Air pollution is the world's largest environmental risk factor for human disease and premature death, resulting in more than 6 million permature deaths in 2019. Currently, there is still a challenge to model one of the most important air pollutants, surface ozone, particularly at scales relevant for human health impacts, with the drivers of global ozone trends at these scales largely unknown, limiting the practical use of physics-based models. We employ a 2D Convolutional Neural Network based architecture that estimate surface ozone MOMO-Chem model residuals, referred to as model bias. We demonstrate the potential of this technique in North America and Europe, highlighting its ability better to capture physical model residuals compared to a traditional machine learning method. We assess the impact of incorporating land use information from high-resolution satellite imagery to improve model estimates. Importantly, we discuss how our results can improve our scientific understanding of the factors impacting ozone bias at urban scales that can be used to improve environmental policy.

Tomato crop health plays a critical role in ensuring agricultural productivity and food security. Timely and accurate detection of diseases affecting tomato plants is vital for effective disease management. In this study, we propose a deep learning-based approach for Tomato Leaf Disease Detection using two well-established convolutional neural networks (CNNs), namely VGG19 and Inception v3. The experiment is conducted on the Tomato Villages Dataset, encompassing images of both healthy tomato leaves and leaves afflicted by various diseases. The VGG19 model is augmented with fully connected layers, while the Inception v3 model is modified to incorporate a global average pooling layer and a dense classification layer. Both models are trained on the prepared dataset, and their performances are evaluated on a separate test set. This research employs VGG19 and Inception v3 models on the Tomato Villages dataset (4525 images) for tomato leaf disease detection. The models' accuracy of 93.93% with dropout layers demonstrates their usefulness for crop health monitoring. The paper suggests a deep learning-based strategy that includes normalization, resizing, dataset preparation, and unique model architectures. During training, VGG19 and Inception v3 serve as feature extractors, with possible data augmentation and fine-tuning. Metrics like accuracy, precision, recall, and F1 score are obtained through evaluation on a test set and offer important insights into the strengths and shortcomings of the model. The method has the potential for practical use in precision agriculture and could help tomato crops prevent illness early on.

Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Embeddings. A novel dataset, featuring diverse handwritten prescriptions from various regions of Pakistan, was utilized to fine-tune the model on different handwriting styles. The Mask R-CNN model segments the prescription images to focus on the medicinal sections, while the TrOCR model, enhanced by Multi-Head Attention and Positional Embeddings, transcribes the isolated text. The transcribed text is then matched against a pre-existing database for accurate identification. The proposed approach achieved a character error rate (CER) of 1.4% on standard benchmarks, highlighting its potential as a reliable and efficient tool for automating medicine name extraction.

As advancements in computing technologies and the expanding use of e-commerce platforms dramatically increase the risk of fraud for financial companies, many are turning to deep learning to better protect themselves and their customers.

During the dot-com boom, online clothing sales were predicted to grow to 40% -50% of total sales. Although online sales of some other kinds of merchandise, such as books, have reached 50% of the market in the past 15 years, the percentage of online clothing sales hovers around 20%. The difficulty in finding the correct size and fit is one of the primary reasons that consumers are reluctant to buy clothes online. And their concern is not groundless; sizing varies among clothing manufacturers, and it is difficult to ascertain fit from online images. Consequently, 30%-40% of online clothing purchases are returned.

Long gone are the days of swapping tapes with friends after school, reading about the latest bands in your weekly magazine or tuning into Top of the Pops at the end of the week for the chart countdown. The excitement of discovering new music has definitely declined as the digitalisation of music has taken over the industry. Spotify is the most popular music streaming service out there and as such, harnesses the most valuable asset a company like this can have – data. Used by over 100 million people, with 30million of those paid subscribers and 55% of those linking their accounts to social media. Around 5million playlists are created or edited daily and in 2015 Spotify users streamed over 20bn hours of music.

Machine learning has readily improved the way we interact with the internet. One such example would be the spam filter which readily separates unwanted emails from an expansive list of received snippets. However, it is quite intriguing to understand the modus operandi involved in the functioning of these spam filters as segregating mails based on the user ID isn't a plausible option. Moreover, most spams are directed from legit email IDs which are in turn hacked by a third party phishing body. Therefore, the only way to differentiate would be the email content which can be translated beforehand using Machine Learning.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.

David Ledbetter, data scientist at the Children's Hospital Los Angeles, shares how his team is using TITAN X GPUs and deep learning to help provide better recommendations of drug treatments for children in their pediatric intensive care unit. To train their models, 13,000 patient snapshots were created from ten years of electronic health records at the hospital to understand the interactions between a patient's vital state, heart rate, blood pressure and the treatments they were given. By understanding the most important relationships in the data, they are then able to generate the probability of survival predictions for the patients moving forward as well as physiology predictions in order to simulate augmented treatments. David presented his research poster "Dr. Watch more scientists and researchers share how accelerated computing is benefiting their work at http://nvda.ly/X7WpH