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What is data mining? | SAS

Descriptive Modeling: It uncovers shared similarities or groupings in historical data to determine reasons behind success or failure, such as categorizing customers by product preferences or sentiment. Sample techniques include:

Predictive Modeling: This modeling goes deeper to classify events in the future or estimate unknown outcomes for example, using credit scoring to determine an individual's likelihood of repaying a loan. Predictive modeling also helps uncover insights for things like customer churn, campaign response or credit defaults. Sample techniques include:

Prescriptive Modeling: With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. You need the ability to successfully parse, filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy.

In the end, you should not look at data mining as a separate, standalone entity because pre-processing (data preparation, data exploration) and post-processing (model validation, scoring, model performance monitoring) are equally essential. Prescriptive modelling looks at internal and external variables and constraints to recommend one or more courses of action for example, determining the best marketing offer to send to each customer. Sample techniques include:

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What is data mining? | SAS

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What is Data Mining? | IBM

Learn about data mining, which combines statistics and artificial intelligence to analyze large data sets to discover useful information.

Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation.

Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques that underpin these analyses can be divided into two main purposes; they can either describe the target dataset or they can predict outcomes through the use of machine learning algorithms. These methods are used to organize and filter data, surfacing the most interesting information, from fraud detection to user behaviors, bottlenecks, and even security breaches.

When combined with data analytics and visualization tools, like Apache Spark, delving into the world of data mining has never been easier and extracting relevant insights has never been faster. Advances within artificial intelligence only continue to expedite adoption across industries.

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets. As mentioned above, data mining techniques are used to generate descriptions and predictions about a target data set. Data scientists describe data through their observations of patterns, associations, and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, like spam detection.

Data mining usually consists of four main steps: setting objectives, data gathering and preparation, applying data mining algorithms, and evaluating results.

1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Data scientists and business stakeholders need to work together to define the business problem, which helps inform the data questions and parameters for a given project. Analysts may also need to do additional research to understand the business context appropriately.

2. Data preparation: Once the scope of the problem is defined, it is easier for data scientists to identify which set of data will help answer the pertinent questions to the business. Once they collect the relevant data, the data will be cleaned, removing any noise, such as duplicates, missing values, and outliers. Depending on the dataset, an additional step may be taken to reduce the number of dimensions as too many features can slow down any subsequent computation. Data scientists will look to retain the most important predictors to ensure optimal accuracy within any models.

3. Model building and pattern mining: Depending on the type of analysis, data scientists may investigate any interesting data relationships, such as sequential patterns, association rules, or correlations. While high frequency patterns have broader applications, sometimes the deviations in the data can be more interesting, highlighting areas of potential fraud.

Deep learning algorithms may also be applied to classify or cluster a data set depending on the available data. If the input data is labelled (i.e. supervised learning), a classification model may be used to categorize data, or alternatively, a regression may be applied to predict the likelihood of a particular assignment. If the dataset isnt labelled (i.e. unsupervised learning), the individual data points in the training set are compared with one another to discover underlying similarities, clustering them based on those characteristics.

4. Evaluation of results and implementation of knowledge: Once the data is aggregated, the results need to be evaluated and interpreted. When finalizing results, they should be valid, novel, useful, and understandable. When this criteria is met, organizations can use this knowledge to implement new strategies, achieving their intended objectives.

Data mining works by using various algorithms and techniques to turn large volumes of data into useful information. Here are some of the most common ones:

Association rules: An association rule is a rule-based method for finding relationships between variables in a given dataset. These methods are frequently used for market basket analysis, allowing companies to better understand relationships between different products. Understanding consumption habits of customers enables businesses to develop better cross-selling strategies and recommendation engines.

Neural networks: Primarily leveraged for deep learning algorithms,neural networksprocess training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold), and an output. If that output value exceeds a given threshold, it fires or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent. When the cost function is at or near zero, we can be confident in the models accuracy to yield the correct answer.

Decision tree: This data mining technique uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.

K- nearest neighbor (KNN): K-nearest neighbor, also known as the KNN algorithm, is a non-parametric algorithm that classifies data points based on their proximity and association to other available data. This algorithm assumes that similar data points can be found near each other. As a result, it seeks to calculate the distance between data points, usually through Euclidean distance, and then it assigns a category based on the most frequent category or average.

