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How Big Data, AI, IoT and Deep Learning Are Powering Modern Healthcare

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Big data, artificial intelligence (AI), internet of things (IoT) and deep learning (DL) are revolutionizing modern healthcare post pandemic. After having made remarkable improvements in finance, retail and marketing, big data, artificial intelligence, internet of things (IoT) and deep learning are now transforming healthcare. The volume of data involved in healthcare studies and analysis makes it a perfect use-case for these ground breaking technologies. Healthcare industry handles an immense load of data that is piling up every day. Sooner or later, we will need big data tools to transform healthcare information into relevant insights that can help the development of health services.


NoCode Journal - NoCode Products Within AI and ML

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You may have heard about artificial intelligence (AI) and machine learning (ML), and are wondering how they can help you. The capacity to control tools in the Artificial Intelligence (AI) and Machine Language (ML) areas with NoCode opens a plethora of possibilities for creators as well as business teams. We'll talk about NoCode solutions that may help you use AI and ML to create sophisticated applications without any programming knowledge in this blog post. You'll be able to develop complicated apps without any coding expertise using these tools! As we venture further into this area, we will continue to update this article. Obviously AI is the fastest and simplest data prediction tool in the world.


XO Propels Private Aviation into the AI Era

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XO, part of Vista, the world's first private aviation ecosystem, published a report outlining how AI, machine learning, data design, and predictive analytics are allowing the company to create a more accessible and affordable future for private aviation. The XO platform is a sophisticated, complex suite of proprietary technology tools that continually monitor and manage occupancy, positioning, and demand. This has allowed XO to transform a legacy industry into one that is transparent, efficient, and accessible, making its membership classes and benefits more meaningful and valuable, thanks to the underlying architecture that links them all. XO's accomplishments are manifest across these four integrated and co-dependent operational, product and service areas. XO continues to innovate and propel an aviation ecosystem for an open future, more widely available than ever before, transparent, efficient, and more sustainable.


Why AI-enabled decision-making is the next step in the supply chain digitalisation journey

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As a result, companies have gone through a decade's worth of digital transformation in just a matter of months, with the pandemic forcing them to refresh archaic processes with AI, machine learning, and data science technologies. Such technological advancements will continue to evolve and further establish themselves as a critical component to managing complex logistical landscapes – from improving efficiency and mitigating the effects of a global labour shortage, to identifying more robust and dependable ways to move commodities. In a world where uncertainty is the only certainty, AI-enabled order and inventory visibility across shipments will also be vital to'keep the wheels in motion.' Most importantly, to provide real-time updates on changes to arrival times and to identify potential disruptions before and as they occur. Take the recent congestion issues at the Port of Los Angeles, for example.


Pros And Cons of AI In Manufacturing DataScienceCentral.com

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The fourth industrial revolution has been a game-changer, with the global economy's expansion driving the adoption of new technologies across sectors. Manufacturers are using AI software in product design, production, supply chain, and logistics. AI analytics and data are helping in improving product quality and efficiency. Advances in machine learning, artificial intelligence (AI), and Big Data have initiated an algorithm-based era. Today companies are able to automate multiple tasks, cutting down on errors as well as downtime and expenditures associated with them using AI.


It's a Marketing Mess! Artificial Intelligence vs Machine Learning

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There are many types of analytics that are used in the security world; some are defined by vendors, others by analysts. Let's begin by using the Gartner analytics maturity curve as a model for the list, with the insertion of one additional term slotted in the middle of the curve: Behavioral Analytics. Descriptive Analytics (Gartner): Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question "What happened?" Baikalov explains that descriptive Analytics is the realm of a SIEM (Security Information and Event Management system) like ArcSight: "these systems gather and correlate all log data and report on known bad activities." Diagnostic Analytics (Gartner): Diagnostic Analytics is a form of advanced analytics which examines data or content to answer the question "Why did it happen?",


What is Machine Learning?

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Although machine learning (ML) has been around for decades, its practical applications are now coming into focus as it helps companies better understand their customers. Available data from sources such as social media, mobile devices, and Internet of Things (IoT) devices is growing rapidly--we're now generating an estimated 2.5 quintillion bytes of data every day. This flood of information has made machine learning more accessible than ever before. To leverage the full potential of machine learning, however, it's important to understand what it is, how it works, why it's important, and the applicable use cases for your business. Machine learning is a subset of artificial intelligence (AI) that allow systems to learn and improve from experience without being explicitly programmed. It involves algorithms that make dynamic decisions and predictions based on historical data rather than following static program instructions for specific tasks and outcomes.


Different ways to Implement Machine Learning with Oracle Analytics

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Predictive Analytics is one of the widely used flavours of Analytics. Nowadays, most of the customers want to leverage machine learning(ML) techniques to identify the likelihood of future outcomes based on historical data. To predict the future KPIs appropriate Machine learning Models require to be developed and used for predictive analytics. This blog is primarily focusing on how to implement machine learning with Oracle analytics to predict future KPIs and then perform analytics in Oracle Analytics Cloud(OAC) or Oracle Analytics Server(OAS). "Please do not use this blog to refer and validate Machine Learning concepts" We can implement ML either in Oracle Analytics Cloud/Oracle Analytics Server or in Oracle Database.


Version Next Now Season 4 -- Greg Kihlström Customer Experience & Digital Transformation

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For years, organizations have leveraged business intelligence dashboards to help users make data-driven decisions. Unfortunately, often the analytics platforms are chosen to fit the data rather than leading with what the company is trying to solve for. The sheer volume of data and lack of context provided can lead to poor decisions and less than ideal outcomes. That's where decision intelligence comes in. Leaders from TEKsystems share their points of view on how organizations are incorporating AI and machine-learning technologies to transform their business intelligence platforms into powerful tools that optimize the decision-making process, create agility and drive the business forward.


Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists' Intelligence?

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Background: Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. Summary: Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve. Key Messages: AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built. Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines; medicine is not an exception.