Outline theiia.org Assignment Example | Topics and Well Written Essays - 1250 words

Outline theiia.org - Assignment Example Employers  and Agencies can fill open positions in their respective departments. One can also post job openings searching for resumes, which match the desired criteria posted in the audit career center (Institute of Internal Auditors 13). 1. SERVICES A. Quality Assurance Here, there is a Quality Assurance and Improvement Program (QAIP) which enables evaluation of the internal audit activity's conformance. This program is also responsible for gauging the efficiency and effectiveness of the internal audit activity, identifying improvement opportunities. Internal assessments are constant, where there are in-house assessments of the in-house audit  activities together with periodic self-assessments and/or reviews (Institute of Internal Auditors 14). 1. MEMBERSHIP A. Benefits of Membership This section focuses on members where it stipulates that members are entitled to guidance, training, and services free or specially priced. Most of the important prospects accessible to IIA members are: 1. Advocacy Resourceful guidance is accessible to members for the advocacy of their role with the main stakeholders. The IIA maintains international recognition due to their presence in Washington DC in the advocacy of the profession. ... The IIA is the pioneer in imaginative interior review preparing, with the provision of quality involving opportunities for learning which is well facilitated for its members and customers. With IIA, there is an assurance that as a member you have the learning and abilities essential for the procurement of the most abnormal amount of surety, knowledge, and possible objectivity which adds value to your organization. There is a commitment to the deliverance of the very best in internal audit training, satisfying our notoriety as the worldwide guide in inward review instruction, by the procurement of the best quality esteem in the business (Institute of Internal Auditors 16). 1. Top-quality Training Programs and Facilitators Here, the IIA raises the bar on quality by best practices in adult instructional design when building and updating courses. IIA also incorporates  the strategy and theory behind how adults learn working closely with subject matter experts in the development and mai ntenance of courses ensuring that they are aligned with the IIA's International Professional Practices Framework (IPPF). Another reason for our superiority and the top quality training programs is IIA’s talent facilitators which facilitate the member to undertake meticulous training and peer review, thus becoming experts at encouraging transfer of knowledge by way of discussions, exercises, and activities (Institute of Internal Auditors 17). 1. CERTIFICATION A. CIA certification The IIA has a very comprehensive certification portfolio serving as the key to unlocking opportunities within the internal audit profession; increasing the level of a member’s credibility while adding clout to the resume. This increases your value to your clients and employer

Social learning theories Essay Example | Topics and Well Written Essays - 4000 words

Social learning theories - Essay Example At the time when populations were expansively diffused, the implications of any particular aggressive act were primarily restricted to individuals whom the act was aimed at. Under circumstances of modernised life, in which the wellbeing of groups of inhabitants depends upon harmonious functioning of complex mutually supporting systems, aggressive behaviour that can be effortlessly carried out without demanding complicated mechanism immediately damages enormous numbers of people (Geen, 2001). Interest over the destructive implications of aggression confuses the reality that such behaviour normally has purposeful importance for the user. Certainly, there is a characteristic distinct to aggression that commonly generates circumstances cultivating its incidence. Dissimilar to social behaviours that are not useful without a degree of reciprocity satisfactory to the partakers, aggression does not necessitate eager receptiveness from others for its effectiveness (Moeller, 2001). An individual can harm and destroy to self-benefit despite of whether the victim agrees to it or not. Through destructive behaviour, or authority through verbal and physical coercion, individuals can gain important resources, modify rules to suit their personal desires, acquire power over and haul out compliableness from others, get rid of circumstances that negatively influence their wellbeing, and break down barriers that hamper or interrupt realisation of desired objectives. Hence, actions that are harsh for the victim can be gratifying for the one committing the aggressive act. Though aggression has several various roots its practical value certainly contributes greatly to the occurrence of such behaviour in the dealings of everyday existence (Moeller, 2001). Throughout the years several theories have been suggested to clarify why individuals act

Review of literature Essay Example | Topics and Well Written Essays - 500 words

Review of literature - Essay Example High consumption of alcohol in men can arouse anger in men, with the consequent result of domestic violence. Evaluation of the impact of treatment for alcoholism one year after the treatment demonstrated reduction of anger tantrums and violence towards the female partners (O’Farrell, Fals-Stewart, Murphy & Murphy, 2003). The evaluation of a 15-week cognitive-behavioral skills training program for men involved in domestic violence against women demonstrated that there was a sharp decrease in the recurrence of the violent behavior in men completing the training program. In addition, in comparison to those who dropped out of the program, those who completed the training program demonstrated a lower rate of physical violence recidivism during the one year follow up period (Hamberger & Hastings, 1988). Cognitive behavioural therapy is a popular treatment program for physically abusive men. The objective in cognitive behavioural therapy is not only to change behavior employing behavioural change models, but also to alter thinking patterns and beliefs that contribute to the violent behavior. However, very few randomised controlled effect evaluations have been conducted on the effectiveness of this treatment strategy for any definite conclusion on the effectiveness of the treatment strategy (Smedslund et al, 2007). The Over Aggression Scale (OAS) measures aggressive behaviors on four aspects of physical aggression directed on others, physical aggression directed on self, verbal aggression, and physical aggression against objects present in the surroundings. These four classifications are further studied on the basis of ratings from one to four, with one being the least severe and four being the mist severe. This tool is extremely useful for evaluating the effect of treatment programs for reducing aggressive behavior, as it permits recording of aggressive incidents for review and comparison