Data mining techniques are widely adopted among business intelligence and data analytics teams, helping them extract knowledge for their organization and industry. Some data mining use cases include:

Companies collect a massive amount of data about their customers and prospects. By observing consumer demographics and online user behavior, companies can use data to optimize their marketing campaigns, improving segmentation, cross-sell offers, and customer loyalty programs, yielding higher ROI on marketing efforts. Predictive analyses can also help teams to set expectations with their stakeholders, providing yield estimates from any increases or decreases in marketing investment.

Educational institutions have started to collect data to understand their student populations as well as which environments are conducive to success. As courses continue to transfer to online platforms, they can use a variety of dimensions and metrics to observe and evaluate performance, such as keystroke, student profiles, classes, universities, time spent, etc.

Process mining leverages data mining techniques to reduce costs across operational functions, enabling organizations to run more efficiently. This practice has helped to identify costly bottlenecks and improve decision-making among business leaders.

While frequently occurring patterns in data can provide teams with valuable insight, observing data anomalies is also beneficial, assisting companies in detecting fraud. While this is a well-known use case within banking and other financial institutions, SaaS-based companies have also started to adopt these practices to eliminate fake user accounts from their datasets.

Partner with IBM to get started on your latest data mining project. IBM Watson Discovery digs through your data in real-time to reveal hidden patterns, trends and relationships between different pieces of content. Use data mining techniques to gain insights into customer and user behavior, analyze trends in social media and e-commerce, find the root causes of problems and more. There is untapped business value in your hidden insights. Get started with IBM Watson Discovery today.

Sign up for a free Watson Discovery account on IBM Cloud, where you gain access to apps, AI and analytics and can build with 40+ Lite plan services.

To learn more about how IBMs data warehouse solution, sign up for an IBMid andcreate your free IBM Cloud accounttoday.

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Gold Mining Market Report 2021: A $249.6 Billion Market by 2026 with 3% CAGR Predicted Between 2021 and 2026 – – Business Wire

DUBLIN--(BUSINESS WIRE)--The "Gold Mining Market 2021-2026" report has been added to's offering.

The global gold mining market is forecast to grow from $214.1 billion in 2021 to $249.6 billion by 2026, at a compound annual growth rate (CAGR) of 3.1% for the period of 2021-2026.

Jewelry as an end-use of the gold mining market should grow from $107.3 billion in 2021 to $124.6 billion by 2026, at a CAGR of 3.0% for the period of 2021-2026.

Central bank as an end-use of gold mining market should grow from $22.7 billion in 2021 to $26.9 billion by 2026, at a CAGR of 3.5% for the period of 2021-2026.

This report covers the gold mining industry. Definitive and detailed estimates and forecasts of the global market are provided, followed by a detailed analysis of the regions, technology, end-uses and on-going trends.

This report covers the technological, economic and business considerations of the gold mining industry, with analyses and forecasts provided for global markets. The report includes descriptions of market forces relevant to the gold mining industry and their areas of application.

Global markets are presented for the size of gold mining segments, along with growth forecasts through 2026. Estimates of sales value are based on the price in the supply chain. It analyzes market-driving forces and examines industry structure. It also analyzes international aspects of all global regions and types of gold mining. Profiles of major global manufacturers also are presented.

This report considers the impact of COVID-19, which impacted the growth rate of every global industry in 2020 and continues to affect market forces.

The report segments the gold mining market by technology: placer mining, hardrock mining, bio-mining and recycling. The market also is segmented into the following end-uses: jewelry, technology, investment and central banks.

The Report Includes:

The global gold mining market is fairly consolidated and the top players account for a significant share of the market. Top manufacturers of gold mining include Newmont Corp., Barrick Gold Corp., AngloGold Ashanti, Polyus, Kinross, Gold Fields, Newcrest, Agnico Eagle, Polymetal, and Kirkland Lake. The top 10 players account for around 22-23% of the total market share, which is anticipated to grow due to increased merger and acquisition activities among manufacturers.

Key Topics Covered:

Chapter 1 Introduction

Chapter 2 Summary and Highlights

Chapter 3 Market and Technology Background

Chapter 4 Market Trends

Chapter 5 Market Breakdown by Technology

Chapter 6 Market Breakdown by End Use

Chapter 7 Gold Mining Demand and Supply

Chapter 8 Market Breakdown by Region

Chapter 9 Competitive Landscape

Chapter 10 Company Profiles

For more information about this report visit

About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Gold Mining Market Report 2021: A $249.6 Billion Market by 2026 with 3% CAGR Predicted Between 2021 and 2026 - - Business Wire

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Convention centers stay hopeful in 2nd year of cancellations –

What was supposed to be a celebrated return to a form of normalcy became topsy-turvy as spiking COVID-19 case counts are disrupting a second fall convention season in Anchorage for event planners and those behind the scenes.

