Category Archives: Machine Learning

Army looks to machine learning to predict, prevent injuries

The Army is harnessing a sensor and machine learning platform currently used by professional and collegiate sports teams to analyze individual soldiers biomechanics and predict and prevent physical injuries.

According to a March 2020 paper in Military Medicine, noncombat injuries are the leading cause of outpatient medical visits among active Army service members, accounting for nearly 60% of soldiers limited duty days and 65% of soldiers who cannot deploy for medical reasons. Besides decreasing the number of soldiers available to deploy, these injuries are expensive to treat and can lead to service-connected disability compensation.

The Armys Mission and Installation Contracting Command will be using Sparta Sciences Sparta Trac system to collect data on movements used in heavy physical training regimes. The system uses force plates, similar to large bathroom scales that are equipped with sensors that assess an athletes core and lower extremity strength. As athletes do various balance, jumping and plank exercises, the system collects and analyzes the data to create a movement signature and show the risk level for musculoskeletal injuries. It also designs customized workouts so soldiers can strengthen weak areas and avoid injuries. The diagnostic test takes five minutes, company officials wrote in an Aug. 18 column for Stars and Stripes.

Force plate technology was singled out for study by the military in the 2021 National Defense Authorization Act. The NDAA encouraged development of a tool that will check warfighters physical fitness to determine combat readiness. Force plate technology and machine learning capabilities are an important part of that tool, according to the NDAA.

Although force plate systems are already used across the military, the NDAA tasked the Secretary of Defense to report on how many military units are using the systems, as well as whether the technology could be scaled to develop individual fitness programs for at-home and deployed warfighters.

About the Author

Mark Rockwell is a senior staff writer at FCW, whose beat focuses on acquisition, the Department of Homeland Security and the Department of Energy.

Before joining FCW, Rockwell was Washington correspondent for Government Security News, where he covered all aspects of homeland security from IT to detection dogs and border security. Over the last 25 years in Washington as a reporter, editor and correspondent, he has covered an increasingly wide array of high-tech issues for publications like Communications Week, Internet Week, Fiber Optics News, tele.com magazine and Wireless Week.

Rockwell received a Jesse H. Neal Award for his work covering telecommunications issues, and is a graduate of James Madison University.

Click here for previous articles by Rockwell. Contact him at [emailprotected] or follow him on Twitter at @MRockwell4.

Visit link:
Army looks to machine learning to predict, prevent injuries - GCN.com

The ability to reason abstractly about events as they unfold is a defining feature of human intelligence. We know instinctively that crying and writing are means of communicating, and that a panda falling from a tree and a plane landing are variations on descending.

Organizing the world into abstract categories does not come easily to computers, but in recent years researchers have inched closer by training machine learning models on words and images infused with structural information about the world, and how objects, animals, and actions relate. In a new study at the European Conference on Computer Vision this month, researchers unveiled a hybrid language-vision model that can compare and contrast a set of dynamic events captured on video to tease out the high-level concepts connecting them.

Their model did as well as or better than humans at two types of visual reasoning tasks picking the video that conceptually best completes the set, and picking the video that doesnt fit. Shown videos of a dog barking and a man howling beside his dog, for example, the model completed the set by picking the crying baby from a set of five videos. Researchers replicated their results on two datasets for training AI systems in action recognition: MITs Multi-Moments in Time and DeepMinds Kinetics.

We show that you can build abstraction into an AI system to perform ordinary visual reasoning tasks close to a human level, says the studys senior author Aude Oliva, a senior research scientist at MIT, co-director of the MIT Quest for Intelligence, and MIT director of the MIT-IBM Watson AI Lab. A model that can recognize abstract events will give more accurate, logical predictions and be more useful for decision-making.

As deep neural networks become expert at recognizing objects and actions in photos and video, researchers have set their sights on the next milestone: abstraction, and training models to reason about what they see. In one approach, researchers have merged the pattern-matching power of deep nets with the logic of symbolic programs to teach a model to interpret complex object relationships in a scene. Here, in another approach, researchers capitalize on the relationships embedded in the meanings of words to give their model visual reasoning power.

