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ABC Doesn’t Have The Backbone To Put Ann Coulter On ‘The View’ – The Federalist

January 4, 2022 | Author: admin

ABCs The View is having a hard time finding a new conservative co-host. The ones whove auditioned either are liberals masquerading as conservatives on air, or have proven theyre too much smarter than the incumbent panelists. Five months after co-star Meghan McCain left the show, the hosts cant experience the latter again.

At the same time, Politico Playbook reported Monday, the new host cant be seen as too chummy with the other co-hosts, as the networks market-research shows that the audience wants to see the women spar.

Sources said that this hurt the chances of Ana Navarro, a regular fill-in on the conservative chair who worked as a surrogate for Joe Biden in 2020: She is perceived by the producers as too friendly with the other hosts and not a traditional Republican, the newsletter continued.

Despite alienating McCain with a toxic work environment, the shows hosts and producers maintain hope of finding a replacement who fits the right criteria outlined by Politico:

Sources close to the show said that the search has stalled as executives struggle to find a conservative cast-member who checks all the right boxes. They will not consider a Republican who is a denier of the 2020 election results, embraced the January 6 riots, or is seen as flirting too heavily with fringe conspiracy theories or the MAGA wing of the GOP. But at the same time, the host must have credibility with mainstream Republicans, many of whom still support Donald Trump.

Right now, we still do need a really conservative voice, Co-Host Sunny Hostin told New York Magazine in November, adding the new permanent addition to the panel must not be duplicative.

If the hosts were really up for a challenge from a unique personality, they would bring repeat guest and conservative firebrand Ann Coulter back to the table, this time for an audition. Shes entertaining on television, fits the criteria, and offers an articulate perspective severely absent from the program.

Even left-wing writer Mickey Kaus agrees.

Ann Coulter seems an obvious choice, Kaus wrote on Twitter. Not Never Trump. Not pro-Trump (the-person). Not election denier. Just MAGA enough! All boxes checked. Also listens before arguing, occasionally changes mind. (Do they want that?)

Considering the shows audition process to this point, it doesnt sound like it.

I was told when I left, they were looking for a real conservative, McCain told Variety in October. I gave them a list. None of them have tested, by the way.

(To be clear, former Trump State Department Spokeswoman Morgan Ortagus, who produced a viral segment challenging Rep. Adam Schiff, D-Calif., on the Steele Dossier, did not audition until a month later.)

The panelists on The View dont take well to being challenged, not from a permanent colleague on a daily basis. If they did, McCain wouldnt have been pushed off the program after four seasons.

Love or hate her, Coulter, an author of 12 books, many of which ended up as New York Times bestsellers, holds her own in dynamic discussions on complex topics. Her prior appearances on the daytime program have shown just that to the visible ire of its liberal panelists.

Will they tolerate welcoming Coulter as a host among their ranks? Almost certainly not.

Its just an honor to be nominated! Coulter told The Federalist in an emailed statement.

Tristan Justice is the western correspondent for The Federalist. He has also written for The Washington Examiner and The Daily Signal. His work has also been featured in Real Clear Politics and Fox News. Tristan graduated from George Washington University where he majored in political science and minored in journalism. Follow him on Twitter at @JusticeTristan or contact him at Tristan@thefederalist.com.

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Data mining applications in healthcare

January 4, 2022 | Author: admin

Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.

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Data Mining Techniques: Top 5 to Consider

January 4, 2022 | Author: admin

Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that youre trying to solve will determine the type of data mining technique that will yield the best results.

In todays digital world, we are surrounded with big data that is forecasted to grow 40%/year into the next decade. The ironic fact is, we are drowning in data but starving for knowledge. Why? All this data creates noise which is difficult to mine in essence we have generated a ton of amorphous data but experiencing failing big data initiatives. The knowledge is deeply buried inside. If we do not have powerful tools or techniques to mine such data, it is impossible to gain any benefits from such data.

This analysis is used to retrieve important and relevant information about data, and metadata. It is used to classify different data in different classes. Classification is similar to clustering in a way that it also segments data records into different segments called classes. But unlike clustering, here the data analysts would have the knowledge of different classes or cluster. So, in classification analysis you would apply algorithms to decide how new data should be classified. A classic example of classification analysis would be Outlook email. In Outlook, they use certain algorithms to characterize an email as legitimate or spam.

It refers to the method that can help you identify some interesting relations (dependency modeling) between different variables in large databases. This technique can help you unpack some hidden patterns in the data that can be used to identify variables within the data and the concurrence of different variables that appear very frequently in the dataset. Association rules are useful for examining and forecasting customer behavior. It is highly recommended in the retail industry analysis. This technique is used to determine shopping basket data analysis, product clustering, catalog design, and store layout. In IT, programmers use association rules to build programs capable of machine learning.

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This refers to the observation for data items in a dataset that do not match an expected pattern or an expected behavior. Anomalies are also known as outliers, novelties, noise, deviations, and exceptions. Often, they provide critical and actionable information. An anomaly is an item that deviates considerably from the common average within a dataset or a combination of data. These types of items are statistically aloof as compared to the rest of the data and hence, it indicates that something out of the ordinary has happened and requires additional attention. This technique can be used in a variety of domains, such as intrusion detection, system health monitoring, fraud detection, fault detection, event detection in sensor networks, and detecting eco-system disturbances. Analysts often remove the anomalous data from the dataset top discover results with an increased accuracy.