Greg Spears, general manager for both of Anchorages city-owned Egan and Denaina convention centers sums up the last year-and-a-half with one word: brutal.

According to Spears, his team has fielded event cancellations totaling roughly $600,000 in just the past month as the resurgent virus, fueled this time by the infamous delta variant, continues to hammer many service-based industries.

Revenue from room rentals this year is trending at about twice the level of last year but is still only about half of what the Egan and Denaina generated in 2019, which Spears referred to as a decent year.

He has taken the approach of doing everything reasonably possible to accommodate everyone who wants to hold in-person gatherings, whether that can still happen now or needs to wait. Once-standard policies of 60 percent deposit refunds for cancellations within 60 days and no refunds inside of 30 days before an event have been relaxed.

It has mostly been around that one-month out timeframe that most event organizers have sought to postpone or outright cancel their plans, Spears said, and staff for the downtown convention centers have done their best to accommodate.

We have been very flexible the last 18-19 months because we understand the situations our clients are in, Spears said in a Sept. 14 interview. Were working with every client so we can hopefully one day get their events back in the building.

While the centers have sizable reserves to draw on from years of profitability, it has not made laying off nearly 100 workers and other unforeseen obstacles any easier to navigate, he added.

The Alaska Oil and Gas Association was among the first local organizations to move its annual conference back in recent weeks. Originally scheduled for Sept. 2 at the Denaina Center, leaders of the industry trade group decided in early August to push the event to mid-January in an attempt to keep attendees as comfortable and safe as possible while also recognizing the deeply rooted desire a growing number of people have to meet again, CEO Kara Moriarty said.

Normally a gathering of about 500 people, the size of the AOGA conference also necessitated considering the potential impact on hospital capacity by holding it as once scheduled, according to Moriarty.

We also consciously made the decision not to go virtual because our audience is ready for an in-person event, she said.

Moriarty also corroborated Spears version of how the cancellations are being handled by convention center officials, saying they were in lock-step with each other on the decision to postpone, again. Once scheduled for May, AOGAs conference had already been moved once this year.

(Denaina Center staff) were really great but we had been in communication with them the whole time, Moriarty said. We moved our date before they had to incur costs they couldnt recover.

She said similar things about the guest speakers and sponsors, nearly all of whom agreed to reschedule or roll deposits to January instead of backing out altogether.

I dont think anyones asked for a refund. At this point, everyone is just, OK, see you in January, Moriarty said.

Alaska Federation of Natives leaders also decided in late August to push their three-day conference one of the largest gatherings annual gatherings of its kind in the state back to mid-December instead of committing to another wholly online event as was done last year. The AFN convention is also held at the Denaina Center when it is in Anchorage.

Visit Anchorage CEO Julie Saupe said Outside groups that hold events in Anchorage are modifying plans as well. Some events first booked for last fall and rebooked for this year are now being pushed to 2023 or 2024 because plans for the interim years have already been made, she said.

More than 1,000 people from Outside were expected to attend the IEEE Signal Processing Society International Conference on Image Processing in Anchorage Sept. 19-22, according to Visit Anchorage spokesman Jack Bonney, who wrote via email that Visit Anchorage officials are working on options with that group and others toward meetings in the city in future years.

Overall, about 70 percent of the events once planned for the Egan and Denaina that have been altered since March 2020 have already been rebooked and more rebookings are expected, according to Bonney.

Anchorage Economic Development Corp. CEO Bill Popp said his group, which holds some of the citys largest luncheons each year, is planning for an in-person 2022 Anchorage Economic Forecast Luncheon in late January after staying virtual for its annual 3-year Economic Outlook presentation held in early August. At the time, COVID-19 cases were just starting to increase significantly across Alaska.

I think people are ready to get back in the three-dimensional world. I think that networking, collegiality are tangible benefits in the minds of most of our constituents, Popp said. The business community, community leadership, the public they all want to be in the room.

Adding to the challenges for Spears have been the widespread labor and supply shortage that has hit the convention centers as well.

