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Will Artificial Intelligence Take Over Your Job in 2022? – Digital Information World

We have been worried about artificial intelligence (AI) and other kinds of robots taking over our jobs for quite some time now. Techno paranoia is by no means a new phenomenon, and it manifests itself in a wide range of ways including situations where people will be concerned about the impact that automation might have on their careers. With the new year right around the corner, what are the chances that AI might make you unemployed in 2022? Peoples opinions on this are quite varied.

The group that is perhaps most fearful of losing their jobs to an AI based program is that of college graduates. Over 69% of people that have completed a college degree fear that they might not be able to get a job or that AI will make their job more or less redundant in the coming years since it would more than likely be able to do whatever job they can handle in a much more efficient manner and they would cost a lot less too.

If you look at all of the respondents to the survey as a whole, you would notice that the average is around 55% for people that are afraid to losing jobs to AI. Hence, college graduates seem to be disproportionately worried about this sort of thing occurring with all things having been considered and taken into account. However, if we were to break down the responses and sort them out by the category of job that we are looking at, it becomes clear that virtually everyone is fearful of the impact of AI.

For example, about 63% of respondents felt that the role of cashiers will be fulfilled by AI in the coming years. This has already started to occur, with Amazon creating stores that you can walk in and out of and your payment will be automatically calculated and deducted from your bank account or some other source of funds. The practicality of such a setup is yet to be tested, but suffice it to say that it already exists in the modern day and many fast food companies are experimenting with this as well.

52% of respondents also said that they felt that the jobs of drivers could be automated and made redundant. This might have something or the other to do with the rise of driverless cars which use a computer program and an algorithm to make it so that you dont have to take the wheel. While these types of cars still have a long way to go before they can become the global standard, they have been getting better on a regular basis and might make traffic jams less prevalent than they are right now.

With all of that having been said and now out of the way, it is important to note that not all of the views that people have about AI are negative. If we were to take the example of the economy, around 45% of respondents felt that AI could do a lot of good in this regard. These respondents felt that if AI were made responsible for things like fiscal policy and the like it could reduce the prevalence of corruption and create a smoother type of system for everyone to enjoy.

However, that doesnt mean that everyone agrees that this is a good idea. 29% of respondents felt that doing so would be disastrous for the economy, but a plurality appear to think otherwise. The fact of the matter is that opinions regarding AI are mixed, but this has absolutely no impact on its growth this year. It will continue to grow in 2022 and the changes that are coming would need to be dealt with as and when they arrive so that people can get accustomed to a new way of living. Take a look at below charts more insights on fear of artificial intelligence and its trends, which comes courtesy of Tidio.

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Artificial intelligence is restoring lost works by Klimt, Picasso and Rembrandt, but not everyone is happy about it – Bowling Green Daily News

