The applications of Expert Systems can be applied in varied commercial and industrial problems for easy characterization. The applications of Expert systems can work in most of the areas of knowledge. The diversity in the complexity of problems that can be solved through the help of applications of Expert systems can be from helping a salesperson in identifying its potential customers to helping in the execution of NASA plan and maintenance of the space shuttle that is to be used in the plan (Masuch et al., 2018).
Expert Systems applications can be classified into five major categories:
Troubleshooting and diagnosis of devices: Application of Expert systems can help in giving recommendations for corrective measures for a device that has started malfunctioning. ES applications are capable of giving recommendations regarding corrective measures of any kind of electronic device (Al Rekhawi et al., 2017).
Planning and scheduling: ES applications can also the organization in planning and scheduling their operations and activities in order to smoothly achieve the goals and objectives of the organization.
Process Controlling and Control: The systems in this category help in identifying the loopholes in the system or predicting the trends both in order to avoid failures so as to implement the corrective measures timely in order to maintain the optimality of the system.
Configuration of Manufactured objects: ES applications help in finding a solution to the complex design if the project by determining the given elements along with the set of constraints in the manufacturing process. Thus, it can be said ES can prove to be useful while designing the products that involve complex engineering designing and manufacturing.
Final decision making: ES systems help variety of sectors in their final decision making including financial services sector. ES systems is capable of assisting bankers with the decision whether they should or not make the loans to a particular individual or business depending upon their credit scores.
As ES systems work on behalf of the human resources, similar to the human expert it is capable of making mistakes just like the human expert. One such instance where system can generate wrong diagnosis is when the expert himself insert wrong rules. Also, most of the times the ES applications work with the incomplete information which increases the chances of wrong diagnosis. Thus, it can be said that the suggestions provided by the ES systems are probabilistic in nature that is certain degree of uncertainty is involved in the results of the diagnosis. However, if we separate the inference engine from the knowledge base and the knowledge is organized in non-sequential manner then it can become easy to correct mistakes once they are detected.
Answer 2.1 If the client observes that the dollar exchange rate is falling that is fall in the value of dollar against the other currencies it will lead to increase in the interest rates prevailing in the economy. Thus, the ES will recommend the client not to make new loan according to the rules set in ES system.
2.2 If the second client observes that the interest rates will remain unchanged in the economy then according to the rules of ES the bond prices prevailing in the economy will also remain unchanged. Thus, the ES will advise the client not to invest in the bonds as the investment will not prove to be beneficial for the client.
3. a) Earlier, individuals had to take into account a lot of information from the brochures and websites in order to take right vacation choice. In this situation, the staff in travel agencies do not have complete information to assist the potential travelers (Sartori & Melen, 2017). Thus, expert systems in the domain of tourism helps in presenting the information that is brochures into machine readable formats in Expert Systems. In this way, it will become easy for the travel to take travel decisions. Another feature of Expert Systems is personalized support system that is Travel Router that can also recommend trips to the users based upon their behavior and characteristics. Through the help of Expert Systems travelers can easily evaluate the suitability of hotels and transport and sort their travel bookings (Ravi & Vairavasundaram, 2016).
b) Expert system works as a personalized travel support system which embodies expertise in the travel field. It is important that expert working with expert system stores every information regarding the destinations across the world. The expert system should be able to answer all the questions regarding every respective destination. Thus, the expert system should have enough knowledge regarding the travel package prices and the destinations so as to assist the users in the final decision-making process (Tan et al., 2016).
c) There are several limitations of working with such systems as below:
The expert needs to have full details and knowledge base about destinations in order to efficiently assist the users with the travel decisions.
Limitation of technology.
Such expert systems are very costly for the organization.
It is important to maintain the quality of human experts at the organization.
4. There are various models with screening applications in order to detect and predict the occurrence of fraud or risk of fraud at any stage of the entire process of travel decision making to travel bookings. Such models include Neutral networks, naïve Bayes models, decision trees, Weighted criteria model and logistic regression (Barboza et al., 2017). All these models can be used by the travel agency for identifying opportunities of fraud and take corrective measures or deterring claims for the activities that involve high certainty of fraudulent practice.
According to the nature of the organization which is a travel company, Weighted screening model can be the most appropriate model for the company. As the model will help the company in rejecting travelers and assigning likelihood score of fraud to each potential traveler that comes to the agency based upon personal and historical data of the clients.
Al Rekhawi, H. A., Ayyad, A. A., & Abu Naser, S. S. (2017). Rickets Expert System Diagnoses and Treatment. International Journal of Engineering and Information Systems (IJEAIS).
Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417.
Masuch, M. (Ed.). (2018). Organization, management, and expert systems: models of automated reasoning (Vol. 23). Walter de Gruyter GmbH & Co KG.
Ravi, L., & Vairavasundaram, S. (2016). A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Computational intelligence and neuroscience, 2016, 7.
Sartori, F., & Melen, R. (2017). Wearable expert system development: definitions, models and challenges for the future. Program, 51(3), 235-258.
Tan, C. F., Wahidin, L. S., Khalil, S. N., Tamaldin, N., Hu, J., & Rauterberg, G. W. M. (2016). The application of expert system: A review of research and applications. ARPN Journal of Engineering and Applied Sciences, 11(4), 2448-2453.
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