Thursday, August 27, 2020

Paper on Cystic Fibrosis. Explains causes, cures and much more!

Paper on Cystic Fibrosis. Clarifies causes, fixes and considerably more! The previous an analysis is caused the better with the goal that early treatment to can slow the movement of lung harm brought about by contamination. Pre-birth tests are accessible to decide whether a child will be brought into the world with cystic fibrosis. In babies, blood tests showing elevated levels of stomach related catalysts propose cystic fibrosis, yet a certaindiagnosis requires a perspiration test to decide the measure of salt in the perspiration. Sweat tests give a substantial analysis in babies more than 24 hours old, and this test is additionally used to affirm finding in more seasoned youngsters and adults.Cystic fibrosis stays serious; existing medicines intend to calm uneasiness and defer the staggering and unavoidable impacts of the infection. Meconium ileus, the intestinal block happening in infants, may require medical procedure. Patients with pancreatic blockage must take pancreatic chemicals with suppers. Indeed, even with such compounds, individuals with cyst ic fibrosis must devour satisfactory measures of protein, nutrients, and higher-than-typical measures of fat to guarantee growth.English: Clubbing of the fingers is an exemplary fe...Those with respiratory contaminations are treated with anti-microbials, frequently in airborne structure. When breathed in, these cured fumes battle disease and assuage tightening of the aviation routes. Utilizing a system called chest active recuperation or postural waste, parental figures of individuals with cystic fibrosis over and over and enthusiastically beat on the patient's back and chest to unstick bodily fluid impeding the aviation routes. Progressively, cystic fibrosis patients with serious, hopeless lung harm go to lung transplantation medical procedure (see Medical Transplantation). In spite of the fact that complexities with transplantation medical procedure may present issues for certain patients, lung or mix heart and lung transplants give almost 80 percent of cystic fibrosis patients wi th serious lung harm a completely new rent on life.Although no fix has yet been discovered, cystic fibrosis presents one of the most encouraging regions of examination in current medication. Researchers are exploring the utilization of quality treatment to...