Country

United States of AmericaUS Virgin IslandsUnited States Minor Outlying IslandsCanadaMexico, United Mexican StatesBahamas, Commonwealth of theCuba, Republic ofDominican RepublicHaiti, Republic ofJamaicaAfghanistanAlbania, People's Socialist Republic ofAlgeria, People's Democratic Republic ofAmerican SamoaAndorra, Principality ofAngola, Republic ofAnguillaAntarctica (the territory South of 60 deg S)Antigua and BarbudaArgentina, Argentine RepublicArmeniaArubaAustralia, Commonwealth ofAustria, Republic ofAzerbaijan, Republic ofBahrain, Kingdom ofBangladesh, People's Republic ofBarbadosBelarusBelgium, Kingdom ofBelizeBenin, People's Republic ofBermudaBhutan, Kingdom ofBolivia, Republic ofBosnia and HerzegovinaBotswana, Republic ofBouvet Island (Bouvetoya)Brazil, Federative Republic ofBritish Indian Ocean Territory (Chagos Archipelago)British Virgin IslandsBrunei DarussalamBulgaria, People's Republic ofBurkina FasoBurundi, Republic ofCambodia, Kingdom ofCameroon, United Republic ofCape Verde, Republic ofCayman IslandsCentral African RepublicChad, Republic ofChile, Republic ofChina, People's Republic ofChristmas IslandCocos (Keeling) IslandsColombia, Republic ofComoros, Union of theCongo, Democratic Republic ofCongo, People's Republic ofCook IslandsCosta Rica, Republic ofCote D'Ivoire, Ivory Coast, Republic of theCyprus, Republic ofCzech RepublicDenmark, Kingdom ofDjibouti, Republic ofDominica, Commonwealth ofEcuador, Republic ofEgypt, Arab Republic ofEl Salvador, Republic ofEquatorial Guinea, Republic ofEritreaEstoniaEthiopiaFaeroe IslandsFalkland Islands (Malvinas)Fiji, Republic of the Fiji IslandsFinland, Republic ofFrance, French RepublicFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabon, Gabonese RepublicGambia, Republic of theGeorgiaGermanyGhana, Republic ofGibraltarGreece, Hellenic RepublicGreenlandGrenadaGuadaloupeGuamGuatemala, Republic ofGuinea, RevolutionaryPeople's Rep'c ofGuinea-Bissau, Republic ofGuyana, Republic ofHeard and McDonald IslandsHoly See (Vatican City State)Honduras, Republic ofHong Kong, Special Administrative Region of ChinaHrvatska (Croatia)Hungary, Hungarian People's RepublicIceland, Republic ofIndia, Republic ofIndonesia, Republic ofIran, Islamic Republic ofIraq, Republic ofIrelandIsrael, State ofItaly, Italian RepublicJapanJordan, Hashemite Kingdom ofKazakhstan, Republic ofKenya, Republic ofKiribati, Republic ofKorea, Democratic People's Republic ofKorea, Republic ofKuwait, State ofKyrgyz RepublicLao People's Democratic RepublicLatviaLebanon, Lebanese RepublicLesotho, Kingdom ofLiberia, Republic ofLibyan Arab JamahiriyaLiechtenstein, Principality ofLithuaniaLuxembourg, Grand Duchy ofMacao, Special Administrative Region of ChinaMacedonia, the former Yugoslav Republic ofMadagascar, Republic ofMalawi, Republic ofMalaysiaMaldives, Republic ofMali, Republic ofMalta, Republic ofMarshall IslandsMartiniqueMauritania, Islamic Republic ofMauritiusMayotteMicronesia, Federated States ofMoldova, Republic ofMonaco, Principality ofMongolia, Mongolian People's RepublicMontserratMorocco, Kingdom ofMozambique, People's Republic ofMyanmarNamibiaNauru, Republic ofNepal, Kingdom ofNetherlands AntillesNetherlands, Kingdom of theNew CaledoniaNew ZealandNicaragua, Republic ofNiger, Republic of theNigeria, Federal Republic ofNiue, Republic ofNorfolk IslandNorthern Mariana IslandsNorway, Kingdom ofOman, Sultanate ofPakistan, Islamic Republic ofPalauPalestinian Territory, OccupiedPanama, Republic ofPapua New GuineaParaguay, Republic ofPeru, Republic ofPhilippines, Republic of thePitcairn IslandPoland, Polish People's RepublicPortugal, Portuguese RepublicPuerto RicoQatar, State ofReunionRomania, Socialist Republic ofRussian FederationRwanda, Rwandese RepublicSamoa, Independent State ofSan Marino, Republic ofSao Tome and Principe, Democratic Republic ofSaudi Arabia, Kingdom ofSenegal, Republic ofSerbia and MontenegroSeychelles, Republic ofSierra Leone, Republic ofSingapore, Republic ofSlovakia (Slovak Republic)SloveniaSolomon IslandsSomalia, Somali RepublicSouth Africa, Republic ofSouth Georgia and the South Sandwich IslandsSpain, Spanish StateSri Lanka, Democratic Socialist Republic ofSt. HelenaSt. Kitts and NevisSt. LuciaSt. Pierre and MiquelonSt. Vincent and the GrenadinesSudan, Democratic Republic of theSuriname, Republic ofSvalbard & Jan Mayen IslandsSwaziland, Kingdom ofSweden, Kingdom ofSwitzerland, Swiss ConfederationSyrian Arab RepublicTaiwan, Province of ChinaTajikistanTanzania, United Republic ofThailand, Kingdom ofTimor-Leste, Democratic Republic ofTogo, Togolese RepublicTokelau (Tokelau Islands)Tonga, Kingdom ofTrinidad and Tobago, Republic ofTunisia, Republic ofTurkey, Republic ofTurkmenistanTurks and Caicos IslandsTuvaluUganda, Republic ofUkraineUnited Arab EmiratesUnited Kingdom of Great Britain & N. IrelandUruguay, Eastern Republic ofUzbekistanVanuatuVenezuela, Bolivarian Republic ofViet Nam, Socialist Republic ofWallis and Futuna IslandsWestern SaharaYemenZambia, Republic ofZimbabwe