Describe Fingerprint photographic technique you would use to recover Assignment

Describe Fingerprint photographic technique you would use to recover such marks following U.K. guidance - Assignment Example In general we have three types of fingerprints mainly latent, plastic and patent prints. Latent prints are formed from oils and sweat from the skin surface. They are invisible to un-aided eye, therefore; they require additional treatments. Patent fingerprints are made from ink, grease, dirt and blood. They are visible to the naked eye. Finally, the plastic fingerprints are in three-dimension, and they are made after pressing the hands on fresh soap, wax or paints. The prints are visible to the naked eye (Houck and Siegel, 2010, p. 29). To improve the visibility of latent fingerprints various lighting techniques can be used to obtain the invisible surfaces. According to Chaikovsky, Argaman, Batman, Sin-David, Barzovski and Yaalon (2005, p. 574), digital multiple exposure technique is applied. The process is simple and productive. Digital photography and computerized image processing with application of layers methodology produce many images that are easily controlled by computer programmes. Production of many images that are combined into a single image enables improved visualization of the selected portions of the latent print without affecting the rest part of the image. According to SIRCHIE (2013, p. 1), Crime scene photos are important in the crime scene inquiry. The photographs complement the investigator data in the form of data. A camera can be used to capture every object of importance within the crime scene. Warren (2014, p. 1), conducted a research to determine usage digital photography for forensic purposes. It was found out that digital imaging devices with spectral filters are very effective in the identification of untreated latent fingerprints in that it is viable and non destructive. According to the National Law Enforcement (2011, p. 6), it is believed that every individual has different body parts hence comparison of the measurements can be used to distinguish between two individuals. The method was developed by

Plan balanced meals snacks Essay Example for Free

Plan balanced meals snacks Essay

Advantages And Disadvantages Of Nuclear Energy Environmental Sciences Essay

Advantages And Disadvantages Of Nuclear Energy Environmental Sciences Essay Nuclear energy is a thriving global industry. Nowadays, there are a total of 435 nuclear power plants in the world. This source energy is not yet used in Malaysia. Nuclear energy can generate electricity by Fission process at the power plant. Its also said that nuclear energy only used a little fuel of uranium only but it can produce high electricity output. In such way, it can benefit our country in term of produce electricity as the government must take precaution about the decreases of supply of non renewable energy that extensively used in Malaysia. Hydroelectric is a renewable energy in Malaysia that produce high power capacity. It produced electricity from the flow of lake or streams. It will give the kinetic energy as the water flows downward very fast. So the kinetic energy has been converted to mechanical by the turbine. From the turbine, it will produce electricity. Tenaga Nasional Berhad (TNB) operates three hydroelectric in peninsular Malaysia which its sum generating power capacity is 1911 megawatts (MW) and operates with a total of 21 dams. The largest hydroelectric power system in Malaysia is at Bakun on the Balui River in Sarawak. It can generate power capacity as high as 2400 MW which is double of power capacity supply in peninsular Malaysia. Coal is a fossil fuel created from plant trapped underground for millions of years without being in contact to air. Because of its nature of long term of produce, it is classified as a non-renewable energy source. Coal is mainly consists of carbon atoms that come from plant material from ancient swamp forests. There are many types of coal. Some contain dangerous material such as sulphur that pollutes the atmosphere further when they burn that will cause to acid rain. Anthracite, bituminous coal, lignite, and sub-bituminous coal are all different types of coal that are used nowadays. [1] In Malaysia, there are 5 generating coal power stations. Sultan Salahuddin Abdul Aziz Shah Power station is the largest coal power station which its produce 2420 MW. In some research, It said that coal will last supply for the power energy at least for the next century.So our government must take precaution above this matter by bring in other energy sources in Malaysia. Oil is our primary source nowadays. For the time being, the increase of oil price has become a big issue in Malaysia. This will affect the price of TNB tariff in the long term as many of the power station use oil to produce energy. This matter will become serious if the fuel price keeps increasing. Oil plants in Malaysia are widely used in Sabah as the total Oil plant around Malaysia is five. Most of the power plants in Sabah use diesel engines to running their system. Gelugor Power system is the only one oil plant in peninsular Malaysia. It can produce around 398MW of generating power. In Malaysia, Gas energy Plant is widely used. Like oil and coal, gas is found underground, it from the million years of heat and pressure that being applied to the underground that can caused them to change to crude oil and natural gas. There is about 21 gas energy plant in Malaysia that can produce generating power from 100MW to 1500MW. Tuanku Jaafar Power system is the highest generating power system in Malaysia as it can produce 1500 MW of power Gas is a non renewable energy so the supply of gas maybe limited nowadays. Its also said that gas contributes a large amount of carbon dioxide that can be harmful to greenhouse warming. Biomass provides heat and energy. We can use many sources of biomass such plants, landfill fumes, agriculture, forest residues and also from the waste of industries and cities. In such ways, it not also minimizes the cost but it also reduces pollution to the environment. Biomass produce energy by basically burning organic matter to released its chemical energy. Using biomass energy contributes CO2 when burned but the carbon dioxide being recaptured and used by other plants. We can create electricity from biomass by direct fired, co firing, gasification and anaerobic digestion. There are nine biomass power plants in Malaysia. Likes Oil power plants, Biomass energy is widely used in Sabah. Many of biomass power plants empty fruit bunch as their fuel. Although biomass a renewable energy, it can only produce small power capacity.