Restarting for the Foo Fighters concert here a couple weeks ago was just a major headache in staffing. We enlisted volunteers, took help from family and friends. I myself took on different roles checking vaccination cards and whatnot, Spears said, adding that several members of his core, full-time staff are also currently sidelined with COVID-19 infections of their own.

However, the clear pent-up demand for large gatherings at some point keeps him upbeat about whats to come.

Our future is bright if we can get COVID under control, Spears said.

Elwood Brehmer can be reached at [emailprotected].

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New Research report on Hematology (CBC) Analyzers Market Insights and Forecast to 2026 | International Key Company’s Siemens, Abbott, SFRI, Roche,…

Overview of the GlobalHematology (CBC) Analyzers Market:

The top-notch Hematology (CBC) Analyzers Market research report has been structured by skilful and experienced team players who work hard to accomplish an absolute growth and success in the business. The Marketing report acts upon Market study and analysis to provide Market data by considering new product development from beginning to launch. The key research methodology employed throughout this report by DBMR research team is data triangulation which entails data mining, analysis of the impact of data variables on the Market, and primary validation. Adopting Hematology (CBC) Analyzers Market report is always beneficial for any company, whether it is small scale or large scale, for Marketing its products or services.

SWOT analysis has been performed while creating Hematology (CBC) Analyzers Market document along with many other standard steps of researching, analysing and collecting data. The report is formulated by considering several steps which can be summarised as follows. 1. Craft a title page 2. Fix a table of contents 3. Edit the report in the executive summary 4. Inscribe an introduction 5. Write the qualitative research segment of the body 6. Note down the survey research section of the body 7. Go over the types of data used in drawing conclusions 8. Allocate findings based on research 9. State conclusions and call the reader to action.

Get a Sample Copy of this Report @ .

The Global Hematology (CBC) Analyzers Market is expected to grow with a moderate CAGR in the forecast period of 2019-2026.

According to the market report analysis, Hematology analyzers are medical devices that are used for categorizing and counting the blood cell count of the patients from the blood samples. These devices are also used for analyzing differential white blood cell counts. According to the different medical devices, the features hematology analyzers are varied as well and based on these features the users can specify which testing service or analysis is required by them.

The most significant key factors driving the growth of the Global Hematology (CBC) Analyzers Market are increasing levels of blood donation activities along with rise in the frequency of blood disorders in patients giving rise to the demand of products and increasing adoption of product in combination with flow cytometry processes.

Access Complete Report Here: .

Major Key Players:

Global Hematology (CBC) Analyzers Market Segmentation:

Market Segment by Products & Services:

Market Segment by Price Range:

Market Segment by End-User:

This study answers to the below key questions:

1 What will the market size be in 2026 and what will the growth rate be?

2 What are the key market trends?

3 What are the challenges to market growth?

4 Who are the key players in this market?

5 What are the market opportunities and threats faced by the key players?

For More Insights Get FREE Detailed TOC of Global Hematology (CBC) Analyzers Market Report 2021 @ .

Major Highlights of TOC: Global Hematology (CBC) Analyzers Market

1 Global Hematology (CBC) Analyzers Market Overview

2 Global Hematology (CBC) Analyzers Market Competitions by Manufacturers

3 Global Hematology (CBC) Analyzers Capacity, Production, Revenue (Value) by Region (2021-2026

4 Global Hematology (CBC) Analyzers Supply (Production), Consumption, Export, Import by Region (2021-2026)

5 Global Hematology (CBC) Analyzers Production, Revenue (Value), Price Trend by Type

6 Global Hematology (CBC) Analyzers Market Analysis by Application

7 Global Hematology (CBC) Analyzers Manufacturers Profiles/Analysis

8 Hematology (CBC) Analyzers Manufacturing Cost Analysis

9 Industrial Chain, Sourcing Strategy and Downstream Buyers

10 Marketing Strategy Analysis, Distributors/Traders

11 Market Effect Factors Analysis

12 Global Hematology (CBC) Analyzers Market Forecast (2021-2026)

13 Research Findings and Conclusion

14 Appendix

About Data Bridge Market Research:

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process.

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New Research report on Hematology (CBC) Analyzers Market Insights and Forecast to 2026 | International Key Company's Siemens, Abbott, SFRI, Roche,...

