Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”
Instead of talking about artificial intelligence at the organizational level in hospitals and in research laboratories, the focus for non-machine learning practitioner should be on understanding the data pipes and what is involved around the model training.
alternative download link:
https://www.dropbox.com/s/9tv673sxkxcnojj/dataStrategyForOphthalmology.pdf?dl=0
ARTIFICIAL NEURAL NETWORKING.
FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant
Knowledge in medical field is characterized by uncertanity and vagueness
Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics
Greek philosopher visualized a basic model of brain function as early as 300 bc
Till date nervous system is not completely understood to human kind.
Face Mask Detection And Attendance SystemDarsh Jain
The Current COVID-19 pandemic has got the world to a halt and took everyones attention, as it was a global pandemic the world knew about it. But some underdeveloped nations suffer from viral outbreaks every year. To protect ourselves from these viral outbreaks there is a need for a continuous supply of Sanitizers and face masks to maintain personal hygiene. To help such nations and give some contribution to the world, a novel idea of Face Mask Detection and Attendance System has been proposed in this paper. Also, the various techniques that can be used for facial recognition and object detection, like the HAAR cascade method, Machine learning Algorithms, Deep Learning, etc. are discussed.
Detecting Fatigue Driving Through PERCLOS: A ReviewCSCJournals
In this paper, we present a literature survey about drowsy driving detection using PERCLOS metric that determines the percentage of eye closure. This metric determines that an eye is closed if the percentage of eye closure is 80% or above. When this percentage is observed for multiple frames of a video camera feed, the driver is determined to be in an unsafe fatigue status. In our research, we found that the PERCLOS metric had a 0.79 to 0.87 correlation coefficient value which exceeds the 0.7 R value needed to be considered a strong correlation coefficient. A higher value than 0.7 indicates a more linear relationship which means that the metric is dependable [1].
Recent advances digital imaging /certified fixed orthodontic courses by India...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Instead of talking about artificial intelligence at the organizational level in hospitals and in research laboratories, the focus for non-machine learning practitioner should be on understanding the data pipes and what is involved around the model training.
alternative download link:
https://www.dropbox.com/s/9tv673sxkxcnojj/dataStrategyForOphthalmology.pdf?dl=0
ARTIFICIAL NEURAL NETWORKING.
FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant
Knowledge in medical field is characterized by uncertanity and vagueness
Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics
Greek philosopher visualized a basic model of brain function as early as 300 bc
Till date nervous system is not completely understood to human kind.
Face Mask Detection And Attendance SystemDarsh Jain
The Current COVID-19 pandemic has got the world to a halt and took everyones attention, as it was a global pandemic the world knew about it. But some underdeveloped nations suffer from viral outbreaks every year. To protect ourselves from these viral outbreaks there is a need for a continuous supply of Sanitizers and face masks to maintain personal hygiene. To help such nations and give some contribution to the world, a novel idea of Face Mask Detection and Attendance System has been proposed in this paper. Also, the various techniques that can be used for facial recognition and object detection, like the HAAR cascade method, Machine learning Algorithms, Deep Learning, etc. are discussed.
Detecting Fatigue Driving Through PERCLOS: A ReviewCSCJournals
In this paper, we present a literature survey about drowsy driving detection using PERCLOS metric that determines the percentage of eye closure. This metric determines that an eye is closed if the percentage of eye closure is 80% or above. When this percentage is observed for multiple frames of a video camera feed, the driver is determined to be in an unsafe fatigue status. In our research, we found that the PERCLOS metric had a 0.79 to 0.87 correlation coefficient value which exceeds the 0.7 R value needed to be considered a strong correlation coefficient. A higher value than 0.7 indicates a more linear relationship which means that the metric is dependable [1].