[1] DISCUSSION NUCLEAR POWER IN MALAYSIA Malaysian government announced that Malaysia is considering having Nuclear power plant by 2021. As the coal power plant has the limit supply of coal, and the price fossil fuel keep increasing, this was the best way to generate power that meet our country energy demand. Green Technology and Water Minister Peter Chin Fah Kui said that our main renewable energy, which is hydroelectric also have a problem of limited land to building a dam for hydro power plant. So the drastic way to overcome this entire problem is to build a costly but efficient nuclear power plant. According to the minister, the safety of power plant would be the high priority on building this technology. [2] How Nuclear Energy Works Nuclear energy is produced naturally under a human control. A nuclear power plant uses steam to generate the power plant. Uranium is the main aspect to produce a steam. Nuclear reaction occurs when uranium atoms split into small particles that can produce large amount of heat during the chain reaction. It will release around 3 neutrons and can produce a large of energy during the fission. This chain reaction will continue until uranium is split. Figure 1 shows fission of Uranium 235. [3] Figure 1 show Fission of Uranium 235 Fission process will occur in the fuel assemblies in the reactor core. Control rods are used to absorb neutrons to control the fission process. In order to keep the heat flowing efficiently, each power plant will have their coolant device that prevent the core becoming too hot and carries heat away. The piping of steam will run through the turbine in order to create electricity. Fission process can produce radioactive, It is important to have a barrier that can protect the release of radioactivity. Nuclear power plant is said to be under human control because it constantly monitored and controlled from a control room by highly trained engineered. It also has a backup system designed if the normal operation is disrupted. Figure 2 shows schematic figure of nuclear power plant. [4] Figure 2 shows schematic of nuclear power plant ADVANTAGES Environmental effect Nuclear energy has the lowest effect on the environment, Its because that the nuclear plants do not produce harmful gases. The water from the power plant also contains no harmful pollutants. Main aspect of Environmental safety of nuclear energy is it does not burn anything to produce electricity compared to other power plant such coil, oil and gas that can produce CO2 as it can increased concentration of greenhouse that responsible for climate change. For example, A 1000MW coal plant produce 44000 tonnes of sulphur oxides and 22000 tonnes of nitrous oxide and also 500 000 tonnes of solid waste that can be harmful to the atmosphere environment compared to the nuclear energy that doesnt produce any noxious gases .Nuclear energy will reduce about 5-10% greenhouse gases reduction by the time of 10 years. Cost Nuclear energy is very cost effective compared to the renewable energy such wind, hydro or solar. It uses Uranium as fuel. Main aspect is it use it can produce huge amount of energy in such a small amount of uranium. Uranium is highly concentrated source which is very cheap and easy transportable. For example, One kilogram Uranium will yield 20 000 times as much energy compare to the same amount of coal.[5] Supply Research said that Coal sufficient for some 300 years, natural gas for 60 years and oil for 40 years. The development of Nuclear power would be a drastic change for this problem. It was known that Uranium would last long for around 50 years, but recycling the plutonium from spent fuel would increase the potential of uses this technology as long as 3000 years at todays level of use. As we know, the consumption of fuel (uranium) of uses this technology is very small but the electricity produce are very high which is the main advantage of this technology.[6] Safety Since the tragedy of Chernobyl and three miles island, the safety of nuclear power plant had been improved. All nuclear power plant needs to be at maximum safety avoid major accidents. Nowadays, the design of newest nuclear reactor has applied the concept of negative feedback loop. This new technology ensures that nuclear power become harder to squeeze. In such ways, nuclear chain reaction that can lead to a explosion almost impossible to happen. So this technology had overcome the safety problem that always been worried by people around the world. DISADVANTAGE Environmental effect Nuclear waste is the main disadvantage in nuclear power energy. After many years of research, the world still has no exact solution to safely dispose the nuclear waste. Nuclear waste nowadays is stored in a disposal site. The problem is Plutonium takes a hundred thousand of years to be no longer radioactive. So the storage site may be full before the plutonium become inactive. In instant, plutonium are very dangerous as it is highly toxic and also can be used to make bomb.[7] Cost Although the price of uranium is cheap, the cost to construct Nuclear power plant is very expensive compared to the other source of power plant. It uses highly expensive technologies. The total cost to use the nuclear technology include construction, safety, insurance and liability in case of accidents or being attack which is can cause to mass destruction and also the cost of nuclear waste Nuclear Proliferation As we know, nuclear technology used a lot of uranium that can be converted to weapons production. Nowadays, there are more than 40 countries used this technology have a big risk about nuclear proliferation. The hazard of nuclear power includes the risk of disaster like nuclear reactor disaster in Chernobyl which hotly debated in the internet. The other problem is also the risk of terrorism and sabotage which can cause mass destruction. RENEWABLE ENERGY IN MALAYSIA Hydropower and biomass are commonly used of renewable energy in Malaysia. After an increasing price of fuel, lack of supply of coal, environmental problem and limited land in building dam for a hydropower, its a sufficient way if Malaysia take a step to develop a new type of renewable energy such wind and solar. Renewable energy plays a big role in supplying electricity in Malaysia as it also reduces the issue of global warming. Since Malaysia is located in equatorial region which receiving average of 8 hours of sunshine, its better for Malaysia government to implement solar technology in Malaysia. Nowadays, solar technology in our country is only use in rural area. For instant, Malaysia actually has built their first solar power station in Tropical Village of Kampung Denai.