The cluster is a collection of data objects; those objects are similar within the same cluster. That means the objects are similar to one another within the same group and they are rather different, or they are dissimilar or unrelated to the objects in other groups or in other clusters. Clustering analysis is the process of discovering groups and clusters in the data in such a way that the degree of association between two objects is highest if they belong to the same group and lowest otherwise. A result of this analysis can be used to create customer profiling.

In statistical terms, a regression analysis is the process of identifying and analyzing the relationship among variables. It can help you understand the characteristic value of the dependent variable changes, if any one of the independent variables is varied. This means one variable is dependent on another, but it is not vice versa. It is generally used for prediction and forecasting.

All of these data mining techniques can help analyze different data from different perspectives. Now you have the knowledge to decide the best technique to summarize data into useful information information that can be used to solve a variety of business problems to increase revenue, customer satisfaction, or decrease unwanted cost.

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Data Mining Process: Models, Process Steps & Challenges …

January 4, 2022 | Author: admin

This Tutorial on Data Mining Process Covers Data Mining Models, Steps and Challenges Involved in the Data Extraction Process:

Data Mining Techniques were explained in detail in our previous tutorial in this Complete Data Mining Training for All. Data Mining is a promising field in the world of science and technology.

Data Mining, which is also known as Knowledge Discovery in Databases is a process of discovering useful information from large volumes of data stored in databases and data warehouses. This analysis is done for decision-making processes in the companies.

Data Mining is carried using various techniques such as clustering, association, and sequential pattern analysis & decision tree.

Data Mining is a process of discovering interesting patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the web, and other information repositories or data that are streamed into the system dynamically.

Why Do Businesses Need Data Extraction?

With the advent of Big Data, data mining has become more prevalent. Big data is extremely large sets of data that can be analyzed by computers to reveal certain patterns, associations, and trends that can be understood by humans. Big data has extensive information about varied types and varied content.

Thus with this amount of data, simple statistics with manual intervention would not work. This need is fulfilled by the data mining process. This leads to change from simple data statistics to complex data mining algorithms.

The data mining process will extract relevant information from raw data such as transactions, photos, videos, flat files and automatically process the information to generate reports useful for businesses to take action.

Thus, the data mining process is crucial for businesses to make better decisions by discovering patterns & trends in data, summarizing the data and taking out relevant information.

Any business problem will examine the raw data to build a model that will describe the information and bring out the reports to be used by the business. Building a model from data sources and data formats is an iterative process as the raw data is available in many different sources and many forms.

Data is increasing day by day, hence when a new data source is found, it can change the results.

Below is the outline of the process.

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Many industries such as manufacturing, marketing, chemical, and aerospace are taking advantage of data mining. Thus the demand for standard and reliable data mining processes is increased drastically.

The important data mining models include:

CRISP-DM is a reliable data mining model consisting of six phases. It is a cyclical process that provides a structured approach to the data mining process. The six phases can be implemented in any order but it would sometimes require backtracking to the previous steps and repetition of actions.

The six phases of CRISP-DM include:

#1) Business Understanding: In this step, the goals of the businesses are set and the important factors that will help in achieving the goal are discovered.

#2) Data Understanding: This step will collect the whole data and populate the data in the tool (if using any tool). The data is listed with its data source, location, how it is acquired and if any issue encountered. Data is visualized and queried to check its completeness.

#3) Data Preparation: This step involves selecting the appropriate data, cleaning, constructing attributes from data, integrating data from multiple databases.

#4) Modeling: Selection of the data mining technique such as decision-tree, generate test design for evaluating the selected model, building models from the dataset and assessing the built model with experts to discuss the result is done in this step.

#5) Evaluation: This step will determine the degree to which the resulting model meets the business requirements. Evaluation can be done by testing the model on real applications. The model is reviewed for any mistakes or steps that should be repeated.

#6) Deployment: In this step a deployment plan is made, strategy to monitor and maintain the data mining model results to check for its usefulness is formed, final reports are made and review of the whole process is done to check any mistake and see if any step is repeated.

[image source]

SEMMA is another data mining methodology developed by SAS Institute. The acronym SEMMA stands for sample, explore, modify, model, assess.

SEMMA makes it easy to apply exploratory statistical and visualization techniques, select and transform the significant predicted variables, create a model using the variables to come out with the result, and check its accuracy. SEMMA is also driven by a highly iterative cycle.

Steps in SEMMA

Both the SEMMA and CRISP approach work for the Knowledge Discovery Process. Once models are built, they are deployed for businesses and research work.

The data mining process is divided into two parts i.e. Data Preprocessing and Data Mining. Data Preprocessing involves data cleaning, data integration, data reduction, and data transformation. The data mining part performs data mining, pattern evaluation and knowledge representation of data.

[image source]

Why do we preprocess the data?

There are many factors that determine the usefulness of data such as accuracy, completeness, consistency, timeliness. The data has to quality if it satisfies the intended purpose. Thus preprocessing is crucial in the data mining process. The major steps involved in data preprocessing are explained below.

Data cleaning is the first step in data mining. It holds importance as dirty data if used directly in mining can cause confusion in procedures and produce inaccurate results.

Basically, this step involves the removal of noisy or incomplete data from the collection. Many methods that generally clean data by itself are available but they are not robust.

This step carries out the routine cleaning work by:

(i) Fill The Missing Data:

Missing data can be filled by methods such as:

(ii) Remove The Noisy Data: Random error is called noisy data.

Methods to remove noise are :

Binning: Binning methods are applied by sorting values into buckets or bins. Smoothening is performed by consulting the neighboring values.

