big data


Developments in Artificial Intelligence for Mental Health Care

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Advances in artificial intelligence have considered computers to help doctors in diagnosing disease and help screen patients' vital signs from any area. Significant advances have been made in artificial intelligence that will soon affect the manner in which mental health care is practiced in everyday clinical settings. The outcome will be increasingly individualized treatment integrating both traditional and evidence-based complementary and alternative medicine (CAM) modalities, progressively viable and more cost-effective medicines of numerous mental health issues, and improved results. In Europe, the WHO assessed that 44.3 million individuals suffer from depression and 37.3 million endure with anxiety. Diagnosis of mental health disorders depend on an age-old method that can be subjective and unreliable, says paper co-creator Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway.


How to Use Big Data and Artificial Intelligence for Demand-Based Pricing in Retail

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You can call yourself a guru of retail pricing if you can make the right pricing decisions for every one of your products, separately and combined, based on their demand elasticity at any given moment. Each of your pricing decisions has to help you reach all of your current business goals and ensure the best shopping experience at the same time. In other words: to find a balance between your profits and traffic. Let's take a step back to where everything begins – your business strategy. The company's strategy is supposed to be converted into its pricing strategy and subsequently into pricing tactics.


Machine Learning and Artificial Intelligence Are Poised to Revolutionize Asthma Care - Pulmonology Advisor

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The advent of large data sets from many sources (big data), machine learning, and artificial intelligence (AI) are poised to revolutionize asthma care on both the investigative and clinical levels, according to an article published in the Journal of Allergy and Clinical Immunology. During 15-minute clinic visits, only a short amount of time is spent understanding and treating what is a complex disease, and only a fraction of the necessary data is captured in the electronic health record. "Our patients and the pace of data growth are compelling us to incorporate insights from Big Data to inform care," the researchers posit. "Predictive analytics, using machine learning and artificial intelligence has revolutionized many industries," including the healthcare industry. When used effectively, big data, in conjunction with electronic health record data, can transform the patient's healthcare experience.


Processing Geospatial Data at Scale With Databricks

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The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data. Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. This boom of geospatial big data combined with advancements in machine learning is enabling organizations across industry to build new products and capabilities. Maps leveraging geospatial data are used widely across industry, spanning multiple use cases, including disaster recovery, defense and intel, infrastructure and health services. For example, numerous companies provide localized drone-based services such as mapping and site inspection (reference Developing for the Intelligent Cloud and Intelligent Edge).


KNIME on Amazon Web Services Now Available to Productionize AI/ML

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KNIME, a unified software platform for creating and productionizing data science, announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials. Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers.


Marketing Analytics and Data Science East 2019 - Day 1/2 Lessons & Reminders

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MADS East 2019 was a two-day conference in December that gave attendees endless opportunities to expose themselves to new ideas in the space of data science for marketing. Some of this year's conference perks included: tables for one-on-one networking, a half-an-hour off the record roundtable with 7 industry leaders, two unique tracks per day, buffet-style lunches, breakfasts, snacks, a refreshing break for cocktails at the Opening Night Party, and NYC Times Square views. This article is my summary of the Day 1 presentations I was able to attend, including lessons and reminders from the speakers. Aside from staying up to date on industry trends, MADS East has also proven itself a valuable opportunity for data and marketing people who are looking to engage with professionals of varying career levels. I was expecting to be the only individual with little background in data or extended industry experience present, but to my surprise, there was a decent balance between early, mid and late-career attendees.


Elsevier Launches 'AI and Big Data in Cancer,' a New Conference on the Translation of Technology, Data and Analytic Innovations into Clinical Practices and Patient Benefits

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AI and Big Data in Cancer: From Innovation to Impact, a new conference from Elsevier, a global information and analytics business specializing in science and health, will bring together experts from all aspects of cancer research and the digital medicine value chain to understand how to translate artificial intelligence and data-driven innovations into new clinical care practices for patients. These leaders, including 2018 Nobel laureate for Medicine, Dr. James Allison, will share pragmatic insights on finding the right partners to move innovations successfully forward. "It is time to shift our conversation from'what-technology-can-do' to'what-medicine-needs' and to raise awareness of what else is necessary to translate an AI-enabled and data-driven innovation into a marketed product," said Dr. Lynda Chin, Conference Chair, Founder and CEO of Apricity Health and Professor at Dell Medical School at the University of Texas, USA. "Understanding what these hurdles are is the first step to overcoming them. "The aim of this conference is to bring innovators together with stakeholders, from patients, clinicians and developers to regulators, payers and investors, so they can network and identify collaborators who can help them accelerate the translation of their innovation into clinical practices," Dr. Chin said. "Insights from the program's 40 key opinion leaders will advance the emerging digital medicine industry, building bridges from computer to clinics," said Laura Colantoni, Vice President for Reference Content, Elsevier, and one of the main organizers for the conference. "We are particularly excited about establishing this conference as a venue for successful innovators, influential facilitators, regulators and payers, as well as investors to find, engage and collaborate with clinicians, researchers and patients to accelerate progress in this area.


How Machine Learning Uncovers Opportunities For Business Optimization

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With ever more data being generated across modern businesses, companies are looking for actionable intelligence to drive optimization, increase margins and avoid supply chain distributions. The sheer volume of data can make it difficult to see trends; this is where machine learning is a revolution for business intelligence. Machine learning is a type of artificial intelligence (AI) that powers computers with the ability to learn without being explicitly programmed. It excels at finding anomalies, patterns and predictive insights in large datasets -- the data lakes -- by reporting on historical data as well as deploying models built to forecast likely outcomes. In particular, machine learning automates "what if" analysis by modeling a range of scenarios and prescribing actions that can help the organization achieve optimal results.


How Machine Learning Uncovers Opportunities For Business Optimization

#artificialintelligence

With ever more data being generated across modern businesses, companies are looking for actionable intelligence to drive optimization, increase margins and avoid supply chain distributions. The sheer volume of data can make it difficult to see trends; this is where machine learning is a revolution for business intelligence. Machine learning is a type of artificial intelligence (AI) that powers computers with the ability to learn without being explicitly programmed. It excels at finding anomalies, patterns and predictive insights in large datasets -- the data lakes -- by reporting on historical data as well as deploying models built to forecast likely outcomes. In particular, machine learning automates "what if" analysis by modeling a range of scenarios and prescribing actions that can help the organization achieve optimal results.


Big data analysis by neural networks Learn Neural Networks

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Big data is used to train and apply machine learning models in banking, insurance, and healthcare. Combining data in a data warehouse is crucial in the development of machine learning algorithms in industry, which creates a problem of data security and limited computing resources. Artificial neural networks in big data are known for their efficiency and effectiveness for small data sets. They cover search websites, ranking algorithms, recommendation and citation systems. The purpose of this paper is to present the progress, challenges, and opportunities for future research regarding the use of artificial neural networks in big data analysis, and the result of possible achievements of certain advances.