The global Healthcare Big Data Analytics market size was valued at $16.87 billion in 2017, and is projected to reach $67.82 billion by 2025, growing at a CAGR of 19.1% from 2018 to 2025. On the other hand, security issues associated with the lack of medical data and skilled staff of sensitive patients have somewhat suppressed growth. Nevertheless, with the growing propensity for cloud-based analytics solutions, emerging trends in healthcare, such as innovations in remote health and geography, have created favorable opportunities in this area. Market Segment by Regions, regional analysis covers North America (United States, Canada and Mexico) Europe (Germany, France, UK, Russia and Italy) Asia-Pacific (China, Japan, Korea, India and Southeast Asia) South America (Brazil, Argentina, Colombia etc.) Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)
"It shows how myriad distributed data streams can be harnessed to fight crime. Through easy-to-read prose, the reader learns how to use both public and private databases and networks to find threats and minimize risks. Besides explaining how data mining is done, the book introduces the reader to such techniques as intelligent agents (software that performs user-delegated tasks autonomously), link analysis (a process involving the mapping of the associations between suspects and locations), and text mining (a process used to identify a document's content based on linguistic analysis) and how they can aid law enforcement. For example, law enforcement in the United Kingdom use text mining to "institutionalize the knowledge of criminal perpetrators and organized gangs and groups," author Jesús Mena writes. Case studies buttress these points.
The new TLM Cash and Liquidity Management, AI and machine learning module is an important development for any financial institution with a treasury department, with its ability to predict when credit is going to arrive; giving the treasurer more control over cash-flows. The proprietary algorithm uses the data and predicts the forecasted settlement time of receipts on an intraday basis. The core of the module is underpinned by sophisticated machine learning technology that continuously improves, meaning the predictions become more accurate and treasurers can make more informed decisions. Nadeem Shamim, Head of Cash & Liquidity Management, SmartStream, says: "Things are going to get tighter in terms of managing liquidity. Collateral is expensive, capital is expensive and there is currently a big drive to reduce excessive use of capital – this is an area where AI and predictive analytics can manage liquidity buffers more efficiently and that can result in significant savings".
Wu Haishan was at Princeton University studying how schools of fish swim together when the crowd behavior of a much bigger group grabbed his attention: 1.35 billion fellow Chinese. It was Lunar New Year back home in 2014, and Baidu Inc., operator of the country's biggest search engine, had created an animation of all the trips people in China make during the holiday -- the largest annual human migration. He soon joined the company as a data scientist in Beijing, where he's tracking user location information to produce economic gauges such as which urban areas are ghost cities and how many people are buying cars. Big-data gurus like Wu are bringing the nation's colossal economy into sharper focus in a more potent way than in other major economies because, unlike most developed nations, China's official stats are often suspect or incomplete and private gauges can disappear. "We were running around pointing a flashlight at various things like labor or ports," said Jeffrey Towson, a professor of investment at Guanghua School of Management at Peking University.
Faculty Of Engineering Cairo University Giza, Egypt Diagnosis of anemia depends upon the observation of variations in color, shape and gray level distribution inside the Red Blood Cells (RBC's). The most important of all is the variation in the outer contour of an individual cell. In this paper, several new closed contour features are presented, together with some techniques for preprocessing and feature extraction. Preprocessing includes contour extraction and run length coding of a closed contour, while features include concavity, unsymmetry and zero crossing of slope density curve. Features are rotation, transformation and scale invariant in addition to being highly noise tolerant. An efficient design of a decision tree is presented for classification.