double Archives - Artificial Intelligence

CSS Boarding Style:

Maruti Kr. — Wed, 12 Apr 2023 09:24:44 +0000

We have eight style in CSS

solid: This style is used to creates a solid line around the element.

border-style: solid;

2. dotted: This style creates a dotted line around the element.

border-style: dotted;

3. dashed: This style creates a dashed line around the element.

border-style: dashed;

4. double: It is used to creates a double line around the element.

border-style: double;

5. inset: This style creates a 3D inset effect around the element.

border-style: inset;

6. outset: The outset value is a border style property in CSS that can be used to define a 3D-like border with a raised or sunken appearance.

border-style: outset;

7. groove: This style creates a 3D groove effect around the element.

border-style: groove;

8. ridge: This style creates a 3D ridge effect around the element.

border-style: ridge;

Boarder Properties:

In CSS, It gives several properties to control the borders of an HTML element.

Some of the most commonly used border properties:

border-style: This property controls the style of the border, such as solid, dashed, dotted, double, groove, ridge, inset, and outset.
border-width: This property sets the width of the border. It can be a specific length, such as 1px, or one of the following keywords: thin, medium, or thick.
border-color: This property sets the color of the border. It can be any valid color value, such as a hex code, an RGB value, or a named color.
border-radius: This property sets the radius of the border corners. It can be a specific length, such as 5px, or a percentage of the element’s size, such as 50%.
border: This is a shorthand property that combines all the above border properties into a single declaration. For example, border: 1px solid black sets a 1-pixel solid black border.

CSS Position Properties: Left, Right, Top, Button

Position in CSS

By using CSS position we can change the place of an element from one place to another place.

They have Five value of CSS position.

Static
Relative
Fixed
Sticky
Absolute

In css position we are using top, button, left, right to the change of the position of an element.

Position Static:

Static means constant. By using position static we can’t change the place of position.

Static is the by default value of css position.

Position Relative:

By using position relative we can change the place of element from its parent position.

Example:




    
    Document
    


    CSS

Output:

Position Fixed:

In position fixed internally the element will take the value and it will be fixed in the position.

In position fixed from the beginning of scrolling the page of the element will not move anywhere, the element will be constant in the given position.

Example:






    Document

    



