleAggregating financial services data without assumptions: A semantic data reference architecture
ISBN978-1-4799-7935-6
PublisherInstitute of Electrical and Electronics Engineers Global Communications Conference Academic conference Science IEEE Xplore, nice, blue, text, trademark png
Published inProceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

We are seeing a sea change down the pike in terms of financial information aggregation and consumption; this could potentially be a game changer in financial services space with focus on ability to commoditize data. Financial Services Industry deals with a tremendous amount of data that varies in its structure, volume and purpose. The data is generated in the ecosystem (its customers, its own accounts, partner trades, securities transactions etc.), is handled by many systems – each having its own perspective. Front-office systems handle transactional behavior of the data, middle office systems which typically work with a drop-copy of the data subject it to intense processing, business logic, computations (such as inventory positions, fee calculations, commissions) and the back office systems deal with reconciliation, cleansing, exception management etc. Then there are the analytic systems which are concerned with auditing, compliance reporting as well as business analytics. Data that flows through this ecosystem gets aggregated, transformed, and transported time and again. Traditional approaches to managing such data leverage Extract-Transform-Load (ETL) technologies to set up data marts where each data mart serves a specific purpose (such as reconciliation or analytics). The result is proliferation of transform…

Citations

IEEE Papers Qudamah Quboa, Nikolay Mehandjiev, “Creating Intelligent Business Systems by Utilising Big Data and Semantics”, Business Informatics (CBI) 2017 IEEE 19th Conference on, vol. 02, pp. 39-46, 2017.
Amarnath Palavalli, Durgaprasad Karri, Swarnalatha Pasupuleti, “xDaaS: Any Data as a Service”, Semantic Computing (ICSC) 2016 IEEE Tenth International Conference on, pp. 166-167, 2016.
Sam Adam Elnagdy, Meikang Qiu, Keke Gai, “Understanding Taxonomy of Cyber Risks for Cybersecurity Insurance of Financial Industry in Cloud Computing”, Cyber Security and Cloud Computing (CSCloud) 2016 IEEE 3rd International Conference on, pp. 295-300, 2016.
 Hevel Jean-Baptiste, Meikang Qiu, Keke Gai, Lixin Tao, “Meta Meta-Analytics for Risk Forecast Using Big Data Meta-Regression in Financial Industry”, Cyber Security and Cloud Computing (CSCloud) 2015 IEEE 2nd International Conference on, pp. 272-277, 2015.
Books… This algorithm assists in detecting the corners of an image by scanning the image and detecting locations with the greatest deviation. Once the algorithm recognizes the corners in the image which can be highlighted as shown in the figure (Gollapudi, 2019). The process began by converting the image to grayscale, then it is possible to apply Harris edge detection, based on the built-in methods of the library. 
ReferenceComputer vision in agriculture, application development using open source tools and systems
… According to [8], traditional machine learning techniques have not completed the ability to manipulate natural network data in its original shape. For decades, the establishment of a machine learning system requires precise engineering and substantial experience in the field to design a feature extractor that transforms raw information into an appropriate internal representation [9]. …
ReferenceA Review of Deep Learning Security and Privacy Defensive Techniques
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