Machine Learning for Personalised Health

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Personalized healthcare and precision medicine are introducing novel opportunities to leverage data about an individual to deliver personalized health profiles and wellness profiles to an individual. This data includes electronic medical records, genomics, lifestyle, and environmental data. However, there are fundamental challenges from collecting such data at an individual level to integrating it to developing machine learning algorithms and tools to deliver on the promise and outcome of personalized healthcare. This SlideShare explores the concept of machine learning for personalized health in brief.

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

Machine Learning for Personalised Health

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Web3 is the future of the internet, where users are in control of their own data and have the power to control their online identity. Existing problems with the Web include centralized control of the internet by a few companies, lack of privacy and security for online users, and inequitable distribution of power and value online. Web3 technology enables decentralized applications and services that run on a peer-to-peer network, making the internet more secure and transparent. By leveraging blockchain technology, Web3 creates a more open and equitable online experience, where users can participate in a decentralized economy and enjoy greater privacy and security. With Web3, the internet is shifting from a centralized system controlled by a few companies to a decentralized network that is open to everyone. There are currently over 100 decentralized applications (dApps) built on Web3 technology, with many more in development. The total value locked in decentralized finance (DeFi) protocols built on Web3 technology has surpassed $60 billion. The Web3 ecosystem has attracted millions of users and is growing rapidly. This SlideShare explores the concept of Web3 in brief.

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Nowadays, product recommendation engines can understand customers in real time and suggest the best matching using several different machine learning algorithms. Unfortunately, many e-commerce stores put basic category levels or best-selling product recommendations on their websites, and never revise them or customize them again. They stand to gain by implementing recommendation engines. Deep learning works similarly to machine learning models, but deep learning uses more than one million data points and can determine whether the prediction is correct. Since deep learning is a self-learning system, manual intervention is no longer required, so the results can be used immediately. The core idea of deep learning-based recommendations is to unite all side-information features into subnetworks of user-related and item-related features and learn the similarity between their latent representations using neural matrix factorization. It has been demonstrated that deep learning with heterogeneous input significantly increases overall recommendation quality which makes it suitable for recommendations with rich side information about items and users. This SlideShare explores the concept of using deep recommendations for e-commerce in brief.

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The internet has a carbon footprint. Behind the rhetoric of digital solutions making our lives lighter, there is a very big problem. The internet, and our usage of it, is not so light at all. The internet as a whole currently accounts for 2% of global emissions. That’s the same as all global aviation. And this figure is increasing rapidly. The more data we consume, the more energy we use, and data consumption shows no signs of slowing as people worldwide grasp for better and faster internet speeds. By 2025, Cisco predicts that the internet will account for 3.5% of global carbon emissions. The internet is not slowing down. As it matures and plays more of an integral part in our lives, we expect more from the internet. As each year passes, people are developing more complex, visually elaborate websites. These websites may look and work well – but behind the scenes they are bloated, using huge amounts of energy each time they are loaded. To put this in perspective: loading the average website uses up the equivalent amount of energy as boiling a kettle for a cup of tea. Environmental guilt has started to creep into everyday life for most people. There is always a sense that we should be doing better. Thankfully, there are many things we can do to reduce our internet carbon footprint. One of the means is using the most efficient file format for graphics (such as SVG) to lessen the power needed to load the site. This SlideShare explores the concept of SVG format in brief.

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HR Software Buyers Guide in 2024 - HRSoftware.com

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Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...

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