With the advent of Industry 4.0, Data Science and Explainable Artificial Intelligence (XAI) has received considerable intrest in recent literature. However, the entry threshold into XAI, in terms of computer coding and the requisite mathematical apparatus, is really high. For fault diagnosis of steel plates, this work reports on a methodology of incorporating XAI based insights into the Data Science process of development of high precision classifier. Using Synthetic Minority Oversampling Technique (SMOTE) and notion of medoids, insights from XAI tools viz. Ceteris Peribus profiles, Partial Dependence and Breakdown profiles have been harvested. Additionally, insights in the form of IF-THEN rules have also been extracted from an optimized Random Forest and Association Rule Mining. Incorporating all the insights into a single ensemble classifier, a 10 fold cross validated performance of 94% has been achieved. In sum total, this work makes three main contributions viz.: methodology based upon utilization of medoids and SMOTE, of gleaning insights and incorporating into model development process. Secondly the insights themselves are contribution, as they benefit the human experts of steel manufacturing industry, and thirdly a high precision fault diagnosis classifier has been developed.

Artificial intelligence allows farmers to spray weeds while keeping the crop untouched. With crop prices in the dumpster and the world's population growing among a changing climate, artificial intelligence is becoming a life-saving measure for many farmers. From automated planting and harvesting to unmanned vehicles for cultivation and soil sampling, AI has begun to make it more cost efficient for producers to do their job. One of the largest roadblocks is herbicides. According to a 2016 University of Illinois study, the chemical prices are on the rise and pose a big threat to a farmer's bottom line.

Tungsten is an essential component of high-performance steels but global production is strongly influenced by China and western countries are keen to develop alternative sources. The work, published in the leading journal Geoscience Frontiers, has been led by Dr Chris Yeomans, from the Camborne School of Mines, and involved geoscientists from the University of Nottingham, Geological Survey of Finland (GTK) and the British Geological Survey. The research applies machine learning to multiple existing datasets to examine the geological factors that have resulted in known tungsten deposits in SW England. These findings are then applied across the wider region to predict areas where tungsten mineralisation is more likely and might have previously been overlooked. The same methodology could be applied to help in the exploration for other metals around the world.

This paper introduces structured machine learning regressions for prediction and nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and we find that it empirically outperforms the unstructured machine learning methods. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data exhibit heavier than Gaussian tails. To that end, we leverage on a novel Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed $\tau$-mixing processes which may be of independent interest in other high-dimensional panel data settings.

PredictMedix (PMED.CN) has been a stock garnering a lot of attention throughout this Covid pandemic. Our team and esteemed writers have covered the company extensively, and the fundamental information which I will summarize, can be read on our latest article here. In terms of a technical approach, the analysis is mine. Looking at the daily chart of PMED, we can clearly see the higher lows and higher highs in an uptrend. On its run from breaking resistance way back at 0.195, we formed two higher lows on the road to the all important 1.00 zone.

Infosys announced its deal with Lanxess, a specialty chemicals company headquartered in Germany. Infosys will support Lanxess in its IT infrastructure digitization strategy and enable its workforce spread across 33 countries with a managed modern workplace. Infosys will setup an end-user centric modern workplace with 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 said. Kai Finke, CIO of Lanxess, said: "Standardized and harmonized workplace services will enable us to increase our service quality and usability on a global basis as well as increase flexibility and scalability which nowadays are getting more and more important."

BizClik Media announced the launch of the August edition of Mining Global Magazine. In this issue, Mining Global steps inside Oren, designed by Shell and IBM as the world's first B2B marketplace platform for the mining industry. We continue the theme of data with CRU Group's Director of Technology & Analytics Will Blake, who tells us about the importance of data for mining enterprises. We also speak with Thorsten Scholz, CTO of Forwood Safety, about how his company is harnessing new technologies to transform the mining industry's safety culture, and eradicate workplace fatalities. Elsewhere in the magazine, Ludovic Donati, CDO of Eramet, introduces the innovative technologies that his company has introduced – including artificial intelligence and machine learning – to keep Eramet ahead as the digital era transforms the mining industry.

"Artificial Intelligence in Oil and Gas Market is growing at a High CAGR during the forecast period 2020-2026.

According to a Nielsen report, brick-and-mortar alcohol dollar sales were up 21% in April 2020 compared to the same period a year ago. Online alcohol sales skyrocketed by 234% over the same period in 2019. However, despite the increase, global sales are decreasing due to the shutdowns in restaurants, bars, live events and travel. Next Century Spirits is a liquor technology startup with $9.6 M in funding. The company uses big data and machine learning to create and filter bespoke distilled spirits.

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.