Recent advances digital imaging /certified fixed orthodontic courses by India...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Motivational overview for why the medical image analysis need a volumetric equivalent of popular ImageNet database used in benchmarking deep learning architectures, and as a basis for transfer learning when not enough data is available for training the deep learning from scratch
"Challenges for AI in Healthcare" - Peter Graven Ph.DGrid Dynamics
Dynamic Talks Portland: The use of AI in many industries has revolutionized operations and efficiency. In healthcare, the progress is just beginning. Despite the promise of AI, why has the development lagged other industries? What issues are unique to healthcare that create challenges for common approaches? How can data scientists overcome these challenges and deliver on the promise of using data to reach multiple goals of improved quality, decreased cost, and greater patient satisfaction?
Clinical applications with a focus on rheumatoid arthritis (RA) management. Quick overview of hand pose tracking for managing rheumatoid arthritis.
For best clinical outcome, you might want to think how to integrate additional modalities like surface electromyography (sEMG) and hand function assessments (like hand grip strength, and finger extension strength) to the clinical prognostics model.
Alternative download link:
https://www.dropbox.com/s/rexzt3d5tsm1vgc/hand_tracking_arthritis_management.pdf?dl=0
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
To explore more, visit: https://skyl.ai/form?p=start-trial
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
Recent advances in diagnostic aids /certified fixed orthodontic courses by In...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
A new perspective of refractive error calculation with mobile application IJECEIAES
In many situations, not standardized and limited access to eye health care in several regions of Indonesia becomes the main challenge for myopia patients to measure and monitor their current refractive error condition. Many apps were proposed to provide low-cost alternative measurement tools rather than expensive tools such as Phoropter with Snellen chart and Retinoscopy, but still, those apps need an Internet connection and manually complex steps to operate. These conditions make myopia patients reluctant to use this kind of service. In this regard, we propose an intuitive diopter level measurement app based on mobile application setup, which implements the concept of measure the user face to smartphone screen distance for the rapid diopter calculation processes and at the same time provides a low-cost alternative refractive measurement tool. This paper highlights our experiences when developing a mobile application that can help patients with myopia measuring their blur line distances and evaluate their diopter levels independently. We conduct a number of human trials with the device on a controlled environment to demonstrate the ability of the proposed app to measure the diopter level. The experimental results show that the proposed app is quite successful in measuring the diopter level of myopia patients with a relatively small range of calculation errors compared to optometrist measurement results.
At the recent ECR 2019 technical exhibition in Vienna, the big news was the advancement in artificial intelligence software. Many CT booth presentations were focused on AI, and no doubt it will be the trend in the upcoming year. Here are some of the AI developments by the biggest names in medical imaging.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
Medical vision: Web and mobile medical image retrieval system based on google...IJECEIAES
The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.
Motivational overview for why the medical image analysis need a volumetric equivalent of popular ImageNet database used in benchmarking deep learning architectures, and as a basis for transfer learning when not enough data is available for training the deep learning from scratch
"Challenges for AI in Healthcare" - Peter Graven Ph.DGrid Dynamics
Dynamic Talks Portland: The use of AI in many industries has revolutionized operations and efficiency. In healthcare, the progress is just beginning. Despite the promise of AI, why has the development lagged other industries? What issues are unique to healthcare that create challenges for common approaches? How can data scientists overcome these challenges and deliver on the promise of using data to reach multiple goals of improved quality, decreased cost, and greater patient satisfaction?
Clinical applications with a focus on rheumatoid arthritis (RA) management. Quick overview of hand pose tracking for managing rheumatoid arthritis.
For best clinical outcome, you might want to think how to integrate additional modalities like surface electromyography (sEMG) and hand function assessments (like hand grip strength, and finger extension strength) to the clinical prognostics model.
