London, Dec. 10, 2019 (GLOBE NEWSWIRE) -- According to a new market research report "Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions), Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), & End User - Global Forecast to 2027", published by Meticulous Research, the AI in Supply Chain Market is expected to grow at a CAGR of 39.4% from 2019 to reach $21.8 billion by 2027. Today supply chain networks are becoming more and more complex owing to progressive globalization. Various well-established supply chain organizations across the globe are increasingly struggling with rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Therefore, the adoption of artificial intelligence technologies in supply chain operations is on the rise in order to create new opportunities & enhance operational capabilities by leveraging new possibilities, fastening processes, and making organizations adaptable to changes in the future. Realizing the fact, various end-use industries are investing heavily in order to reap the profits in highly dynamic and competitive market environments.

Global health care expenditure has been projected to grow from US $7.7 trillion in 2017 to US $10 trillion in 2022 at a rate of 5.4% [1]. This translates into health care being an average of 9% of gross domestic product among developed countries [2,3]. Some key global trends that have led to this include tax reform and policy changes in the United States that could impact the expansion of health care access and affordability (Affordable Care Act) [4], implications on the United Kingdom's health care spend based on the decision to leave the European Union [5], population growth and rise in wealth in both China and India [6-8], implementation of socioeconomic policy reform for health care in Russia [9], attempts to make universal health care effective in Argentina [10], massive push for electronic health and telemedicine in Africa [11], and the impact of an unprecedented pace of population aging around the world [12]. From clinicians' perspective there are many important trends that are affecting the way they deliver care of which the growth in medical information is alarming. It took 50 years for medical information to double in 1950. In 1980, it took 7 years. In 2010, it was 3.5 years and is now projected to double in 73 days by 2020 [13].

This is the seventh post of my column Deep Learning for Life Sciences where I give concrete examples of how Deep Learning can already now be applied in Computational Biology, Genetics and Bioinformatics. In the previous posts, I demonstrated how to use Deep Learning for Ancient DNA, Single Cell Biology, OMICs Data Integration, Clinical Diagnostics and Microscopy Imaging. Today we are going to dive into the exciting History of Human Evolution and learn that it is straightforward to borrow methodology from the Natural Language Processing (NLP) and apply it to Human Population Genetics in order to infer regions of Neanderthal introgression in modern human genomes. When ancestors of Modern Humans migrated out of Africa 50 000 - 70 000 years ago, they encountered Neanderthals and Denisovans, two groups of ancient hominins that populated Europe and Asia at that time. We know that Modern Humans interbred with both Neanderthals and Denisovans since there is evidence of the presence of their DNA in genomes of Modern Humans of non-African origin.

At IGF 2019, several policy initiatives, reports, and publications were launched or used as background material for discussions.

A Canadian university is working with a Canadian and American education technology company to research the use of artificial intelligence in K-12 classrooms. Specifically, the project will explore the impact of AI-driven learning experiences on student outcomes, including academic growth and social emotional learning. Participants will also develop research and best practices on the responsible use of AI in regards to equity, student privacy and teachers' abilities to personalize their students' learning experiences. The initiative involves Thierry Karsenti, a professor at the University of Montreal and Canada research chair on information and communication technologies, and Classcraft CEO, Shawn Young. Karsenti has recently been involved in a research effort that delivers mobile education on smartphones using AI to adapt professional development for teachers in Africa.

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Peter Kairouz and H. Brendan McMahan conceived, coordinated, and edited this work.

Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model due to the imbalances in the dataset for discriminating attacks from the overall dataset. Multilayer Perceptron (MLP) technique will provide improvement in accuracy and increase the performance of detecting the attack and benign data from a balanced dataset. We have worked on the UGR'16 dataset publicly available for this work. Data wrangling has been done due to prepare test set from in the original set. We fed the neural network classifier larger input to the neural network in an increasing manner (i.e. 10000, 50000, 1 million) to see the distribution of features over the accuracy. We have implemented a GAN model that can produce samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan). We have been able to generate as many samples as necessary based on the data sample we have taken from the UGR'16. We have tested the accuracy of our model with the imbalance dataset initially and then with the increasing the attack samples and found improvement of classification performance for the latter.

NFL and AWS will use data and analytics to co-develop the "Digital Athlete," a platform that aims to improve player safety, treatment, and ultimately, predict and prevent injury Building on the existing Next Gen Stats (NGS) partnership and as the NFL marks its 100th season, AWS and NFL will innovate together to shape the future of football. The partnership aims to leverage AWS's artificial intelligence (AI) and machine learning (ML) services to provide a deeper and more profound understanding of the game than ever before, making transformational change possible in football, other sports, and potentially other industries. The NFL and AWS will develop new tools and generate deeper and better-informed insights into player injuries, specifically the impact of a variety of factors such as game rules, equipment, and rehabilitation and recovery strategies. Over time, the collaboration aims to also build the capability to predict the risk of player injuries before they happen. "The NFL is committed to reimagining the future of football," said NFL Commissioner Roger Goodell.

Millions of roads around the world have yet to be mapped, and that's a real problem, particularly in the developing world. Missing map data can harm disaster response, community planning, and local economies. And while government-run and tax-funded projects like the U.K.'s Ordnance Survey have produced enormous corpora, they've largely failed to freely and widely distribute them. That's motivated crowdsourcing efforts like OpenStreetMap, which recruit thousands of volunteers to catalog roads, buildings, and bridges every day. It's an arduous process, but one buoyed by Facebook, which has worked with communities and partners to fine-tune a tool -- Map With AI -- that automates several of the most time-consuming steps.

Automated decision making comes of age.