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London open: Stocks fall as investors digest jobs data; miners in the red – ShareCast

London stocks edged lower in early trade on Tuesday as investors digested the latest UK jobs data, weighed down by weakness in the mining sector and a disappointing update from Ocado.

At 0840 BST, the FTSE 100 was down 0.3% at 7,046.51.

Figures released earlier by the Office for National Statistics showed worker numbers bounced back in August to pre-pandemic levels, while job vacancies hit a record high as the recovery from the Covid-19 crisis continues.

The number of workers on payrolls increased by 241,000 from August to 29.1m, while the number of job vacancies in the June to August period rose 35% to a new record high of 1.03m, with the biggest jump in hiring seen in hotels and restaurants.

Meanwhile, the unemployment rate fell to 4.6% in July from 4.7% the month before, in line with analysts expectations.

In the three months to July, average earnings fell to 8.3% from 8.8% but were ahead of expectations of 8.2%.

ONS deputy national statistician Jonathan Athow said: "Early estimates from payroll data suggest that in August the total number of employees is around the same level as before the pandemic, though our surveys show well over a million are still on furlough.

"However, this recovery isnt even: in hard-hit areas such as London and sectors such as hospitality and arts and leisure, the numbers of workers remain well down on pre-pandemic levels.

"The overall employment rate continues to recover, particularly among groups such as young workers who were hard hit at the outset of the pandemic, while unemployment has fallen.

"Vacancies reached a new record high. Not surprisingly, this is driven above all by hospitality, the sector with the highest proportion of employers reporting their job openings are hard to fill."

In equity markets, Ocado slumped after the online supermarket said sales at Ocado Retail dropped by considerably more than expected over its third quarter, but only due to a conflagration at its customer fulfilment centre at Erith.

Miners were under the cosh as copper prices fell, with BHP, Glencore, Anglo American, Rio Tinto and Antofagasta all lower. Mining giant BHP was also hit by a downgrade to equalweight at Barclays.

British Airways and Iberia parent IAG flew lower after a downgrade to neutral from outperform at BNP Paribas.

On the upside, JD Sports Fashion surged to the top of the FTSE 100 after it reported record first-half results, boosted by pent-up demand after UK stores reopened from lockdown and acquisitions in the US.

Pre-tax profit before exceptional items jumped to 439.5m in the six months to the end of July from 61.9m a year earlier as revenue rose to 3.89bn from 2.54bn. JD said it expected annual headline pre-tax profit for the full year to be at least 750m.

Trainline rallied as it predicted a return to profit in the first half of the year as ticket sales recovered from the damage inflicted early in the pandemic. Group net ticket sales were 666m in the three months to the end of August to stand at 71% of the total two years earlier. In the first half net ticket sales of 1bn were 54% of the level two years earlier.

Market Movers

FTSE 100 (UKX) 7,046.51 -0.31%FTSE 250 (MCX) 23,809.11 0.14%techMARK (TASX) 4,685.12 0.13%

FTSE 100 - Risers

JD Sports Fashion (JD.) 1,141.50p 8.82%Ashtead Group (AHT) 5,918.00p 1.44%Royal Mail (RMG) 493.60p 1.27%M&G (MNG) 207.10p 1.17%SSE (SSE) 1,650.00p 1.13%InterContinental Hotels Group (IHG) 4,663.00p 1.13%Bunzl (BNZL) 2,592.00p 0.97%Kingfisher (KGF) 358.80p 0.93%B&M European Value Retail S.A. (DI) (BME) 583.80p 0.86%Barratt Developments (BDEV) 701.80p 0.69%

FTSE 100 - Fallers

Ocado Group (OCDO) 1,795.50p -4.77%International Consolidated Airlines Group SA (CDI) (IAG) 145.40p -1.96%BHP Group (BHP) 2,040.50p -1.92%Glencore (GLEN) 333.60p -1.85%Anglo American (AAL) 3,037.50p -1.79%HSBC Holdings (HSBA) 374.40p -1.56%Rio Tinto (RIO) 5,211.00p -1.44%Antofagasta (ANTO) 1,438.50p -1.37%Melrose Industries (MRO) 177.50p -1.20%Rolls-Royce Holdings (RR.) 110.62p -1.18%

