Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

"Graduates at IBM are coming into roles where you provide services to a range of different industries as opposed to just working in one," she says. "From a career perspective, they can move across different industries but they also move across different functions. You retain your core expertise but you also get to do different jobs because of the diversity of our clients." IBM works with dozens of Australia's biggest companies using Watson to drive machine learning and data analysis within business, and has the advantage of being able to supply whole teams of experts as challenges arise. In contrast, even big industry employers often have only a few specialists in key areas.

The artificial intelligence (AI) in health care market is set to top $34 billion by 2025, according to some estimates -- and it's no real wonder why. One startup that's successfully maintained pole position is Health Catalyst, a Salt Lake City, Utah-based health care big data company founded in 2009 by Steven Barlow and Thomas Burton. It aims to drive clinical and operational performance improvements in state and regional health plan providers, physician groups, and extended care facilities through its suite of analytics apps. And it's raising capital to help further progress toward that goal. Health Catalyst today announced that it has secured $100 million in series F equity and debt financing led by health care investment firm OrbiMed, with participation from existing partners Sequoia Capital, Norwest Venture Partners, Sands Capital Ventures, UPMC Enterprises, and Kaiser Permanente Ventures.

Accenture has announced plans to launch a new Applied Intelligence Studio for Mining in Johannesburg. The studio will apply the latest in data science and artificial intelligence technologies with new data sources for real-time co-creation of innovative digital solutions that can help mining companies solve some of their hardest analytical problems. It is expected to open in February 2019. "They are increasingly looking to apply advanced analytics to reimagine processes, unlock trapped value, and drive operational excellence in their businesses today and position themselves for growth tomorrow." "Volatile commodity prices, rising input costs and changing global demand for commodities require mining companies to rethink their strategies and business models to remain competitive," said Rachael Bartels, a senior managing director who leads Accenture's mining business globally.

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

As in many other sectors, artificial intelligence (AI) technologies are beginning to emerge in the chemical process industries (CPI). While AI-assisted solutions, and other associated technologies, such as robotic process automation (RPA), Internet of Things (IoT), automated drones and quantum computing, are still relatively new for many CPI applications, developers and users alike are realizing their potential benefits for expediting research and development (R&D), predictive maintenance, process optimization and more. Within its Smart Operations initiative, Henkel AG & Co. KGaA (Düsseldorf, Germany; www.henkel.com) is utilizing AI capabilities in its global process operations and supply chain. "We use AI to run efficient analyses of complex data arrays for achieving higher production performance, quick product innovation and scaleup for our self-adjusting production systems," explains Sandeep Sreekumar, global head of Adhesive Digital Operations at Henkel. "Our focus is not only on collecting internal manufacturing data, but also on actively working with customers on data collection opportunities during product usage to make improvements and adjust to changing customer needs," says Sreekumar.

In the present paper, a method of defining the industrial process parameters for a new product using machine learning algorithms will be presented. The study will describe how to go from the product characteristics till the prediction of the suitable machine parameters to produce a good quality of this product, and this is based on an historical training dataset of similar products with their respective process parameters. In the first part of our study, we will focus on the ultrasonic welding process definition, welding parameters and on how it operate. While in second part, we present the design and implementation of the prediction models such multiple linear regression, support vector regression, and we compare them to an artificial neural networks algorithm. In the following part, we present a new application of Convolutional Neural Networks (CNN) to the industrial process parameters prediction. In addition, we will propose the generalization approach of our CNN to any prediction problem of industrial process parameters. Finally the results of the four methods will be interpreted and discussed.

Research studies have shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition performance. Therefore, it is important to analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. This paper proposes a method that can automatically map the gaze fixations collected using a wearable eye-tracker to the predefined areas of interests. The proposed method detects these areas or objects (i.e., hazards) of interests through a computer vision-based segmentation technique and transfer learning. The mapped fixation data is then used to analyze the viewing behaviors of workers and compute their attention distribution. The proposed method is implemented on an under construction road as a case study to evaluate the performance of the proposed method.

Experts warn ML should be used "for its correct purpose" – not for prying long-term strategies from sparse information Data is the latest of many hopes for banks and other investors looking for improved returns in a lacklustre environment. Several banks have begun to point their research teams at big data – using internal data, purchased databases or new research to collect huge quantities of data points, which can then be analysed using the new technology of machine learning (ML). UBS seems to be in the lead at present, but Morgan Stanley, BNP Paribas and many others are following. And this combination is being applied elsewhere as well; last week Risk looked at HSBC's client intelligence unit, which is aimed at using internal client data to generate new sales leads for existing customers. Standard Chartered's data analytics group earned a 2019 Risk Award for quant of the year for its head Alexei Kondratyev, based on the group's machine learning work.

You might think that digital technologies, often considered a product of "the West", would hasten the divergence of Eastern and Western philosophies. But within the study of Vedanta, an ancient Indian school of thought, I see the opposite effect at work. Thanks to our growing familiarity with computing, virtual reality and artificial intelligence, "modern" societies are now better placed than ever to grasp the insights of this tradition. Vedanta summarises the metaphysics of the Upanishads, a clutch of Sanskrit religious texts, likely written between 800 and 500 BCE. They form the basis for the many philosophical, spiritual and mystical traditions of the Indian sub-continent.