[8] Many citizens nowadays also use solar photovoltaic application as their water heater in their home. This shows that solar technology is no longer a new technology in Malaysia. Its better if our government to t ake opportunity to build a mega solar power supply in our country to overcome the problem of environmental effect if using non renewable energy such coil and oil. COMPARISON BETWEEN NUCLEAR ENERGY AND RENEWABLE ENERGY Safety The main difference is renewable energy doesnt have the tendency to create a military weapon the way the nuclear energy does. For instant, a sabotage of nuclear power plant would release a large amount of radioactive which is very harmful to human. Theres no type of renewable energy that can be used as weapon of mass destruction. Supply Renewable energies have very limitless source. It stated that nuclear energy can long last about 3000 years more because of the uranium supply is larger enough and also because of the ability of recycling plutonium. Both of this source energy has the long term of supply which would be the main factor of using this source. Environment Renewable energy is absolutely environmental friendly. Nuclear energy also doesnt produce any noxious gas which can increase the concentration of greenhouse problem or global warming. But the problem of using nuclear energy is to deal with the nuclear waste. Nuclear waste radioactive remains for hundred thousand of years. Until now, any of the world government body doesnt have the solution to solve this problem. Cost Nuclear power is more expensive compare with renewable energy. Even though we know that nuclear power plant only use little fuel (uranium) means little cost in term of supply but other cost like construction, safety, and waste are yet highly expensive. Solar energy is very affordable and very appropriate for our country as Malaysia gets a good sunlight each year. This could be the main advantage of using this technology. CONCLUSION Energy sources that are sustainable, environmental friendly and cost effective would be the main factor of choosing source energy. Renewable energy can achieve the sustainable we need. Renewable energy supplies 19% of energy and nuclear energy only supply 16% of world electricity.[9] Renewable energy not only have limitless source, its also easily organize and absolutely no risk to human and global. Nuclear energy maybe has its own advantage but the dangerous of nuclear waste has overcome all the benefit of nuclear energy. Theres still a big problem about the radioactive effect that can be harmful to human. So the investment on building nuclear power plant energy would be not a good option.

Intelligent Software Agent

Intelligent Software Agent Chapter 1 Intelligent Software Agent 1.1 Intelligent Agent An Agent can be defined as follows: â€Å"An Agent is a software thing that knows how to do things that you could probably do yourself if you had the time† (Ted Seller of IBM Almaden Research Centre). Another definition is: â€Å"A piece of software which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The software should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended results† (G.W.Lecky Thompson). [1] [2] [3] [4] An Intelligent Agent can be divided into weak and strong notations. Table 1.1 shows the properties for both the notations. Weak notation Strong notation Autonomy Mobility Social ability Benevolence Reactivity Proactivity Rationality Temporal continuity Adaptivity Goal oriented Collaboration Table 1.1 1.1.1 Intelligency Intelligence refers to the ability of the agent to capture and apply domain specific knowledge and processing to solve problems. An Intelligent Agent uses knowledge, information and reasoning to take reasonable actions in pursuit of a goal. It must be able to recognise events, determine the meaning of those events and then take actions on behalf of a user. One central element of intelligent behaviour is the ability to adopt or learn from experience. Any Agent that can learn has an advantage over one that cannot. Adding learning or adaptive behaviour to an intelligent agent elevates it to a higher level of ability. In order to construct an Intelligent Agent, we have to use the following topics of Artificial Intelligence: Knowledge Representation Reasoning Learning [5] 1.1.2 Operation The functionality of a mobile agent is illustrated in 1.1. Computer A and Computer B are connected via a network. In step 1 a mobile Agent is going to be dispatched from Computer A towards Computer B. In the mean time Computer A will suspend its execution. Step 2 shows this mobile Agent is now on network with its state and code. In step 3 this mobile Agent will reach to its destination, computer B, which will resume its execution. [7] 1.1.3 Strengths and Weaknesses Many researchers are now developing methods for improving the technology, with more standardisation and better programming environments that may allow mobile agents to be used in products. It is obvious that the more an application gets intelligent, the more it also gets unpredictable and uncontrollable. The main drawback of mobile agents is the security risk involved in using them. [8] [9] The following table shows the major strengths and weaknesses of Agent technology: Strengths Weakness Overcoming Network Latency Security Reducing Network traffic Performance Asynchronous Execution and Autonomy Lack of Applications Operating in Heterogeneous Environments Limited Exposure Robust and Fault-tolerant Behavior Standardization Table 1.2 1.2 Applications The followings are the major and most widely applicable areas of Mobile Agent: Distributed Computing: Mobile Agents can be applied in a network using free resources for their own computations. Collecting data: A mobile Agent travels around the net. On each computer it processes the data and sends the