Alternative download link:
https://www.dropbox.com/s/rexzt3d5tsm1vgc/hand_tracking_arthritis_management.pdf?dl=0
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
To explore more, visit: https://skyl.ai/form?p=start-trial
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
Recent advances in diagnostic aids /certified fixed orthodontic courses by In...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
A new perspective of refractive error calculation with mobile application IJECEIAES
In many situations, not standardized and limited access to eye health care in several regions of Indonesia becomes the main challenge for myopia patients to measure and monitor their current refractive error condition. Many apps were proposed to provide low-cost alternative measurement tools rather than expensive tools such as Phoropter with Snellen chart and Retinoscopy, but still, those apps need an Internet connection and manually complex steps to operate. These conditions make myopia patients reluctant to use this kind of service. In this regard, we propose an intuitive diopter level measurement app based on mobile application setup, which implements the concept of measure the user face to smartphone screen distance for the rapid diopter calculation processes and at the same time provides a low-cost alternative refractive measurement tool. This paper highlights our experiences when developing a mobile application that can help patients with myopia measuring their blur line distances and evaluate their diopter levels independently. We conduct a number of human trials with the device on a controlled environment to demonstrate the ability of the proposed app to measure the diopter level. The experimental results show that the proposed app is quite successful in measuring the diopter level of myopia patients with a relatively small range of calculation errors compared to optometrist measurement results.
At the recent ECR 2019 technical exhibition in Vienna, the big news was the advancement in artificial intelligence software. Many CT booth presentations were focused on AI, and no doubt it will be the trend in the upcoming year. Here are some of the AI developments by the biggest names in medical imaging.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
Medical vision: Web and mobile medical image retrieval system based on google...IJECEIAES
The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.
Dear Reader,
We are a leading system integrator and IT solutions provider in Mumbai for automating your enterprise needs with reliable solutions since 30 years.
We are happy to publish 69th issue of our monthly newsletter "TechTalk". The earlier issues, can be found in the Newsletter section at www.goapl.com
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Computer vision, in its most basic sense, is trying to make the machine do what the human brain can do with vision. That's why we call it artificial vision. Its most basic task is to recognize objects. Recognizing and grouping objects. The aim is to make sense of the content of digital images.
In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours.
In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency.
For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL.
In this system, there are four entities User, Ambulance and Hospital. The user must register and log in using a username and password. After logging in, the user can Book Ambulance, Book Hospital, View Nearby Hospitals, View Previous Booked Ambulances and Hospitals, and it can also change its password.
When the user books an ambulance and hospital, a booking request is sent to the respective representatives of the ambulance and hospital. In view, Nearby Hospitals the user can view the nearest hospitals in their location. The ambulance driver has to register and then login in using a username and password.
After logging in, the driver can view booking requests, nearby hospitals and their previous bookings i.e., previously accepted requests. In Booking requests, it can either accept or decline the user requests. The hospital has to register and log in using a username and password. After login in the hospital representative can view the booking request and either accept or decline the user request.In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours.
In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency.
For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and
Face Recognition Based Attendance System with Auto Alert to Guardian using Ca...ijtsrd
Now a days the wise attending management system victimization face detection techniques. Daily attending marking could also be a typical and vital activity in colleges and colleges for checking the performance of students. Manual attending maintaining is tough methodology, significantly for large cluster of students. Some machine driven systems developed to x beat these difficulties, have drawbacks like worth, faux attending, accuracy, meddlesomeness. To beat these drawbacks, there is need of good and automatic attending system. We've a bent to unit implementing attending system victimization face recognition. Since face is exclusive identity of person, the problem of pretend attending and proxies could also be resolved. The system uses native binary pattern face recognition technique because it is fast, straightforward and has larger success rate. Also, its pro vision to have an effect on intensity of sunshine draw back and head produce draw back that produces it effective. This wise system could also be degree effective because of maintain the degree will less squat recognition system is planned supported appearance based choices that concentrate on the shortened squatter image rather than native countenance. The remainder step in squatter recognition system is squatter detection Viola Jones squatter detection methodology that capable of method photos terribly whereas achieving higher detection rates is utilized. The complete squatter recognition methodology could also be divided into a pair of parts squatter detection and squatter identification. For face detection, Viola Jones face detection methodology has been used out of the many face detection ways that. Once face detection, face is cropped from the actual image to urge obviate the background. Chemist faces and shear faces ways that are used for face identification. Average photos of subjects area unit used as coaching job set to spice up the accuracy of identification. Diksha Ghare | Prajakta Katakdhod | Shraddha Ujgare | Komal Suskar | Prof. Amruta Surana ""Face Recognition Based Attendance System with Auto Alert to Guardian using Call and SMS"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23928.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23928/face-recognition-based-attendance-system-with-auto-alert-to-guardian-using-call-and-sms/diksha-ghare
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
1. Computer Vision
Sanjay S
Computer Science Department, RMKEC
Abstract
Computer vision is the process of using
machines to understand and analyze imagery
(both photos and videos). While these types
of algorithms have been around in various
forms since the 1960’s, recent advances
in Machine Learning, as well as leaps forward
in data storage, computing capabilities, and
cheap high-quality input devices, have driven
major improvements in how well our software
can explore this kind of content.