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OPPO is creating Innovative Experiences with Artificial Intelligence as it unveils its Smart Glass Technologies – BellaNaija

The keen emphasis on innovative mobile technology experience is bringing the likes of Neural Processing Unit (NPU), artificial intelligence (AI), and augmented reality (AR) to the forefront in Nigeria and major global tech giants are not leaving any stones unturned in the drive towards creating smarter cities around the world.

It is widely believed that the Nigerian populace has overwhelmingly embraced the use of mobile technologies as an integral part of their everyday lives. According to Wikipedias world listing of countries by number of mobile phones in use, based on Nigerian Telecommunication Commission data, as of 2020, Nigeria ranks 7thand 1st in Africa. This also comes with adaptation to sophisticated mobile technologies.

It is noteworthy that tech company, Oppo, is playing big in this area as it recently unveiled its NPU and Smart Glass technologies at its 2021 Oppo Inno Day event in Lagos. NPU is technology planned to continue to accelerate the efficiency of artificial intelligence (AI) applications and improve functionality, this is how people are bringing the dream of smarter societies to reality.

The fast-growing mobile telecommunication company promises that it will revolutionize the way Nigerians use mobile gadgets driven by high technological advancements in everyday life.

This is an obvious reason a global giant like this is now showcasing its passionate capacity in significant technological improvements in the features that now come in phones, and other relatable gadgets. For instance, there have been tremendous changes in both software and hardware strategies of the brand and it is quite interesting to see these cutting-edge technologies become features so relevant and adaptable in the daily lives of an average Nigerian.

In the same vein, the Oppo Nigeria Marketing Manager, Jennifer Okorhi had explained at the event that the new smart glass is built around a groundbreaking monocle waveguide design. She further added that it also has an innovative application as a teleprompter, which allows an adaptive text display to make presentations at work or in public hitch-free. This is a testament to how adaptable these technologies can become in Nigeria, as Nigerians are known over the world to easily adapt technology in all ramifications.

The company substantiated its new strategy in creating an innovative experience for Nigerians as it unveiled several exciting Artificial Intelligence (AI) and camera technologies at the 2021 edition. Stakeholders at the maiden edition in Nigeria held a consensus that there is a huge potential for the technologies that Oppo is bringing to Nigeria.

This is considering that a large population of Nigerians is already leveraging technological applications for solving almost everyday challenges in agriculture, financial services, and even in the corporate space.

It is through events like this that stakeholders are being carried along in the innovative technology space, a strategy the future-looking tech companies are deploying to keep Nigerians informed on current realities in the tech space and how they can better leverage these new technologies.

Participants from all works of life; both private and public commended the new features that the company is adding to its fleet of mobile phones and Gadgets. The Honourable Commissioner of Science and Technology at Lagos State Government, Hakeem Fahm noted at the Lagos Inno Day celebrations that innovation is a practical way of introducing new ideas while he also expressed satisfaction in what the tech giant is doing as it also aligns with what the government of Lagos State aims to achieve with technology.

A Nigerian actress, Stephanie Coker, expressed confidence in the versatility and dynamism that the company puts in its products. The Oppo products; phones, smart glasses, all look ecstatically pleasing and this is hard to find, sometimes you find the nice-looking phone but its very heavy, Stephanie said.

While sharing his view after the Inno Day showcase, Big Brother Housemate, Pere Egbi who also experienced using the Oppo Reno 5 series during the reality show, noted at the showcase event in Lagos that the newly unveiled products are ecstatically pleasing to the eyes. Pere also expressed satisfaction with the huge internal memory size and sleekness of the unveiled Oppo foldable phones.

There is no doubt that these gadgets will be easily adoptable by Nigerians who savor this amazing innovative technology easily. Oppo is showing resilience and commitment to creating valuable experiences for its teeming customers to derive maximum satisfaction from the products being churned out.

In Nigeria, Oppo continues to drive diverse innovative experiences for its teeming customers by providing premium and top-quality mobile gadgets.

