Dr. Sachin R Sakhare is working as a Professor in the Department of Computer Engineering of Vishwakarma Institute of Information Technology, Pune, India. He has 26 Years of experience in engineering education. He is recognised as PhD guide by Savitribai Phule Pune University and currently guiding 7 PhD scholars. He is a life member of CSI, ISTE and IAEngg. He has Published 39 research communications in national, international journals and conferences, with around 248 citations and H-index 6.

The world is driven by connections--from financial and communication systems to social and biological processes. As connectedness continues to accelerate, it's not surprising that interest in graph algorithms has exploded because they are based on mathematics explicitly developed to gain insights from the relationships between data. Graph analytics can uncover the workings of intricate systems and networks at massive scales--for any organization. We are passionate about the utility and importance of graph analytics as well as the joy of uncovering the inner workings of complex scenarios. Until recently, adopting graph analytics required significant expertise and determination, because tools and integrations were difficult and few knew how to apply graph algorithms to their quandaries.

Faire is an online wholesale marketplace built on the belief that the future is local -- there are over 2 million independent retailers in North America and Europe doing more than $2 trillion in revenue. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town -- we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses everywhere can compete with these big box and e-commerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally.

At Vision Analytics, we are a company specialized in the development of computer vision solutions. Our neural networks allow (among other things) to generate value and information from an image, allowing smart decision-making based on data and real analytics. One of the problems facing artificial intelligence these days, and in particular the area of computer vision, is the lack of data. Surprising as it may seem, we generate more than 50 thousand photos per second (phototutorial), but we only have access to a few of these photos and from these, only some of them are valid for the task we are facing on. The problem of the lack of data is so heavy that companies like Vision Analytics (visionanalytics.ai) This is where our partner Veris Behavior (verisbehavior.com)

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In fact, odds are you've already worked for a company that uses AI and/or machine learning tools to some extent! But AI will also have wide-reaching consequences for the economy be on the workplace. In fact, AI is sure to shape the future of live shopping and e-commerce marketplaces for years to come. Let's take a look at six major ways AI will change e-commerce and live shopping in the near future. For starters, AI technology developments will allow the further development of visual search capabilities and programs.

Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.

In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few.

Afresh Technologies Inc., a startup using artificial intelligence to help grocery store operators reduce food waste, on Thursday announced that it has closed a $115 million funding round. The Series B round was led by Spark Capital. More than a half-dozen other backers participated as well, including Walter Robb, the former co-chief executive officer of Whole Foods. The investment brings Afresh's total outside funding to $148 million. Founded in 2017, Afresh provides a software platform that enables grocery store operators to track fresh food sales.

Images with natural context can provide better results than plain background. The following is an example image of the front yard of a house. The following is an example image of the front yard of a different house with a different background. Include images with varying lighting so that it covers the different lighting conditions that occur during inference (for example, with and without flash). You can also include images with varying saturation, hue, and brightness.