results back to the central server. Software Distribution and Maintenance: Mobile agents could be used to distribute software in a network environment or to do maintenance tasks. Mobile agents and Bluetooth: Bluetooth is a technology for short range radio communication. Originally, the companies Nokia and Ericsson came up with the idea. Bluetooth has a nominal range of 10 m and 100 m with increased power. [38] Mobile agents as Pets: Mobile agents are the ideal pets. Imagine something like creatures. What if you could have some pets wandering around the internet, choosing where they want to go, leaving you if you dont care about them or coming to you if you handle them nicely? People would buy such things wont they? [38] Mobile agents and offline tasks: 1. Mobile agents could be used for offline tasks in the following way: a- An Agent is sent out over the internet to do some task. b- The Agent performs its task while the home computer is offline. c- The Agent returns with its results. 2. Mobile agents could be used to simulate a factory: a- Machines in factory are agent driven. b- Agents provide realistic data for a simulation, e.g. uptimes and efficiencies. c- Simulation results are used to improve real performance or to plan better production lines. [10[ [11] [12] 1.3 Life Cycle An intelligent and autonomous Agent has properties like Perception, Reasoning  and Action which form the life cycle of an Agent as shown in 1.2. [6] The agent perceives the state of its environment, integrates the perception in its knowledge base that is used to derive the next action which is then executed. This generic cycle is a useful abstraction as it provides a black-box view on the Agent and encapsulates specific aspects. The first step is the Agent initialisation. The Agent will then start to operate and may stop and start again depending upon the environment and the tasks that it tried to accomplish. After the Agent finished all the tasks that are required, it will end at the completing state. [13] Table 1.3 shows these states. Name of Step Description Initialize Performs one-time setup activities. Start Start its job or task. Stop Stops jobs, save intermediate results, joins all threads and stops. Complete Performs one-time termination activities. Table 1.3 1.4 Agent Oriented Programming (AOP) It is a programming technique which deals with objects, which have independent thread of control and can be initiated. We will elaborate on the three main components of the AOP. a- Object: Grouping data and computation together in a single structural unit called an ‘Object. Every Agent looks like an object. b- Independent Thread of control: This means when this developed Agent which is an object, when will be implemented in Boga server, looks like an independent thread. This makes an Agent different from ordinary object. c- Initiation: This deals with the execution plan of an Agent, when implemented, that Agent can be initiated from the server for execution. [14] [15] [16] [17] 1.5 Network paradigms This section illustrates the traditional distributed computing paradigms like Simple Network Management Protocol (SNMP) and Remote Procedure Call (RPC). 1.5.1 SNMP Simple Network Management Protocol is a standard for gathering statistical data about network traffic and the behavior of network components. It is an application layer protocol that sits above TCP/IP stack. It is a set of protocols for managing complex networks. It enables network administrators to manage network performance, find and solve network problems and plan for network growth. It is basically a request or response type of protocol, communicating management information between two types of SNMP entities: Manager (Applications) and Agents. [18] Agents: They are compliant devices; they store data about themselves in Management Information Base (MIB) (Each agent in SNMP maintain a local database of information relevant to network management is known as the Management Information Base) and return this data to the SNMP requesters. An agent has properties like: Implements full SNMP protocol, Stores and retrieves managed data as defined by the Management Information Base and can asynchronously signal an event to the manager. Manager (Application): It issues queries to get information about the status, configuration and performance of external network devices. A manager has the following properties: Implemented as a Network Management Station (the NMS), implements full SNMP Protocol, able to Query Agents, get responses from Agents, set variables in agents and acknowledge asynchronous events from Agents. [18] 1.3 illustrates an interaction between a manager and an Agent. The agent is software that enables a device to respond to manager requests to view or update MIB data and send traps reporting problems or significant events. It receives messages and sends a response back. An Agent does not have to wait for order to act, if a serious problem arises or a significant event occurs, it sends a TRAP (a message that reports a problem or a significant event) to the manager (software in a network management station that enables the station to send requests to view or update MIB variables, and to receive traps from an agent). The Manager software which is in the management station sends message to the Agent and receives a trap and responses. It uses User Data Protocol (UDP, a simple protocol enabling an application to send individual message to other applications. Delivery is not guaranteed, and messages need not be delivered in the same order as they were sent) to carry its messages. Finally, there is one application that enables end user to control the man ager software and view network information. [19] Table 1.4 comprises the Strengths and Weaknesses of SNMP. Strengths Weaknesses Its design and implementation are simple. It may not be suitable for the management of truly large networks because of the performance limitations of polling. Due to its simple design it can be expanded and also the protocol can be updated to meet future needs. It is not well suited for retrieving large volumes of data, such as an entire routing table. All major vendors of internetwork hardware, such as bridges and routers, design their products to support SNMP, making it very easy to implement. Its traps are unacknowledged and most probably not delivered. Not applicable It provides only trivial authentication. Not applicable It does not support explicit actions. Not applicable Its MIB model is limited (does not support management queries based on object types or values). Not applicable