ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

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Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News

Metallic alloys for aerospace components are expected to be made faster and more cheaply with the application of machine learning in Project MEDAL.

This is the aim of Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design,which is being led by Intellegens, a Cambridge University spin-out specialising in artificial intelligence, the Sheffield University AMRC North West, and Boeing. It aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise additive manufacturing (AM) for new metal alloys.

How collaboration is driving advances in additive manufacturing

Project MEDALs research will concentrate on metal laser powder bed fusion and will focus on so-called parameter variables required to manufacture high density, high strength parts.

The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10m initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

In a statement, Ben Pellegrini, CEO of Intellegens, said: The intersection of machine learning, design of experiments and additive manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, added James Hughes, research director for Sheffield University AMRC North West. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Aerospace components must withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace sector.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1m. Project MEDAL aims to accelerate this process.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80 per cent, Pellegrini said: The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.

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Project MEDAL to apply machine learning to aero innovation - The Engineer

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes. IPA solutions go beyond simple, rule-based tasks.

Machine Learning (ML) Intelligent Process Automation Marketresearch is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

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Some of the important players in Machine Learning (ML) Intelligent Process Automation market are Automation Anywhere, Inc., UiPath., Blue Prism Limited., Pegasystems Inc., AntWorks, NICE Ltd., KOFAX INC., Softomotive Ltd., SAP SE, AutomationEdge, eggplant., LarcAI, Kryon Systems, Autologyx, Sanbot Innovation Technology., Ltd, Cinnamon, Inc., Wipro Limited, Xerox Corporation, Tata Consultancy Services Limited., IBM Corporation.

Machine Learning (ML) Intelligent Process Automation Market is growing at a High CAGR during the forecast period 2021-2027. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes. IPA solutions go beyond simple, rule-based tasks.

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Machine Learning (ML) Intelligent Process Automation market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Machine Learning (ML) Intelligent Process Automation markets trajectory between forecast periods.

Global Machine Learning (ML) Intelligent Process Automation Market research report offers:

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Regions Covered in the Global Machine Learning (ML) Intelligent Process Automation Market Report 2021: The Middle East and Africa(GCC Countries and Egypt) North America(the United States, Mexico, and Canada) South America(Brazil etc.) Europe(Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The cost analysis of the Global Machine Learning (ML) Intelligent Process Automation Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Key questions answered in the report include:

Table of Content (TOC)

Global Machine Learning (ML) Intelligent Process Automation Market Report 2021 Growth, Trend and Forecast to 2027

Chapter 1 Machine Learning (ML) Intelligent Process Automation Market Overview

Chapter 2 Global Economic Impact on Machine Learning (ML) Intelligent Process Automation Industry

Chapter 3 Global Machine Learning (ML) Intelligent Process Automation Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region (2014-2021)

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions (2014-2021)

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Machine Learning (ML) Intelligent Process Automation Market Forecast (2021-2027)

Chapter 13 Appendix

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Forecast On Machine Learning (ML) Intelligent Process Automation Market Witness the Growth of Great Billion by 2027 With Top Companies Like Automation...

Machine learning was shown to identify patients with rheumatoid arthritis (RA) who present an increased chance of achieving clinical response with sarilumab, with those selected also showing an inferior response to adalimumab, according to an abstract presented at ACR Convergence, the annual meeting of the American College of Rheumatology (ACR).

In prior phase 3 trials comparing the interleukin 6 receptor (IL-6R) inhibitor sarilumab with placebo and the tumor necrosis factor (TNF-) inhibitor adalimumab, sarilumab appeared to provide superior efficacy for patients with moderate to severe RA. Although promising, the researchers of the abstract highlight that treatment of RA requires a more individualized approach to maximize efficacy and minimize risk of adverse events.

The characteristics of patients who are most likely to benefit from sarilumab treatment remain poorly understood, noted researchers.

Seeking to better identify the patients with RA who may best benefit from sarilumab treatment, the researchers applied machine learning to select from a predefined set of patient characteristics, which they hypothesized may help delineate the patients who could benefit most from either antiIL-6R or antiTNF- treatment.

