Project information Cancer is a leading cause of death worldwide. In many cancers, surgery, where the surgeon removes malignant tissue, plays a pivotal role. A significant development in surgical procedures is tracking surgical instruments in conjunction with preoperative MRI/CT imaging to guide the procedure, ensuring more accurate, safer, and less invasive procedures. This project will develop novel deep learning-based image-guided surgery techniques for surgical procedures in the abdomen. In this region, interoperative motility, proximity to major vessels, and critical structures like nerves are important considerations for using image-guided techniques.

BOSTON--(BUSINESS WIRE)--BriefCam, the industry's leading provider of Video Content Analytics and VIDEO SYNOPSIS solutions, today announced availability for BriefCam Video Content Analytics on Axis deep learning cameras in December 2021. Edge video processing on Axis deep learning cameras is a new hybrid deployment option for the BriefCam platform that reduces the total cost of ownership of a real-time video analytics deployment, enables operation in low-bandwidth environments, and makes for faster real-time alerting. Through the Axis Application Development Partner Program, BriefCam is one of the first to leverage the AXIS Camera Application Platform (ACAP) to enable comprehensive analytics directly on Axis Communications' deep learning camera series. BriefCam analytics are enabled on the Axis deep learning cameras AXIS P3255 and AXIS Q1615 Mk III, which feature a dual chipset of ARTPEC-7 and a deep-learning processing unit (DLPU), as well as the ARTPEC 8 camera series. By enabling BriefCam analytics on the edge, along with post processing and management capabilities, users experience up to six times faster real-time alerting, a 5-10x reduction in bandwidth, and up to 55% less real-time GPU processing servers for real-time processing.

Researchers in China and the United States recently explored how an attention-based deep neural network (ABNN) could help improve sonar systems.  The research was published in the Journal of the Acoustical Society of America by the Acoustical Society of America through AIP Publishing.  Qunyan Ren is co-author of the research.  “We found the ABNN was […]

Recent deep neural networks (DNNs) often predict extremely well, but sacrifice interpretability and computational efficiency. Interpretability is crucial in many disciplines, such as science and medicine, where models must be carefully vetted or where interpretation is the goal itself. Moreover, interpretable models are concise and often yield computational efficiency. In our recent paper, we propose adaptive wavelet distillation (AWD), a method which distills information from a trained DNN into a wavelet transform. Surprisingly, we find that the resulting transform improves state-of-the-art predictive performance, despite being extremely concise, interpretable, and computationally efficient!

This blog post proposes an approach to crack Pseudo-Random Number Generators (PRNGs) using machine learning. By cracking here, we mean that we can predict the sequence of the random numbers using previously generated numbers without the knowledge of the seed. We started by breaking a simple PRNG, namely XORShift, following the lead of the post published in [1]. We simplified the structure of the neural network model from the one proposed in that post. Also, we have achieved a higher accuracy. This blog aims to show how to train a machine learning model that can reach 100% accuracy in generating random numbers without knowing the seed.

Start Online Job Oriented best Deep Learning Course in Delhi, We provide the best online or offline Deep Learning training course with qualified and experienced Trainers and all training provide on Live project-based and after training, we help their students for great placement and provide a professional certificate.

Scientific context: The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take into account and automatically exploit the multitude of human testimonies related to an industrial accident and its consequences on the environment and health. By involving the population in the collection and analysis of data, particularly through social networks, and by providing effective means for interpreting this data, the proposed solution should contribute to providing answers to the worrying problem of industrial accidents and their consequences.

Microsoft and chip manufacturer Nvidia have created a vast artificial intelligence that can mimic human language more convincingly than ever before. But the cost and time involved in creating the neural network has called into question whether such AIs can continue to scale up. The new neural network, known as the Megatron-Turing Natural Language Generation (MT-NLG) has 530 billion parameters, more than tripling the scale of OpenAI's groundbreaking GPT-3 neural network that was considered the state of the art up until now.

Python is one of the most popular programming languages on the market and currently takes first place with 33.18% of the market share. And this figure should not be surprising since Python is an extremely easy-to-learn programming language and incredibly flexible at the same time. It is excellent for many purposes, and Machine Learning is one such purpose. Python has many different libraries of complete tools for integrating machine learning technologies into business projects. In this article, we'll take a look at 10 well-known machine learning libraries in Python.

In a previous blog article, we outlined several deep learning techniques for OCR. We mentioned how these techniques are used for text detection and text recognition, which are the two primary building blocks of an OCR system. In this article, we will go deeper into how deep learning is being used for text detection, which is the first block when doing OCR. We are planning to release more articles where we will explore more deep learning techniques for text detection and text recognition. Object detection is a field in deep learning that is applied in several computer vision tasks, including text detection. There are mainly two types of object detection models: one-stage detectors and two-stages detectors.