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Machine Learning saves Energy by predicting accuracy of weather forecasts – RtoZ.Org – Latest Technology News

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Sophisticated heating and cooling systems in Buildings adjust themselves based on the predicted weather. But when the forecast is imperfect – as it often is – buildings can end up wasting energy. A new approach developed by Cornell Researchers predicts the accuracy of the weather forecast using a machine learning model trained with years' worth of data on forecasts and actual weather conditions. The Researchers combined that predictor with a mathematical model that considers building characteristics including the size and shape of rooms, the construction materials, the location of sensors and the position of windows. The result is a smart control system that can reduce energy usage by up to 10 percent, according to a case study the research team conducted on Toboggan Lodge, a nearly 90-year-old building on Cornell's campus.


GoldSpot Discoveries Corp. to Apply Machine Learning on Cerrado Gold Inc.'s Minera Don Nicolas Project

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Toronto, Ontario--(Newsfile Corp. - September 16, 2020) - GoldSpot Discoveries Corp. (TSXV: SPOT) (the "Company" or "GoldSpot") has been engaged by Cerrado Gold Inc. ("Cerrado") to apply machine learning and its proprietary data science expertise to identify new exploration targets on Cerrado's Minera Don Nicolas (MDN) project, located in Santa Cruz, Argentina. In its analysis, GoldSpot will work with Cerrado's technical team to integrate and analyze geological and remote sensing data available in the area. The process will explore the potential for gold mineralization within the MDN properties, to produce GoldSpot Smart Targets which fuse geoscience knowledge with data science insights. "Minera Don Nicolas is in the mineral and data rich Deseado Massif, an area where GoldSpot is having significant success, particularly at Yamana Gold's Cerro Moro project. MDN has robust property-wide datasets and we look forward to supporting Cerrado's technical team and advancing exploration efforts. The project has significant potential with a land package of more than 273,000 hectares," stated Denis Laviolette, Executive Chairman and President of GoldSpot Discoveries.


Bayer Crop Science posted on LinkedIn

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Knowledge is power, but our capacity to learn is even more important. The same is true for machine learning. See how #artificialintelligence is constantly...


Machine-learning helps sort out massive materials' databases

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Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which can measure up to 7,800 m2 in a single gram of material. As a result, MOFs are extremely versatile and find multiple uses: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples. Because of their popularity, material scientists have been rapidly developing, synthesizing, studying, and cataloguing MOFs. Currently, there are over 90,000 MOFs published, and the number grows every day.


Spectroscopy and Chemometrics News Weekly #37, 2020

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Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry" LINK "Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches" LINK "NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley" LINK "Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy" LINK "Multi-task deep learning of near infrared spectra for improved grain quality trait predictions" LINK "Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri'Ya') Using Vis/NIR Online Half-transmittance Technique" LINK "Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and ...


Top 8 Data Mining Techniques In Machine Learning

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Data mining is considered to be one of the popular terms of machine learning as it extracts meaningful information from the large pile of datasets and is used for decision-making tasks. It is a technique to identify patterns in a pre-built database and is used quite extensively by organisations as well as academia. The various aspects of data mining include data cleaning, data integration, data transformation, data discretisation, pattern evaluation and more. Below, we have listed the top eight data mining techniques in machine learning that is most used by data scientists. Association Rule Learning is one of the unsupervised data mining techniques in which an item set is defined as a collection of one or more items.


Artificial intelligence helps researchers up-cycle waste carbon - Express Computer

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Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources. "Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage," says Professor Ted Sargent, one of the senior authors on a new paper published today in Nature.


AIoT: Why it has been labelled as the catalyst to IoT Strategy

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As you may already know, IoT connects a vast array of portable devices, home appliances, wearables, and other electronics/machines over a network. Connected devices can signal their environment and be remotely monitored, controlled, and maintained. While all this works well on paper, there is a catch (and a rather obvious one). Round the clock monitoring naturally leads to a never-ending influx of complex data. For instance, a car manufacturing company may want to monitor everything from tire pressure to fuel performance in order to push the boundaries of future models.


Catalyst of change: Bringing artificial intelligence to the forefront - The Financial Express

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Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.


Catalyst of change: Bringing artificial intelligence to the forefront

#artificialintelligence

Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.