Language representations allow us to integrate contextual information learned from text databases into our visual models, says study co-author Mathew Monfort, a research scientist at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL). Words like running, lifting, and boxing share some common characteristics that make them more closely related to the concept exercising, for example, than driving.

Using WordNet, a database of word meanings, the researchers mapped the relation of each action-class label in Moments and Kinetics to the other labels in both datasets. Words like sculpting, carving, and cutting, for example, were connected to higher-level concepts like crafting, making art, and cooking. Now when the model recognizes an activity like sculpting, it can pick out conceptually similar activities in the dataset.

This relational graph of abstract classes is used to train the model to perform two basic tasks. Given a set of videos, the model creates a numerical representation for each video that aligns with the word representations of the actions shown in the video. An abstraction module then combines the representations generated for each video in the set to create a new set representation that is used to identify the abstraction shared by all the videos in the set.

To see how the model would do compared to humans, the researchers asked human subjects to perform the same set of visual reasoning tasks online. To their surprise, the model performed as well as humans in many scenarios, sometimes with unexpected results. In a variation on the set completion task, after watching a video of someone wrapping a gift and covering an item in tape, the model suggested a video of someone at the beach burying someone else in the sand.

Its effectively covering, but very different from the visual features of the other clips, says Camilo Fosco, a PhD student at MIT who is co-first author of the study with PhD student Alex Andonian. Conceptually it fits, but I had to think about it.

Limitations of the model include a tendency to overemphasize some features. In one case, it suggested completing a set of sports videos with a video of a baby and a ball, apparently associating balls with exercise and competition.

A deep learning model that can be trained to think more abstractly may be capable of learning with fewer data, say researchers. Abstraction also paves the way toward higher-level, more human-like reasoning.

One hallmark of human cognition is our ability to describe something in relation to something else to compare and to contrast, says Oliva. Its a rich and efficient way to learn that could eventually lead to machine learning models that can understand analogies and are that much closer to communicating intelligently with us.

Other authors of the study are Allen Lee from MIT, Rogerio Feris from IBM, and Carl Vondrick from Columbia University.

Go here to see the original:
Toward a machine learning model that can reason about everyday actions - MIT News

Four Different Machine Learning Algorithms Were Deployed to Analyze 9 Million Data Points Across 673 Global Banks, Including 65 Gcc Banks, to Explain up to 98% of What Drives Bank Valuations

Iridium Quant Lens Shows That Investor Relations Adds up to 24.2% of Gcc Bank Valuations

IR Quality Is the 3rd Most Important Factor Impacting Price/ Tangible Book Value of Gcc Banks

The quality of investor relations can add up to 24.2 percent to a listed companys market capitalization, according to a new data science project by Iridium Advisors that uses four different Machine Learning algorithms to calculate the impact of 30 financial and non-financial valuation drivers.

Oliver Schutzmann, CEO of Iridium Advisors, said: Many boards and management teams in emerging markets have not yet invested sufficiently in investor relations because they do not fully understand the value it adds. To this background, we sought to take a scientific and systematic approach to show how the business value they create can be translated into market value, and thereby quantify the value of investor relations. With the insights gained from Iridium Quant Lens Machine Learning algorithms, we can now help business leaders understand what exactly drives their market value and show them how to unlock material valuation potential.

Recommended AI News: Zixi And Telstra Partner For Global Live Video Distribution

The Iridium Quant Lens machine learning (ML) platform was built on the foundations of classic finance theory that a companys stock price is derived through an evaluation of risk relative to return factors by equity market participants. In order to identify the financial and non-financial drivers of bank valuations, four different machine learning algorithms were deployed to consider 30 risk and return metrics, compiled from over 9 million data points, and covering 673 banks globally. The Quant Lens algorithms were run separately for all banks and for 65 GCC banks over different time horizons ranging from 1 to 10 years.

Iridiums algorithms proved successful in decomposing valuation drivers and, in aggregate, explained 86% of valuation variability for the test data set and 91 percent of the full data set. Furthermore, some individual models, such as the 3-year models for GCC banks, explained up 95 percent of the test data set and 98 percent of the full data set.