Saturday, August 22, 2020

The History Of The Community Mobilization Sociology Essay

The History Of The Community Mobilization Sociology Essay The term network has different importance. We can characterize network is where individuals living in the specific topographical region and they shares their normal qualities, interests and they follows a specific method of living. The term network may allude to the national network or worldwide network. In science a network is a gathering of communicating individuals sharing a populated situation. Aside from a geological territory a network is a gathering or society, helping one another. In human networks conviction, assets, needs, intrigue and various different conditions might be available in like manner, which likewise influences the personality of the members and their level of cohesiveness. In each general public different kinds of networks may happen. A few classifications are as per the following:- Geographic people group: It ranges from the nearby neighborhood, suburb, town, town or city, district, country or even the planet in general. These allude to networks of area. Networks of culture: It ranges from the nearby club, sub-culture, ethnic gathering, strict, multicultural or pluralistic human progress, or the worldwide network societies of today. They might be incorporated as networks of need or character, for example, debilitated people, or delicate matured individuals. Networks are settled; one network can contain another-for instance a geographic network may contain various ethnic networks. Character is likewise one of the significant factor to evaluate the sort of network. For instance on the off chance that a gathering of individuals shows the basic personality other than area, at that point it would be a network dependent on their basic intrigue. Likewise an expert network can likewise exist in the public arena where a gathering of individuals are with the equivalent or related occupations. There are various things that quandary individuals together to their various networks or that they may share for all intents and purpose with the others living around them, which incorporates convictions and qualities, language, domain, religion, culture and occupations. Network Mobilization: A solid network feeling is likewise clear on occasion of emergency and change for families, for example, marriage and passing. In a network people shared their we feeling with one another. As we realize that appropriate administration and use of assets is the most ideal route for the advancement of the network. Along these lines network activation is consistently noticeable for the turn of events and upliftment of any network. For preparing the network individuals make their arrangements and afterward do the things as needs be. They assume responsibility, changing, building up their locale and their lives too. Network assembly is a procedure which permits individuals in the network to: Distinguish needs and advance network interests. Advance great administration and popularity based dynamic. Distinguish explicit gatherings for undertaking explicit issues. Distinguish all the accessible assets in the network and prepare or produce new assets. Networks can prepare to work for changes that will satisfy the social, enthusiastic, money related and physical necessities of individuals. Network activation is a procedure to make mindfulness among individuals in regards to the current circumstance of the network so as to energize positive change later on For people group preparation correspondence is one of the significant instrument to accomplish it. Through appropriate and successful correspondence different techniques for assembling the network can be executed. Here we are talking about the different strategies for network activation by given entertainers according to the fig 1.1. Activating the network Ideological groups Social Movements Singular gatherings Ideological groups The utilization of long range interpersonal communication locales by ideological groups and other politically dynamic gatherings has been expanded. These gatherings have understood that utilizing of person to person communication possibly expands individuals activation. The Internet has become a quick and ease specialized instrument and empowers snappy and simple collection of political data among potential voters. For example Mr. Barack Obama fruitful utilized Twitter, Flicker, You Tube, Facebook among others to impart and prepare individuals. He likewise had a $2 million dollar custom crusading and interpersonal interaction site constructed called As ideological groups are presently effectively captivating with long range informal communication destinations as a major aspect of their techniques to activate the greatest individuals in the network. In the current situation, the utilization of web based life is extremely critical to assemble the individuals and netw orks for aggregate activity. We realize that today a great many individuals are associated somehow to the web. Individuals are effectively open on web. It is another method of arriving at huge quantities of individuals to assemble them for a collective endeavor. Facebook, Mixit, Twitter can likewise be powerful for the abovementioned. To produce mindfulness among individuals and to activate them in huge number to make a network autonomous. Papers, radio broadcasts and the TV have the position to advise thousands additional individuals about different crusades for activating any network. By composing a public statement, an ideological group can feature the realities, people groups requests, and what individuals need the media to discuss with respect to the turn of events and nonstop strengthening of their locale. Gatherings can likewise compose a question and answer session. So they can welcome the columnists to go to a gathering where representatives from the network association stand up openly about the issue and the arrangement of the issues of network. Every one of these systems ideological groups can apply to prepare the network and individuals too to create mindfulness among them and to give them a stage where individuals can talk about issues their locale issues likewise activate the assets. Aside from above, Political conventions and the utilization of neighborhood dialects are instrumental in the assembly of gatherings for social activity. Social Movements Social developments are a kind of gathering activity. They are huge casual groupings of people or associations which center around explicit political or social issues for their turn of events. As it were, they do, oppose or fix a social change in the general public. Social development crusades assume a basic job in assembling and keeping up the lowered systems of social development networks. We examine the significance of such development battles and their impacts on development networks, ensuing aggregate activity, and social and political change. Our exploration adds to a comprehension of the state of social development networks and the associations among nearby and worldwide crusades. Network activation is frequently utilized by grassroots-based social developments, which incorporates progressive developments. The procedure for the most part appears as enormous open social affairs, for example, mass gatherings, walks, marches, parades and shows. Those social occasions as a rule are a piece of a dissent activity. These are the various ways by which a network can be activated and can speak loudly for the interest of advancement of the network. At the end of the day, network activation looks to encourage change inside the network for its turn of events. Present day Western social developments got conceivable through training (the more extensive scattering of writing), and expanded portability of work because of the industrialization and urbanization of nineteenth century social orders. Social developments have been and kept on being firmly associated with majority rule political frameworks. Every so often, social developments have been associated with democratizing countries, yet more regularly they have created after democratization. Current developments regularly use innovation and the web to assemble individuals universally. Adjusting to correspondence patterns is a typical topic among fruitful developments. Research is starting to investigate how backing associations connected to social developments utilize internet based life to encourage metro commitment and aggregate activity. It is a sort of political association we have to consider is the social development. A development, by its very nature, isn't something that can be effectively nailed down since, as Glaser (2003) recommends, it speaks to a free network of similar individuals who share a wide scope of thoughts and suppositions. This kind of definition, along these lines, covers a scope of practices, a genuine case of which may be something like the earthy person development a general class of individuals who, in an assortment of ways, are worried about securing the physical condition. Della Porta and Diani (1999) refine this general thought by considering social developments as far as Informal systems the development in general is approximately organized. Individuals meet up, at different occasions, based on: Shared convictions and backing for a general arrangement of thoughts, normally based around: Conflictual issues, particularly, despite the fact that not really, issues of national and worldwide i mportance. Some portion of the purpose behind social developments is that issues of worry to development followers/individuals are either not being tended to by ideological groups or, in the event that they are, the developments disciples are unequivocally restricted to the strategies being proposed/instituted. This is one motivation behind why such developments frequently include: Protest in a scope of structures, (for example, common insubordination, exhibits or exposure stunts). At the end of the day, as Schweingruber (2005) puts it, social developments include: Ceaseless, enormous scope, sorted out aggregate activity, roused by the craving to sanction, stop, or opposite change in some region of society. Social developments are any expansive social coalitions of individuals who are associated through their common enthusiasm for blocking or influencing social change. Albeit social developments don't need to be officially composed. Social development is consistently a significant device to acquire the change society and just as to activate the networks for their own turn of events. By orchestrating individuals in mass gatherings, marches, walks, conversations on different iss

Friday, August 21, 2020

College Essay Writing - Describe Your Essay Idea

College Essay Writing - Describe Your Essay IdeaYour descriptive essay idea is one of the most difficult parts of your college application. That is why it is absolutely crucial that you devote a certain amount of time to making sure that your essay contains only high quality descriptive ideas. This is one of the hardest parts of the entire process, but that does not mean that it cannot be done. The first thing that you must understand is that there are only two different kinds of readers for a descriptive essay: the reader who will grade your essay and the reader who will read your essay.If you do not know what sort of reader your prospective college or university is, then you should simply stop right here and read this entire article. In this article, I am going to share with you a very important fact about descriptive essays. To give you an example, let's say that you happen to have a very vivid imagination and you write down all of the best ideas that you can think of. Now you are going to want to put those descriptive essays on the Internet.Your potential employer will spend his or her time looking through all of the articles that you submit. If you have a number of different ideas in one, then he or she is going to read the same thing over again. It is important that you make sure that you concentrate on only one idea at a time.You can do this by concentrating on a single concept at a time and writing out an entire idea for each paragraph. The idea should not contain any of the others in a series. When you write out the entire idea for each paragraph, then you will be able to focus on just one and still be able to give it the quality that it deserves.Your goal is to get into your school's administration, so you want to write as much as possible. You can also use your experiences as an inspiration. This means that instead of using different descriptions of things that you have seen, you will instead be focusing on describing some of the most difficult situa tions that you have encountered.You do not have to write out life situations as well. Instead, you may choose to focus on completely different situations if you feel that your previous experiences are not sufficiently descriptive. Writing out a story or movie trailer for an automobile will really help you to gain confidence about your writing ability.Summarize your writing. The summary section of your essay will allow you to summarize the purpose of your entire essay. In addition, you will also be able to summarize your points in a way that will be easy for your audience to follow. Your summary should contain the main points of your essay as well as key phrases that they can identify with.Finally, don't forget to include this informative resource at the end of your descriptive essay ideas. Your work is not over when your potential employer reads your essay. Make sure that you are prepared to communicate your whole thought process as well as give a little background information about yourself.