FTSE 250 - Risers

Trainline (TRN) 392.40p 3.81%Contour Global (GLO) 195.20p 2.31%Hammerson (HMSO) 34.03p 2.04%Future (FUTR) 3,746.00p 1.79%888 Holdings (888) 400.20p 1.68%WH Smith (SMWH) 1,590.00p 1.63%Frasers Group (FRAS) 673.50p 1.51%Countryside Properties (CSP) 531.50p 1.43%UK Commercial Property Reit Limited (UKCM) 77.30p 1.31%Cineworld Group (CINE) 64.08p 1.30%

FTSE 250 - Fallers

Ferrexpo (FXPO) 361.20p -4.80%Restaurant Group (RTN) 120.00p -3.54%Cairn Energy (CNE) 184.80p -2.17%Petropavlovsk (POG) 19.83p -1.54%PureTech Health (PRTC) 345.50p -1.29%Fidelity China Special Situations (FCSS) 347.50p -1.28%TP Icap Group (TCAP) 160.00p -1.26%Hill & Smith Holdings (HILS) 1,862.00p -0.85%Shaftesbury (SHB) 618.50p -0.80%Hochschild Mining (HOC) 153.90p -0.77%

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London open: Stocks fall as investors digest jobs data; miners in the red - ShareCast

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Jon Najarian Says People Invested In These Stocks ‘Are Going To Continue To Just Mint Money’ – Benzinga – Benzinga

Cybersecurity, data miningand cryptocurrency are three great places for investors to put money right now, Market Rebellion co-founder Jon Najarian said Thursday on CNBC's "Fast Money Halftime Report."

Global cybersecurity company IronNet Inc (NYSE:IRNT) is up over 400% since Labor Day.

"The volume in this one has been through the roof in both stock and options," Najarian said.

Related Link:IronNet Sees Sky-High WallStreetBets Interest

Companies that have real solutions in cybersecurity or data mining, like Palantir Technologies (NYSE:PLTR), are going to continue to perform very well, according to Najarian.

Everyone thought Ark Invest founder Cathie Wood was crazy for repeatedly buying Palantir when it dipped, but Najarian said they'vebeen proven wrong.

Najarian notedhe also likes crypto as a place to put money to work right now: "I think cybersecurity, crypto and people in data mining are going to continue to just mint money."

IRNT, PLTR Price Action: IronNet is up 25.7% at $40.35 and Palantir is up 5.04% at $28.44 at time of publication.


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Jon Najarian Says People Invested In These Stocks 'Are Going To Continue To Just Mint Money' - Benzinga - Benzinga

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The multi-colored Kevin Cubes in Fortnite are heading for the center of the map – Gamepur

Before the beginning of Fortnites latest chapter, many players hoped to see a return of Kevin the Cube. Well, they got more than they bargained for, as multiple cubes can now be found on the map. Much like the original Kevin, they are also slowly making their way across the map, and thanks to some data mining, we now know where they are going.

According to Hypex, the cubes are all on their way to the center of the map. There are a number of distinct differences between these cubes and the original Kevin that should also prove worrying. These cubes were unleashed by the alien motherships destruction and were being used to power it. Upon impact with the island, they caused the formation of the Sideways, a strange alternative dimension filled with monsters.

Finally, these cubes also appear to be sentient, so they actually know what they are doing. Kevin was already a very disruptive presence on the island, but multiple cubes are bound to bring significant changes. The fact that they are heading toward the center of the island is also worrying.

The center of the island has long been the place where major events happen in the world of Fortnite. From Thanos trying to rip the Zero Point out of the island and eat it, to the appearance of the Spire and its eventual destruction, the center of the map has been a hotspot over recent seasons.

There is the chance that if all the Kevins make it to the center of the map, they might be able to bring the type of changes that players have been hoping for as Chapter 3 begins to get closer.

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The multi-colored Kevin Cubes in Fortnite are heading for the center of the map - Gamepur

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Dios Exploration to Drill Windfall Geotek Provided AI Targets on the K2 Project Located in James Bay, Quebec – Junior Mining Network

Brossard, Quebec - TheNewswire - September 15th, 2021 Windfall Geotek (TSXV:WIN) (OTC:WINKF) (FSE:L7C2) a leader in the use of Artificial Intelligence (AI) with advanced knowledge-extraction techniques since 2005 in the mining sector is pleased to announce that its partner Dios Exploration (TSX-V:DOS) will drill Gold AI targets on Dios Explorations K2 project located in the Eeyou Istchee James Bay region, Quebec (Figures 1-3).

