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Shamshad Ansari

President and CEO at Accure, Inc.

Centreville, United States

Shamshad (Sam) Ansari is an author, inventor, and thought leader in the fields of computer vision, machine learning, artificial intelligence, and cognitive science. He has extensive experience in high scale, distributed, and parallel computing. Sam currently serves as an Adjunct Professor at George Mason University, teaching graduate- level programs within the Data Analytics Engineering department of the Volgenau School of Engineering. His areas of instruction encompass machine learning, natural language processing, and computer vision, where he imparts his knowledge and expertise to aspiring professionals.

Having authored multiple publications on topics such as machine learning, AI, RFID, and high-scale enterprise computing, Sam’s contributions extend beyond academia. He holds four US patents related to healthcare AI, showcasing his innovative mindset and practical application of technology.
Throughout his extensive 20+ years of experience in enterprise software development, Sam has been involved with several tech startups and early-stage companies. He has played pivotal roles in building and expanding tech teams from the ground up, contributing to their eventual acquisition by larger organizations. At the beginning of his career, he worked with esteemed institutions such as the US Department of Defense (DOD) and IBM, honing his skills and knowledge in the industry.

Currently, Sam serves as the President and CEO of Accure, Inc., an AI company that he founded. He is the creator, architect, and a significant contributor to Momentum AI, a no-code platform that encompasses data engineering, machine learning, AI, MLOps, data warehousing, and business intelligence. Throughout his career, Sam has made notable contributions in various domains including healthcare, retail, supply chain, banking and finance, and manufacturing. Demonstrating his leadership skills, he has successfully managed teams of software engineers, data scientists, and DevSecOps professionals, leading them to deliver exceptional results. Sam earned his bachelor’s degree in engineering from Birsa Institute of Technology (BIT) Sindri and subsequently a Master’s degree from the prestigious Indian Institute of Information Technology and Management Kerala (IIITM-K).

Available For: Advising, Authoring, Consulting, Influencing, Speaking
Travels From: Centreville, VA
Speaking Topics: Machine Learning, AI, Generative AI, SecureGPT, Computer Vision, Democratizing AI

Shamshad Ansari Points
Academic 25
Author 127
Influencer 50
Speaker 0
Entrepreneur 160
Total 362

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Company Type: Company
Minimum Project Size: $25,000+
Average Hourly Rate: $200-$300
Number of Employees: 51-250
Company Founded Date: Undisclosed
Media Experience: 10 years

Areas of Expertise

Agile
AI 33.96
Analytics 36.62
Big Data 35.49
Business Strategy
Digital Disruption
Digital Transformation
Digital Twins 39.59
Entrepreneurship
Generative AI 30.48
HealthTech
Innovation
IoT 30.16
IT Leadership
IT Operations
IT Strategy
Predictive Analytics
RPA
Startups
Supply Chain 30.17

Industry Experience

Healthcare
High Tech & Electronics

Publications

1 Adjunct Professor
Potential for Using Deep Learning for Digital-Twin System Validation Testing
IEEE
October 31, 2022
Abstract:
One of the challenges in designing and operating systems composed of interacting components is validating that the emergent behavior of the system does not cause one or more components to migrate, over time, into a hazardous operating state. Many modern airline accidents can be characterized as Interaction Accidents – no component failed, but the interaction of components resulted in a hazardous state.Due to the dependence of time, emergent behavior cannot be evaluated by analysis of the design. In theory it can be evaluated by Digital-Twin agent-based simulations. However, the running these simulations to uncover rare event emergent hazardous states is prohibitive due to: (1) the combinatorics of initial states of each of the components, and the (2) combinatorics of the time dimension (i.e. small variations in timing can result in very different outcomes). Deep Learning Neural Networks (DLNN) have shown promise to capture the underlying combinatoric behavior as well as compress the time dimension.This paper demonstrates the application of DLNN to identify emergent behavior from components with hybrid moded/continuous behavior that plays out over time. DLNNs were trained and tested for three systems with increasing behavioral complexity. The DLNNs accurately were able to represent the time dependent behavior for which they were trained/tested. The DLNNs were also able to learn and predict emergent behavior for behaviors that were not included in the training/testing data (up to 63% of the missing cases). The implications of these results are discussed.

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Tags: AI, Digital Twins

7 Article/Blogs
Handwritten Signature Extraction and Matching
Import from medium.com
February 20, 2023
Signature extraction and matching is a vital tool in preventing fraud, verifying identity, ensuring legal compliance, and improving efficiency in various industries. For example:Financial Services: Handwritten signature extraction and matching can be used by financial institutions to verify signatu

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Tags: AI, Digital Twins, Generative AI

How to Train YOLO Model to Detect Distracted Drivers
Import from medium.com
August 01, 2022
Distracted driving is any activity that diverts a driver’s attention while driving a motor vehicle. This includes activities such as texting, talking on the phone, drinking, doing makeup and hair, fiddling with the stereo and radio systems and talking to fellow passengers.Distracted driving is one

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Tags: AI, Digital Twins, Generative AI

Impulse Data Warehousing and OLAP Solution Outperforms Google BigQuery by 3x
Import from medium.com
August 19, 2021
A comprehensive benchmarking of Accure’s Impulse data warehousing and OLAP solution was performed and compared against the price-performance of Google BigQuery (GBQ). Impulse outperformed GBQ by 3x on an average in all queries executed on both the platforms against the same dataset (described belo

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Tags: AI, Digital Twins, Generative AI