Monday, May 25, 2020

The Wisdom in Chinese Proverbs Essay - 1202 Words

Chinese Proverbs Proverbs are common sayings that are used to illustrate a point to give advice to people on how to live. Chinese proverbs are the common sayings that are used during the time of ancient China. They have existed for thousands of years. According to Chinese Proverb Stories, a website that describes the different kinds of proverbs, Historical Chinese proverbs are used to illustrate a point, describe a legendary figure, or describe a historical event ( Many of these proverbs derived from ancient literature. These proverbs are usually associated with myths or historical facts to educate about morality. Many of these Chinese proverbs came from philosophers and poets such as Lao-Tzu, Confucius,†¦show more content†¦Ã¤ ¸â‚¬Ã¨ ¯ ºÃ¥ Æ'金 —, YÄ « Nuà ² QiÄ n JÄ «n, is a proverb that stresses about the trustworthiness of a person. It is translated into English as A very trustworthy person will always do what he has promis ed. This phrase probably derived from around the time of the Han Dynasty. It probably derived from the Han Dynasty because the story of this proverb revolved around a man named Ji Bu during the Han Dynasty. Ji Bu was an upright and honest person who was serious about the issues of justice; he was always determined to do the things he had promised. Due to his honorable morality, Emperor Liu Bang wanted to arrest JI Bu and execute him, however, a man with the surname Zhou helped protect Ji Bu due to his admiration of Ji Bu. Mr. Zhou sent Ji Bu to a peasants house and pleaded with Emperor Liu Bang about the good things that Ji Bu had done, which made the emperor reversed his order of wanting to execute Ji Bu. Ji Bu was then assigned to be governor of Hedong Province by the emperor. Once he was governor of the province, he still remained friends and loyal to the people around him and continued to do the things he had promised to do for the people. Therefore the moral of this proverb is that a trustworthy person will receive the good from others because a trustworthy person will always keep up with his words. æ ° ´Ã¦ » ´Ã§Å¸ ³Ã§ © ¿ — , ShuÄ ­ DÄ « Shà ­ ChuÄ n, is proverb that deals with ones determination and success. The story of this proverb revolves around the Song Dynasty. That time period was a period ofShow MoreRelated Graduation Speech Essay564 Words   |  3 PagesA boy once approached Socrates with the desire to obtain wisdom. Without saying a word, Socrates led the boy down to the edge of the sea and walked in, beckoning him to follow. When they were standing waist deep in the water, Socrates pushed the boy down, completely submerging his head. He held him thus for a couple of minutes, until the boy was almost to the point of death, before letting him up. Sputtering and angry, the boy demanded to know why Socrates had held him underwater for so long. 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Their religion, Hinduism, is based on silence, â€Å"The Hindu believes that ‘self-realization, salvation, truth, wisdom, peace, and bliss are all achieved in a state of meditation and introspection when the individual is communicating with himself or herselfRead MoreHealth Quotes2317 Words   |  10 Pagesneglects to diet wastes the skill of his doctors.   ~Chinese Proverb Physical ills are the taxes laid upon this wretched life; some are taxed higher, and some lower, but all pay something.   ~Lord Chesterfield The... patient should be made to understand that he or she must take charge of his own life.   Dont take your body to the doctor as if he were a repair shop.   ~Quentin Regestein Sickness comes on horseback but departs on foot.   ~Dutch Proverb, sometimes attributed to William C. Hazlitt