    Lorem ipsum dolor sit, amet consectetur adipisicing elit. Sint quam, distinctio, debiti
        nulla eos doloribus blanditiis libero at consequatur consectetur. Commodi, natus! Enim, recusandae omnis id
        impedit sequi nulla dolor deserunt consequatur laudantium nesciunt, excepturi, dolorum
        deleniti unde
        ducimus
        minima eum obcaecati nam! Fugit corporis impedit, aut quibusdam iste assumenda blanditiis consectetur quas
        corrupti perspiciatis odio natus neque est explicabo velit ab cum beatae minima hic eligendi sapiente qui rem
        ratione. Amet accusamus aut quibusdam ducimus tempora fugiat molestiae debitis corporis, consequatur commodi
        esse ratione eum eaque, dicta veritatis ex quis est sint? Minus commodi ea rerum animi dolor magnam laudantium
        laboriosam recusandae porro optio corporis tempore perspiciatis, earum quis
        velit cum maiores fugiat doloremque, quod minus expedita quas aspernatur reprehenderit sunt. Eligendi similique
        voluptatum dolorum cupiditate, vero libero delectus exercitationem consectetur et, molestias nesciunt non
        laudantium quaerat veritatis, dolorem provident architecto obcaecati. Hic quibusdam voluptas laudantium libero
        numquam adipisci voluptate? Quam suscipit aut consequuntur ullam officiis voluptate facere culpa doloremque
        repudiandae iste, quae possimus odio, fuga cumque debitis cupiditate! Quo, repellat. Qui repellat, minus illo
        iure voluptatum tenetur nisi dicta ut, quae voluptatibus veniam. Quia tempore suscipit similique fuga sed vitae,
        neque vero dolore a culpa eos quod ullam magnam totam magni consequuntur nihil ducimus explicabo? Id dolore
        reprehenderit quia, vitae excepturi, eligendi assumenda ex vel at nisi quibusdam porro dolores perspiciatis
        dolorum consequatur culpa. Quos, rem accusantium? Voluptatem earum reprehenderit, ipsa pariatur enim, provident
        rerum doloribus, ullam voluptatum harum deserunt! Placeat totam rem aliquam culpa earum repellat neque, velit
        consectetur quo odit explicabo modi animi reiciendis at delectus ratione harum impedit, incidunt omnis. Esse
        eius eum ratione, nobis expedita hic ullam, cumque molestiae odit consequuntur id ad maxime inventore
        distinctio. Error ullam corporis corrupti illum quidem perspiciatis rerum? Voluptatibus molestiae temporibus
        laudantium harum animi non impedit vel, iusto vero quos nam neque ea amet aliquam atque, facilis assumenda?
        Ratione assumenda eligendi, beatae numquam quam inventore quos hic aliquam quas voluptatibus voluptates quo
        rerum blanditiis obcaecati vel magnam consequatur eos debitis ad quibusdam quidem nostrum, perferendis esse
        modi? Alias explicabo dolore quasi est accusamus maxime repellendus id impedit soluta reprehenderit reiciendis
        perferendis corporis, aliquid sed quis in delectus aut dolorum doloribus quos earum voluptatem rerum.
        Necessitatibus voluptate, deserunt cupiditate assumenda expedita incidunt quibusdam excepturi, adipisci ullam
        quisquam facilis mollitia! Necessitatibus maiores repudiandae mollitia odio commodi laboriosam nihil asperiores,
        accusantium, officiis dolores deleniti dolorum, illo molestias accusamus laborum omnis animi numquam quas? Nulla
        odit explicabo doloribus ab iure, a adipisci, autem corporis cum vel, veniam minus animi cumque eos! Placeat
        minus, officiis facere consequatur, facilis explicabo alias fuga sapiente deserunt a quod qui cum. Natus
        reprehenderit assumenda perspiciatis inventore quibusdam tempore atque vel aperiam nostrum cum, ullam placeat
        libero quo aliquam molestiae quam consequuntur, officia numquam modi. Molestias consequatur aperiam id autem,
        recusandae repellat voluptates sapiente similique obcaecati minus rem asperiores facere reprehenderit, quod,
        illum aspernatur distinctio eligendi. Delectus alias reprehenderit recusandae incidunt, provident officia rerum    quam veritatis ipsa! Quas accusamus adipisci animi sunt quasi. Corrupti, sed. Corporis amet error ex ipsa natus
        quibusdam velit, tempore quos tenetur quam excepturi eum omnis nostrum animi at. explicabo nam distinctio labore
        officiis autem cum dicta hic
        magnam, in vel maxime ipsam adipisci ab assumenda repellat. Perspiciatis!

Output:

Position Sticky:

In css sticky the element will move in the beginning but when the element will teach to the fived value it will be constant or fixed in that position.

Example:

 




    Document

    




    Lorem, ipsum dolor sit amet consectetur adipisicing elit. Laborum accusantium voluptatum vitae at odio, debitis
        est incidunt harum? Magni blanditiis autem at laborum, corrupti deleniti, quae illum reprehenderit animi
        accusamus architecto accusantium quas nulla ab dicta nam, quasi quidem pariatur aliquid cupiditate enim? Vero
        debitis ab quos quaerat, ut vel, porro dignissimos, repellendus illum facilis nostrum consequatur dolore
        Consequuntur, totam at corrupti commodi velit voluptatibus porro illo! Expedita ipsa dignissimos dicta eligendi
        quisquam voluptatibus explicabo, totam natus? Ipsam quisquam debitis repellat quibusdam consectetur. Quos
        repellat repudiandae non amet aut, 
CSS positionsequi commodi dignissimos alias nulla voluptate, quas earum eum aliquid
        exercitationem dolore numquam debitis ea totam libero corrupti molestiae officiis recusandae aspernatur eaque?
        Ipsa magnam architecto, doloribus quos qui, sequi aliquam voluptatem quibusdam quia impedit dolores laudantium
        totam eaque provident voluptates voluptate at ipsam a. Nam consectetur vitae quisquam dignissimos rem tempore
        officia debitis, soluta veniam magni itaque saepe sint tenetur. Ab totam atque sequi, quidem asperiores officia
        aliquid dolores aliquam molestias quo natus quisquam voluptatum. Fugit inventore ratione vitae error et

Output:

Position Absolute:

By using position absolute we can attach the child element to the nearest relative parent.