Following their extraction of data from the sarilumab clinical development program, the researchers utilized a decision tree classification approach to build predictive models on ACR response criteria at week 24 in patients from the phase 3 MOBILITY trial, focusing on the 200-mg dose of sarilumab. They incorporated the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm, including 17 categorical and 25 continuous baseline variables as candidate predictors. These included protein biomarkers, disease activity scoring, and demographic data, added the researchers.

Endpoints used were ACR20, ACR50, and ACR70 at week 24, with the resulting rule validated through application on independent data sets from the following trials:

Assessing the end points used, it was found that the most successful GUIDE model was trained against the ACR20 response. From the 42 candidate predictor variables, the combined presence of anticitrullinated protein antibodies (ACPA) and C-reactive protein >12.3 mg/L was identified as a predictor of better treatment outcomes with sarilumab, with those patients identified as rule-positive.

These rule-positive patients, which ranged from 34% to 51% in the sarilumab groups across the 4 trials, were shown to have more severe disease and poorer prognostic factors at baseline. They also exhibited better outcomes than rule-negative patients for most end points assessed, except for patients with inadequate response to TNF inhibitors.

Notably, rule-positive patients had a better response to sarilumab but an inferior response to adalimumab, except for patients of the HAQ-Disability Index minimal clinically important difference end point.

If verified in prospective studies, this rule could facilitate treatment decision-making for patients with RA, concluded the researchers.

Reference

Rehberg M, Giegerich C, Praestgaard A, et al. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Presented at: ACR Convergence 2020; November 5-9, 2020. Accessed January 15, 2021. 021. Abstract 2006. https://acrabstracts.org/abstract/identification-of-a-rule-to-predict-response-to-sarilumab-in-patients-with-rheumatoid-arthritis-using-machine-learning-and-clinical-trial-data/

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Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis - AJMC.com Managed Markets Network

We successfully made it through 2020 and 2021 is finally upon us. While some things, like the way businesses operate, have changed drastically, others remain the same. In the current times, companies are increasingly going online and operating with newer tech solutions to keep up with the changes that the pandemic has brought about in the market.

Companies across the world are adopting Data Science and Machine Learning to understand complex business problems, extract meaningful insights and formulate ways to resolve them. Theyre being used across several sectors and for diverse use cases. These can be anything from banking & finance departments using machine learning algorithms to identify forged signatures to supply chain and manufacturing companies using it for smarter inventory management. In the same vein, airline companies are using data science to map flight delay and develop loyalty programs, and the gaming industry is applying it to improve gaming models based on insights.

These job roles offer some of the highest salaries. Therefore, many engineering graduates in India are interested to pursue their M. tech in Data Science and Machine Learning. The salary scale in this domain ranges from Rs 4 Lakhs per annum to Rs 25 Lakhs per annum, considering various factors. In India, the average pay scale of a Data Scientist is estimated to be Rs 7 Lakhs per annum. Hence the incredible demand. Check out all the lucrative roles you can bag with these skills:

1. Data AnalystAs a data analyst, you will be responsible for various tasks, including visualisation, munging and processing of massive amounts of data. You will also have to perform queries on the databases from time to time. One of the most important skills to gain for you, as a data analyst would be optimisation. This is because you will have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.

2. Data EngineersAs a Data Engineer, you build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimised. You will also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

3. Database AdministratorYour job profile is pretty much self-explanatory: You will be responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on your requirements. You will also be responsible for database backups and recoveries.

4. Machine Learning EngineerAs a Machine Learning Engineer, you will be in high demand today. However, the job profile comes with its challenges. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc., you would also be expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.

5. Data ScientistYou have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, you are expected to perform predictive analysis and run a fine-toothed comb through an unstructured/disorganised data to offer actionable insights. You could also do this by identifying trends and patterns that can help the companies in making better decisions.

6. Data ArchitectAs a Data Architect, you create the blueprints for data management so that the databases can be easily integrated, centralised, and protected with the best security measures. You must also ensure that the Data Engineers have the best tools and systems to work with. Some other related job roles worth mentioning include Statistician, Business analyst, Data and Analytics Manager.

For those whod love to upskill, Great Learning has emerged as one of Indias leading professional learning services with a footprint in 140 countries and has delivered 55 million+ learning hours. With a curriculum formulated by industry experts, their programs have helped learners successfully transition to new domains and grow in their fields. They offer courses on some of the hottest topics of today Data Science and Machine Learning, Artificial Intelligence etc.