Friday, May 15, 2020

Intelligent Agent - Free Essay Example

Sample details Pages: 31 Words: 9438 Downloads: 8 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Chapter 1 Intelligent Software Agent 1.1 Intelligent Agent An Agent can be defined as follows: 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: 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). Don’t waste time! Our writers will create an original "Intelligent Agent" essay for you Create order [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 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 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 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 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: 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: 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 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: 1- Data gathering: Data warehousing, web crawling. 2- 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. 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 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 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 input for a system of cluster analysis is a set of samples and a measure of similarity (or dissimilarity) between two samples. The output from cluster analysis is a number of clusters that form a partition, or a structure of partitions, of the data set. One additional result of cluster analysis is a generalised description of every cluster, and this is especially important for a deeper analysis of the data sets characteristics. K-Means clustering algorithm is the most commonly used algorithm employing a square-error criterion. It is used for finding the similar patterns due to its simplicity and fast execution. It starts with a random, initial partition and keeps re-assigning the samples to clusters, based on the similarity between samples and clusters, until a convergence criterion is met. Typically, this criterion is met when there is no re-assignment of any sample from one cluster to another that will cause a decrease of the total squared error. K-Means algorithm is popular because it is easy to implement, and its time and space complexity is relatively small. A major problem with this algorithm is that it is sensitive to the selection of the initial partition and may converge to a local minimum of the criterion function if the initial partition is not properly chosen. Clustering in data mining is a useful technique for discovering interesting data distribution and pattern in the data sets. It is actually a way to organize similar patterns into groups. It appears extensively in machine learning literature and in most data mining suits. It is probably the father of the entire family of clustering algorithms. This algorithm was introduced by J.B.MacQueen in 1967. [31] 2.5.1 Cluster and Clustering Clustering of data is a method by which a large set of data is grouped into clusters of smaller sets of similar data. It is concerned with finding structure in a large set of data. It is defined as: The process of organising objects into groups whose members are similar in some way. A cluster is therefore a collection of objects which are similar between them and dissimilar to objects belonging to other clusters. Similarity criterion is a distance and this is called distance-based clustering. 2.5.2 Applications of Clustering Clustering techniques are used for combining observed examples into clusters (groups) which satisfy two main criteria. Firstly, each group or cluster is homogeneous; examples that belong to the same group are similar to each other and secondly, each group or cluster should be different from other clusters, i.e., examples that members belong to one cluster should be different from the examples of other clusters. 2.5.3 Clusters Analysis Depending on the clustering technique, clusters can be expressed in different ways: * Identified clusters may be exclusive, so that any example belongs to only one cluster. * They may be overlapping; an example may belong to several clusters. * They may be probabilistic, whereby an example belongs to each cluster with a certain probability. * Clusters might have hierarchical structure, having crude division of examples at highest level of hierarchy, which is then refined to sub-clusters at lower levels. [31] 2.5.4 K-Means Algorithm The algorithm has as an input a predefined number of clusters that is the k from its name. Means stands for an average, an average location of all the members of a particular cluster. When dealing with clustering techniques, one has to adopt a notion of a high dimensional space, or space in which orthogonal dimensions are all attributes from the table of data we are analysing. The value of each attribute of an example represents a distance of the example from the origin along the attribute axes. In order to use this geometry efficiently, the values in the data set must all be numeric (categorical data must be transformed into numeric ones) and should be normalised in order to allow fair computation of the overall distances in a multi-attribute space. The basic mechanism of all clustering algorithms is represented in 2.4. K-Means algorithm is an iterative procedure, where final results depend on the values selected for initial centroids A Centroid is an artificial point in the space of records which represents an average location of the particular cluster. The coordinates of this point are averages of attribute values of all examples that belong to the cluster. Finding useful pattern in large datasets has attracted considerable interest recently and of the most widely studied problems in this area is the identification of clusters or densely populated regions, in a multi-dimensional data set. Given the desired number of clusters k and a dataset of N points and a distance-based measurement function, two major problems with this K-means Clustering Algorithm remain: Wrong choice of initial centroids: This problem deals with the production of erroneous results. The algorithm depends upon this selection. If the selection is correct or accurate then the result will be fine otherwise it may fail. Selection of initial partition: The algorithm is sensitive in selection of the initial partition and may converge to a local minimum of the criterion function if the initial partition is not properly chosen. It has two primary steps first, the assignment step where the instances are placed in the closest class and the second one, re-assignment step where the class centroids are recalculated from the instances to the class. [33] [22] The K-means-clustering algorithm is a popular approach to finding clusters due to simplicity of implementation and fast execution. It appears extensively in the machine learning literature and in most data mining suites of tools. However, its implementation makes an algorithm whose behaviour is complex. The following are very important points to understand this behaviour: * How do we prepare the data for clustering? * How do we formulate the problem and use the clustering tool? * How do we interpret the clusters and use them? [34] [22] Centroid of a cluster Centroid is an artificial point in the space of records which represents an average location of the particular cluster. The coordinates of this point are averages of attribute values of all examples that belong to the cluster. The basic steps of K-means Algorithm K-Means algorithms basic steps are shown in 2.5. The interpretation of this flow chart in 2.5 is follows: Enter the number of clusters and number of iterations, which are the required and basic inputs of the K-Means