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Pregnancy Tests False Positives

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What is a false positive?



One definition of a False Positive is: “A positive test result in a subject that does not possess the attribute for which the test is being conducted.”

Relating this definition to inappropriate images, typically digital pornography, a false positive refers to a benign image, which is incorrectly reported as containing inappropriate content.

Conversely a ‘False Negative' refers to an image containing inappropriate content, which is not identified as inappropriate and therefore is not reported.

When using software to scan file systems for inappropriate images, auditors are usually more concerned with false positives than false negatives for one simple reason - they can see and measure the false positives because they have been detected. False negatives on the other hand are images which have been missed by the scanning software and therefore do not get presented to the auditor.

The false positive / false negative balance

When creating software applications to find inappropriate images on computer systems, the application developer has to deal with the False Positive versus False Negative balance, such that the software only presents a human auditor with images that are likely to contain inappropriate content yet at the same time does not ignore images containing inappropriate content.

At one extreme, an application could return every image encountered for the auditor to review. While this would definitely capture all inappropriate images, this is clearly unmanageable since the auditor would have to review every image on a computer system.

At the other extreme, an application might only return images which are definitely inappropriate. While this would greatly reduce the number of images viewed by the auditor, a large number of inappropriate images would not be reported by the application if there is any uncertainty at all. Therefore most applications try for a ‘happy medium' whereby they expect return a manageable number of false positives but will also capture almost all the inappropriate images.

The realities of image analysis.

When setting out to determine whether or not a network contains inappropriate images, it is important to recognize that:

• no software algorithm is 100 percent accurate;

• images are stored in hundreds of files types;

• a high proportion of files residing on the network will be capable of containing images; • a human auditor will have to view and classify the suspect images.

In most cases, there will be a large number of suspect images which need to be reviewed by an auditor many of which will be false positives.

Therefore it is essential to be able to:

• Identify false positives quickly;

• Clear false positives en masse;

• Ensure the same false positives never appear in future scans.

Irish company Approach

That irish company's image algorithms are tuned to look for multiple facets of an image and make a decision based upon statistical likelihood values that an image contains inappropriate content.

In the case of digital pornography, the Irish company‘s image algorithms look for key elements in seven facets of an image:

Such comprehensive analyses result in a published accuracy rate of around 95%
Pregnancy Tests False Positives
Unless you live in a cave --and one without a computer at that-- you're very familiar with the huge time waster that sifting through mountains of email spam can be. And while spam is a huge annoyance for employees and end users everywhere, the real costs for businesses are in time and money and they're increasing every day. As costs rise, IT people (at SMEs in particular) are desperate for ways to deal with it effectively.

It's estimated that 100 billion spam emails fly back and forth, dirtying up the internet every day. A lot of these emails end up on in business email boxes, and employees are forced to spend their mornings sifting through mountainous piles of spam. All of which is time they're spending not working.

The Good, the Bad, and the Ugly of Spam Filters

Installing anti-spam filters is really the only effective way to deal with day-to-day spam within a company. These filters work to separate the wheat from the chaff, so to speak, allowing the good emails through and tossing the junk in the trash. They do this by combining several different types of technology and updating almost constantly. And while the highest quality spam filters do provide relief, sifting out about 98% of a company's spam, they're still imperfect.

Why? Unfortunately, spam filters occasionally have trouble telling what is wheat and what is chaff (to stretch an already-tired metaphor), and toss some important emails out along with the junk. And while they don't occur that often, 'false positives' can still become a problem for business owners.

One solution to the problem of false positive is adjusting the sensitivity of the company's spam filters. The way this works is simple: the higher the sensitivity, the more spam is caught. But the higher the sensitivity, the more valid emails will be taken out with the trash. Increasing the sensitivity of spam filters can cost companies a heck of a lot more money than too much spam after all, customers don't like it when their emails go ignored.

The Direct and Indirect Cost of Anti-Spam Filters

Even one little missed email can get a company in deep water. A law company based out of Colorado, for example, missed an email containing some important information about a court date and ended up missing an important day in court. The cost? They were required to pay all of the fees for the opposing counsel.

How did this happen? To combat spam, the company's IT department had turned up the sensitivity of their email filters. Apparently, the United States District sounded pretty spammy to these super hyper filters, and it costed this company thousands of dollars in fees. Ouch.

While this is a nice and obvious example of how false positives can lose a company money, in most cases it's simply not that cut and dried. Most monetary losses aren't direct; they come from a loss of business and credibility. When an email from a client is gobbled up by the spam filter and a big business deal falls through because the client feels ignored, it takes a lot of work and public relations to get that client back. And sometimes it never happens.

In addition to directly losing a company money, false positives can affect productivity just as much as an overload of spam can. If employees are used to seeing a lot of their important emails get junked, they will have to spend valuable time sifting through their spam folder for genuine emails. How much can that cost? More than you'd think. According to Ferris Research, recovering a genuine email from a spam folder costs an average of $3.50 of that employee's time. Especially if you don't experience many false positives, it may not seem like much but in a company of 500 people who have to fish out two emails from their spam folder a month, it adds up to $42,000. Does that sound like an itty-bitty expense to you?

It doesn't to most other business owners, either. And to reduce the number of false positives --and the amount of business lost-- many businesses don't use any anti-spam filters at all. And while it's hard to judge them for wanting to prevent a loss of business, going without a spam filter opens them up to all sorts of problems. Taking care of spam is just too important.

Striking a Balance

But how do you deal with spam and reduce false positives at the same time? The best method is to set aside all mail marked as spam into a special folder so that users will have access to it, and can check it often. While this may seem time-consuming, with good anti-spam software it really doesn't have to be. The better the software is at recognizing obvious spam, the fewer emails will end up in the junk folder.

As spam filters improve, so do spammers. Because of this, spam filters will probably never be 100% effective-- the ones that will delete every piece of spam will generally delete a few important emails along with it. And the ones that let all the important stuff though will also let in a bit of junk. But using a good, well-configured anti spam software and integrating it with your email server can really reduce the amount of spam that end users receive, as well as the rate of false positives you experience.
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About Author
Both Colm Doherty & Jesmond Darmanin are contributors for EditorialToday. The above articles have been edited for relevancy and timeliness. All write-ups, reviews, tips and guides published by EditorialToday.com and its partners or affiliates are for informational purposes only. They should not be used for any legal or any other type of advice. We do not endorse any author, contributor, writer or article posted by our team.

Colm Doherty has sinced written about articles on various topics from Legal Matters, Computers and The Internet and SPAM. This article was written by Colm Doherty of Pixalert – http://www.pixalert.com . PixAlert is the market leader in produ. Colm Doherty's top article generates over 5400 views. to your Favourites.

Jesmond Darmanin has sinced written about articles on various topics from Online Security, Management and Business Promotion. Jes Darmanin works at GFI Software: a company that develops the leading for Exchange and other mail servers. More information about GFI MailEssentials - The. Jesmond Darmanin's top article generates over 1300 views. to your Favourites.
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