Binning is done by smoothing by bin i.e. each bin is replaced by the mean of the bin. Smoothing by a median, where each bin value is replaced by a bin median. Smoothing by bin boundaries i.e. The minimum and maximum values in the bin are bin boundaries and each bin value is replaced by the closest boundary value.

When multiple heterogeneous data sources such as databases, data cubes or files are combined for analysis, this process is called data integration. This can help in improving the accuracy and speed of the data mining process.

Different databases have different naming conventions of variables, by causing redundancies in the databases. Additional Data Cleaning can be performed to remove the redundancies and inconsistencies from the data integration without affecting the reliability of data.

Data Integration can be performed using Data Migration Tools such as Oracle Data Service Integrator and Microsoft SQL etc.

This technique is applied to obtain relevant data for analysis from the collection of data. The size of the representation is much smaller in volume while maintaining integrity. Data Reduction is performed using methods such as Naive Bayes, Decision Trees, Neural network, etc.

Some strategies of data reduction are:

In this process, data is transformed into a form suitable for the data mining process. Data is consolidated so that the mining process is more efficient and the patterns are easier to understand. Data Transformation involves Data Mapping and code generation process.

Strategies for data transformation are:

Data Mining is a process to identify interesting patterns and knowledge from a large amount of data. In these steps, intelligent patterns are applied to extract the data patterns. The data is represented in the form of patterns and models are structured using classification and clustering techniques.

This step involves identifying interesting patterns representing the knowledge based on interestingness measures. Data summarization and visualization methods are used to make the data understandable by the user.

Knowledge representation is a step where data visualization and knowledge representation tools are used to represent the mined data. Data is visualized in the form of reports, tables, etc.

RDBMS represents data in the form of tables with rows and columns. Data can be accessed by writing database queries.

Relational Database management systems such as Oracle support Data mining using CRISP-DM. The facilities of the Oracle database are useful in data preparation and understanding. Oracle supports data mining through java interface, PL/SQL interface, automated data mining, SQL functions, and graphical user interfaces.

A data warehouse is modeled for a multidimensional data structure called data cube. Each cell in a data cube stores the value of some aggregate measures.

Data mining in multidimensional space carried out in OLAP style (Online Analytical Processing) where it allows exploration of multiple combinations of dimensions at varying levels of granularity.

List of areas where data mining is widely used includes:

#1) Financial Data Analysis: Data Mining is widely used in banking, investment, credit services, mortgage, automobile loans, and insurance & stock investment services. The data collected from these sources is complete, reliable and is of high quality. This facilitates systematic data analysis and data mining.

#2) Retail and Telecommunication Industries: Retail Sector collects huge amounts of data on sales, customer shopping history, goods transportation, consumption, and service. Retail data mining helps to identify customer buying behaviors, customer shopping patterns, and trends, improve the quality of customer service, better customer retention, and satisfaction.

#3) Science and Engineering: Data mining computer science and engineering can help to monitor system status, improve system performance, isolate software bugs, detect software plagiarism, and recognize system malfunctions.

#4) Intrusion Detection and Prevention: Intrusion is defined as any set of actions that threaten the integrity, confidentiality or availability of network resources. Data mining methods can help in intrusion detection and prevention system to enhance its performance.

#5) Recommender Systems: Recommender systems help consumers by making product recommendations that are of interest to users.

Enlisted below are the various challenges involved in Data Mining.

Data Mining is an iterative process where the mining process can be refined, and new data can be integrated to get more efficient results. Data Mining meets the requirement of effective, scalable and flexible data analysis.

It can be considered as a natural evaluation of information technology. As a knowledge discovery process, Data preparation and data mining tasks complete the data mining process.

Data mining processes can be performed on any kind of data such as database data and advanced databases such as time series etc. The data mining process comes with its own challenges as well.

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The best AI, analytics, and big data conferences of 2022 – TechBeacon

January 4, 2022 | Author: admin

Attending AI, analytics, big data, and machine-learning conferences helpsyou learn about the latest advancements and achievements in these technologies, things that would likely take too long and too much effort to research and master on your own. And you get this information from keynote speakers who are tops in their fieldsinformation you can use to help you in your work.

These events allow you to develop your skills in these areas while interacting with others who share the same interests and who can help you solve problems.

To that end, TechBeaconhascreated the following shortlist of AI, analytics, big data, and MLconferences for 2022.

Twitter:@WAICannesWeb:worldaicannes.comDate:February 1012Location:Cannes, France, and virtualCost:Free or up to 1,490

A high-level conference program will hostover 100 speakers from around the world and from rich and diverse backgrounds. Attendees will get a complete vision of AI in all its complexity and challenges through the conference's five tracks.

Who should attend:AIprofessionals and enthusiasts

Twitter:@datascisalonWeb:datascience.salon/miami//Date:February 16Location:Miami, Florida, USA, and virtualCost: $595

The Data Science Salon is a focused conference that grew into a diverse community of data scientists, machine-learning engineers, analysts, and other senior data specialists. Attendees can improve their data skills viaengaging live presentations and panel discussions, learn how to apply state-of-the art AI and ML techniques in the enterprise, and meet and connect with leading data scientists in a casual atmosphere.

Who should attend: Senior data scientists anddata decision makers

Twitter:@teamreworkWeb:re-work.co/events/ai-ethics-summit-2022Date:February 1718Location:San Francisco, California, USA, and virtualCost: In-person, $1,695 to$2,095; virtual pass, $249

At this event, attendees will learn aboutresponsible and ethical approaches to developing AI for the common good. They will also hear from expert speakers on recent, relevant developments and the progression of AI ethics.