algorithm, then compute the initial centroids by providing the formula shown in equation 3 and 4 below. Calculate the distance either by Euclideans distance or City Block (Manhattan) distance formulae (equations 5 and 6). On the basis of these distances, generate the partition by assigning each sample to the closet cluster. Compute new cluster centers as centroids of the clusters, again compute the distances and generate the partition. Repeat this until the cluster memberships stabilises. Now the initial centroid will be C(ci, cj).Where: max X, max Y, min X and min Y represent maximum and minimum values of X and Y attributes respectively. k represents number of clusters and i,j and n vary from 1 to k where k is an integer. In this way, we can calculate the initial centroids; this will be the starting point of this algorithm by using the square-errors. Where d(xi, xj) is the distance between xi and xj. xi and xj are the attributes of a given object, where i and j vary from 1 to N where N is total number of attributes of a given object. i,j and N are integers. There are different techniques available to use the initial points (it is required as an input). The following are the different methods to calculate the initial points: * Corner: In this method all the values in the data sets are scaled to be in [-1, 1], the set of all the clusters close to the vertices (-1,,-1) is considered. This is usually a bad set of initial points since it lies on the boundary of the data, and can be considered an outlier. * Bins: This method consists in dividing the space in bin and then takes random points inside each bin, this assures that the set of initial points are distributed covering the entire dataset. * Centroid: This method consists of choosing all the starting clusters close to the mass centroid of the dataset. Each cluster center is calculated by adding a small random perturbation to the centroid of the dataset. * Spread: The cluster centers are distributed randomly and trying to cover the entire space. This method is similar to bins. * PCA: The data points are projected in the space of the principal component, a clustering procedure is applied to this one-dimensional set. The cluster centres are calculated depending on the obtained clusters in the one-dimensional space. Among the above mentioned methods, in order to calculate the initial points, we will opt for the centroid method because it has been advocated throughout the literature that the centroid method produces the best results. Stages of Clustering Applications: Typically, a clustering application involves the following stages as illustrated in 2.6: * Develop a data set. (In order to develop a data set one needs to define a substantive problem or issue). * Data pre-processing. (In this stage missing and unreliable entries are checked). * Clustering data. (After Applying the Algorithm, the data set will be clustered into the required number of clusters). * Interpretation of clusters. (The most important stage, after the clustering, the interpretation of these clusters). * Drawing conclusions. (The final stage is drawing conclusions with respect to the issue in question from the result). The view is supported by the fact that, typically, clustering results are not supported to solve the entire substantive problem but rather contribute to an aspect of it. On the other hand, clustering algorithms are supposed to be applied to situations and issues at which the users knowledge of the domain is not quite deep but rather developing. K-Means is implemented in statistical packages like Statistical Package for Social Science (SPSS) and data mining programs such as Clementine and MineSet. [35] 2.5.5 Strengths and Weaknesses One of the most basic, straightforward clustering techniques is the classic K-means algorithm, in use for several decades. It is probably the father of this entire family of algorithms. K-Means forms clusters in numeric domains, partitioning instances into disjoint clusters. Variations of K-Means where the Euclidean distance function is replaced by another distance have been proposed. It has been shown that the performance of the method is strongly related to the distance used. In summary, only the K-means algorithm and its equivalent in an artificial neural networks domain-the Kohonen network have been applied for clustering on large data sets. Other approaches have been tested, typically, on small data sets. The reasons behind the popularity and weaknesses of K-Means algorithms are as follows: Strengths Weaknesses Its time complexity is O(nkl), where n is the number of samples, k is the number of clusters, and 1 is the number of iterations taken by the algorithm to converge. Typically, k and 1 are fixed in advance and so the algorithm has linear time complexity in the size of the data set. Although this algorithm is easy to implement, it has the drawback of being greedy in the sense that it tends to get stuck in a local minimum that usually depends on the initial centre provided. This makes this algorithm very sensitive to initial points. As an alternative to the problem of local minimum, stochastic methods with a close relation to physics, such as variations of simulated annealing, have been applied to clustering optimisation problems. Its space complexity is O(k + n), and if it is possible to store all the data in the primary memory, access time to all elements is very fast and the algorithm is very efficient. If an obvious distance measure does not exist we must define it, which is not always easy, especially in multidimensional spaces. It is an order-independent algorithm. For a given initial distribution of clusters, it generates the same partition of the data at the end of the partitioning process irrespective of the order in which the samples are presented to the algorithm. The Results of the clustering algorithm (that in many cases can be arbitrary itself) can be interpreted in different ways. Not applicable Current clustering techniques do not address all the requirements adequately (and concurrently). Not applicable Dealing with large number of dimensions and large number of data items can be problematic because of time complexity. Not applicable The effectiveness of the method depends on the definition of distance (for distance-based clustering). Table 2.2 2.5.6 Challenges in Clustering Most of the issues related to automatic cluster detection are connected to the kinds of questions wanted to answer in the data mining project, or data preparation for their successful application. * Distance measure: Most clustering techniques use for the distance measure is the Euclidean distance formula (square root of the sum of the squares of distances along each attribute axes). Non-numeric variables must be transformed and scaled before the clustering can take place. Depending on these transformations, the categorical variables may dominate clustering results or they may be even completely ignored. * Choice of the right number of clusters: If the number of clusters k, in the K-Means method, is not chosen to match the natural structure of the data, the results will not be good. The proper way to alleviate this is to experiment with different values for k. In principle, the best k value will exhibit the smallest intra-cluster distances and largest inter-cluster distances. More sophisticated techniques measure these qualities automatically, and optimise the number of clusters in a separate loop (Auto Class). * Cluster interpretation: Once the clusters are discovered they have to be interpreted in order to have some value for the data mining project. There are different ways to utilise clustering results: i. Cluster membership can be used as a label for the separate classification problem. Some descriptive data mining technique (like decision trees) can be used to find descriptions of clusters. ii. Clusters can be visualised using 2D and 3D scatter graphs or some other visualisation technique. iii. Differences in attribute values among different clusters can be examined, one attribute at a time. * Application issues: Clustering techniques are used when we expect natural groupings in examples of the data. Clusters should then represent groups of items (products, events, customers) that have a lot in common. Creating clusters prior to application of some other data mining technique (decision trees, neural networks) might reduce the complexity of the problem by dividing the space of examples. This space partitions can be mined separately and such two-step procedure might exhibit improved results (descriptive or predictive) as compared to data mining without using clustering. 