How computer vision works
One of the major open questions in both
Neuroscience and Machine Learning is: how
exactly do our brains work, and how can we
approximate that with our own algorithms? The
reality is that there are very few working and
comprehensive theories of brain computation;
so despite the fact that Neural Nets are
supposed to “mimic the way the brain work
The same paradox holds true for computer
vision – since we’re not decided on how the
brain and eyes process images, it’s difficult to say
how well the algorithms used in production
approximate our own internal mental processes.
For example, studies have shown that some
functions that we thought happen in the brain
of frogs actually take place in the eyes. We’re a
far cry from amphibians, but similar uncertainty
exists in human cognition.
That’s a lot of memory to require for one image,
and a lot of pixels for an algorithm to iterate over.
But to train a model with meaningful accuracy –
especially when you’re talking about Deep
Learning – you’d usually need tens of thousands
of images, and the more the merrier. Even if you
were to use Transfer Learning to use the insights
of an already trained model, you’d still need a
few thousand images to train yours on.
2. Computer vision on Medical field
The research of computer vision, imaging
processing and pattern recognition has made
substantial progress during the past several
decades. Also, medical imaging has attracted
increasing attention in recent years due to its
vital component in healthcare applications.
Investigators have published a wealth of basic
science and data documenting the progress and
healthcare application on medical imaging. Since
the development of these research fields has set
the clinicians to advance from the bench to the
bedside, the Journal of Healthcare
Engineering set out to publish this special issue
devoted to the topic of advanced computer
vision methods for healthcare engineering, as
well as review articles that will stimulate the
continuing efforts to understand the problems
usually encountered in this field. The result is a
collection of fifteen outstanding articles
submitted by investigators.
Usage of CV in Medical field
X-Rays
The role of X-rays is to identify if there is any
abnormalities or damage to a human organ or
body part. Computer vision can be trained to
classify scan results just like a radiologist would
do and pinpoint all potential problems in a
single take. This is a healthier approach with
radiation exposure limited as much as possible,
especially in the case of children and the elderly.
CT scans
This method is used to detect tumours, internal
bleeding, and other life-threatening conditions.
The advantage of using computer vision here is
that the entire process can be automated with
increased precision, since the machine could
identify even those details that are invisible to
the human eye. A recent study at the University
of Central Florida proved that while trained
physicians had only 65% accuracy in detecting
lung cancer, the machine was right in 95% of the
cases.
This result becomes even more critical when we
are talking about brain damage, strokes, or
internal bleeding when every second can make a
difference.
3. Object Detection
Image classification involves assigning a class
label to an image, whereas object localization
involves drawing a bounding box around one or
more objects in an image. Object detection is
more challenging and combines these two tasks
and draws a bounding box around each object
of interest in the image and assigns them a class
label. Together, all of these problems are
referred to as object recognition.
Object recognition is a general term to describe
a collection of related computer vision tasks that
involve identifying objects in digital
photographs. Image classification involves
predicting the class of one object in an
image. Object localization refers to identifying
the location of one or more objects in an image
and drawing abounding box around their
extent. Object detection combines these two
tasks and localizes and classifies one or more
objects in an image.
Retail and Retail Security
Amazon recently opened to the public
the Amazon Go store where shoppers need not
wait in line at the checkout counter to pay for
their purchases. Located in Seattle, Washington,
the Go store is fitted with cameras specialized in
computer vision. It initially only allowed Amazon
employee shoppers, but welcomed the public
beginning in early 2018.