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Ann Coulter: Happy Kwanzaa, the holiday brought to you by the FBI – Today’s News-Herald

It seems like all I hear these days is how liberals are red-hot for teaching history, while retrograde troglodytes on the right are demanding that we suppress the teaching of history by banning critical race theory (CRT). Haranguing students, day in day out, about their white privilege is just teaching history.

On this beloved Kwanzaa week, heres some history for you.

Celebrated exclusively by white liberals, Kwanzaa is a fake holiday invented in 1966 by black radical/FBI stooge Ron Karenga -- aka Dr. Maulana Karenga, founder of United Slaves, the violent nationalist rival to the Black Panthers. Liberals have become so mesmerized by multicultural gibberish that they have forgotten the real history of Kwanzaa and Karengas United Slaves. Kwanzaa emerged not from Africa, but from the FBIs COINTELPRO.

In what was ultimately a foolish gambit, during the madness of the 60s, the FBI encouraged the most extreme black nationalist organizations in order to discredit and split the left. The more preposterous the group, the better. (Its the same function Alexandria Ocasio-Cortez serves today.)

By that criterion, Karengas United Slaves was perfect.

Despite modern perceptions that blend all the black activists of the 60s, the Black Panthers did not hate whites. Although some of their most high-profile leaders were drug dealers and murderers, they did not seek armed revolution.

No, those were the precepts of Karengas United Slaves. The United Slaves were proto-fascists, walking around in dashikis, gunning down Black Panthers and adopting invented African names. (I will not be shooting any Black Panthers this week because I am Kwanzaa-reform, and we are not that observant.)

Its as if David Duke invented a holiday called Anglika, which he based on the philosophy of Mein Kampf -- and clueless public schoolteachers began celebrating the made-up, racist holiday.

In the category of the-gentleman-doth-protest-too-much, back in the 70s, Karenga was quick to criticize Nigerian newspapers that claimed that certain American black radicals were CIA operatives.

Now we know the truth: The FBI fueled the bloody rivalry between the Panthers and United Slaves. In the annals of the American 60s, Karenga was the Father Gapon, stooge of the czarist police. Whether Karenga was a willing FBI dupe, or just a dupe, remains unclear.

In one barbarous outburst, Karengas United Slaves shot two Black Panthers to death on the UCLA campus: Al Bunchy Carter and John Huggins. Karenga himself served time, a useful stepping-stone for his current position as the chair of the Africana Studies Department at California State University at Long Beach.

The left has forgotten the FBIs tacit encouragement of this murderous black nationalist cult founded by the father of Kwanzaa. The esteemed Cal State professors invented holiday is a nutty blend of schmaltzy 60s rhetoric, black racism and Marxism. The seven principles of Kwanzaa are the same as those of the Symbionese Liberation Army, another invention of The Worst Generation.

In 1974, Patty Hearst, kidnap victim-cum-SLA revolutionary, famously posed next to the banner of her alleged captors, a seven-headed cobra. Each snakehead stood for one of the SLAs revolutionary principles: Umoja, Kujichagulia, Ujima, Ujamaa, Nia, Kuumba and Imani -- the exact same seven principles of Kwanzaa.

When Karenga was asked to distinguish Kawaida, the philosophy underlying Kwanzaa, from classical Marxism, he essentially said that, under Kawaida, we also hate whites. (And heres something interesting: Kawaida, Kwanzaa and Kuumba are also the only three Kardashian sisters not to have their own shows on the E! network.)

While taking the best of early Chinese and Cuban socialism (is that the mass murder or the seizure of private property?), Karenga said Kawaida practitioners believe ones racial identity determines life conditions, life chances and self-understanding.

Theres an inclusive philosophy for you!

Sing to Jingle Bells:

Kwanzaa bells, dashikis sell

Whitey has to pay;

Burning, shooting, oh what fun

On this made-up holiday!

New York Times bestselling author and syndicated columnist Ann Coulter is a graduate of Cornell University and the University of Michigan Law School. Ann is a regular contributor to conservative news sites Human Events and Breitbart. She is a native of New Canaan, Conn.

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

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

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

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.

Learn more about how an enterprise data governance solution can help you solve organizational challenges read our eBook Data Governance 101: Moving Past Challenges to Operationalization.

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Data Mining Process: Models, Process Steps & Challenges …

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.

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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.

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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.

Stay tuned to our upcoming tutorial to know more about Data Mining Examples!!

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

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

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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

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.

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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:

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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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