Who should attend: AI ethicists, AI strategists, AI technologists, data engineers, fellows, lead for responsible AI, professors, PhD students, program leads, andresearch scientists

Twitter:@teamreworkWeb:re-work.co/events/deep-learning-landscape-summit-2022Date:February 1718Location:San Francisco, California, USA, and virtualCost: Varies

This event brings together the latest technology advancements as well as practical examples to apply AI to challenges in business and society. Attendees will hear about advances in deep learning and smart AI from leading innovators. They will also learn from industry experts in speech and image recognition, neural networks, and big data.

Who should attend: Developers, data scientists, DevOps specialists, IT decision makers, and anyone interested in combining DevOps practices with machine learning

Twitter: @weCONECTMediaWeb: industryofthingsworldusa.comDate: March 1718Location: San Diego, California, USA, and virtualCost: Standard ticket, $3,995; digital ticket, $695

Organizers say that this event is the leading business platform for smart manufacturing and industrial IoT. The event focuses on real world, end-user case studies on how smart manufacturing technologies are changing businesses and industries. Speakers from around the world offer insights intoconcepts and present current strategies. Topics include automation, machine-to-machine communication, interoperability, analytics, and new business models.

Part of the Industry of Things World global event series, this conference has become the meeting point for senior executives who want to learn more about the industrial Internet. A sister conferenceis scheduled for September inBerlin, Germany.

Who should attend: IoT specialists and strategists, IoT novices, cloud computing adopters, big data analytics experts, and anyone else involved in digital transformation

Twitter: @EnterpriseDataWeb: edw2022.dataversity.netDate: March 2025Location: San Diego, California, USACost: $995 to $3,395

For 26 years now, Enterprise Data World has been recognized as the most comprehensive educational conference on data management in the world. This year's event will offer in-depth training opportunities for data-driven professionals from around the world. Industry experts will share case studies, experiences, and knowledge about various topics, including data governance, data architecture, blockchain technology, data integration, data modeling, metadata management, data and information quality, business analytics, data science, big data and enterprise information management, and more.

Who should attend: Chief data officers; data and information architects; vice presidents and directors of analytics, business intelligence, and data governance; information quality professionals; data scientists; and big data engineers

Twitter: @odscWeb: odsc.com/bostonDate: April 19 21Location: Boston, Massachusetts, USA, and virtualCost:In-person, $799 to $4,663; virtual, $299 to $2,663

ODSC East 2022 will be more inclusive than everfrom in-person sessions to digital experiences available for everyone, attending from anywhere. The idea is to combinesmall, immersive, in-person experiences with innovative and insightful digital ones. ODSC East will help attendees stay current with the most recent and exciting developments in data science.

Who should attend: Data scientists, software engineers, analysts, managers, and CxOs

Twitter: @Gartner_inc,#GartnerDAWeb: gartner.com/en/conferences/emea/data-analytics-ukDate:May 911Location: London, UKCost:2,650 to3,550

This conference addresses the most significant challenges that data analytics leaders face as they build the innovative and adaptable organizations of the future. Attendees will learn how to deliver continued value in an uncertain world with strategies and innovations backed by data and analytics.

Who should attend: Analytics and business information practitioners,business analysts,data scientists,analytics and business information program leaders,enterprise information leaders,andMDM program managers

Twitter: @ai_expoWeb: ai-expo.net/northamerica/Date:May 1112Location: Santa Clara, California, USACost:TBA

This event is for the enterprise technology professional seeking to explore the latest innovations, implementations, and strategies to drive businessforward. It will provide insight from more than 250 speakers sharing their industry knowledge and real-life experiences in solo presentations, expert panel discussions, and in-depth fireside chats. Topics includedemystifying AI, creating an AI-powered organization, machine learning, decision science, RPA and automation, data analytics, chatbots, and computer vision.

Who should attend: IT decision makers, CTOs, developers and designers, heads of innovation, chief data officers, chief data scientists, brand managers, data analysts, people from startups, tech providers, C-level executives, andventure capitalists

Twitter: @infotodayWeb: dbta.com/DataSummit/2022/default.aspxDate:May 1718Location: Boston, Massachusetts, USACost:Showcase only, $50 ($25, early bird); per-workshop fee,$395($295, early bird); various keynote, boot camp, and summit combos, $495(early bird)to $1,095

At Data Summit 2022, attendees will hear about approaches the world's leading companies are taking to solve key challenges in data management. Whether attendees' interests lie in the technical possibilities and challenges of new and emerging technologies or inusing big data for business intelligence, analytics, and other business strategies, Data Summit 2022 has something for everyone, according to organizers.

Who should attend: CIOs, chief data officers, database administrators, application developers, IT directors and managers, software engineers, data architects, technology specialists, data analysts, project managers, data scientists, and business directors and managers

Twitter: @WorldDataSummitWeb: worlddatasummit.comDate:May 1820Location: Amsterdam,NetherlandsCost:Conference, 1,295; workshop day, 795; conference andworkshop, 1,595

Thisthree-day conference will help attendees get a better understanding of developing an analytical model for their business and customer growth. Experts will discuss all aspects of data analysis, how to work with unstructured data, and how to upgrade data visualization and interpretability to the next level. Attendees can also dig deeper into customer analytics or increase their technical knowledge by attending a workshop.

