An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst's creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects. Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for structuring discussions about data science.

Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore, several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired by the Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model. It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop neurons, which their activation function follows Deteriorating Stop operator. Deteriorating Stop operators can generate non-congruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps the neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, besides input data the rate at which input data changes, is included as well in order to give the model the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has the capability of the simulation of both rate-independent and rate-dependent hysteresis with either congruent or non-congruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training the model which is based on a combination of the Genetic Algorithm and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it to various hysteresis from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses.

--In this paper, we propose an efficient algorithm for mining novel'Set of Contrasting Rules'-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has no efficient mining algorithm. We propose SCR-Apriori algorithm, which results in the same set of SCR-patterns as the state-of-the-art approache, but is less computationally expensive. We also show experimentally that by incorporating the knowledge about the pattern structure into Apriori algorithm, SCR-Apriori can significantly prune the search space of frequent itemsets to be analysed. I NTRODUCTION Association rules learning is a popular technique in data mining [1]. However, it is known that finding rules of high quality is not always an easy task [2]. This issue is even more significant in domains where the reliability of the obtained knowledge is required to be high (for example, in medicine). Also, association rules mining techniques usually generate a huge number of rules that have to be analysed by a human in order to choose meaningful and useful ones [3].

CHICAGO & LONDON--(BUSINESS WIRE)--Artificial Intelligence (AI) is widely expected to drive important benefits across the health system, from increasing efficiency to improving patient outcomes, but it also may be key to making healthcare more human. Benefits range from increasing the amount of time clinicians can spend with patients and on cross-care team collaboration to enhancing the ability to deliver preventative care. According to a new study of more than 900 healthcare professionals in the U.S. and U.K. conducted by MIT Technology Review Insights with GE Healthcare, nearly half of medical professionals surveyed said AI is already increasing their ability to spend time with and provide care to patients. Additionally, more than 78 percent of healthcare business leaders who reported they have deployed AI in their operations also reported that AI has helped drive workflow improvements, streamlining operational and administrative activities and delivering significant efficiencies toward transforming the future of healthcare. "Of any industry, AI could have the most profound benefits on human lives if we can effectively harness it across the healthcare system," said Kieran Murphy, President and CEO, GE Healthcare.

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared " Raman Spectroscopy and NIR Spectroscopy as Possible AID in Localisation of Solitary Pulmonary Nodules" LINK NIR spectroscopy has potential for rapid on farm analysis of slurry nutrient content. Wouter Saeys, IFSConf LINK "Modeling for SSC and Firmness Detection of Persimmon Based on NIR Hyperspectral Imaging by Sample Partitioning and Variables Selection" LINK " Application of the NIR Spectroscopy in the Researches of Orthopedics Diseases" LINK "FT-NIR による油脂の迅速な品質管理" LINK "Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm." LINK "Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy." LINK "Characterization of the Processing Conditions upon Textural Profile Analysis (TPA) Parameters of Processed Cheese Using Near-Infrared Hyperspectral Imaging" LINK "Total aromatics of diesel fuels analysis by deep learning and near-infrared spectroscopy" LINK "Rapid Assessment of Soil Quality Indices Using Infrared Reflectance Spectroscopy" LINK "Quantitative Determination of the Fiber Components in Textiles by Near-Infrared Spectroscopy and Extreme Learning Machine" LINK "Non-Destructive Method for Predicting Sapodilla Fruit Quality Using Near Infrared Spectroscopy" LINK "Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging" LINK " MENGUKUR BERAT VOLUME TANAH DI LAPANGAN MENGGUNAKAN NEAR INFRARED SPECTROSCOPY MEASUREMENT OF SOIL BULK DENSITY IN …" LINK "Hyperspectral Characteristics of Coastal Saline Soil with Visible/near Infrared Spectroscopy" LINK "Monitoring Soil Surface Mineralogy at Different Moisture Conditions Using Visible Near-Infrared Spectroscopy Data" LINK "Near infrared spectroscopy for assessing mechanical properties of Castanea sativa wood samples" Modulus of elasticity LINK " Development of near-infrared spectroscopic sensing system for online real-time monitoring of milk quality during milking" LINK " Advances in Near-Infrared Spectroscopy and Related Computational Methods" LINK "Morphological, Physicochemical and FTIR Spectroscopic Properties of Bee Pollen Loads from Different Botanical Origin" LINK "Fourier transform infrared imaging and quantitative analysis of pre-treated wood fibers: A comparison between partial least squares and multivariate curve resolution with alternating least squares methods in a case study" LINK "Antioxidant Activity of Blueberry (Vaccinium spp.)

