Collaborating Authors

neural network

Natural Language Processing of Radiology Text Reports: Interactive Text Classification


This report presents a hands-on introduction to natural language processing (NLP) of radiology reports with deep neural networks in Google Colaboratory (Colab) to introduce readers to the rapidly evolving field of NLP. The implementation of the Google Colab notebook was designed with code hidden to facilitate learning for noncoders (ie, individuals with little or no computer programming experience). The data used for this module are the corpus of radiology reports from the Indiana University chest x-ray collection available from the National Library of Medicine's Open-I service. The module guides learners through the process of exploring the data, splitting the data for model training and testing, preparing the data for NLP analysis, and training a deep NLP model to classify the reports as normal or abnormal. Concepts in NLP, such as tokenization, numericalization, language modeling, and word embeddings, are demonstrated in the module.

Less Talk, More Action: How Emotion Intelligence Reads What You Don't Say


One area of tech innovation pulling ahead is Emotion Intelligence. Artificial Intelligence (AI), EI uses facial mapping, eye tracking and other experience measurement data points. Know more about EI in this article.

Pinaki Laskar on LinkedIn: #DeepLearning #machinelearning #artificialintelligence


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner In deep learning, the'deep' talks more about the architecture and not about the level of understanding that the algorithms are capable of producing. Take the case of a video game. A deep learning algorithm can be trained to play Mortal Kombat really well and will even be able to defeat humans once the algorithm becomes very proficient. Change the game to Tekken and the neural network will need to be trained all over again. This is because it does not understand the context.

Minute Article - Member Blogs - By Madhavi Desai


Using the blend of technologies similar to Artificial Intelligence like Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, etc, These decision support systems outshines its ability to analyze patterns, simplify processes by examining large amounts of volumetric data, and spot business opportunities. With the help of computerized models using self-learning technologies like data mining, pattern recognition, and natural language processing, Cognitive computing synthesizes the data fed to machine learning algorithms from different information sources to suggest the best possible answers. Pitching on the grounds of learning, reasoning, and self-correction and assisting humans to make smarter decisions, Cognitive Computing applications include speech recognition, sentiment analysis, face detection, risk assessment, and fraud detection.

Detection and Semiquantitative Analysis of Cardiomegaly, Pneumothorax, and Pleural Effusion on Chest Radiographs


To develop and evaluate deep learning models for the detection and semiquantitative analysis of cardiomegaly, pneumothorax, and pleural effusion on chest radiographs. In this retrospective study, models were trained for lesion detection or for lung segmentation. The first dataset for lesion detection consisted of 2838 chest radiographs from 2638 patients (obtained between November 2018 and January 2020) containing findings positive for cardiomegaly, pneumothorax, and pleural effusion that were used in developing Mask region-based convolutional neural networks plus Point-based Rendering models. Separate detection models were trained for each disease. The second dataset was from two public datasets, which included 704 chest radiographs for training and testing a U-Net for lung segmentation.

Thousands of galaxies classified in the blink of an eye


Astronomers have designed and trained a computer program that can classify tens of thousands of galaxies in just a few seconds, a task that usually takes months to accomplish. In research published today, astrophysicists from Australia have used machine learning to speed up a process that is often done manually by astronomers and citizen scientists around the world. "Galaxies come in different shapes and sizes," said lead author Mitchell Cavanagh, a Ph.D. candidate based at the University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). "Classifying the shapes of galaxies is an important step in understanding their formation and evolution, and can even shed light on the nature of the Universe itself." Cavanagh said that with larger surveys of the sky happening all the time, astronomers are collecting too many galaxies to look at and classify on their own.

How AI Is Helping Space Debris Removal Efforts


As the space race heats up, debris has become a burning issue. Since the beginning of the space age in the 1950s, thousands of satellites and rockets have been sent to space and are marooned there. The Union of Concerned Scientists Satellite database has listed more than 4,084 operational satellites currently orbiting the Earth. In 2010, this number was less than a thousand. In the distant future, this problem can extend to the lunar surface and the asteroid belt (the current count stands at 34,000 pieces of space junk bigger than 10 centimetres in size and millions of smaller pieces).

Deep Learning Approach to Detect Banana Plant Diseases


Hello folks:) This is my final year research project based on deep learning. Let me give an introduction about my project first. When we talk about banana it's a famous fruit that commonly available across the world, because it instantly boosts your energy. Bananas are one most consumed fruit in the world. According to modern calculations, Bananas are grown in around 107 countries since it makes a difference to lower blood pressure and to reduce the chance of cancer and asthma.

5 Best Free Artificial Intelligence and Deep Learning Courses for Beginners in 2021 - Best of Lot


Hello guys, if you are interested in learning about Artificial Intelligence and how to build AI and looking for free online resources then you have come to the right place. Earlier, I have shared free Machine Learning and Free Data Science courses and in this article, I am going to share free Artificial Intelligence and deep learning courses for beginners. These free courses are created from Udemy, Coursera, edX, and Pluralsight and created by experts and trusted by thousands of people who wanted to learn Artificial Intelligence. Clicking on this article link shows that you are very interested to understand and learn more about artificial intelligence but wait! Learning artificial intelligence is not that easy and never will be.