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Bangalore based Great Learning can help you unleash the potential of an M-Tech in Data Science & Machine - Times of India

Sri Lanka revives port deal with India and Japan for sea terminal Business Standard First quasi-judicial body to strengthen its digital back-end

Topics CERC|artificial intelligence|machine learning

Shreya Jai | New Delhi Last Updated at January 15, 2021 06:10 IST

The apex power sector regulator, the Central Electricity Regulatory Commission (CERC), is planning to set up an artificial intelligence (AI)-based regulatory expert system tool (REST) for improving access to information and assist the commission in discharge of its duties. So far, only the Supreme Court (SC) has an electronic filing (e-filing) system and is in the process of building an AI-based back-end service.

The CERC will be the first such quasi-judicial regulatory body to embrace AI and machine learning (ML). The decision comes at a time when the CERC has been shut for four ...

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Business Standard has always strived hard to provide up-to-date information and commentary on developments that are of interest to you and have wider political and economic implications for the country and the world. Your encouragement and constant feedback on how to improve our offering have only made our resolve and commitment to these ideals stronger. Even during these difficult times arising out of Covid-19, we continue to remain committed to keeping you informed and updated with credible news, authoritative views and incisive commentary on topical issues of relevance. We, however, have a request.

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First Published: Fri, January 15 2021. 06:10 IST

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CERC plans to embrace AI, machine learning to improve functioning - Business Standard

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There's something so interesting about knowing what our favorite celebrities keep in their refrigerator...or refrigerators in Kim Kardashian's case. In fact, it's pretty common for brands to get a little more clout when a star gives them some free press (like Brad Pitt and Red Vines!), which is what makes the fact that Katy Perry has plans to go vegan some food for thought this afternoon.

The singer-songwriter shared a tweet on her account about the lifestyle change, saying that she follows a pretty plant-based diet already but has hopes to make the full transition soon. "Im about 95% ready to be 100% VEGAN... my dog Nugget has been joining me on this journey for the past 4 monthz. Pray for us ok," she wrote.

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In the past, Katy has expressed her distaste for certain meats, particularly during her pregnancy with baby Daisy. In March of 2020 she shared on Instagram Live that she was "not really into red meat" and was thankful for Impossible Burgers because they were a great alternative when she had a craving.

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The recent news that Hidden Valley is releasing a plant-based and dairy-free Ranch dressing may also be a big reason Katy is finally ready to take the plunge. She shared a photo of the upcoming product on her Instagram with the caption "OMG PRAYERS ANSWERED," so that probably means she'll be purchasing the dressing when it hits markets in April.

By then, Katy will probably have made the fully plant-based transition, and luckily she won't be on the journey alone because, as she shared, her dog has been along for the ride as well.

Orlando Bloom On Who Baby Daisy Looks Like

Katy Perry Shares Her Pregnancy Cravings

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Katy Perry Shared That She's Going Totally Vegan Soon - Delish.com

On January 20, the Indian village of Thulasendrapuram will celebrate the inauguration of Kamala Harris, the first Black and South Asian woman elected as Vice President of the United States, with vegan treats. This week, every family in the villagethe birthplace of Harris grandfatherreceived a basket of vegan biryani, plant-based meat from Indian startup GoodDot, oat milk from Good Mylk, vegan sweets, and more courtesy of the animal-rights group People for the Ethical Treatment of Animals (PETA).

PETA India is delighted to help Thulasendrapuram celebrate the milestone of Vice President-elect Harris inauguration with vegan goodies, especially since she has sponsored and supported so much legislation in the US against cruelty to animals, PETA India spokesperson Radhika Suryavanshi said. Vegan food saves animals lives, helps preserve the planet, and safeguards human health, so everyone wins.

Harris stance on meatDuring a CNN town hall in 2019 during Harris run for president, the now VP-elect responded to a question about whether she would support a change in the dietary guidelines to reduce the consumption of red meat. I love cheeseburgers from time to time, I just do. But [we must also consider] what we do in terms of creating incentives that we will eat in a healthy way, that we will encourage moderation, and that we will be educated about the effect of our eating habits on our environment, Harris said. We have to do a much better job with that and the government has to do a much better job with that.

