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Internet of Things and Fog Computing-Enabled Solutions for Real-Life Challenges (Hardcover): Anil Saroliya, Ajay Rana, Vivek... Internet of Things and Fog Computing-Enabled Solutions for Real-Life Challenges (Hardcover)
Anil Saroliya, Ajay Rana, Vivek Kumar, Jose Sebastian Gutierrez Calderon, Senthil Athithan
R2,854 Discovery Miles 28 540 Ships in 12 - 17 working days

In today's world, the use of technology is growing rapidly, and people need effective solutions for their real-life problems. This book discusses smart applications of associated technologies to develop cohesive and comprehensive solutions for the betterment of humankind. It comprehensively covers the effective use of the Internet of Things (IoT), wireless sensor network, wearable sensors, body area network, cloud computing, and distributed computing methodologies. The book comprehensively covers IoT and fog computing sensor supported technologies or protocols including web of things, near-field communication, 6LoWPAN, LoRAWAN, XMPP, DDS, LwM2M, Mesh Protocol, and radio-frequency identification. The book- Discusses smart applications to develop cohesive and comprehensive solutions for real-life problems. Covers analytical descriptions with appropriate simulation and prototype models. Examines the role of IoT and fog computing technologies during global emergency situations. Discusses key technologies including cloud computing, 5G communication, big data, artificial intelligence, control systems, and wearable sensors. The text is primarily written for graduate students, and academic researchers working in diverse fields of electrical engineering, biomedical engineering, electronics and communication engineering, computer engineering, and information technology.

Data Scientists at Work (Paperback, 1st ed.): Sebastian Gutierrez Data Scientists at Work (Paperback, 1st ed.)
Sebastian Gutierrez
R1,010 R819 Discovery Miles 8 190 Save R191 (19%) Ships in 10 - 15 working days

Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. "Data scientist is the sexiest job in the 21st century," according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (Andre Karpis ts enko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees' earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.

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