For position absolute we need parent child relationship between the element(chain – like structure).

In which parent we want to attached the child element to that parent give position relative and to the child element give position: absolute

  
  
  
  
  
  
This heading has an absolute position  
 The heading below is placed 150px from the left and 250px from the top of the page.

Output:

The post CSS Boarding Style: appeared first on Artificial Intelligence.

Data’s Double Edges: How To Use Machine Learning To Solve The Problem Of Unused Data In Risk Management

aiuniverse — Thu, 17 Jun 2021 05:35:21 +0000

Source – https://www.forbes.com/

Gary M. Shiffman, Ph.D. is the Founder and CEO of Giant Oak and Co-Founder and CEO of Consilient. He is the creator of GOST and Dozer.

According to my company’s research, a full 25% of PPP fraud casesbrought by the Department of Justice could have been easily prevented. The fraud is so obviously clumsy that it is embarrassing to whomever approved the loans.

Decision-makers consume a lot of data. The world is awash in data, and data is there to be used — or not used — like at no other time. As a result, risk measurement systems today perform far better than the systems of even just three years ago. But what if yesterday’s performance was poor, in absolute terms? Can improvement over last year justify missing obviously blatant threats to your organization? I want to focus this article on obvious but undiscovered risk and the data not used in analytics.

Artificial Intelligence and Machine Learning (AI/ML) enable qualitative changes to risk management, which deliver large step increases in quantitative performance, leaving a gaping question. If asked, “How much improvement is enough?,” then “any improvement” might sufficiently answer the question. But “any” feels like an inattentive answer. The very existence of data demands decisions most executives have not been trained to make: What data can be excluded from the analysis? And yet these decisions on what data to use and exclude require great care, like receiving a double-edged razor in an unprotected hand.

About a decade ago, when “big data” was the buzz, I remember joining industry discussions as executives rushed to formulate initiatives and responses. Leaders would often clench their fists while arguing that there is such a thing as too much data.

The Biden Cybersecurity EO: The Good, The Bad And The Ugly—But Mostly Good

Too much data overwhelms humans, so the reaction of the 2010s made sense at the time. However, data also creates more accurate ML models. Amazon’s market capitalization in 2011 was $78 billion and grew to an astounding $1.7 trillion by 2021; the growth came from understanding the value of more data, not less. Risk professionals in 2021 similarly understand that happiness with less data can cut a career short.

Machine Learning tools are available, posing a new “big data” challenge for the 2020s: missing threats because of data not used. Market leaders have moved from fearing too much data to too little data in analytics.

To limit the use of data in risk discovery leaves threats undiscovered, exposing decision-makers to ex post facto criticism: “How did you miss that? It was so obvious!” The data is free and publicly available. Read news reports of PPP fraud cases, for example. People who did not have companies or employees received large amounts in Covid-19 relief dollars. “How did they miss that?” you might think. The bank and government screeners used too little data and missed obvious information. They erred in selecting the data not used.

Critics of using more data, even in 2021, rightly complain that added data still creates too many “false positives,” especially in unstructured data. Like oiling a blade in a sawmill, data helps for a while but eventually gums up the moving parts. Data has a history of gumming up the risk discovery process.

To prevent these big-data frustrations in the past, data-as-a-service vendors emerged. Firms in these markets use hundreds or thousands of people to filter data, creating highly curated data sets, and they sell this high-cost data at a high price to risk professionals in many industries, financial institutions and law enforcement agencies.

Unfortunately, human-based filtering absolutely separates risk management professionals from massive amounts of valuable data. For example, financial services firms spend $180.9 billion on financial crime compliance worldwide, according to a 2020 LexisNexis study, and yet financial institutions capture less than 1% of the criminal proceeds. Fifty-seven percent of that $180.9 billion is spent on labor. The large effort masks the lack of progress.

