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In this post we will look at goal tracking software. Some is free, some is not, some is available online as the web service for free or small monthly fee and some needs to be downloaded and installed. The review can help to choose right tool for your goal setting needs.

1. GoalEnforcer – Visual Goal Setting Software helps to develop your ideas, track your tasks, start planning immediately , boost your productivity, visualize your goals and accomplish more. GoalEnforcer software can be downloaded for Windows and MAC OS. The download includes goal setting tutorial. There are several levels with different prices and features. This software can be applied to many goal setting related tasks such as project management , To Do list, task list, task manager, checklist, planner, brainstorming and many more.

2. Joes Goals – is a simple online tool to keep track of goals. It is 100% free. No download is required. It can be shared with your friends, have as many goals as you want, show track of your progress by using daily score, track your successes and failures through logbook.

3. 101 SMART goals – is a free online goal setting and goal tracking tool which helps to set goals, track or focus on goals.

4. Google calendar – is online free tool provided from Google. Several posts on the web describe how to use Google calendar for goal tracking purposes. Google Calendar as a To Do List & Task Management App Goal Setting Using Google Calendar How to use Google Tasks in Calendar

5. Weekly planner is online web service. The creation of it was inspired by the ‘7 Habits of Highly Effective People’. WEEK PLAN helps thousands of users be more effective. There is also opportunity to create application on top of week plan software using API

Thus there many different tools with different features and functionality and we looked at some of them at this post.

One of the new and exciting features of HTML5 is the ability to build chart without any plugin or server side programming. This can be done using the canvas element and some javascript coding. The Canvas is the HTML5 element and allows to draw graphics, 2D shapes on the web page using javascript. Google Chrome supports now HTML5, some other browsers may have problems with supporting HTML5 as of the time of this writing.

Here is the code example of canvas. The example is just drawing one line from (10,10) to (80,80). The output is shown under the code box.
<br /><br /> <canvas id=”myCanvas” width=”200″ height=”100″ style=”border: 1px solid #c3c3c3;”><br /><br /> </canvas><br /><br /> <script type=”text/javascript”><br /> var c=document.getElementById(“myCanvas”);<br /> var ctx=c.getContext(“2d”);<br /> ctx.moveTo(10,10);<br /> ctx.lineTo(80,80);<br /> ctx.stroke();<br /> </script></p> <p>

The good resource on HTML5 canvas tag is w3schools.com

Here is one more link if you want learn more on canvas element in HTML5. http://thinkvitamin.com/code/how-to-draw-with-html-5-canvas/ How to Draw with HTML 5 Canvas   By Jamie Newman

There are many different ways to do charting due to different javascript libraries:

RGraph: HTML5 Javascript charts library RGraph is a HTML5 Javascript charts library. Using the new HTML5 canvas tag, RGraph creates Javascript charts in the web browser.  RGraph supports over twenty different types of Javascript charts, including fuel gauges,  thermometer charts, waterfall charts. It is very cool looking charts, but there is one time fee for commercial usage.

A tag cloud or word cloud is one more way to put navigation on web site. There are different ways to create tag cloud. [1] For example collocate cloud can be found at scottishcorpus.ac.uk website [2]. From Wikipedia “a collocate cloud provides a more focused view of a document or corpus. Instead of summarizing an entire document, the collocate cloud examines the usage of a particular word. The resulting cloud contains the words which are often used in conjunction with the search word.” [1]

The post in the blog [3] describes second generation of tag clouds and the trend for the future. Looks like tag clouds will stay here and will get more complicated, useful, powerful, dynamic and with more different features.

Having your own script to generate tag cloud provides more flexibility and power in creating tag cloud and automating posting/updating it on the web. Thus I created some perl script, online demo and service for tag cloud. In the future it will be updated with more features like different colors, automatic updates and other features. You can find it at Tag Cloud Generator Please fill free to provide any feedback or suggest features to add.

[1] From Wikipedia, the free encyclopedia

[2] Collocate cloud

[3] Second Generation Tag Clouds by Joe Lamantia.com

Artificial Intelligence (AI) may be the section of information technology concentrating on creating machines that may engage on actions that humans consider intelligent. It’s the cutting-fringe of information technology.This generally involves borrowing qualities from human intelligence, and using them as calculations inside a computer friendly way. A pretty much flexible or efficient approach could be taken with respect to the needs established, which influences how artificial the intelligent behavior seems.