News Highlights

Windfall Geotek signed a strategic deal in June 2021 to sell Dios Exploration targets to be drilled within the year. Increasing target validation is a core part of the new strategy to help further improve our technology and prove credibility in the market

Dios also received positive soil samples inside the area of the AI target showing a potential positive sign of gold mineralization

The largest Windfall Geotek AI target covers an area of 0.87 km2 on the DIOS K2 Property

The AI analysis consisted of area of 148, 969 km2 in the James Bay Region that includes four geological sub provinces the Minto, La Grande, Opinaca-Opatica subprovince, and Ashuanipi complex (from north to south), consisting of volcano-plutonic and sedimentary assemblages

Geology indicates a contact between basalts & felsic tuffs, highlighted also by DIOSs VLF anomalies within the AI target

Positive soil samples inside the area of the AI target confirming as a valid gold target

Dinesh Kandanchatha, Chairman of Windfall Geotek commented: We are excited to progress to the next phase of our relationship with the DIOS Team. We at Windfall, leverage an outcome based model for our business, and those outcomes are validated by drill programs. Both management teams share a commitment to efficient use of capital and are excited about the potential of Windfalls technology and DIOS geological expertise to find and develop new resources.

Marie- Josee Girard, Presidente & Geologist of Dios Exploration Inc commented : We are very excited with this several km long gold-copper target area, pointed at with several combined methods, from Dios research and field work and Windfall Geotek AI targeting

Figure 1 - Windfall Geotek AI Targets with Satellite Imagery base layer

Figure 2 - Windfall Geotek AI Targets with regional geology base layer

Figure 3 - Windfall Geotek AI Targets with Geophysics base layer

The scientific and technical data contained in this press release was reviewed and prepared under the supervision of Grigor Heba, Ph.D., P.Geo.,Principal Geologist and a Qualified Person as defined by National Instrument 43-101.About Windfall Geotek Powered by Artificial Intelligence (AI) since 2005

Windfall is an Artificial Intelligence company that has been in business for over 15 years developing its proprietary CARDS analysis (AI) and data mining techniques. Windfall Geotek can count on a multidisciplinary team that includes professionals in geophysics, geology, Artificial Intelligence, and mathematics. It combines available public and private datasets including geophysical, drill hole and surface data. The algorithms designed and employed by Windfall are calculated to highlight areas of interest that have the potential to be geologically similar to other gold deposits and mineralization. The Company's objective is to develop a new royalty stream by significantly enhancing and participating in the exploration success rate of mining and to continue the Land Mine detection application as a high priority. Windfall has played a part in numerous past discoveries utilizing its methodology as described at:

For further information, please contact:for

President and CTO of Windfall Geotek

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Website: http://www.windfallgeotek.comPhone: 1-855-946-5145

Additional information about the Company is available under Windfall Geoteks profile on SEDAR at Neither the TSX Venture Exchange nor does its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accept responsibility for the adequacy or accuracy of this release.

FORWARD-LOOKING STATEMENTS: This news release contains forward-looking statements, which relate to future events or future performance and reflect managements current expectations and assumptions. Such forward-looking statements reflect managements current beliefs and are based on assumptions made by and using information currently available to the Company. Investors are cautioned that these forward-looking statements are neither promises nor guarantees, and they are subject to risks and uncertainties that may cause future results to differ materially from those expected. These forward-looking statements are made as of the date hereof and, except as required under applicable securities legislation, the Company does not assume any obligation to update or revise them to reflect new events or circumstances. All forward-looking statements made in this press release are qualified by these cautionary statements and by those made in our filings with SEDAR in Canada (available at http://WWW.SEDAR.COM).

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Dios Exploration to Drill Windfall Geotek Provided AI Targets on the K2 Project Located in James Bay, Quebec - Junior Mining Network

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Automating the Right Processes: Task Capture vs Process Mining? –

The growth of robotic process automation (RPA) over the past few years has been one of technologys great success stories.A recent Gartner announcement on the state of the market indicates that spending on RPA software is likely to surpass $2.4 billion in 2022. At that pace, 85% of large and very large organizations will have deployed some form of RPA by the end of next year.

On the face of it, RPAs huge popularity makes perfect sense. Properly executed, RPA is able to automate a wide range of repetitive, rules-based processes, vastly reducing the time spent on routine tasks, as well as the potential for human error which often accompanies such work. More importantly, RPA significantly improves productivity and drives innovation by freeing up employees to bring their creativity and problem-solving abilities to more meaningful work.