Accelerate Data Science, AI and Process Automation With Momentum
Import from medium.com
June 20, 2021
Momentum is a suite of software platforms that enables data engineers, scientists and analysts to efficiently solve machine learning problems and automate business processes.Fig 1. Momentum AI home page screen shotDemocratize AI DevelopmentMomentum enables end-to-end enterprise automation without a

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Tags: AI, Digital Twins, Generative AI

Building A Realtime Pothole Detection System Using Machine Learning and Computer Vision
Import from medium.com
March 15, 2021
Figure 1: Screenshot of potholes detected by a camera installed on a moving vehicle. The potholes are marked with rectangular regions with detection confidence (image by author)Potholes are formed due to wear and tear and weathering of roads. They cause not only discomforts to citizens but also dea

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Tags: AI, Digital Twins, Generative AI

Getting Started with Computer Vision with Machine Learning
Import from medium.com
January 02, 2021
This short article is an attempt to provide with a concise information on different tools and libraries you would need to get started with developing computer vision system or applications. This is not an exhaustive list of all libraries available in the market. The exact toolset will depend on your

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Tags: AI, Digital Twins, Generative AI

Building Deep Autoencoders with Keras and TensorFlow
Import from medium.com
August 03, 2020
In this tutorial, we will explore how to build and train deep autoencoders using Keras and Tensorflow.The primary reason I decided to write this tutorial is that most of the tutorials out there, including the official Keras and TensorFlow ones, use the MNIST data for the training. I have been asked

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Tags: AI, Digital Twins, Generative AI

2 Books
Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python 2nd ed. Edition
Apress
November 18, 2023
Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition’s publication. All code used in the book has also been fully updated.

This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you’ll gain a thorough understanding of them. The book’s source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python.

Upon completing this book, you’ll have the knowledge and skills to build your own computer vision applications using neural networks

What You Will Learn

Understand image processing, manipulation techniques, and feature extraction methods
Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO
Utilize large scale model development and cloud infrastructure deployment
Gain an overview of FaceNet neural network architecture and develop a facial recognition system
Who This Book Is For

Those who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students.

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Tags: AI

Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python 1st ed. Edition
Apress
July 15, 2020
Apply computer vision and machine learning concepts in developing business and industrial applications ​using a practical, step-by-step approach.



The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section.



Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.



The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning.

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Tags: AI

1 Journal Publication
Java & RFID tags
Dr Dobb's Journal
November 01, 2005
An RFID system consists of an antenna and transceiver, which read data transmitted by a transponder via radio frequency (RF). The combined transceiver and antenna are called the" RFID reader." The transponder, referred to as the" RFID tag," is an integrated circuit containing RF circuitry. It transmits data when it comes within the electric or magnetic field of the antenna. The antenna transmits data to a processing unit where it is manipulated according to whatever business needs. The reader is connected to the processing unit or a computer's communication port. Commands are sent from the computer to the reader, which then activates the

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Tags: IoT, Supply Chain

4 Patents
INTENT-BASED CLUSTERING OF MEDICAL INFORMATION
20230335298
October 19, 2023
Abstract: A medical information navigation engine (“MINE”) includes a medical information interface, a reconciliation engine, and an intent-based presentation engine. The medical information interface receives medical information from a plurality of medical sources, which is subsequently reconciled by the reconciliation engine. The intent-based presentation engine clusters the reconciled medical information by applying at least one clustering rule to the reconciled medication information. The clustered reconciled medical information can be presented to a user.

Patent Number 20230335298

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Tags: AI, Analytics, Big Data

SYSTEMS AND METHODS FOR PATIENT RETENTION IN NETWORK THROUGH REFERRAL ANALYTICS
USPTO
February 13, 2023
Abstract: A medical information navigation engine (“MINE”) is capable of inferring referral activity not reported into a referral workflow system by utilizing intent-based clustering of medical information. The intent based clustering reconciles received medical data, from a variety of sources, and then clusters the data by applying one or more clustering rules. After the referrals not otherwise reported are inferred, they may be utilized to generate metrics that can be utilized to enhance patient care, and reduce costs. Metrics may be generated for both in-network and out-of-network referrals in order to distinguish differences in reporting activity.

Patent Number 20230197291

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Tags: AI, Analytics, Big Data

Systems and methods for event stream platforms which enable applications
USPTO
May 11, 2015
Abstract: Systems and methods to generate a final event stream are provided. The system collects information from a wide variety of sources, and then parses, normalizes, and indexes the information. This generates an initial event stream that can be tagged and then iteratively processed to generate a final event stream. The processing includes first order logic querying and knowledge extraction to infer additional events which is added to the event stream. The final event stream is used by a knowledge exchange for consumption by applications. These applications may be internal applications and/or third party applications. This system may be particularly useful in use with medical information, or any other big data enterprise system.

Patent Number 9639662

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Tags: AI, Analytics, Big Data

Medical information navigation engine (MINE) system
USPTO
August 31, 2011
Abstract: A method of transacting medical information includes receiving medical information from a medical sources, identifying, mapping, and consolidating the received medical information by a back-end medical processor, providing access to specific relevant data, based on a user's security privileges, within the identified, mapped, and consolidated medical information, based on user-specific functions or roles by a front-end medical processor, and generating user-customized processed medical information to a plurality of users, with at least a portion of the user-customized processed medical information being provided to each of the plurality of users based on its relevancy to each user's specific function or role and each user's associated security privileges.

Patent Number 10176541

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Tags: AI, Analytics, Big Data

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