In December 2020, Harris was also challenged to go vegan for the month of January by JIVINITI Womens Coalition, a diverse group of organizations composed mostly of women of color led by nonprofit The Virsa Foundation. The Vice President-elect has not publicly accepted the challenge.

Love the plant-based lifestyle as much as we do? Get the BEST vegan recipes, travel, celebrity interviews, product picks, and so much more inside every issue of VegNews Magazine. Find out why VegNews is the worlds #1 plant-based magazine by subscribing today!

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Kamala Harris' Ancestral Indian Village to Celebrate Inauguration with Vegan Feast - VegNews

You can give your tofu scramble and avocado a rest.

For a meal thats considered the most important of the day, breakfast should never be boring. And if youre trying to keep them plant-based, they can get repetitive and old quickly. Theres only so much avocado toast one can take.

But having food allergies or living a plant-based or vegan diet doesnt mean that you have to sacrifice flavor and variety. By swapping out cows milk and eggs for non-dairy and plant-based alternatives (try applesauce!) and cooking with organic ingredients, you can still enjoy breakfast staples.

Here are a few companies that offer nationwide shipping on pancakes, muffins, waffles, and even French toast mixes in a variety of actually tasty flavors that wont make you want to get back into bed.

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These Companies Are Making Vegan-Friendly Breakfast More Exciting - Thrillist

In February, Alabama-based The Veggie, a Black woman-owned vegan catering and meal delivery company, is launching its first brick-and-mortar caf. Founded in 2017 by Chef Adyre Mason, The Veggie specializes in prepared vegan comfort foods such as seafood pasta (fettuccine cooked with sweet red peppers, white garlic cream sauce, and topped with chives and soy-based shrimp and lobster); White Chikn Chili (roasted and shredded hearts of palm, navy beans, onion, and green chili broth topped with coconut cream and served with a side of cornbread); and Spinach and Root Enchiladas (enchiladas baked in green chili cream sauce stuffed with roasted golden beets, spinach, and shiitake mushrooms).

Currently available in a variety of weekly meal plans for delivery in Huntsville, AL and within a 25-mile radius of the city, The Veggie Quick Service Caf will continue offering Masons prepared vegan meal delivery service alongside grab-and-go, ready-to eat-meals such as the companys signature black-eyed pea salad, vegan chikn salads, family-style dinners, and single meals from the meal delivery menu for customers who choose not to purchase a full meal preparation package. The caf will also offer a variety of fresh, made-to-order dishes including fan-favorites Crabless Cake Sammy and Buffalo Shrimp PoBoy and new menu items such as the ChickUN Basket and Pizza Bowl.

Our service is centered around two main pillars, convenience and variety, and customers can definitely expect to see this in our new caf, Mason told VegNews. We are providing our customers with a level of variety and types of vegan food that they cant readily find in this area.

From engineer to vegan chefAn engineer for 10 years, Mason decided to cash in her 401(k), quit her job as a Weapon Systems Engineer for an Army program, and focus on launching The Veggie. Launched just one year after her mothers passing, Mason made it a point to focus on foods that can help improve peoples healtha topic she is familiar with herself. After suffering from adult acne, joint pain, high cholesterol, and digestive distress in her early 20s, Mason transitioned to a plant-based diet and drastically improved her health.

While The Veggie continues making vegan foods more readily available in Huntsville, AL and surrounding communities, Mason has plans to provide plant-based meals to those who are unable to afford them. Growing up, I watched my mom use her own money, even as a single parent, to regularly feed the homeless, Mason said. One of my long-term goals with The Veggie is to provide healthy meals as regularly as possible to the homeless in Huntsville and in the cities we expand to in the future.

The Veggie Quick Service Caf will be located in the Lowe Mill Arts and Entertainment District at 2211 Seminole Drive, Railroad Room 5. Out-of-state customers can purchase The Veggies cinnamon rolls online for nationwide shipping.

Love the plant-based lifestyle as much as we do? Get the BEST vegan recipes, travel, celebrity interviews, product picks, and so much more inside every issue of VegNews Magazine. Find out why VegNews is the worlds #1 plant-based magazine by subscribing today!

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A New Black Woman-Owned Vegan Caf Is Coming to Alabama - VegNews