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Tag: ComputerVision

7 Innovative Examples of XR Technologies in the Healthcare Industry

VR & AR technologies will transform health care in the future, given their rapid and relevant development. They make the clinical experience of patients more immersive.
Artificial intelligence has matured into a fundamental technology used in a variety of fields such as robotics, computer vision, and natural language processing. It is pretty obvious that this progress would interfere with the health care sector, which needs constant improvement. Consequently, the combination of AI and XR provides extensive biotechnology applications and enables digital interaction with the physical…

Tabular Classification and Regression Made Easy with Lightning Flash

Illustration Photo by Oleg Magni from PexelsMachine LearningThis post presents solving Tabular primary data via the two most common Machine Learning (ML) tasks — classification and regression, with Lightning Flash, which makes it very simple.When it comes to articles on deep learning, advances in Computer Vision or Natural Language Processing (NLP) receive the lion's share of the attention. Advancement in CV and NLP is fantastic and super exciting; however, many data scientists' day-to-day tasks revolve around tabular data processing.Tabular data classification and regression are…

Top 10 AI Jobs Available in Government Agencies Across the World

by Sayantani Sanyal
November 25, 2021
The government and public sector stand to gain exceptional benefits from the integration of AI in its daily operations. As artificial intelligence and machine learning gain momentum, an increasing number of government agencies have also started to use AI tools to improve decision-making and national security. The use of AI in government must take into account privacy and security, compatibility with legacy systems, and evolving workloads. The heart of AI in government services involves techniques like deep learning, computer vision, speech…

The 5 Biggest Data Science Trends In 2022

The emergence of data science as a field of study and practical application over the last century has led to the development of technologies such as deep learning, natural language processing, and computer vision. Broadly speaking, it has enabled the emergence of machine learning (ML) as a way of working towards what we refer to as artificial intelligence (AI), a field of technology that’s rapidly transforming the way we work and live.

The 5 Biggest Data Science Trends In 2022
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Data science encompasses the theoretical and practical application of ideas, including…

Microsoft AI Open-Sources ‘SynapseML’ For Developing Scalable Machine Learning Pipelines

Source: https://www.microsoft.com/en-us/research/blog/synapseml-a-simple-multilingual-and-massively-parallel-machine-learning-library/

Microsoft has announced the release of SynapseML, an open-source library that simplifies and speeds up the creation of machine learning (ML) pipelines. SynapseML can be used for building scalable and intelligent systems to solve various types of challenges, including anomaly detection, computer vision, deep learning, form and face recognition, Gradient boosting, microservice orchestration, model interpretability, reinforcement…

10 AI Project Ideas in Computer Vision

The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.

Sources of Training Data for Computer Vision Annotation

We are aware of how autonomous or driverless vehicles process vast amounts of data to develop the sense of their environment. Computer vision is one of the levels in autonomous cars which analyzes every object on the path and plans the next action for the vehicle and takes the decision as per the learning. Then, there is an underlying safety framework which functions as per the ODD or Operational Design Domain, involving attributes of the operating environment; atmospheric conditions; dynamic elements or moveable objects.
Let us explore more about the available sources of computer vision…

ElectrifAi Announces Computer Vision and MLaaS for Oil, Gas and Energy at ADIPEC

Delivering fast and reliable machine learning business solutions.JERSEY CITY, N.J., Nov. 14, 2021 /CNW/ — ElectrifAi, one of the world’s leading companies in practical artificial intelligence (Ai) and pre-built machine learning (ML) models, today announced availability of Computer Vision (CV) and Machine Learning as a Service (MLaaS) for the Oil, Gas and Energy industries at ADIPEC in Abu Dhabi. ElectrifAi will be exhibiting at Booth 13605.ElectrifAi (PRNewsfoto/ElectrifAi)More than ever, oil and gas companies need to leverage the power of Ai, ML and CV to drive operational and cost…

FuboTV acquires computer vision startup Edisin.ai

Edisin.ai tracks and identifies athletes and on-field objects
FuboTV wants to increase non-subscription revenues
Acquisition will aid betting and interactive gaming products
US-based sports streaming service FuboTV has acquired Indian artificial intelligence (AI) startup Edisn.ai to strengthen attempts to generate non-subscription revenues and deepen engagement with its viewers.
The New York-based firm pitches itself as a “cable TV replacement service”, providing subscribers who want to ‘cut the…

Exclusive: Lessons from Levi’s data science bootcamp

Since starting at Levi’s in San Francisco in August 2019, Ronald Pritipaul has supervised the photography of a library of 10,000 garments and coordinated the design process for men’s denim jackets and bottoms. Now, the lifelong “denim head” has a new role — associate data project manager for computer vision.Curiously, Pritipaul doesn’t have a background in computer science. What he does have is an ingrained loyalty to Levi’s and a thorough knowledge of the day-to-day problems that need solving. He’s one of 40 recent graduates of Levi’s first machine learning “bootcamp,”…

