Goto

Collaborating Authors

Results


With deep learning algorithms, standard CT technology produces spectral images

#artificialintelligence

In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research. "We hope that this technique will help extract more information from a regular single-spectrum X-ray CT scan, make it more quantitative, and improve diagnosis," said Wang, who is also the director of the Biomedical Imaging Center within the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer. Conventional CT scans produce images that show the shape of tissues within the body, but they don't give doctors sufficient information about the composition of those tissues.


Genius Tool to Compare Best Time-Series Models For Multi-step Time Series Modeling

#artificialintelligence

The intensity of the growth of the covid-19 pandemic worldwide has propelled researchers to evaluate the best machine learning model that could the people affected in the distant future by considering the current statistics and predicting the near future terms in subsequent stages. While different univariate models like ARIMA/SARIMA and traditional time-series are capable of predicting Number of Active cases, daily recoveries, Number of deaths, they do not take into consideration the other time-varying factors like Medical Facilities (Volume of Testing, ICU beds, Hospital Admissions, Ventilators, Isolation Units, Quarantine Centres, etc). As these factors become important we build a predictive model that can predict the Number of Active Cases, Deaths, and Recoveries based on the change in Medical Facilities as well as other changes in infrastructure. Here in this blog, we try to model Multi-step Time Series Prediction using Deep learning Models on the basis of Medical Information available for different states of India. A typical multi-step predictive model looks as the below figure, where each of the predicted outcomes from the previous state is treated as next state input to derive the outcome for the second-state and so forth.


Colorizing images with Deep Learning

#artificialintelligence

Since the beginning of the photography, Image colorization may have been reserved for those with artistic talent in the past, but now thanks to Artificial Intelligence, is it possible to colorize black and white images and video with outstanding quality. One interesting example is the paper Fully Automatic Video Colorization with Self-Regularization and Diversity ( you can read it here), which refers to one experiment by the Hong Kong University of Science and Technology, which presents a fully automatic method for colorizing black and white films without any human guidance or references. Typical image colorization methods require some sort of labeled reference. A key innovation of this paper is a novel framework consisting of a colorization network with self-learning techniques. The researchers used the ranked diversity loss function proposed in a CVPR paper to differentiate different solution modes.


Financial Time Series Forecasting with Deep Learning

#artificialintelligence

In this episode of the Data Exchange I speak with Murat Özbayoğlu, Chair of Artificial Intelligence Engineering at TOBB University of Economics and Technology in Ankara, Turkey. I've long been fascinated with finance and trading. My first job after I left academia was as the lead quant in a hedge fund, and ever since, I've tried to stay abreast of what tools and techniques quants and data scientists in finance are using. Forecasting in this setting usually means price prediction or price movement (trend) prediction. Output of forecasting models are used to inform investment decisions.


Artificial intelligence solutions built in India can serve the world

#artificialintelligence

The RAISE 2020 summit (Responsible AI for Social Empowerment) has brought issues around artificial intelligence (AI) to the centre of policy discussions. Countries across the world are making efforts to be part of the AI-led digital economy, which is estimated to contribute around $15.7 trillion to the global economy by 2030. India, with its "AI for All" strategy, a vast pool of AI-trained workforce and an emerging startup ecosystem, has a unique opportunity to be a major contributor to AI-driven solutions that can revolutionise healthcare, agriculture, manufacturing, education and skilling. AI is the branch of computer science concerned with developing machines that can complete tasks that typically require human intelligence. With the explosion of available data expansion of computing capacity, the world is witnessing rapid advancements in AI, machine learning and deep learning, transforming almost all sectors of the economy.


Deep Learning is Already Dead: Towards Artificial Life with Olaf Witkowski

#artificialintelligence

Olaf Witkowski is the Chief Scientist at Cross Labs, which aims to bridge the divide between intelligence science and AI technology. A researcher of artificial life, Witkowski started in artificial intelligence by exploring the replication of human speech through machines. He founded Commentag in 2007, and in 2009 moved to Japan to continue research, where he first became interested in artificial life. In his own words, Witkowski says, "artificial intelligence means that you are trying to copy human intelligence as best as possible. Artificial life says, okay, that's good, but let's try to understand human intelligence and recreate it from the fundamental knowledge we have acquired. It's a bit like the Richard Feynman quote: what I cannot create, I do not understand."


The future of AI depends on 9 companies. If they fail, we're doomed.

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. If artificial intelligence will destroy humanity, it probably won't be through killer robots and the incarnation--it will be through a thousand paper cuts. In the shadow of the immense benefits of advances in technology, the dark effects of AI algorithms are slowly creeping into different aspects of our lives, causing divide, unintentionally marginalizing groups of people, stealing our attention, and widening the gap between the wealthy and the poor. While we're already seeing and discussing many of the negative aspects of AI, not enough is being done to address them. And the reason is that we're looking in the wrong place, as futurist and Amy Webb discusses in her book The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity. Many are quick to blame large tech companies for the problems caused by artificial intelligence.


COVID-19 Classification of X-ray Images Using Deep Neural Networks

#artificialintelligence

In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image.


Research lab opens in India focused on deep learning

#artificialintelligence

Medical equipment manufacturer Wipro GE Healthcare has partnered with the Indian Institute of Science (IISc) to open a research lab. The lab is located at the Department of Computational and Data Sciences (CDS) in Bangalore. Work will also be done on digital interfaces to produce sophisticated diagnostic and medical image reconstruction techniques. This research unit will involve around fifty students and three faculty members of IISc to begin with. They will work closely with clinicians as well as Wipro GE Healthcare to integrate computational models into clinical workflows, to help doctors improve patient outcomes.


{ C Language } Deep Learning From Ground Up

#artificialintelligence

Free Coupon Discount - { C Language } Deep Learning From Ground Up, Build Artificial Intelligence Applications in C Created by Israel Gbati Preview this Udemy Course - GET COUPON CODE Welcome to the { C Language } Deep Learning From Ground Up course. We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch in c language. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network all the way to building fully functions deep learning models using c language only. By the end of this course you will be able to build neural networks from scratch without libraries, you will be able to understand the fundamentals of deep learning from a c language perspective and you will also be able to build your own deep learning library in c.