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Car Rental System Literature Review

The Rental vehicles web system is a web-based that creates by JavaScript. Our software application aims to be a complete solution for a web based car rental broker companies with the support of call center functionality in Langkawi Island It can be easy for tourist , travel agency ,and Private visitor to online make research , comparison vehicles prices and make reservation , online payment when they travel .
It also allows the administrator managed data source in short time. It accepts new reservation vehicles, View vehicles model, view date reservation for vehicles, and travel information so that customers can easy look out travel location they need. The system will add on the new feature like validation form to tell the user key in the correct value. The system wills keep records for the customers, and purchases items. So that administrator can manage customers reservation lists.
1.2 Problem background
The web-based that creates by administrator has many useful functions. It contains auto save data, show customer products, contact and feedback form. The web system is focuses on web-based orders, products and data binding. This system is just design for online web-based system implementation, used for administrative purpose. This system will show customers details, order details, product details, payment lists and also including online product itemized bills.
1.3 Project aim
The main mission of Rental vehicles web-based system is looking for Lankawi island rental vehicles market. It can be easy provide for tourist, travel agency, and Private visitor to online make research, comparison vehicles prices and make reservation when they travel
1.4 Project objective
In the objectives, we aim to improve the Rental vehicles business market in langkawi Island and we also consider to provide customers able to user easy way to make reservation .Example customer can use internet access to web-based system to, looking for vehicles detail or prices and so on .
-The system will save all customers details for feature references.
– Customers can save Time, save cost, get information on the web-based and can access in any time any where
– Develop for Rental Vehicles business market in Langkawi Island.
-Easy administrator manages reservation data or processing data with customers.
-More suitable to customer with administrator to communication on internet
1.5 Project scope
The scope of this project is create a user friendly web-based system for customers. The web-based system must be secured and easy to manage by administrator. The following feature to be focused on my web system. In booking list, this function enables customers to see booking date when customer booking . In registration account this function enables customers to create user details and information. In vehicles model list this function enables customers to view all types of vehicles . In payment list this function enables customers to view total of pay booking list. Last, in feedback-form enables customers to send admin suggestion.
1.6 The important of the project
The important weakness in the current system are include, connects between web pages and layout theme or maybe more. the connection at storing data to web system loading will be slow, because the system is trying to load data files on the web-based first then only will load the contents. Sometimes it may fails and need to refresh the page again and again. Then is the layout theme, the administrator was weak in web designing. Some of the webpage tables or images maybe corrupted.
1.7 Summary
In summary, my design ideas is force on the web-based it can be completely to store all the customers overall details and reservation data into the online application system. Although this is my first time to create this kind of web-based application system and also it a challenge of myself .
Chapter 2 Literature review Introduction This chapter were provides the literature review which is related to the project development and make reference to existence of other systems. The sources are refer from the book, articles, journals and also sources from internet.
2.2 Car rental System
In this twenty first century car rental system it are famous using to tourism
Web-based system understands as server components of distributed applications which use the HTTP protocol to exchange data between servers and clients(browser). By this definition, the principal problem of Web-based system development becomes apparent From business perspectives. Web-based systems can be classified as follows (Kaiser, 2000):
2.3 Technologies Research and Development
This chapter we are provides technologies research and development technique for this project. We will explain why we using java technology to plan develop on this project, and it also compares the other open source technology.
Extensible Markup Language (XML) in 1974 Charles Goldfarb, Ed Mosher and Ray Lorie invented GML at IBM.
Advantages of XML
Readability: It is text-based and therefore human readable. Moreover, given the use of text tags to demarcate the data, data represented by XML is usually somewhat understandable to the reader without reference to any other file or definition.
Universality: It supports the Unicode Standard, so text from any character set can be used. Moreover, the text and elements can be specified in different character sets.
Disadvantages of XML
Inefficient: XML was not originally defined as a database storage platform. It was designed to accommodate the exchange of data between nodes of dissimilar systems. Compared to other storage algorithms XML is relatively inefficient. The XML tags, which make it readable to humans, require additional storage and bandwidth.
XML Stylesheet: XML can be used to transmit and store documents for visual display. However, storage is not its primary or original purpose. To use it for anything beyond basic display of the data using markup requires an additional file the XML Stylesheet. XML Stylesheets are conceptually identical to HTML CSS files, but with a different syntax
Java was created in 1991 by James Gosling et al. of Sun Microsystems. Initially called Oak, in honor of the tree outside Gosling’s window, its name was changed to Java because there was already a language called Oak.
Advantages of java
Chapter 3 Research methodology 3.1 Introduction
This chapter we are provides the method and approach which have used to development on this System. We will discuss different type of model objects in this system project and a detailed explanation of each phase in developing on this project.
A methodology is part of important technique use to managing and controlling for research in project to achieve the specified objectives within a given time. In order to complete this research, there are five major phases involved.
Each phase involved in this project have significance in achieving the objectives of the project. This project begins with knowledge acquisition phase and finished with the documentation of result. As mentioned in the objective of this project, data matching technique is applied to provide car rental service to the users.
3.2 Project Methodology
On this project we need found the best methodology which to easy improve , high market value in future develop on this project model . Finally were choosing Waterfall model to become planed and develop idea for this system project.
The Waterfall Model is the earliest method of structured system development and his create by Winston W. Royce in 1970 It is a highly structured development process, first used on DoD software projects in the 1970s. It is the traditional approach to software development and was derived from defense and aerospace project lifecycles now day The Waterfall Model is still common and widely used in software develop filed.
Requirement Analysis