Who should attend: Directors and managers ofdata analytics and modeling,data science,customer and market insight analytics,data integration and artificial intelligence,business analytics,predictive analytics,data architecture,andstatistics

Twitter: @Monitorama,#monitoramaWeb: monitorama.com/2022/pdx.htmlDate: June 2729Location: Portland, Oregon, USACost: $700

This conference focuses on techniques and tools used to monitor complex applications and infrastructure. Attendees can hear talks by industry experts and community leaders about the newest approaches to monitoring and observability, including AIOps. Past conferences have covered topics such as rethinking user experiencefor AI-driven monitoring toolsand how AI helps observe decentralized systems.

Who should attend: Monitoring engineers, developers, production engineers, software architects and engineers, reliability engineers, and network architects

Twitter: @scienceppWeb: mldm.deDate: July 1621Location:New York, New York, USACost: TBA

The aim of thisconference is to bring together researchers from all over the world who deal with machine learning and data mining. They willdiscuss the status of the recentresearch and other topics.

Who should attend: Anyone interested in ML and data mining

Twitter: @dremioWeb: dremio.com/subsurface/events/summer-2021/Date (2021): July 21Location (2021):New York, New York, USACost (2021): Free

This event explores the latest open-source innovations and offers real-world use cases. Attendees will hear firsthand from technology leaders at companies includingNetflix, USAA, Adobe, Microsoft, and AWS about their experiences architecting and building modern data lakes. Participants will also learn how to innovate with open-source technologies, such as Apache Iceberg, Amundsen, InfluxDB IOx, Apache Parquet, and more.

Who should attend: Data architects and data engineers

Twitter: @Gartner_inc,#GartnerDAWeb: gartner.com/en/conferences/apac/data-analytics-australiaDate: July 2526Location:Sydney, AustraliaCost:A$2,750 toA$3,475(group discounts available)

Attendees at this event will learn how to establish the strategy, organization, culture, and skills needed to align to business priorities and outcomes, and leverage key data and analytics trends.

Who should attend: Chief data officers, chief analytics officers, andsenior data, and analytics and business leaders

Twitter: @Ai4ConferencesWeb: ai4.io/2021/Date (2021): August 1719Location (2021):VirtualCost (2021): $395 (general registration)

This event brings together business leaders and data practitioners to facilitate the adoption of AI and machine-learning technology. Organizers say their mission is to help provide a common framework for what AI means to enterprises as well as the future of the world.

Who should attend: Individuals who hold nontechnical, senior-level positions and/or technical roles at their organizations;data scientists;and data practitioners

Twitter: @RealBIEvent,#REALBIEVENTWeb: realbusinessintelligence.comDate (2021): September 2122Location (2021):Cambridge, Massachusetts, USA, and virtualCost(2021): $299 to $499

Organized by Dresner Advisory Services, this conference is designed for IT and business leaders looking for strategies for successfulbusiness intelligence, analytics, and information and performance management. It emphasizes using real-world best practices and proven methods to produce pragmatic and actionable takeaways.

Who should attend: Business and IT leaders, CIOs, chief data officers, business intelligence and data governance practitioners, and data scientists

Twitter: @teamreworkWeb: re-work.co/events/deep-learning-summit-london-2022Date: September 2223Location:London, UK,and virtualCost:TBA

This event brings together the latest technology advancements as well as practical examples for how to apply AI to solve challenges in business and society. The mix of academia and industry allows attendees to meet with AI pioneers at the forefront of research as well as explore real-world case studies to discover the business value of AI.

Who should attend: Developers, data scientists, DevOps specialists, IT decision makers, and anyone interested in combining DevOps practices with machine learning

Twitter: @teamreworkWeb: re-work.co/events/ai-for-good-summit-seattle-2022Date: November 1011Location:Seattle, Washington, USACost:TBA

Thisevent will explore responsible and practical applications of machine learning and deep learning to improve individual lives and society, including achieving environmental sustainability, increasing access to education and healthcare, reducing AI bias, and boosting transparency.

Who should attend:Chief data officers, CEOs, CIOs, founders, government leaders, policy advisors, professors, andchief AI officers

Twitter: @odscWeb: odsc.com/california/Date (2021): November 1518Location (2021):San Francisco, California, USACost(2021):$299 to $1,299 (time-sensitive discounts available)

The ODSC West Conference 2021 is billed as one of the largest applied data science training conferences in the world. Instructors include some of the core contributors tomany open-source tools, libraries, and languages. Attendees will hear about the latest AI and data science topics, tools, and languages from some of the best and brightest in the field.

Who should attend: Data scientists, software engineers, analysts, managers, and CxOs

Twitter: @datascisalonWeb: datascience.salon/finance-and-technology/Date (2021): December 1Location (2021):VirtualCost(2021):Free to $45

Theconference brings together specialists in the finance and technology data science fields to educate one another, illuminate best practices, and innovate new solutions in a casual atmosphere.

Who should attend: Senior data scientists anddata decision makers

Twitter: @TRAIF2021Web: responsibleaiforum.comDate (2021): December 58Location (2021):VirtualCost(2021):45 to 675

This three-day event brings together members of industry, civil society, government, and academia to discuss the most relevant and pressing issues related to the responsible use of AI through shared stories, cutting-edge research, and practical applications. Organizers also aim to encourage exchange between research and practice through productive discussion and demonstration.

Who should attend: Anyone involved the field of artificial intelligence

***

Make your choices soon and mark your calendars.Prices may vary based on how early you register. Also, remember that hotel and travel costs are generally separate from the conference pricing.