The opportunity to create intelligent machines has intrigued humans since ancient occasions, now using the creation of the PC and half a century of research into AI programming techniques, the imagine wise machines has become a real possibility. Scientists are coming up with systems which could mimic human thought, understand speech, beat the very best human chess player, and numerous other achievements nothing you’ve seen prior possible.

Applications of Artificial Intelligence It’s the key technology in lots of present day novel programs, varying from banking systems that identify attempted charge card fraud, to telephone that understand speech, to software systems that notice when you are getting problems and provide appropriate advice. These technologies wouldn’t exist today with no sustained federal support of fundamental AI research in the last 30 years.

Today, the most popular section of artificial intelligence is neural systems, that are showing effective in many disciplines for example voice recognition and natural-language processing. In robotics, computer systems are actually broadly utilized in set up plants, but they’re capable only of limited tasks with the substitute intelligence factor. Robots have great difficulty determining objects according to appearance or feel, plus they still move and take care of objects clumsily.

Presently, no computer systems exhibit full artificial intelligence (that’s, can simulate human behavior). The finest advances have happened within the area of games playing.

Benefits of Artificial Intelligence Artificial intelligence wouldn’t need any sleep. This is a benefit because it wouldn’t be interrupted from the tasks for sleep, along with other problems that plague biological minds like bathroom breaks and eating.

Unemotional thought on problems. While a man-made mind could theoretically have feelings, it might be better for performance whether it were designed for unemotional reasoning. When individuals make choices, sometimes individuals choices derive from emotion instead of logic. This isn’t always the easiest method to make choices. Once a man-made thoughts are been trained in an activity, that mind may then be replicated effortlessly, in comparison towards the training of multiple people for the similar task. Less errors and defects, reduced time and assets.

Disadvantages of Artificial Intelligence Lacks a persons touch. Human characteristics are occasionally overlooked. The opportunity to replace an individual job. This brings about humans feeling insecure and could possess the anxiety about losing their job. Human abilities could be changed utilizing a machine and for that reason can promote feelings of inferiority among employees and staff. Artificial Intelligence can malfunction. This kind of technology could be misused to result in mass scale destruction

General Regression Neural Networks (GRNN) is one of the type of neural networks with a one pass learning algorithm. The simplicity of algorithm for GRNN is one of the advantage of this neural network and many researchers use it. In one of recent paper was proposed algorithm that is using an ensemble of several General Regression Neural Networks to improve the accuracy of forecasting. [1] In another paper MOPSO algorithm was applied for improvement radial basis function (RDF) which is used also in GRNN [2]

A simple example can explain how GRNN is working. Let’s say several people decided to establish some fund. One contributed $2, another $3 and yet another $100. Now we calculate 2 numbers – one is a sum of dollar amount times the person number :
1*$2+2*$3+3*$100=$306
and another number is just the sum of $ amount:
$2+$3+$100=$105
Now divide 306 / 105 and we get ~ 3
Thus our algorithm calculated top contributor – person number 3. This is exactly what last 2 layers of GRNN are doing.
The output of GRNN is result of division of 2 numbers from 2 nodes of previous layer. Now you probably noticed that in the example above the top contributor ($100 contribution ) was a way ahead of others. The hidden layer in GRNN is responsible for this.

The pattern that is very close to training data will get big value while the other will get very small values. This is what RBF is doing. And the sigma parameter is used to adjust the RBF function output.

This is how GRNN is working. And at minimum it only requires one parameter sigma. Some researchers describe how to apply particle swarm optimization to find optimal number of hidden neurons. And we can use a several network to get even more computing power or better accuracy. Ensemble learning is very well described in [3].

You can find many different ways how to combine 2 or more neural networks. Feel free to grab perl code for GRNN so you can play with it. You can also look at few more data examples and some math. General Regression Neural Networks Thus the forecasting with GRNN is easy and new papers appear that do research on using RGNN

[1] Iffat A. Gheyas, Leslie S. Smith. A Neural Network Approach to Time Series Forecasting, Proceedings of the World Congress on Engineering 2009 Vol II , July 1 – 3, London, U.K.
[2] S.N. Qasem and S.M. Shamsuddin Generalization Improvement of Radial Basis Function Network Based on Multi-Objective Particle Swarm Optimization, Journal of Artificial Intelligence, 2009
[3] http://www.scholarpedia.org/article/Ensemble_learning#Algebraic_combiners