2.5.7 Applications The followings are the possible application rather than areas of this K-Means Clustering Algorithms: * Marketing: Finding groups of customers with similar behaviour given a large database of customer containing their properties and past records. * Biology: Classification of plants and animals given their features. * Libraries: Book ordering. * Insurance: Identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds. * City-planning: Identifying groups of houses according to their house type, value and geographically location. * Earthquake studies: Clustering observed earthquake epicenters to identify dangerous zones. * WWW: Document classification; clustering web log data to discover groups of similar access patterns. * Medical Sciences: Classification of medicines; patient records according to their doses etc. [36] Chapter 3 Results and Discussion 3.1 Results and Discussion The data mining algorithm has been implemented in the form of a multiagent system, comprising two agents. The first agent implements K-means algorithm with proposed initial centroids formula and the second one is based on ID3 algorithm for the interpretation of its results, under the Kaariboga framework, an experimental Java-based mobile agent framework [Annex]. Choosing a clustering algorithm, however, can be a difficult task; even finding just the most relevant approaches for a given dataset is not obvious. Finding a good similarity function depends strongly on the dataset and the determinant elements are the nature of the data and the desired number of clusters. Another element is the type of input and tools that the algorithm requires because some algorithms only handle the numeric inputs and others categorical. For simplicity the tests were performed on datasets which have numerical attributes values. The chosen datasets are: * Iris data set: This data set contains information about the flowers. There are 3 different classes of flowers. It has 150 records with 5 attributes. After applying the process of normalisation, only 100 are selected. * Vote data set: This data set is about the polls in USA. There are 2 different classes of polls i.e. Republicans or Democrats. There are 300 records and 17 attributes. The data set is normalised and the same number of records for clustering is chosen. * Breast data set: This is a medical data set, containing information about the Breasts diseases. There are 2 different classes of Breasts diseases. It has total of 700 records and 11 attributes. After applying the normalisation process, only 233 records are selected for clustering. * Diabetes data set: This is also a medical data set, containing information about Diabetes diseases. We have divided this data set into 5 different classes. It has total 513 records and 8 attributes. Similarly, we normalise this dataset and select only 256 records for clustering. The vertical partitions of each datasets by selecting the proper number of attributes have been created which are suitable for this clustering algorithm. The clusters are created on the basis of the classes in these four datasets, that is, for dataset iris 3 clusters, for dataset vote 2 clusters, for dataset breast 2 clusters and for dataset diabetes 5 clusters; which is also the value of k in this algorithm. Another required input is the number of iterations, which is n, for these datasets the value of n = 100 is chosen. These datasets are illustrated in Appendix A. There is no predefined rule, how many clusters will be created and what will be the number of iterations? It always depends upon the user. If the result is not according to the requirements, the values of k (number of clusters) and n (number of iterations) can be changed every time until the required and better results are found. This is a weakness of this algorithm. Once the clusters are discovered, they have to be interpreted in order to have some value; this is another major issue in this algorithm. There are different ways to utilise clustering results; clusters membership can be used as a label for the separate classification problems, some descriptive data mining techniques like ID3 (decision tree) can be used to find descriptions of clusters and clusters can be visualised using 2D or 3D scattered graphs. We have used 2D scattered graphs and ID3 for visualizing as well as interpreting the results of K-means clustering algorithm. The K-means algorithm with the proposed initial centroids formula was tested on both peer-to-peer and client-server network. There is no difference between results obtained from both modes. The results are demonstrated in tables 3.1, 3.2, 3.3, 3.4 and 3.5. K-Means Number of Clusters Number of Iterations Record/cluster Percentage record/cluster Euclideans distance 3 100 1 = 38 2 = 50 3 = 12 38 50 12 City Block (Manhattan) distance 3 100 1 = 27 2 = 38 3 = 35 27 38 35 Table 4.1-Iris a flower dataset K-Means Number of Clusters Number of Iterations Record/cluster Percentage record/cluster Euclideans distance 2 100 1 = 181 2 = 119 60 39 City Block (Manhattan) distance 2 100 1 = 115 2 = 185 38 61 Table 3.2 Vote a polls dataset K-Means Number of Clusters Number of Iterations Record/cluster Percentage record/cluster Euclideans distance 2 100 1 = 142 2 = 91 60 39 City Block (Manhattan) distance 2 100 1 = 178 2 = 55 76 23 Table 3.3 Breast a medical dataset K-Means Number of Clusters Number of Iterations Record/cluster Percentage record/cluster Euclideans distance 5 100 1 = 52 2 = 26 3 = 11 4 = 91 5 = 76 20 10 4 35 29 City Block (Manhattan) distance 5 100 1 = 83 2 = 11 3 = 58 4 = 81 5 = 23 32 4 22 31 8 Table 3.4 Diabetes a medical dataset K-Means/Data sets Iris Vote Breast Diabetes Euclideans distance C1 = 38 C2 = 50 C3 = 12 C1=181 C2=119 C1 = 142 C2 = 91 C1 = 52 C2 = 26 C3 = 11 C4 = 91 C5 = 76 City-Block (Manhattan) distance C1 = 27 C2 = 38 C3 = 35 C1=115 C2=185 C1 = 178 C2 = 55 C1 = 83 C2 = 11 C3 = 58 C4 = 81 C5 = 23 Table 3.5 Summary of datasets We have noticed in all the four datasets the results obtained from this proposed formula are satisfactory and consistent. So, the further tests have been demonstrated, the formula is transcendental, that is, independent of size and nature of the data. For visualising the obtained clusters we will first apply scattered graph technique only on dataset iris, by taking petal_length, petal_width and class attributes of this dataset. 3.1 shows a scattered graph of all data points in iris dataset before applying the K-means algorithm. References [1] Cawsey, Alison. The Essence of Artificial Intelligence. ISBN: 0135717795,1997. [2] Hermans, Bjorn. Intelligent Software Agents on the Internet: an inventory of Currently offered functionality in the information society a prediction of (near-) future developments. The Netherlands, 9th July, 1996. [3] Kiniry, J., and Zimmerman, Daniel. A Hands-On look at Java Mobile Agents, IEEE, 1997. [4] Robert, S.G. Mobile Agents: Overcoming Early Hype and a Bad Name. 