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Crimson Hexagon Unveils Image Analysis Beyond Logo Identification for Deepest Consumer Insight through Social Data

Crimson Hexagon, a leading provider of consumer insights from social data to inform strategic enterprise decision-making, released its proprietary image analysis capabilities, developed in-house, to help brands and agencies unlock insights within photo data. By applying Crimson Hexagon’s analytics engine to images to recognize scenes, objects, actions, faces and logos, brands learn who, how and where consumers engage with their products.

Transportation, Public Safety and the Modern Power Grid Thrive with Integrated Smart City Data

In this special guest feature, Steph Stoppenhagen, Smart City Business Development Director for Black & Veatch’s Smart Integrated Infrastructure business, outlines how a growing awareness, understanding and acceptance of data-based technology is rapidly changing how city officials manage community services.

A Wave of Abundance from Big Ocean Data

In this contributed article, Matthew Mulrennan, Director of the Ocean Initiative at XPRIZE, and Dr. Jyotika Virmani, Senior Director for Planet & Environment at XPRIZE and prize lead for the Shell Ocean Discovery XPRIZE, explain how advancing big data collection in ocean science can improve the business of conservation and protection of our underwater resources and provide early warnings for water quality risks to human health and in lead to new underwater discoveries.

The Next Generation of Managing Enterprise Data: Intelligent Data Identification

In order to thrive in today’s market, businesses must demand more from their data – more insights, more agility and more flexibility. Intelligent Data Identification capabilities go beyond the metadata repository to leverage 80+ profiling statistics for core data insights and delivering on key value-add business use cases.

Predicting and Preventing Power Outages Using Big Data

Texas A&M University researchers have developed an intelligent model that can predict a potential vulnerability to utility assets and present a map of where and when a possible outage may occur. Dr. Mladen Kezunovic, along with graduate students Tatjana Dokic and Po-Chen Chen, have developed the framework for a model that can predict weather hazards, vulnerability of electric grids and the economic impact of the potential damage.

Why Technology is the Next Frontier in Mental Health

In this special guest feature, Kouris Kalligas, CEO and co-founder at Therachat, highlights how different data technologies, such as artificial intelligence, machine learning, digital journaling, data analytics and data visualization are being integrated into our everyday lives via smartphones and modernizing how mental health services are delivered.

Is Customized Healthcare a Near Term Reality?

In this special guest feature, Abdul Hamid Halabi, the global business development lead for healthcare and life sciences at NVIDIA, discusses how personalized or precision medicine is becoming a reality with the help of a machine learning method called deep learning.

SKA Signs Big Data Cooperation Agreement with CERN

SKA Organisation and CERN, the European Laboratory for Particle Physics, signed an agreement formalising their growing collaboration in the area of extreme-scale computing. The agreement establishes a framework for collaborative projects that addresses joint challenges in approaching Exascale computing and data storage, and comes as the LHC will generate even more data in the coming decade and SKA is preparing to collect a vast amount of scientific data as well.

A New Weapon in the Fight Against Opioid Addiction: Behavioral Analytics

In this special guest feature, David Hom, Chief Evangelist at SCIO Health Analytics®, supports his belief that sophisticated behavioral analytics is the key to uncovering patients who are likely to be or become addicted to prescription opioids.

Using Machine Learning with Health Data: The Challenges and Pitfalls

In this contributed article, Elad Ferber, CTO and Co-founder of Spry Health, points out that when considering health data, the level of required customization for machine learning algorithms is very high for 3 reasons: the inherent complexity of the human body, the accessibility and relevance of data sources, and integration into the existing healthcare system.