Abstract: The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short TermMemory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE).
In February 2020, with COVID-19 spreading rapidly around the globe and antigen tests hard to come by, some physicians turned to artificial intelligence (AI) to try to diagnose cases1. Some researchers tasked deep neural networks -- complex systems that are adept at finding subtle patterns in images -- with looking at X-rays and chest computed tomography (CT) scans to quickly distinguish between people with COVID-based pneumonia and those without2. "Early in the COVID-19 pandemic, there was a race to build tools, especially AI tools, to help out," says Alex DeGrave, a computer engineer at the University of Washington in Seattle. But in that rush, researchers did not notice that many of the AI models had decided to take a few shortcuts. The AI systems honed their skills by analysing X-rays that had been labelled as either COVID-positive or COVID-negative.
COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods. Natural language processing (NLP) techniques have been developed to assess peoples' sentiments on various topics.
"Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2". "The disease first originated in December 2019 from Wuhan, China and since then it has spread globally across the world affecting more than 200 countries. The impact is such that the World Health Organization(WHO) has declared the ongoing pandemic of COVID-19 a Public Health Emergency of International Concern." As of 29th April, there are a total of 31,30,191 cases with 2,17,674 deaths in more than 200 countries across the world. So, in this particular scenario, one primary thing that needs to be done and has already started in the majority of the countries is Manual testing, so that the true situation can be understood and appropriate decisions can be taken.
The COVID-19 outbreak is causing havoc on the world's economy and public health. Until now, more than 27 million confirmed cases have been reported around the world. Because of the rising number of confirmed instances and the challenges posed by COVID-19 mutations, it is critical to classify healthy and infected people as soon as possible in order to control and treat COVID-19. During initial phases of, manual testing technique was used to detect the COVID -19 patients. However, manual testing has several disadvantages, like limited availability of testing kits, high prices, and inefficient blood tests. As a result, Deep Learning can be used to overcome these obstacles in order to provide a more effective and efficient treatment.
This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.
Real-world time-series datasets often violate the assumptions of standard supervised learning for forecasting -- their distributions evolve over time, rendering the conventional training and model selection procedures suboptimal. In this paper, we propose a novel method, Self-Adaptive Forecasting (SAF), to modify the training of time-series forecasting models to improve their performance on forecasting tasks with such non-stationary time-series data. SAF integrates a self-adaptation stage prior to forecasting based on `backcasting', i.e. predicting masked inputs backward in time. This is a form of test-time training that creates a self-supervised learning problem on test samples before performing the prediction task. In this way, our method enables efficient adaptation of encoded representations to evolving distributions, leading to superior generalization. SAF can be integrated with any canonical encoder-decoder based time-series architecture such as recurrent neural networks or attention-based architectures. On synthetic and real-world datasets in domains where time-series data are known to be notoriously non-stationary, such as healthcare and finance, we demonstrate a significant benefit of SAF in improving forecasting accuracy.
A team of researchers from IIT Jodhpur have developed an automated Artificial Intelligence (AI) solution that can differentiate a COVID-19 infected lung from a non-infected one using X-ray images. According to a report by news agency ANI, the solution is based on a deep-learning-based algorithm called COMiT-Net which studies chest X-ray images and differentiates between a healthy and an infected lung by pointing out the abnormalities. It is worth mentioning that detecting COVID-19 using X-ray images has been an in-use technique however this technology promises a unique advantage. IIT Jodhpur researchers develop COVID-19 diagnosis technique using chest X-ray images Read @ANI Story https://t.co/tWrrkACmRE#IIT The new technology was tested in an experiment wherein the experts analysed 2,500 chest X-ray images and achieved 96.8% of sensitivity.
TinyML is a sort of machine learning in which deep learning networks are shrunk to fit on a piece of hardware. Artificial Intelligence and intelligent gadgets are combined in this project. In your pocket lies 45x18mm of Artificial Intelligence. Suddenly, your Arduino board's do-it-yourself weekend project has a little machine learning model implanted in it. New embedded machine learning frameworks will enable the spread of AI-powered IoT devices.
Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such as Space2Vec) were proposed to directly encode any point in 2D space as a high-dimensional vector, and has been successfully applied to various (geo)spatial prediction tasks. However, a map projection distortion problem rises when applying location encoding models to large-scale real-world GPS coordinate datasets (e.g., species images taken all over the world) - all current location encoding models are designed for encoding points in a 2D (Euclidean) space but not on a spherical surface, e.g., earth surface. To solve this problem, we propose a multi-scale location encoding model called Sphere2V ec which directly encodes point coordinates on a spherical surface while avoiding the mapprojection distortion problem. We provide theoretical proof that the Sphere2Vec encoding preserves the spherical surface distance between any two points. We also developed a unified view of distance-reserving encoding on spheres based on the Double Fourier Sphere (DFS). We apply Sphere2V ec to the geo-aware image classification task. Our analysis shows that Sphere2V ec outperforms other 2D space location encoder models especially on the polar regions and data-sparse areas for image classification tasks because of its nature for spherical surface distance preservation.