Full Wave Inversion (FWI) imaging scheme has many applications in engineering, geoscience and medical sciences. In this paper, a surrogate deep learning FWI approach is presented to quantify properties of materials using stress waves. Such inverse problems, in general, are ill-posed and nonconvex, especially in cases where the solutions exhibit shocks, heterogeneity, discontinuities, or large gradients. The proposed approach is proven efficient to obtain global minima responses in these cases. This approach is trained based on random sampled set of material properties and sampled trials around local minima, therefore, it requires a forward simulation can handle high heterogeneity, discontinuities and large gradients. High resolution Kurganov-Tadmor (KT) central finite volume method is used as forward wave propagation operator. Using the proposed framework, material properties of 2D media are quantified for several different situations. The results demonstrate the feasibility of the proposed method for estimating mechanical properties of materials with high accuracy using deep learning approaches.

Marketing today is on the threshold of change. In the past, marketing as we knew it was largely dominated by 30-second TV spots and other mass media such as print, outdoor, radio and so on. The number-crunching only came into play while deciding which medium to back in the advertising campaign and for what price to buy the media. But, look around today and there are the likes of Google, Facebook, Twitter and others who apply complex algorithms such as Page Rank, Adsense, marketing mix modelling, content marketing and so on along with technology (analytics, digital marketing, search engine optimisation (SEO) and search engine marketing (SEM) to make marketing a lot more data-driven. Similarly, in music the magic of maths plays a huge role.

WIRE)--Citrine Informatics has been named an Enterprise of the Year Gold winner in the Best in Biz Awards, the only independent business awards program judged by prominent editors and reporters from top-tier publications in North America. Citrine Informatics' artificial intelligence technology is used by the world's largest materials and chemicals companies to accelerate the product development cycle. Since 2011, Best in Biz Awards' entrants have spanned the spectrum, from the most innovative local companies and start-ups to some of the most recognizable global brands. With more than 700 entries, the 9th annual program attracted a record number of entries from an impressive array of public and private companies of all sizes and spanning all geographic regions and industries in the U.S. and Canada. Best in Biz Awards 2019 honors were conferred in 80 different categories, including Company of the Year, Fastest-Growing Company, Most Innovative Company, Best Place to Work, Customer Service Department, Executive of the Year, Most Innovative Product, Enterprise Product, Best New Service, CSR Program, Event and Blog of the Year.

CHICAGO – Agribusiness is increasingly turning to natural and sustainable alternatives to chemicals as consumers rebuff genetically modified foods and concerns grow over Big Ag's role in climate change. At the heart of the trend are innovations that harness beneficial microorganizms in the soil, including seed-coatings of naturally occurring bacteria and fungi that can do the same work as traditional chemicals, from warding off pests to helping plants flourish, according to a global patent study by research firm GreyB Services. Much of the research in crop biotech is centered in the United States, China, Germany, Japan and South Korea, according to the U.N. agency WIPO. "Both entrepreneurs and investors are saying, 'Hey, the writing is on the wall, we're entering a post-chemical world,'" said Rob LeClerc, chief executive officer of AgFunder, an online venture-capital platform. "The seed companies who have billions in market cap are like'We need to do something,' and everyone recognizes the opportunity."

In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.