It does not support manager-to-manager communications. Not applicable The information it deals with neither detailed nor well-organized enough to deal with the expanding modern networking requirements. Not applicable It uses UDP as a transport protocol. The complex policy updates require a sequence of updates and a reliable transport protocol, such as TCP, allows the policy update to be conducted over a shared state between the managed device and the management station. Table 1.4 1.5.2 RPC A remote procedure call (RPC) is a protocol that allows a computer program running on one host to cause code to be executed on another host without the programmer needing to explicitly code for this. When the code in question is written using object-oriented principles, RPC is sometimes referred to as remote invocation or remote method invocation. It is a popular and powerful technique for constructing distributed, client-server based applications. An RPC is initiated by the caller (client) sending a request message to a remote system (the server) to execute a certain procedure using arguments supplied. A result message is returned to the caller. It is based on extending the notion of conventional or local procedure calling, so that the called procedure need not exist in the same address space as the calling procedure. The two processes may be on the same system, or they may be on different systems with a network connecting them. By using RPC, programmers of distributed applications avoid the details of the interface with the network. The transport independence of RPC isolates the application from the physical and logical elements of the data communications mechanism and allows the application to use a variety of transports. A distributed computing using RPC is illustrated in 1.4. Local procedures are executed on Machine A; the remote procedure is actually executed on Machine B. The program executing on Machine A will wait until Machine B has completed the operation of the remote procedure and then continue with its program logic. The remote procedure may have a return value that continuing program may use immediately. It intercepts calls to a procedure and the following happens: Packages the name of the procedure and arguments to the call and transmits them over network to the remote machine where the RPC server id running. It is called â€Å"Marshalling†. [20] RPC decodes the name of the procedure and the parameters. It makes actual procedure call on server (remote) machine. It packages returned value and output parameters and then transmits it over network back to the machine that made the call. It is called â€Å"Unmarshalling†. [20] 1.6 Comparison between Agent technology and network paradigms Conventional Network Management is based on SNMP and often run in a centralised manner. Although the centralised management approach gives network administrators a flexibility of managing the whole network from a single place, it is prone to information bottleneck and excessive processing load on the manager and heavy usage of network bandwidth. Intelligent Agents for network management tends to monitor and control networked devices on site and consequently save the manager capacity and network bandwidth. The use of Intelligent Agents is due to its major advantages e.g. asynchronous, autonomous and heterogeneous etc. while the other two contemporary technologies i.e. SNMP and RPC are lacking these advantages. The table below shows the comparison between the intelligent agent and its contemporary technologies: Property RPC SNMP Intelligent Agent Communication Synchronous Asynchronous Asynchronous Processing Power Less Autonomy More Autonomous but less than Agent More Autonomous Network support Distributed Centralised Heterogeneous Network Load Management Heavy usage of Network Bandwidth Load on Network traffic and heavy usage of bandwidth Reduce Network traffic and latency Transport Protocol TCP UDP TCP Packet size Network Only address can be sent for request and data on reply Only address can be sent for request and data on reply Code and execution state can be moved around network. (only code in case of weak mobility) Network Monitoring This is not for this purpose Network delays and information bottle neck at centralised management station It gives flexibility to analyse the managed nodes locally Table 1.5 Indeed, Agents, mobile or intelligent, by providing a new paradigm of computer interactions, give new options for developers to design application based on computer connectivity. 20 Chapter 2 Learning Paradigms 2.1 Knowledge Discovery in Databases (KDD) and Information Retrieval (IR) KDD is defined as â€Å"the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data† (Fayyad, Piatetsky-Shapiro and Smith (1996)). A closely related process of IR is defined as â€Å"the methods and processes for searching relevant information out of information systems that contain extremely large numbers of documents† (Rocha (2001)). KDD and IR are, in fact, highly complex processes that are strongly affected by a wide range of factors. These factors include the needs and information seeking characteristics of system users as well as the tools and methods used to search and retrieve the structure and size of the data set or database and the nature of the data itself. The result, of course, was increasing numbers of organizations that possessed very large and continually growing databases but only elementary tools for KD and IR. [21] Two major research areas have been developed in response to this problem: * Data warehousing: It is defined as: â€Å"Collecting and ‘cleaning transactional data to make it available for online analysis and decision support†. (Fayyad 2001, p.30)  · Data Mining: It is defined as: â€Å"The application of specific algorithms to a data set for purpose of extracting data patterns†. (Fayyad p. 28) 2.2 Data Mining Data mining is a statistical term. In Information Technology it is defined as a discovery of useful summaries of data. 2.2.1 Applications of Data Mining The following are examples of the use of data mining technology: Pattern of traveller behavior mined: Manage the sale of discounted seats in planes, rooms in hotels. Diapers and beer: Observation those customers who buy diapers are more likely to buy beer than average allowed supermarkets to place beer and diapers nearby, knowing many customers would walk between them. Placing potato chips between increased sales of all three items. Skycat and Sloan Sky Survey: Clustering sky objects by their radiation levels in different bands allowed