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Data Mining Tools Market is Flourishing at Healthy CAGR with Growing Demand, Industry Overview and Forecast to 2026 Industrial IT – Industrial IT

January 4, 2022 | Author: admin

The latest research on Data Mining Tools Market concisely segments the industry based on types, applications, end-use industries, key regions, and competitive landscape. Also, the report provides a detailed evaluation of the gross profit, market share, sales volume, revenue structure, growth rate, and the financial position of the major market players. The scope of development for new entrance or established companies in the Data Mining Tools business was also highlighted in the report.

In the report, a concise presentation has been included concerning the product or service. Moreover, the various trends and affecting factors of the Data Mining Tools Market. These variables have helped decide the behavior of the market during the forecast period and empowered our specialists to make effective and precise predictions about the market future.

Key Features of Data Mining Tools Research Report:

Request for Sample Copy of Data Mining Tools Market with Complete TOC and Figures & Graphs at https://www.in4research.com/sample-request/18376

The study also involves the important Achievements of the market, Research & Development, new product launch, product responses, and regional growth of the most important competitors operating in the market on a universal and local scale.

Top players Covered in Data Mining Tools Market Study are:

For more Customization in Data Mining Tools Market Report: https://www.in4research.com/customization/18376

Data Mining Tools Market Segmentation

Data Mining Tools market is split by Type and by Application. For the period 2018-2026, the growth among segments provides accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. This analysis can help you expand your business by targeting qualified niche markets.

Market Segmentation by Type:

Market Segmentation by Applications:

Regions covered in Data Mining Tools Market report:

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Research Objective Data Mining Tools Market Research:

The report is useful in providing answers to several critical questions that are important for the industry stakeholders such as manufacturers and partners, end-users, etc., besides allowing them in strategizing investments and capitalizing on market opportunities.

Key Target Audience:

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Four challenges for the future of mining and how to overcome them – Mining Technology

January 4, 2022 | Author: admin

There are a lot of exciting changes afoot in the mining industry, and we are already seeing the benefit of new technology and its impact on safety, productivity, and sustainability. From utilising big data to automation, digital twins and simulation training, the digital era is revolutionising mining and tunnelling.

But this doesnt mean that the future of mining wont have its challenges. Whether it is getting to grips with new technology or adapting to a changing planet, there are going to be obstacles to overcome. Fortunately, companies such as Normet continue to lead the way in digitalised and sustainable underground mining to make the transition as smooth as possible.

From providing the fuel we need to sustain modern life or supplying construction and electronic industries with the resources that they require, we need the mining industry and we need it to perform well. However, as sustainability becomes a global priority, there is increasing pressure to change the way that mines operate and decrease waste.

For Normet, sustainability is key. From its electric battery-operated vehicles to replace diesel, to SmartScan technology to reduce shotcrete waste, and TBM additives to reduce water waste during tunnelling, the company is working to help its customers on sustainability journey and help mining companies to meet increasingly strict global regulations for a more sustainable future.

Typically, industries that are capital intensive and work with regulated workflows are slower to pick up new technology. The mining industry has been particularly notorious for this technology adoption problem in the past.

Part of this can be attributed to an if its not broke, dont fix it attitude, especially from those who have worked in the mining industry for a long time and have seen the benefits of traditional methods. However, the attitudes are changing fast. Customers are showing more and more interest in green technologies and how to leapfrog to new level with technology.

Key ingredient with the leapfrog for mine operators is to communicate and collaborate with innovators in order to better understand how technology can be utilised. Normet helps its mining and tunnelling partners to continuously improve their processes, increase the safety and productivity of their underground activities, and improve the sustainability of their operations.

Automation and robotics are taking on many of the repetitive or dangerous tasks, and new machinery needs a workforce that knows how to keep it efficiently maintained. Workers in the mining industry neednt be worried about being replaced with technology, but there is pressure on the industry to provide adequate upskilling training.

Part of the solution lies in attracting young people with the right skills, and for those already working in the industry there are new and exciting training methods to help them become proficient with updated technology.

Simulation training might conjure images of 80s-esque science fiction or clunky VR gaming, but advancing capabilities have allowed for extremely accurate simulation industry training that precisely follows movement and enables assessment and feedback every step of the way. One example is the EFNARCs Nozzleman scheme, which provides effective simulation-based training for sprayed concrete that utilises advanced VR.

To produce the resources that we need, mining operations are delving deeper and deeper. This poses a number of problems, especially when it comes to keeping workers safe. The deeper the mine, the more adverse the environment, and the greater the risk of rock fracturing. This has put pressure on mining innovators to provide solutions to keep mines stable and workers safe.

Normet offers a solution for fracturing rock with its self-drilling dynamic bolts that can provide rock reinforcement without needing to pre-drill. Additionally, its sprayed concrete solutions optimise the application of shotcrete for maximum efficiency and minimum waste. The best way to keep workers safe is to provide remotely operated solutions that can keep them out of harms way, from electrified vehicles to Internet of Things technology.

For more information about the services Normet provides, download the whitepaper below or visit their website.

By clicking the Submit button above, you accept and acknowledge that your data will be used as described in the Normet Privacy Policy.

We will also collect and use the information you provide for carefully considered and specific purposes, where we believe we have a legitimate interest in doing so, for example to send you communications about similar products and services we offer. We will always give you an option to opt out of any future communications from us. You can find out more about our legitimate interest activity in our privacy policy here. We includes Verdict Media Limited and other GlobalData brands as detailed here.