While there is no denying RPAs success Deloitte has predicted automation initiatives will hit almost universal business adoption in the next 2-3 years many companies complain that it has failed to deliver on its lofty promise. Near constant break-fix cycles leading to automation downtime and the inability of vendors to deliver on the ease of execution have inhibited the ability of many users to effectively scale their RPA initiatives. This, in turn, has impacted anticipated returns in productivity and profitability.

Although the blame for RPA failing to reach its full potential can be traced to numerous causes, a prime culprit may be automating the wrong processes. To be truly effective, RPA relies on automating those processes which showcase its strengths. These typically are processes that rely on clearly identified, predictive business rules, occur frequently, and have structured, readable output (such as an Excel spreadsheet). Processes with little or no exception rates that require rekeying data across multiple systems also represent good candidates for automation.

Bottom line: If companies implementing RPA choose to automate the wrong processes, it simply means they will be making the same mistakes as before, just faster. For that reason alone, it is essential for organizations to capture and analyze their existing processes before jumping into any automation initiative.

Only by taking the time to properly capture processes can a business make an informed decision about where RPA can best be deployed. Doing so also makes a data-backed business case for spending on automation.

To that end, many organizations are turning to either task capture or process mining software to help them identify which processes are best to automate. Unfortunately, that raises another question. While these businesses recognize they need help, which software tool is right for them?

Task capture software, as its name suggests, focuses on the numerous actions (tasks) performed to execute a specific step in a business process. Originally, this was done by having a business analyst sit with employees to monitor their work tasks, ask questions, and then map the results.

While potentially effective, this approach was time intensive and depended in large part on a sound methodology, an analyst with the right skill set to elicit needed information, and employees willingness to participate.

Task capture software replaces the human analyst with an automated recorder which captures employee interactions in the different applications they use, taking screenshots and recording data such as keystrokes, clicks, data entry, etc. This is then combined with context recognition to uncover the low-level details of how specific tasks are executed.

Ultimately, all of this information is compiled into a package that serves as a guide for RPA developers to automate those tasks and increase operational efficiency, reduce errors, and improve employee engagement.

Beyond fast-tracking the automation process, task capture software enables users to build a complete database of task and process documentation that captures even the most complex workflows. That institutional knowledge can then be shared in a way that aligns the work of business analysts with RPA developers and informs future developments.

Process mining software, on the other hand, targets business processes any cluster of related, structured work in which a specific sequence of activities produces a product or service for a particular customer. Processing an invoice, for example, is a business process.

In traditional business process management, process mining monitors and analyzes existing processes via interviews and workshops, resulting in an idealized picture of a process.

Process mining software automates this procedure, using tools to investigate data stored in the enterprise systems event logs including software logins, interactions in that software, and logoffs to discover and present end-to-end processes that an organization is performing to complete work.

Typically, these digital footprints are combined with powerful analysis techniques to present the process that has been successfully mined along with process variants and suggestions for optimizing and automating.

The benefits of process mining software include its ability to survey and analyze processes across the entire enterprise comprehensively and accurately, target bottlenecks and inefficient processes, and drive compliance and continuous process and efficiency improvements.

Ironically, process minings greatest strength its ability to mine and present a lot of data is also its apparent weakness. Process mining can produce such an overwhelming amount of data that it may require the support of data scientists to make sense of it all.

It also tends to be expensive, particularly when compared to task capture software which is both cost-effective and offers the kind of precise, detailed information businesses can use to drive transformative initiatives.

Given that, if an organization has the right governance framework and budget in place and its primary goal is to discover end-to-end business processes, then process mining software might be the preferred option.

If a company is trying to uncover the tasks its employees perform to improve workforce efficiency, identify additional automation opportunities, and get a better grasp of current processes, however, task capture software probably makes more sense.

Because many of the automation opportunities identified by task capture are less complex than those captured through process mining, they represent good candidates for citizen designers and as such, are ideal for organizations which are also concerned about a more cost-effective, user-friendly approach.

Tony Higgins is the Chief Product Officer at Blueprint Software Systems and is responsible for the vision and evolution of Blueprints Enterprise Automation Suite, a powerful digital process discovery, design and management solution that enables enterprise organizations tocapture, identify, design, and manage high-value automations with speed and precision in order to scale the scope and impact of their RPA initiatives.


Automating the Right Processes: Task Capture vs Process Mining? -

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