What are advantages and disadvantages of artificial neural networks?

The artificial neural networks, since they can simulate the biological nervous system are used in many real life applications which are also their one of the biggest advantages. 

They have made it easy for carrying out complex processes such as:

Ø  Function approximation

Ø  Regression analysis

Ø  Time series prediction

Ø  Fitness approximation

Ø  Modeling

- With the artificial neuron networks, the classification based on sequence and pattern recognition along with other difficult things such as the sequential decision making and the novelty detection is possible. 

- A number of operations falling under the data processing category such as clustering, filtering, compression and blind source separation etc. are also carried out with the help of artificial neural networks. 

- Artificial neural networks can be considered as the backbone of the robotics engineering field. 

- They are used in computer numerical control and in directing the manipulators.

- It offers advantages in the following fields of:

1. System control (this including process control, vehicle control and natural resources management),
2. System identification,
3. Game – playing,
4. Quantum chemistry
5. Decision making (in games such as poker, chess, backgammon and so on.)
6. Pattern recognition (including face identification, radar systems, object recognition and so on.)
7. Sequence recognition (in handwritten text recognition, speech, gesture etc.)
8. Medical diagnosis
9. Financial applications i.e., in automated trading systems
10. Data mining
11. Visualization
12. E – mail spam filtering

- Today several types of cancers can be diagnosed using the artificial neural networks. 

- HLND is an ANN based hybrid system for detection of lung cancer. 

- The diagnosis carried out with this is more accurate plus the speed of radiology is more. 

- These diagnoses are then used for making some models based up on information of the patient. 

- Its following theoretical properties are nothing but an advantage to the industry:

1. Computational power: It provides a universal function approximator i.e., the multilayer perceptron or MLP.
2. Capacity: This property indicates about the ability of ANN to model almost any given function. It has a relation with both the notion of complexity and information contained in a network.
3. Convergence: This property is dependent on a number of factors such as:

Ø  Number of existing local minima which in turn depends up on model and the cost function.

Ø  Optimization method used

Ø  Impracticality of few methods for a large amount of parameters.

4. Generalization and statistics: Over training is quite a prominent problem in the applications where it is required to create a system that is capable of generalizing in unseen examples. This in turn leads to problem of the over specified or the convoluted systems along with the network exceeding the limit of the parameters. There are two solutions offered by ANN for this problem:

-   Croos – validation and

-   Regularization

Disadvantages of Artificial Neural Networks

1. It requires a lot of diverse training for making the artificial neural networks ready for the real world operations which is a drawback more prominent in the robotics industry.

2. Many storage and processing resources are required for implementing large software neural networks using ANNs.

3. The human has the ability to process the signals via a graph of neurons. A similar simulation of even a very small problem can call for excessive HD and RAM requirements.

4. Time and money cost for building ANNs is very large. 

5. Furthermore, simulation of the signal transmission through all the connections and associated neurons is required.     

What are different aspects of Game Testing?

Today’s world is fantasized with games. Children and youth both are addicted to games. With the cutting edge technology, gaming technology has reached new heights. Now games have become so exciting with their real world technologies. With the growing demand for games, there always a tough competition between the games developing companies. They struggle with technology to deliver the best quality games. So like software programs and applications, games also have to be tested for quality and performance.

Therefore, game testing is considered to be an important part of game development.

OBJECTIVE OF GAME TESTING
1. Game testing can thought of as a kind of software testing which aims at testing the quality of the games.
2. The main objective of game testing is to find all the errors and bugs in the gaming software and preparing their documentation.
3. Games require very high technology and thus call for the need of analytic competence.
4. It is developed using computer expertise and endurance.
5. There are some critical evaluation skills on which it is based.

WHY GAME TESTING BECAME NECESSARY?

1. In the era of early video games, all the development and testing process was carried out by the developer of the game only since the games were small and primitive technology was used.
2. But, today the games are becoming very lengthy and complex due to the advent of high technologies.
3. So, there is a need for qualitative and quantitative assessment of the games which can be achieved by game testing.
4. Today’s game companies have separate expert game testers.
5. The main aim of game testers is to find note any problems that they discover in the documentations and reports.
6. They should have the skills to finish the games at the most difficult level. Game testing is carried out on the program before releasing its alpha version.

HOW GAME TESTING IS PERFORMED?
A critical component of a game is quality even though there are no standard methodologies to judge the video quality.

1. Different testers and developers have their own methodologies.
2. There is a separate quality assurance staff in game companies to judge the quality of the game.
3. Starting is commenced as soon as the first code of the game is written and it continues till the whole program is completed.
4. The progress of the game is monitored by the QA staff.
5. When the whole program is completed, a test plan is written and test cases are prepared and the testing is carried out accordingly.
6. This is done before alpha stage.
7. As the beta stage approaches, one of the aspects of the game is daily testing according to the plan.
8. List of the features is prepared which are to be included and which are to be excluded.
9. Beta stage testing includes contribution from volunteer testers.
10.The game is played and tested.
11.The discovered glitches are noted down.
12.The game play should be creative since then only most of the bugs will be discovered.
13.There is a time period before the deadline which is termed as crunch time.
14.In this period the features that were added later are tested.
15.The bugs discovered are ranked according to the severity they can cause:

- Critical bugs/ A bugs: that can cause the game to crash.
- Essential problems/ B bugs: bugs that require attention even though the game runs well.
- Obscure problems/ C bugs: these bugs are small but require to be corrected.

The following people are involved in testing of a game:
- Game producer
The person sets the deadlines for testing based upon quality assurance and marketing.
- Lead tester
The person manages list of bugs and most responsible for the quality of game.
- Testers
The person checks the working of the game.
- Software development engineer in test
The person builds automated frame works and test cases.