astronomers to distinguish between galaxies, nearby stars, and many other kinds of celestial objects. Comparison of genotype of people: With/without a condition allowed the discovery of a set of genes that together account for many case of diabetes. This sort of mining will become much more important as the human genome is constructed. [22] [23] [24] 2.2.2 Communities of Data Mining As data mining has become recognised as a powerful tool, several different communities have laid claim to the subject: Statistics Artificial Intelligence (AI) where it is called â€Å"Machine Learning† Researchers in clustering algorithms Visualisation researchers Databases: When data is large and the computations is very complex, in this context, data mining can be thought of as algorithms for executing very complex queries on non-main-memory data. 2.2.3 Stages of data mining process The following are the different stages of data mining process, sometimes called as a life cycle of data mining as shown in 2.1: Data gathering: Data warehousing, web crawling. Data cleansing: Eliminate errors and/or bogus data e.g. Patients fever = 125oC. 3- Feature extraction: Obtaining only the interesting attributes of the data e.g. â€Å"data acquired† is probably not useful for clustering celestial objects as in skycat. 4- Pattern extraction and discovery: This is the stage that is often thought of as â€Å"data mining† and is where we shall concentrate our efforts. 5- Visualisation of the data: 6- Evaluation of results: Not every discovered fact is useful, or even true! Judgment is necessary before following the softwares conclusions. [22] [23] [24] 2.3 Machine Learning There are five major techniques of machine learning in Artificial Intelligence (AI), which are discussed in the following sections. 2.3.1 Supervised Learning It relies on a teacher that provides the input data as well as the desired solution. The learning agent is trained by showing it examples of the problem state or attributes along with the desired output or action. The learning agent makes a prediction based on the inputs and if the output differs from the desired output, then the agent is adjusted or adapted to produce the correct output. This process is repeated over and over until the agent learns to make accurate classifications or predictions e.g. Historical data from databases, sensor logs or trace logs is often used as training or example data. The example of supervised learning algorithm is the ‘Decision Tree, where there is a pre-specified target variable. [25] [5] 2.3.2 Unsupervised Learning It depends on input data only and makes no demands on knowing the solution. It is used when learning agent needs to recognize similarities between inputs or to identify features in the input data. The data is presented to the Agent, and it adapts so that it partitions the data into groups. This process continues until the Agents place the same group on successive passes over the data. An unsupervised learning algorithm performs a type of feature detection where important common attributes in the data are extracted. The example of unsupervised learning algorithm is â€Å"the K-Means Clustering algorithm†. [25] [5] 2.3.3 Reinforcement Learning It is a kind of supervised learning, where the feedback is more general. On the other hand, there are two more techniques in the machine learning, and these are: on-line learning and off-line learning. [25] [5] 2.3.4 On-line and Off-line Learning On-line learning means that the agent is adapting while it is working. Off-line involves saving data while the agent is working and using the data later to train the agent. [25] [5] In an intelligent agent context, this means that the data will be gathered from situations that the agents have experienced. Then augment this data with information about the desired agent response to build a training data set. Once this database is ready it can be used to modify the behaviour of agents. These approaches can be combined with any two or more into one system. In order to develop Learning Intelligent Agent(LIAgent) we will combine unsupervised learning with supervised learning. We will test LIAgents on Iris dataset, Vote dataset about the polls in USA and two medical datasets namely Breast and Diabetes. [26] See Appendix A for all these four datasets. 2.4 Supervised Learning (Decision Tree ID3) Decision trees and decision rules are data mining methodologies applied in many real world applications as a powerful solution to classify the problems. The goal of supervised learning is to create a classification model, known as a classifier, which will predict, with the values of its available input attributes, the class for some entity (a given sample). In other words, classification is the process of assigning a discrete label value (class) to an unlabeled record, and a classifier is a model (a result of classification) that predicts one attribute-class of a sample-when the other attributes are given. [40] In doing so, samples are divided into pre-defined groups. For example, a simple classification might group customer billing records into two specific classes: those who pay their bills within thirty days and those who takes longer than thirty days to pay. Different classification methodologies are applied today in almost every discipline, where the task of classification, because of the large amount of data, requires automation of the process. Examples of classification methods used as a part of data-mining applications include classifying trends in financial market and identifying objects in large image databases. [40] A particularly efficient method for producing classifiers from data is to generate a decision tree. The decision-tree representation is the most widely used logic method. There is a large number of decision-tree induction algorithms described primarily in the machine-learning and applied-statistics literature. They are supervised learning methods that construct decision trees from a set of input-output samples. A typical decision-tree learning system adopts a top-down strategy that searches for a solution in a part of the search space. It guarantees that a simple, but not necessarily the simplest tree will be found. A decision tree consists of nodes, where attributes are tested. The outgoing branches of a node correspond to all the possible outcomes of the test at the node. [40] Decision trees are used in information theory to determine where to split data sets in order to build classifiers and regression trees. Decision trees perform induction on data sets, generating classifiers