The technology that runs behind the Go store is
called Just Walk Out. As shown in this
one-minute video, shoppers activate the IOS or
Android mobile phone app before entering the
gates of the store.
StopLift
ScanItAll’s computer vision technology works
with the grocery store’s existing ceiling-installed
video cameras and point-of-sale (POS) systems.
Through the camera, the software “watches”
the cashier scan all products at the checkout
counter. Any product that is not scanned at the
POS is labeled as a “loss” by the software. After
being notified of the loss, the company says it is
up to management to take the next step to
accost the staff and take measures to prevent
similar incidents from happening in the future.
Using algorithms, Stoplift claims that ScanItAll
can identify sweethearting behaviors such as
covering the barcode, stacking items on top of
one another, skipping the scanner and directly
bagging the merchandise.
Automotive - Tesla
Another company that claims it has developed
self-driving cars is Tesla, which claims that all its
three Autopilot car models are equipped for full
self-driving capability.
Each vehicle, the website reports, is fitted with
eight cameras for 360-degree visibility around
the car with a viewing distance of 250 meters
around. Twelve ultrasonic sensors enable the car
to detect both hard and soft objects. The
company claims that a forward-facing radar
enables the car to see through heavy rain, fog,
dust and even the car ahead.
Its camera system, called Tesla Vision, works with
vision processing tools which the company
4. claims are built on a deep neural network and
able to deconstruct the environment to enable
the car to navigate complex roads.
Agriculture -Cainthus
Animal facial recognition is one feature that
Dublin-based Cainthus claims to offer. Cainthus
uses predictive imaging analysis to monitor the
health and well-being of crops and livestock.
Cainthus uses predictive imaging analysis to
monitor the health and well-being of crops and
livestock.
Cainthus also claims to provide features like
all-weather crop analysis in rates of growth,
general plant health, stressor identification, fruit
ripeness and crop maturity, among others.
Cargill, a producer and distributor of agricultural
products such as sugar, refined oil, cotton,
chocolate and salt, recently partnered with
Cainthus to bring facial recognition technology
to dairy farms worldwide. The deal includes a
minority equity investment from Cargill although
terms were not disclosed.
Benefits and challenges
Using computer vision systems can translate into
saving months before learning about
life-threatening conditions. In the case of some
cancer forms, this could easily be the difference
between saving or losing a patient. The benefit
of these systems is that they can be trained to
spot even slightest abnormalities.
Money is also an important issue here. Every
wrong diagnosis translates into additional costs,
more tests, more hospital days, and improper
treatment, not to mention the psychological
impact of potentially bad news that turn out to
be false.
The great news about using computer vision is
that the same algorithms can be reused for other
patients and data from other medical centres
can be easily transferred to the system for
further algorithm training, thus enhancing the
accuracy rates consistently.
The real challenge of training such a system is in
finding the right sets of relevant images for
training, including of rare cases. To get an
excellent accuracy, such training sets should
have proper tagging and enough variations to
avoid over-training on simple cases.
Concerns about data privacy and personal
security are also on the priority list, but with
proper data anonymisation techniques, one
patient’s data can save the lives of many others.
The futuristic dream of completely automated
diagnosis still has countless technical and ethical
barriers, but consistent advancements have been
made in the last years. AI can be used at various
stages of the hospital-patient relationship, from
easier admission via chatbots to personalised
treatment based on DNA analysis.
5. Medical image analysis is already becoming a
field where AI proves to bring groundbreaking
results.
Reference
1) http://www.academia.edu/Docume
nts/in/Computer_Vision?page=2
2) https://machinelearningmastery
.com/object-recognition-with-dee
p-learning/
3) Computer Vision: Algorithms and
Applications – “Computer Vision
4) Convolutional Neural Networks
(Deeplearning.ai and Coursera)
5) https://www.healtheuropa.eu/co
mputer-vision-accuracy-of-diagno
stics/93650/