Managing Computer Vision Tasks with CVAT

CogitoMachine learning model structuring and processing is not as easy as it may sound. Without the availability of required data, it is difficult to imagine the accuracy of results. At the core of several AI programs wherein complex computations are done, machine learning algorithms also enable the systematic rendering of learning tasks.As much as the quality of data is central to an algorithm, following the stages of applying the data for performance decides the accuracy of prediction. Whether the data is limited or available in ample amounts, imagining data annotation manually isn’t…

EFPN: Extended Feature Pyramid Network for Small Object Detection

When small things create big problemsObject detection has been a breakthrough in computer vision applications since the first few days of intelligent machine systems. Despite being investigated for a long time, this topic seems to never get old and has become one of the must-known problems in video understanding and computer vision. I hope that you already got some background in object detection because I am going to ignore mentioning several fundamental ideas around object detection like…

Drilling into the SSD Model for Object Detection

Object detection brings up several challenges in pattern recognition and computer vision, such as identifying and detecting various objects, and finding the location of each object in overlapping images. In object detection, the “object” is identified by the image given as input and “location” of that object is traced. Currently, there exists several algorithms that analyze the input image and provide output in terms of the detected objects, where each of them is associated with the…

The Beauty of Dance, Seen Through the Power of Touch

It’s nothing short of amazing what trained dancers can do with their bodies, and a real shame that visually-impaired people can’t enjoy the experience of, say, ballet. For this year’s Hackaday Prize, [Shi Yun] is working on a way for visually-impaired people to experience dance performances via haptic feedback on a special device.
This platform, which is called Kinetic Soul, uses Posenet computer vision to track a dancer’s movements. Posenet detects the dancer’s joints and…

Machine Learning

Image by: AuthorIn computer vision, semantic segmentation is one of the most important components for fine-grained inference (CV). To achieve the appropriate precision levels, models must grasp the context of the environment in which they operate. As a result, through pixel accuracy, semantic segmentation supplies them with that insight.Before we dig deep into the topic, let us understand what is semantic segmentation.The goal of semantic segmentation is to group pixels in a meaningful way….

https://pub.towardsai.net/machine-learning-23997460cbc4?source=rss—-98111c9905da—4

3. Real-World Applications Of Machine Learning In Healthcare

Real-World Applications of Machine Learning
Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.… Read more...

Better Quantifying the Performance of Object Detection in Video

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📌 Join us free for TransformX on Oct 6-7

We’re excited to partner with Scale AI on TransformX Conference that explores the shift from research to reality within AI and ML. Join us free for access live and on-demand on October 6-7. Check out the full agenda featuring 60+ keynotes, fireside chats, expert panels, and plenty of networking opportunities to customize your TransformX experience.CLAIM FREE TICKETFULL AGENDA🔦 A Few Highlights of the Agenda 🔦Explore 60+ sessions covering a range of topics like computer vision,…

How to Transfer Fundamental AI Advances into Practical Solutions for Healthcare

In this special guest feature, Dave DeCaprio, CTO and Co-founder, ClosedLoop.ai, discusses what it really takes to make AI that physicians trust. Dave has more than 20 years of experience transitioning advanced technology from academic research labs into successful businesses. His experience includes genome research, pharmaceutical development, health insurance, computer vision, sports analytics, speech recognition, transportation logistics, operations research, real time collaboration,…

Continue reading: https://insidebigdata.com/2021/09/29/how-to-transfer-fundamental-ai-advances-into-practical-solutions-for-healthcare/

Source: insidebigdata.com

Real-World Applications Of Machine Learning In Healthcare

Real-World Applications of Machine Learning

Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.

Read more...

Computer Vision in Agriculture – KDnuggets

Deep Learning in the Field: Modern Computer Vision for Agriculture

 
In today’s fast-paced world of city living and stressful work-life imbalances, especially on the (hopefully) tail-end of a year of pandemic quarantine measures, many young workers are yearning to get closer to nature and family. In the face of re-emerging commutes and the push-and-pull of back-to-the-office versus hybrid or fully-remote working, many young robots would rather ditch the status quo and return to the countryside to scratch a living from the land like their ancestors before them. And they’ll bring lasers, too.

Of course, we’re not talking about the weary office drones being herded back to the office after a year of blissfully working at home, but of robots armed with deep learning computer vision systems and precision actuators for a new breed of farming automation.

Read more...

U-Net Image Segmentation with Convolutional Networks

In our age, semantic segmentation on image data is frequently used for computer vision. U-Net is a backbone network that contains convolutional neural networks for masking objects.

🧶U-Net takes its name from its architecture similar to the letter U as seen in the figure. The input images are obtained as a segmented output map at the output.

You can access the basic level information and working architecture of the U-Net network in the article Image Segmentation with U-Net. This article describes the step-by-step coding of the U-Net in the Python programming language.

Step 1: Obtaining the dataset

In this step, if your dataset will be pulled from an existing code, you can load it from the file as follows. If your data set is in the active folder you are working in, you can work by loading the data from the file.… Read more...