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Experiences with Software Engineering of Mobile Agent Applications, Australian Software Engineering Conference (ASWEC) 2004. [11] Wayne, Jansen., Peter, Mell., Tom, Karygiannis., and Don, Marks. (1999). Applying Mobile Agents to Intrusion Detection and Response. National Institute of Standards and Technology Computer Security Division NIST Interim Report (IR) 6416 October 1999 at , 1999. [12] Suna, Alexandru., Fallah-Seghrouchni, Amal El., and Klein, Giles. Using Mobile Agents for resource sharing, IEEE/WIC/ACM, IAT 2004. [13] Sundsted, T. Agents on the move, Bolster your client apps by adding agent mobility. URL: howto.html, 1998. [14] Suna, Alexandru., and Fallah-Seghrouchni, Amal El. A Programming language for Autonomous and Mobile Agents, IEEE/WIC, IAT 2003. [15] Wang, Ji., Shen, Rui., and Zhu, Hong. Scenario Mechanism in Agent-Oriented Programming, IEEE, APSEC 2004. [16] Suna, Alexandru., and Fallah-Seghrouchni, Amal El. Programming Mobile Intelligent Agents: an Operational Semantics, IEEE/WIC/ACM, IAT 2004. [17] Tripathi, A.R., Ahmed, T., and Karnik, N.M. Experiences and future challenges in mobile agent programming. Microprocessors and Microsystems, Vol 25. March 2003, pp.121, 129. , 2003. [18] Stallings, W. SNMP, SNMPv2 and RMON: Practical Network Management. Addison-Wesley, 1999. [19] Stallings, W. SNMP and SNMPv2: The Infrastructure for Network Management. IEEE Communications Magazine, vol. 36, n_3, p. 37-43, March 1998. [20] Darby, Chad., Griffin, John., Haan, Psacal den., Haan, Peter den.,Konstantinou, A.V., Li, Sing., MacLean. Sean., Mitchell II, G. E., Peach, Joel.,Wansch, Peter., and Wright, William. Beginning Java Networking. Wrox Press. ISBN: 81-7366-399-8, 2001. [21] Donovan, Anne Maric. Knowledge Discovery in Databases and Information Retrieval in Knowledge Management Systems. Knowledge Management System, LIS385T, The University of Austin, April 22, 2003. [22] Kantardzic, Mehmed. Data Mining: Concepts, Models, Methods, and Algorithms. ISBN: 471228524 John Wiley Sons., 2003. [23] Ullman, J. D. Data Mining: A knowledge discovery in databases. URL: [24] Threaling, Kurt. An Introduction to Data Mining, , 2003. [25] Mannila, Heikki. Data mining: Machine Learning, Statistics and Databases, Proceedings of the 8th IEEE, SSDBM 96., 1996. [26] US Census Bureau. Iris, Diabetes, Vote and Breast datasets. Publicly available at URL: [27] Keedwell, Ed., Bessler, Florian., Narayanan, Ajit., and Savic, Dragan. From Data Mining to Rule Refining, A new tool for post data mining rule optimization, IEEE, ICTAI 2000. [28] Quinlan, J.R. C4.5: Programs for Machine Learning. San Francisco: Morgan Kanfmann. ISBN: 1-55860-238-0., 1993. [29] Bentayeb, Fadila., Darmont, Jerome., and Udrea, Cedric. Efficient Integration of Data Mining Techniques in Database Management Systems,IEEE, IDEAS 2004. [30] Zhang, Jianping., Bloedorn, Eric., Rosen, Lowell., and Venese, Daniel. Learning Rules from Highly Unbalanced Data Sets, IEEE, ICDM 2004. [31] MacQueen, J.B. Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1:281-297 [32] Fung, Glenn. A Comprehensive Overview of Basic Clustering Algorithms. June 22, 2001. [33] Davidson, Ian. Understanding K-Means Non-hierarchical Clustering, SUNY Albany Technical Report 2002. [34] Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R.,and Wu, A.Y. An Efficient k-Means Clustering Algorithm: Analysis and Implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No. 7, July 2002. [35] Mirkin, Boris. Towards Comprehensive Clustering of Mixed Scale Data with K-Means. School of Computer Science and Information Systems, Birkbeck College, University of London, Mallet Street, London, WC1E 7HX., 1998. [36] Skrypnik, Irina., Terziyan, Vagan., Puuronen, Seppo., and Tsymbal, Alexey. Learning Feature Selection for Medical Databases. CBMS 1999. [37] Horvat, H., Cvetkovi, D., Milutinovi, D., Kovi, P., and Kovaevi, V. Mobile Agents and Java Mobile Agent Toolkits. Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS-33). CD, ROM, IEEE Computing Society, Los Alamos, CA, p.10. , 2000. [38] Kaariboga. A Java- Based Experimental Mobile Agent Framework. URL: http// , 2004. [39] Pakistan. Map of Pakistan. URL:, 2004. [40] Appendix A Appendix B An agent program for the ID3 algorithm under kaariboga agent framework package org.kaariboga.agents; import java.lang.*; import*; import java.util.*; import java.awt.*; import java.awt.event.*; import javax.swing.*; import javax.swing.event.*; import org.kaariboga.core.Kaariboga; import org.kaariboga.core.KaaribogaEvent; public class dtAgent1 extends Kaariboga { String filename1; String outputfile; public dtAgent1(String name) { super(DECISIONTREE2_+name); } public void onArrival() { System.out.println(This Agent will print the decision rules); } public void onDestroy() throws ArrayIndexOutOfBoundsException { System.out.println(The Agent is going to be destroyed:); } public void KaaribogaDestroyRequest(KaaribogaEvent e) { System.out.println(The agent Destroyed); } public void run() { JOptionPane dialog = new JOptionPane(); filename1 = dialog.showInputDialog (null, Enter Data File Name); dtmain1 dtm = new dtmain1(); long startTime = System.currentTimeMillis(); try { String in = dtm.inputFile(filename1); } catch(Exception e) { System.out.println(Unable to open this Data File:+e); } System.out.println(The Decision Rules are); dtm.decisionRules(); // actual program which will print the decision rules long endTime = System.currentTimeMillis(); long totalTime = (endTime startTime)/1000; System.out.println(Total Time to Build a Decision Tree is: +totalTime+ Second); } } Appendix C A class diagram for ID3 algorithm. A class diagram for K-means clustering algorithm using City Block formula. A class diagram for K-means clustering algorithm using Euclideans distance. Message exchange between agents on different servers. Message exchange between Agents on same server Glossary Agent Oriented Programming (AOP): It is a programming technique which deals with an object, which has independent thread of control and can be initiated. Centroid of a cluster: The centroid of multi-dimensional data points is the data point that is the mean of the values in each dimension. For XY data, the centroid is the point at (mean of the X values, mean of the Y values). Average or mean value of the objects contained in the cluster on each variable. Clustering: Clustering of data is a method by which large a set of data is grouped into clusters of smaller sets of similar data. It is concerned with finding structure in a large set of data. It is defined as: The process of organizing objects into groups whose members are similar in some way. Cluster: A cluster is therefore a collection of objects which are similar between them and a dissimilar to the objects belonging to other clusters. Similarity criterion is a distance this is called distance-based clustering. Information Retrieval (IR): The methods and processes for searching relevant information out of information systems that contain extremely large numbers of documents. Intelligent Agent: 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. Knowledge Discovery in Databases (KDD): The nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. Learning Intelligent Agent (LIAgent): An agent which is capable of to do classification, clustering and prediction using learning algorithms is called a Learning Intelligent Agent (LIAgent).