and prediction models. A decision tree examines the data set and uses information theory to determine which attribute contains the information on which to base a decision. This attribute is then used in a decision node to split the data set into two groups, based on the value of that attribute. At each subsequent decision node, the data set is split again. The result is a decision tree, a collection of nodes. The leaf nodes represent a final classification of the record. ID3 is an example of decision tree. It is kind of supervised learning. We used ID3 in order to print the decision rules as its output. [40] 2.4.1 Decision Tree Decision trees are powerful and popular tools for classification and prediction. The attractiveness of decision trees is due to the fact that, in contrast to neural networks, decision trees represent rules. Rules can readily be expressed so that humans can understand them or even directly used in a database access language like SQL so that records falling into a particular category may be retrieved. Decision tree is a classifier in the form of a tree structure, where each node is either: Leaf node indicates the value of the target attribute (class) of examples, or Decision node specifies some test to be carried out on a single attribute value, with one branch and sub-tree for each possible outcome of the test. Decision tree induction is a typical inductive approach to learn knowledge on classification. The key requirements to do mining with decision trees are:  · Attribute value description: Object or case must be expressible in terms of a fixed collection of properties or attributes. This means that we need to discretise continuous attributes, or this must have been provided in the algorithm.  · Predefined classes (target attribute values): The categories to which examples are to be assigned must have been established beforehand (supervised data).  · Discrete classes: A case does or does not belong to a particular class, and there must be more cases than classes. * Sufficient data: Usually hundreds or even thousands of training cases. A decision tree is constructed by looking for regularities in data. [27] [5] 2.4.2 ID3 Algorithm J. Ross Quinlan originally developed ID3 at the University of Sydney. He first presented ID3 in 1975 in a book, Machine Learning, vol. 1, no. 1. ID3 is based on the Concept Learning System (CLS) algorithm. [28] function ID3 Input: (R: a set of non-target attributes, C: the target attribute, 2.4.3 Functionality of ID3 ID3 searches through the attributes of the training instances and extracts the attribute that best separates the given examples. If the attribute perfectly classifies the training sets then ID3 stops; otherwise it recursively operates on the m (where m = number of possible values of an attribute) partitioned subsets to get their best attribute. The algorithm uses a greedy search, that is, it picks the best attribute and never looks back to reconsider earlier choices. If the dataset has no such attribute which will be used for the decision then the result will be the misclassification of data. Entropy a measure of homogeneity of the set of examples. [5] Entropy(S) = pplog2 pp pnlog2 pn (1) (2) 2.4.4 Decision Tree Representation A decision tree is an arrangement of tests that prescribes an appropriate test at every step in an analysis. It classifies instances by sorting them down the tree from the root node to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance, and each branch descending from that node corresponds to one of the possible values for this attribute. This is illustrated in 2.3. The decision rules can also be obtained from ID3 in the form of if-then-else, which can be use for the decision support systems and classification. Given m attributes, a decision tree may have a maximum height of m. [29][5] 2.4.5 Challenges in decision tree Following are the issues in learning decision trees: Determining how deeply to grow the decision tree. Handling continuous attributes. Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs and Improving computational efficiency. 2.4.6 Strengths and Weaknesses Following are the strengths and weaknesses in decision tree: Strengths Weaknesses It generates understandable rules. It is less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. It performs classification without requiring much computation. It is prone to errors in classification problems with many class and relatively small number of training examples. It is suitable to handle both continuous and categorical variables. It can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting field must be sorted before its best split can be found. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and compared. It provides a clear indication of which fields are most important for prediction or classification. It does not treat well non-rectangular regions. It only examines a single field at a time. This leads to rectangular classification boxes that may not correspond well with the actual distribution of records in the decision space. Table 2.1 2.4.7 Applications Decision tree is generally suited to problems with the following characteristics: a. Instances are described by a fixed set of attributes (e.g., temperature) and their values (e.g., hot). b. The easiest situation for decision tree learning occurs when each attribute takes on a small number of disjoint possible values (e.g., hot, mild, cold). c. Extensions to the basic algorithm allow handling real-valued attributes as well (e.g., a floating point temperature). d. A decision tree assigns a classification to each example. i- Simplest case exists when there are only two possible classes (Boolean classification). ii- Decision tree methods can also be easily extended to learning functions with more than two possible output values. e. A more substantial extension allows learning target functions with real-valued outputs, although the application of decision trees in this setting is less common. f. Decision tree methods can be used even when some training examples have unknown values (e.g., humidity is known for only a fraction of the examples). [30] Learned functions are either represented by a decision tree or re-represented as sets of if-then rules to improve readability. 2.5 Unsupervised Learning (K-Means Clustering) Cluster analysis is a set of methodologies for automatic classification of samples into a number of groups using a measure of association, so that the samples in one group are similar and samples belonging to different groups are not similar. The inpu