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A Fast-Growing Association of Helium Mining on Radio Network With Helium Rent – The Daily Hodl

January 4, 2022 | Author: admin

January 3, 2022 Hong Kong, Hong Kong

The company has been working in the radio network business for several years and has been able to build structures all over the world. The existing radio stations make it easy to connect new Helium hotspot mining devices to the network and to share the income with your customers.

Hotspot hosts earn HNT crypto based on how much they contribute to the network through proof-of-coverage challenges and by witnessing proof-of-coverage challenges.

The customers participate in the sales for a certain period of time. With the flood of money, the association of radio networks would like to expand its network to make it stronger and faster. As a company with large quantities of devices such as Bobcat and Sensecap, delivery times are significantly shorter and are not up to 10 months as is normal.

HeliumRent recommends its customers take a double track. For short-term and quick participation in the Helium network, it is advisable to enter the plans available here. For those who want to wait 10 months or more for their own devices, this is also a viable option.

We are excited about the future and look forward to more news about the Helium network and its alternative 5G technology through the Helium hotspots. Helium is a network meant to help IoT devices like e-scooters (Lime, Bird), simple sensors and pet trackers get low-volume data to the internet quickly and at very low cost. This network is using car sharing companies and smart city solutions as well.

HeliumRent is part of the Association of Radio Network stations with antenna locations all over the world. The headquarter of Association of Radio Network is based in Hong Kong in the Mira Place Tower. Helium has network coverages in more different countries, and the network increases daily with new live mining hotspots for Helium mining and data transfer.

For more information, visit here.

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A Fast-Growing Association of Helium Mining on Radio Network With Helium Rent - The Daily Hodl

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What are the Various Types of Blockchain? – Enterprise Security Mag

January 4, 2022 | Author: admin

Blockchain technology is a relatively new concept initially developed to support Bitcoin. However, the technology's popularity has grown, and more people are discovering that blockchain technology can be used for purposes other than cryptocurrency.

FREMONT, CA: Blockchain is a distributed ledger technology (DLT) intended to foster an environment of trust and confidence. Blockchain is a distributed ledger technology that is replicated and distributed across an entire network of computer systems. It enables access to information for all designated nodes or members, allowing them to record, share, and view encrypted transactional data on their blockchain.

Blockchain technology collects and stores data in groups, also known as "blocks," and each block can contain a finite amount of data. When a block reaches its capacity, it is chained to the entire previous block, forming a data chain, thus earning the clever name "blockchain."

Blockchain security is a comprehensive risk management system for blockchain networks, incorporating assurance services, cybersecurity frameworks, and industry best practices to mitigate fraud and cyber-attack risks.

Because blockchain technology is based on consensus, cryptography, and decentralization principles, its data structures are inherently secure. Each new block of information is inextricably linked to all previous blocks in a way that makes tampering nearly impossible. Additionally, a consensus mechanism (authorized users) validates and agrees on all transactions within a block, ensuring that each transaction is true and accurate. As a result, there is no single point of failure and no way for a user to modify transaction records.

However, blockchain security goes beyond the technology's inherent security features. This is how.

What Are the Different Kinds of Blockchain?

Private Blockchains: Invitations to private blockchain networks are required. Users must be validated either by the network's central administrator or starter or by the network's administrator using a rule set. Typically, businesses that utilize private blockchains establish a permissioned network. Permissioned networks place restrictions on who can join and what types of transactions they can initiate. In either case, participants must be invited or granted permission to join. Private blockchains are frequently used in internal, business-secure environments to manage access, authentication, and record-keeping tasks. Typically, transaction data is kept private.

Public Blockchains: Public blockchains are designed with participation and transparency in mind. The consensus mechanism is "decentralized," which means that anyone can validate network transactions, and the software code is open-source and freely available (e.g., Bitcoin and Ethereum).

The primary characteristic of public blockchain networks is decentralization via crypto-economics, which is used to ensure network cooperation. This means that the network lacks a political control point in public blockchains, and the software system's architecture lacks a central point of failure.

The consensus algorithm's design, network governance, ownership of cryptographic "private keys," and economic incentives determine how decentralized a blockchain is. Consider the concept of "data mining," which rewards users for validating transactions. This incentive incentivizes people to join the network and assist in validating transactions.

Governance considerations include who develops the software code, who is allowed to participate in the consensus mechanism, and who can participate in the communal governance activities that keep the network running. Consensus mechanisms for public blockchains are primarily "Proof-of-Work" (PoW) or "Proof-of-Stake" (PoS) (PoS).

However, anyone can join and validate transactions on a public blockchain, a significant distinction between public and private blockchains.

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What are the Various Types of Blockchain? - Enterprise Security Mag

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Do lakhs of MSMEs no longer want benefits of MSME registration? Heres what govt data suggests – The Financial Express

January 4, 2022 | Author: admin

Ease of Doing Business for MSMEs: There are currently lakhs of enterprises in the MSME sector, considered the backbone of the Indian economy, that perhaps might not want benefits offered by the government to Udyam-registered MSMEs, according to experts. The inference was drawn based on the available data by the Ministry of MSME. Lets understand this below step-by-step:

There was a total of 21,96,902 enterprises that had filed for Entrepreneurs Memorandum (EM-II) between 2007 and 2015, as per data from the MSME Ministrys 2020-21 annual report. From September 2015 onwards, with the introduction of the new online registration portal called Udyog Aadhaar Memorandum (UAM), there were 1,02,32,451 (1.02 crore) registrations till June 30, 2020, when it was replaced with the latest portal Udyam Registration. India, otherwise, had 6.33 crore unincorporated MSMEsas per the National Sample Survey (NSS) 73rd round, conducted by National Sample Survey Office during 2015-16.