Recommended AI News: Litmus and Oden Partner to Offer Complete IIoT Solution for Smart Manufacturing

A significant finding of this study was that the quality of investor relations based on the classification into IR-Agnostic, IR-Basic and IR-Emerging archetypes is a highly material factor consistently influencing valuations of GCC banks. In fact, for most models it was the third most important factor impacting price to tangible book value (P/TBV) and explained 6% of share price variability on average.

In addition, the impact of upgrading investor relations is significant, with each upgrade step in a 2-stage upgrade path, commanding a 12 percent valuation premium on average and a complete move along the investor relations upgrade path adding 24 percent to market capitalization.

To illustrate the impact of IR Quality with real-world examples, one bank (Bank A) currently operates at an IR-Emerging level which adds 0.16x to its P/TBV valuation. Given the banks current market capitalization of USD 33 billion, this translates to almost USD 3 billion of its market value, or the equivalent of USD 220 million in net profits. Considering the valuation uplift achieved by the IR-Emerging level this is a compelling return on investment, being typically achievable with a USD 1.0 million annual IR budget. The converse is true for low IR quality. Bank X currently operates at an IR-Agnostic level, which in fact subtracts -0.07x from its P/TBV valuation.

Recommended AI News: ZelaaPayAE Partners with Tron to Empower Users with a Dual Chain Solution

Go here to see the original:
Iridium Unveils the World's First ML Algorithms That Decode the Value of Investor Relations - AiThority

The report also tracks the latest Machine Learning as a Service Market dynamics, such as driving factors, restraining factors, and industry news like mergers, acquisitions, and investments. It provides market size (value and volume), market share, growth rate by types, applications, and combines both qualitative and quantitative methods to make micro and macro forecasts in different regions or countries.

Prominent players profiled in the study: Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, Xeround

Sample Report with Latest Industry Trends @ https://www.statsandreports.com/request-sample/314706-global-machine-learning-as-a-service-market-size-status-and-forecast-2019-2025

Acknowledge the Global Machine Learning as a Service market with the assist of our expert analyst moderating the worldwide fluctuations. This market report will answer all your queries regarding growth of your business in this Covid-19 pandemic.

This Report provides an overview of the Machine Learning as a Service market, containing global revenue, global production, sales, and CAGR. Also describe Machine Learning as a Service product scope, market overview, market opportunities, market driving force, and market risks. The forecast and analysis of the Machine Learning as a Service market by type, application, and region are also presented. The next part of the report provides a full-scale analysis of Machine Learning as a Service competitive situation, sales, revenue and global market share of major players in the Machine Learning as a Service industry. The basic information, as well as the profiles, applications, and specifications of products market performance along with Business Overview, are offered.

Product Type: Private clouds, Public clouds, Hybrid cloud

Application: Personal, Business

Geographical Regions: North America, Europe, Central & South America, Asia-Pacific, and the Middle East & Africa, etc.

Get Reasonable Discount on this Premium Report @https://www.statsandreports.com/check-discount/314706-global-machine-learning-as-a-service-market-size-status-and-forecast-2019-2025

Machine Learning as a Service Market

Scope of the Machine Learning as a Service Report:

Worldwide Machine Learning as a Service Market 2020, Market Size Value CAGR (XX %) and revenue (USD Million) for the historical years (2016 to 2018) and forecast years (2020 to 2026), with SWOT analysis, Industry Analysis, Demand, Sales, Market Drivers, Restraints, Opportunities and Forecast to 2026 cover in this research report.

This report covers the current scenario and growth prospects of the Machine Learning as a Service Market for the period 2020-2026. The study is a professional and in-depth study with around tables and figures which provides key statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the domain.

Finally, all aspects of the Global Machine Learning as a Service Market are quantitatively as well qualitatively assessed to study the Global as well as regional market comparatively. This market study presents critical information and factual data about the market providing an overall statistical study of this market on the basis of market drivers, limitations and future prospects.