To protect oneself from the double-edged sword of data availability in 2021, use more data in risk measurement to decrease the universe of unused data and use AI/ML to decrease the false positives challenges which vex human screeners and investigators. This is the balance to keep in mind: Use more data and reduce errors by replacing manual human curation with machine learning.

AI/ML can solve much of the double-edged nature of data abundance. Technology delivers effectiveness with efficiency. The key is reindexing the publicly available information on the internet, a task too massive for a human but easy enough for well-trained ML models, and then to perform entity resolution (ER) on that massive mess of unstructured data.

In addition, organizational changes can be implemented — for example, routine testing of ML model output with measurements of efficiency and effectiveness, such as precision and recall against a known set of test data. To do this, organizations may want to consider training management to better understand the measurement of ML systems. Including someone fluent in AI/ML performance on your company’s board also makes sense in today’s world of important data exclusion decisions.

If this technology exists, why is it not pervasive across every bank in the U.S.? The answer is that it takes time for the widespread adoption of new technology. There is no villain. There is no government branch or bank CEO fighting adamantly against it — in fact, joint regulatory agencies, FinCEN and the Bank Policy Institute are encouraging it. AI/ML, which is already so pervasive in our cell phones and homes, will soon start impacting the risk world, such as AML/CFT and Customer Due Diligence.

Decision-makers consume a lot of data but need the ability to use more. Entity resolution across massive public and unstructured data will soon be a part of every risk management organization. The most successful risk management managers of the 2020s will find innovative ways to utilize more data, protect privacy and improve both effectiveness and efficiency.

The post Data’s Double Edges: How To Use Machine Learning To Solve The Problem Of Unused Data In Risk Management appeared first on Artificial Intelligence.

Big data: IPK researchers double accuracy in predicting wheat yields

aiuniverse — Sat, 12 Jun 2021 05:31:17 +0000

Source – https://www.eurekalert.org/

The enormous potential of Big Data has already been demonstrated in areas such as financial services and telecommunications. An international team of researchers led by the IPK Leibniz Institute has now tapped the potential of big data for the first time on a large scale for plant research. To this end, data from three projects were used to increase the predictive accuracy for yield in hybrid varieties of wheat.

“We were able to draw on the largest dataset published to date, which contains information from almost a decade of wheat research and development,” says Prof. Dr. Jochen Reif, Head of the Breeding Research Department at IPK. The results, which could herald a new era for plant breeding, have now been published in the magazine Science Advances.

Finally, data on more than 13,000 genotypes tested in 125,000 yield plots were analysed. For comparison: In a breeding programme, plants are tested in 20,000 yield plots every year. “It was clear to us that we would have to increase the population sizes in order to ultimately develop robust predictive models for yield,” says Prof. Dr. Jochen Reif, “so in this case it was really once: ‘a lot goes a long way'”. The effort was worth it, he said. “We were able to double the predictive accuracy for yield in our study.”

The research team used data from the two previous projects HYWHEAT (funded by the Federal Ministry of Research and Education) and Zuchtwert (funded by the Federal Ministry of Food and Agriculture) as well as from a programme of the seed producer KWS. Basically, the challenge in such studies is to prepare the information to a uniform quality level and thus enable a common analysis. “Since we were responsible for the designs of the experiments from the start, we were able to plan them in such a way that a small proportion of the same genotypes were always tested across the projects, thus enabling an integrated analysis in the first place,” says Prof. Dr. Jochen Reif.

The scientist is firmly convinced that it pays off to use Big Data for plant breeding and research. “We have ultimately worked on the future of all of us”, says the IPK scientist. “We have succeeded in showing the potential of Big Data for breeding yield-stable varieties in times of climate change.”

According to Prof. Dr. Jochen Reif, the current model study has a significance that goes far beyond one crop type and hopefully heralds a cultural change in breeding. “We were able to show the great benefits of Big Data for plant breeding. However, the possibilities for this are only possible through a trusting cooperation of all stakeholders to share data and master the challenges of the future together.”

Ultimately, this is also the entry point for the use of artificial intelligence (AI). “The successful use of AI also stands and falls in plant breeding and research with curated and comprehensive data. Our current study is an important door opener for this path.”

The post Big data: IPK researchers double accuracy in predicting wheat yields appeared first on Artificial Intelligence.