Artificial Intelligence Essay

This paper is the introduction to Artificial intelligence (AI). Artificial intelligence is exhibited by artificial entity, a system is generally assumed to be a computer. AI systems are now in routine use in economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games like computer chess and other video games.
We tried to explain the brief ideas of AI and its application to various fields. It cleared the concept of computational and conventional categories. It includes various advanced systems such as Neural Network, Fuzzy Systems and Evolutionary computation. AI is used in typical problems such as Pattern recognition, Natural language processing and more. This system is working throughout the world as an artificial brain.
Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered “somewhat intelligent”. It is related to the similar task of using computers to understand human intelligence.
We can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do. We discussed conditions for considering a machine to be intelligent. We argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent.
INTRODUCTION :- Artificial intelligence (AI) :-
Artificial intelligence (AI) is defined as intelligence exhibited by an artificial entity. Such a system is generally assumed to be a computer.
Although AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behaviour, learning and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, speech, and facial recognition. As such, it has become a scientific discipline, focused on providing solutions to real life problems. AI systems are now in routine use in economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games like computer chess and other video games.
History :-
The intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Intelligent artifacts appear in literature since then, with real mechanical devices actually demonstrating behaviour with some degree of intelligence. After modern computers became available following World War-II, it has become possible to create programs that perform difficult intellectual tasks.
1950 – 1960:-
The first working AI programs were written in 1951 to run on the Ferranti Mark I machine of the University of Manchester (UK): a draughts-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.
1960 – 1970 :-
During the 1960s and 1970s Marvin Minsky and Seymour Papert publish Perceptrons, demonstrating limits of simple neural nets and Alain Colmerauer developed the Prolog computer language. Ted Shortliffe demonstrated the power of rule-based systems for knowledge representation and inference in medical diagnosis and therapy in what is sometimes called the first expert system. Hans Moravec developed the first computer-controlled vehicle to autonomously negotiate cluttered obstacle courses.
1980’s ONWARDS :-
In the 1980s, neural networks became widely used with the back propagation algorithm, first described by Paul John Werbos in 1974. The 1990s marked major achievements in many areas of AI and demonstrations of various applications. Most notably Deep Blue, a chess-playing computer, beat Garry Kasparov in a famous six-game match in 1997.
Categories of AI :- AI divides roughly into two schools of thought:
Conventional AI.
Computational Intelligence (CI).
Conventional AI :-
Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI).
Methods include:
Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
Case based reasoning
Bayesian networks
Behavior based AI: a modular method of building AI systems by hand. Computational Intelligence (CI) :-
Computational Intelligence involves iterative development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing.
Methods include:
Neural networks: systems with very strong pattern recognition capabilities.
Fuzzy systems: techniques for reasoning under uncertainty, has been widely used in modern industrial and consumer product control systems.
Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms).
Typical problems to which AI methods are applied :- Pattern recognition
Optical character recognition
Handwriting recognition
Speech recognition
Face recognition
Natural language processing, Translation and Chatter bots
Non-linear control and Robotics
Computer vision, Virtual reality and Image processing
Game theory and Strategic planning
Other fields in which AI methods are implemented :- Automation.
Hybrid intelligent system.
Intelligent agent.
Intelligent control.
Automated reasoning.
Data mining.
Behavior-based robotics.
Cognitive robotics.
Developmental robotics.
Evolutionary robotics.
Knowledge Representation.
American Association for Artificial Intelligence (AAAI) :- Founded in 1979, the American Association for Artificial Intelligence (AAAI) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions.
APPLICATIONS OF AI :- Game Playing :-
You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation–looking at hundreds of thousands of positions.
Speech Recognition :-
In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
Understanding Natural Language :-
Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
Computer Vision :-
The world is composed of three-dimensional objects, but the inputs to the human eye and computer’s TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
Expert Systems :-
A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed.
Heuristic Classification :-
One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).
Conclusion :- We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. AI systems are now in routine use in various field such as economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games etc.
AI is an exciting and rewarding discipline. AI is branch of computer science that is concerned with the automation of intelligent behavior. The revised definition of AI is – AI is the study of mechanisms underlying intelligent behavior through the construction and evaluation of artifacts that attempt to enact those mechanisms. So it is concluded that it work as an artificial human brain which have an unbelievable artificial thinking power.
Programs with Common Sense :- John McCarthy, In Mechanization of Thought Processes, Proceedings of the Symposium of the National Physics Laboratory, 1959.
Artificial Intelligence, Logic and Formalizing Common Sense :- Richmond Thomason, editor, Philosophical Logic and Artificial Intelligence. Klüver Academic, 1989.
Concepts of Logical AI :- Tom Mitchell.
Machine Learning.
McGraw-Hill, 1997.
Logic and artificial intelligence :- Richmond Thomason.
In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Fall 2003.

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