You can Buy This Report from Here: https://www.statsandreports.com/placeorder?report=314706-global-machine-learning-as-a-service-market-size-status-and-forecast-2019-2025&type=SingleUser

Thank you for reading our report. For more information on customization, please reach out to us. Our team will ensure the report is tailored according to your needs.

About Us

Stats and Reports is a global market research and consulting service provider specialized in offering wide range of business solutions to their clients including market research reports, primary and secondary research, demand forecasting services, focus group analysis and other services. We understand that how data is important in todays competitive environment and thus, we have collaborated with industrys leading research providers who works continuously to meet the ever-growing demand for market research reports throughout the year.

Contact:

Stats and ReportsMangalam Chamber, Office No 16, Paud RoadSankalp Society, Kothrud, Pune, Maharashtra 411038Phone: +1 650-646-3808Email: [emailprotected]Website: https://www.statsandreports.comFollow Us on: LinkedIN | Twitter |

More:
Machine Learning as a Service Market to Witness Astonishing Growth by 2026 | Amazon, Oracle Corporation, IBM and more - The News Brok

Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions.We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders.We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity.Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health.Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.Michael Leo Birnbaum, Prathamesh Param Kulkarni, Anna Van Meter, Victor Chen, Asra F Rizvi, Elizabeth Arenare, Munmun De Choudhury, John M Kane. Originally published in JMIR Mental Health (http://mental.jmir.org), 01.09.2020.

PubMed

Read more:
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early...

The retail banking sector has been hit with numerous scams during the past few years. Cybercriminals are now also beginning to increasingly go after much larger corporate accounts by launching sophisticated malware and phishing attacks, according to Beate Zwijnenberg, chief information security officer at ING Group.

Zwijnenberg recommends using advanced AI defense systems to identify potentially fraudulent transactions which may not be immediately recognizable by human analysts.

Financial institutions across the globe have been spending a lot of money to deal with serious cybersecurity threats.

Theyve been using static, rules-based verification processes to identify suspicious activity. Theyve also been using more advanced biometric authentication methods. Banks throughout the world keep looking for better or more efficient ways to ensure that their platforms remain secure, while trying to lower the costs involved with maintaining a high level of security.

Artificial intelligence (AI) and machine learning (ML) are now being used to analyze thousands of transactions in real-time. These advanced technologies allow security professionals to quickly and accurately check for potentially fraudulent activities. In many cases, cybersecurity experts are able to take action before bad actors can carry out fraudulent transactions.

As reported by PYMNTS, Amsterdams ING Group, which manages nearly a trillion euros in assets, has been using AI/ML tech to protect its platform against attacks from cybercriminals.

Zwijnenberg told the news outlet:

The real-time aspect of online fraud means that you need to intervene immediately because otherwise, the money is transferred and its gone for good. So, the real-time element [of artificial intelligence] is quite important.

She added:

Fraudsters are after the data or the money, but until recently, the techniques had not changed. If you have a traditional bank branch, they try to get into the safe and physically get the money out, and for digital banks, its not much different. It is only the modus operandi that has changed.

Zwijnenberg revealed that cybercriminals are increasingly targeting wholesale banking and are consistently applying the same phishing techniques to different types of customers. She confirmed that phishing scams are the most common in both business banking and wholesale banking. Identity theft has also become a major problem, Zwijnenberg noted.

She explained that using machine learning algorithms is a good idea when the amount of data is becoming bigger and bigger over time. She added that its like finding the needle in the haystack, and you benefit from applying AI and ML to make sure that you really only look into the specific areas that call for it.

Banks and government offices were recently targeted by malicious malware (a P2P botnet) which had maliciously mined privacy-oriented cryptocurrency Monero (XMR) by hogging the computing resources of targeted computers.

Cyberattacks in the UK and the US have increased as more consumers and businesses conduct financial transactions online.

Last month, over 300,000 potentially fraudulent sites with fake celeb endorsements were identified by the UKs National Cyber Security Center, with half of them being related to cryptocurrency.

Read the original post:
AI and Machine Learning Algorithms are Increasingly being Used to Identify Fraudulent Transactions, Cybersecurity Professional Explains - Crowdfund...