Former MSME Minister Nitin Gadkari had announced the revised guidelines on July 2 last year for MSMEs to reinstate retail trade (street vendors were also allowed to register) and wholesale trade under the MSME category. As of November 29, 5,33,404 registrations of retail and wholesale trades were recorded on the Udyam portal. This data was shared by MSME Minister Narayan Rane in Lok Sabha in reply to a question during the winter session.

The overall registration count on the Udyam portal was around 64.10 lakh as of December 31, 2021, which included retail trade and wholesale trade count. However, the share of new registrations, which could be new MSMEs altogether and/or those that were unregistered, and MSMEs switching from UAM or EM-II registrations on the Udyam portal, wasnt known.

Also read: Rs 4.5 lakh cr ECLGS: Breaking down the mega post-Covid credit guarantee scheme for MSMEs since launch

Comments from MSME Ministrywerentimmediately available for this story.

Importantly, the government had extended the validity of the EM-II and UAM registrations (obtained before June 30, 2020) from March 2021 to December 2021. The Reserve Bank of India (RBI) hadclarified last yearthat existing EMs II and/or UAMs of MSMEs would remain valid till March 31, 2021, even as MSMEs were required to register on the Udyam portal before March this year to avail various benefits by the government including incentives such as Priority Sector Lending.

It is expected that existing EM Part-II and UAM holders would be able to migrate to the new system of Udyam Registration, which was launched on 1st July 2020, and would avail the benefits of government schemes, thereby paving the way for strengthening MSMEs and leading to their faster recovery, boost to their economic activity and creation of jobs, MSME ministry had said in a statement on the extension provided. However, it hadnt issued any notification or statement on further extension of the validity.

According to experts, if it is assumed that all 58.7 lakh Udyam portal registrations (retail and wholesale trade registrations excluded) as of December 31, 2021, were existing MSMEs that switched from EM-II/UAM registrations, it meant that 57 per cent (of over 1 crore MSMEs) had moved to Udyam portal while rest 43 per cent (43.55 lakh) hadnt registered. However, Udyam portal data likely included new registrations as well, hence, the share of existing MSMEs registering on the portal might be below 57 per cent.

I believe sufficient time was given to businesses to get onto Udyam portal till December 2021. After all, it was the entrepreneurs choice to register as MSME on the portal or not. If they didnt register despite benefits offered by the government then it suggests that they were not interested in it. On the other hand, it could also be a matter of duplicity in registrations under UAM/EM-II and hence the actual registrations on Udyam might be right. However, if we remove the duplicity issue, then it is a concern as to why those businesses have not registered. Benefits under Udyam portalmight not be very effective for these enterprises and hence, they are comfortable with a regular business registration to operate,Anil Bhardwaj, Secretary-General, Federation ofIndianMSMEs (FISME) told Financial Express Online.

Among overall benefits of registering on the Udyam portal also included freedom from renewal of registration, any number of activities including manufacturing or service could be added to one registration, MSMEs could register themselves on GovernmenteMarketplace(GeM) and also simultaneously onboardTReDSplatform. Registration was also intended to help MSMEs in availing benefits of government schemes such as Credit Guarantee Scheme, Public Procurement Policy, etc. MSMEs can register on the portal free of cost and without any documents except their Permanent Account Number and Aadhaar details.

There must be many new enterprises under the MSME ambit post revision of the MSME definition who didnt register on the portal. MSMEs who didnt register on the Udyam portal might not be considered MSMEs henceforth and would not be eligible for MSME benefits. However, MSMEs havent benefitted much from government sops. The biggest challenge for many MSMEs has been to raise collateral-free loans. Also, those who dont want to sell to the government via public procurement, dont have to register on the portal. Moreover, MSMEs have been wary of sharing investment and turnover details through the portal,Mukesh Mohan Gupta, President, Chamber of Indian Micro, Small & Medium Enterprises (CIMSME) toldFinancial ExpressOnline.

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As of January 3, 2022,60,65,339 were microenterprises registered on the Udyam portal followed by3,14,332 small and 34,102 medium enterprises, data showed. However, it is important to note that the 64 lakh registrations came in 18 months (July 2020-December 2021) in comparison to over 1 crore UAM registrations that were recorded in nearly 5 years. The pandemic has underscored the need to move to a digital platform. This move hasnt been easy and a lot of adjustment has been required. For some MSMEs, this has become the only mode of surviving. We are getting a good response on the Udyam portal. If you look at the earlier model, it had taken around five years to reach 10 million (registrations), here in 14-15 months, we are reaching almost 6 million, MSME secretary BB Swain had said at a virtual CII event in November last year.

I dont think MSMEs have stopped seeing the benefits of registering on the portal. With government schemes, they get preferential treatment. Also, it doesnt seem enough time has been given for existing MSMEs to register on the Udyam portal. It is a remote possibility that MSMEs would not find it beneficial to register on the portal during the Covid period with multiple schemes offered by the government. The general feedback we have got from MSMEs is that they have found it useful. The government should do data mining on the reasons for such MSMEs who havent switched to the newportal, Harjinder Kaur Talwar, Vice President, FICCI CMSME told Financial Express Online.

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