The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data-- and model-development--related issues. These issues should be carefully addressed by allowing a flexible, customized, and industry-specific knowledge discovery framework; in our case, this takes the form of the cross-industry standard process for data mining (CRISP-DM). This framework is designed to ensure active cooperation between different phases to adequately address data- and model-related issues. In this paper, we review several extensions of CRISP-DM models and various data-robustness-- and model-robustness--related problems in machine learning, which currently lacks proper cooperation between data experts and business experts because of the limitations of data-driven knowledge discovery models.

What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.

Relevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machine (SVM) based on the Bayesian sparsity model. Compared with the regression problem, RVM classification is difficult to be conducted because there is no closed-form solution for the weight parameter posterior. Original RVM classification algorithm used Newton's method in optimization to obtain the mode of weight parameter posterior then approximated it by a Gaussian distribution in Laplace's method. It would work but just applied the frequency methods in a Bayesian framework. This paper proposes a Generic Bayesian approach for the RVM classification. We conjecture that our algorithm achieves convergent estimates of the quantities of interest compared with the nonconvergent estimates of the original RVM classification algorithm. Furthermore, a Fully Bayesian approach with the hierarchical hyperprior structure for RVM classification is proposed, which improves the classification performance, especially in the imbalanced data problem. By the numeric studies, our proposed algorithms obtain high classification accuracy rates. The Fully Bayesian hierarchical hyperprior method outperforms the Generic one for the imbalanced data classification.

This article is co-written together with Syed Nazmus Sadat who Studies Forestry and Environmental Science at Shahjalal University of Science & Technology, Sylhet in Bangladesh. How can artificial intelligence help in efforts to prevent deforestation? Deforestation has an incredibly adverse impact on planet earth. The forests cover close to a third of the land area on our planet and provide us with purer air and fresher water. Eighty percent of the world's land based wildlife live in forests [1].

Brossard, Quebec - TheNewswire - July 23, 2020 - Windfall Geotek (TSXV:WIN) is a leader in the use of Artificial Intelligence (AI) in the mining sector for digital exploration and is pleased to announce that it has signed an agreement with BTU Metals Corp to provide new high probability gold targets develop during an internal project on the Red Lake Mining Camp. As reported by BTU Metals Corp earlier this week, the exploration team has confirmed positive results received from ongoing work using Windfall's proprietary'CARDS' Artificial Intelligence ("AI") system on the identified high-grade gold targets. BTU is pursuing both high-grade gold targets and copper-dominant massive sulfide targets on its 200 square kilometer property, that shares a 35 kilometer border with Great Bear Resources Ltd ("Great Bear"). One-third of the property area has been analyzed, from which 35 high priority targets have been identified at a high correlation rate with known gold mineralization within the Red Lake camp. More validation and follow-up investigation of the CARDS AI generated gold targets are being conducted by BTU geologists and the highest priority targets are expected to be drill-ready later this summer.

The University of Lincoln is leading on a project to create what is widely considered to be the world's first robotic farm. A consortium responsible for delivering'Robot Highways' has won a bid to create the robotic arm after Innovate UK allocated funding of £2.5 million. With an aim to be delivered by 2025 across the UK, a fleet of robots will perform a multitude of on-farm functions as one operation, powered by renewable energy. The project aims to reduce sector reliance on seasonal labour, estimating a 40% reduction in the workers required. It will also provide solutions for moving the sector toward a carbon zero future.

Infosys has announced a strategic partnership with Lanxess for digitizing the IT infrastructure and enable its global workforce spread across 33 countries with a secure and fully managed modern workplace. As part of this transformation, Infosys will setup an end-user centric modern workplace with globally standardized device/workplace landscape (for Office, Functional and Virtual users) based on a Device as a Service (DaaS) construct, backed with NextGen unified communication and collaboration platforms. The global workforce of Lanxess will be supported by a multi-lingual artificial intelligence-powered service desk operating from Europe and India. Infosys will also transform Lanxess to a future-ready end user IT landscape over the course of the partnership. This will ensure a seamless and harmonized workplace experience for Lanxess' global workforce.

We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.

Machine learning and AI can improve the functionalities of the chemical industry through the identification of chemical molecules -reactiveness and toxicity level during process engineering. And it can also be used to check the availability of raw materials, feasibility of compound, measuring blending temperature of certain chemical, and environmental concerns during production, transportation, and storage. And in the future, it is expected to increase the speed of the production process through ML technologies at its core. The industries interdependent on the chemical industry like agriculture, construction, automotive, cosmetics, energy, consumer products, transportation, etc., are implementing technology at their core; and the development of these industries will increase the growth of the chemical industry. Hence, the RoI on machine learning goes beyond the product revenue and chemical process.

There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by sensor malfunction. To address this fundamental issue, this paper presents an incremental Bayesian tensor learning method for reconstruction of spatiotemporal missing data in SHM and forecasting of structural response. In particular, a spatiotemporal tensor is first constructed followed by Bayesian tensor factorization that extracts latent features for missing data imputation. To enable structural response forecasting based on incomplete sensing data, the tensor decomposition is further integrated with vector autoregression in an incremental learning scheme. The performance of the proposed approach is validated on continuous field-sensing data (including strain and temperature records) of a concrete bridge, based on the assumption that strain time histories are highly correlated to temperature recordings. The results indicate that the proposed probabilistic tensor learning approach is accurate and robust even in the presence of large rates of random missing, structured missing and their combination. The effect of rank selection on the imputation and prediction performance is also investigated. The results show that a better estimation accuracy can be achieved with a higher rank for random missing whereas a lower rank for structured missing.