Wednesday, May 6, 2020

The Main Weapon Used For Murder Or Manslaughter Is Handguns

The main weapon used for murder or manslaughter is handguns. There is no other weapon as gun that can kill someone in seconds. 72% of handguns are used in homicides are handguns. The other types of guns include rifles and shotguns. In article â€Å"weapon use and violent crime† Craig Perkins states â€Å"Between 1993 and 2001, about 26% (or an annual average of 2.3 million) of the estimated 8.9 million violent crimes in the United States were committed by offenders armed with guns, knives, or objects used as weapons. Basically guns are the main reason for criminals to commit crimes such as killing of innocent citizen or robberies. These statistics show that numbers are increasing every year. To stop these number from increasing government, need strict rule of checking background before selling gun to any citizen. This will benefits government as well as citizen. First, Gun control would reduce the rate of homicide and gun control would surely reduce crime. It will place a qu alification for people who are eligible to have a gun. People with criminal history won’t be allowed to purchase a license gun. It will make tough for any criminal to buy a gun. That would lead to less people having guns and only the more responsible people in the world owning guns. This act will help police and other law enforcement to reduce crimes rates in community. In an article, â€Å"The Facts That Neither Side Wants to Admit about Gun Control†, by Justin King, acknowledges, â€Å"Australia enacted its gun banShow MoreRelatedGun Control Is A Decent Answer For The United States1183 Words   |  5 PagesThroughout the years there have been numerous of debate confrontations about regardless of whether weapon control is a decent answer for the United States. Therefore, two extremely particular sides have shaped: one for gun control and one against it. 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Investigators concluded that Vicki Friedli was most likely shot with a .40-caliber handgun, possibly a Glock. Hayward, Vicki’s boyfriend and one of the victims of the killing, was shot with a 12-gauge shotgun. In 2007, after a search in Pape’s then home, authorities found the shotgun and shells that could have possibly been used for the weapons killing the victims. Furthermore, a Glock holster was found, but not the actual Glock. With the evidence mentioned, thereRead MoreStop Frisk Policy? Racist or not? Essay2309 Words   |  10 Pagesanything while patting Katz outer garment so he had no reason to order Katz to take of his coat. The three men John W. Terry Chilton, and Katz were taken to the police station where both men who had guns on them were charged with carrying concealed weapons. John W. Terry then filed a lawsuit o n the city of Ohio his defense attorney asked for the gun not to be admissible during the trial but the judge in charge of the case found no need to remove the valuable evidence because the detective had followed

Tuesday, May 5, 2020

International Business for Case of Royal Mail-

Question: Discuss about theInternational Businessfor Case of Royal Mail. Answer: Introduction: International business has been obtaining formidable impetus in the recent times with the inclinations of enterprises to accomplish strategic advantage in the global business environment. The last two decades have witnessed the rapid and sustainable growth of international business. Markets have evolved into global entities for majority of services and goods as well as a wide assortment of financial instruments. The case of Royal Mail also presents a lucid interpretation of an enterprise engaging in international business action. The illustration of the literature pertaining to international business such as concepts and models would be helpful in drawing critical inferences into the international business action of Royal Mail (Ahammad, et al., 2016). Royal Mail to Engage in International Business The event can be illustrated as the recent implications for the Royal Mail to engage in international business are reflective of the reduction in the number of letters it delivers in UK. The characteristics of Royal mail could be prominently observed in its reputation as the designated Universal Postal Service Providers of UK as well as the plausible implications for supporting customers, communities all over the country and the businesses in UK (Bury, 2017). The engagement of the company in one of the profound transformations in the industry of delivery services in British history is reflective of the barriers for the vision of the organization to realize its position as the best delivery company in UK and Europe (Cheng, et al., 2014). As per Darendeli Hill, the notable characteristics which can be perceived in the existing market scenario in the UK reflects on the declining share of letter volumes in delivery service and the profound implications for the growth of the parcel business through involvement of online retailers such as Amazon (Darendeli Hill, 2016). On the contrary, the GLS or General Logistics Systems, which is the international delivery arm of Royal Mail, has depicted considerable improvements in business performance which depicted growth by 9% as compared to the domestic business which depicted a slight reduction of 2%. The declining share of letters delivered by Royal Mail by 6% over the course of the last year creates potential implications for the organization to consider international business action. Furthermore, the increasing popularity of online shopping has provided a promising impetus to the volumes of parcel deliveries which increased by 3% in the recent year. Therefore, Royal Mail has to consider emphasizing on the international business arm, GLS, and embark on overseas acquisitions that can expand the GLS in varying geographies. As per Helm Gritsch, despite the risks of losing revenue to the dedicated parcel delivery companies such as Amazon, Royal Mail can leverage the international business volumes generated by GLS to address the setbacks faced by the organization in domestic business performance. Therefore the validity of reflecting on relevant literature pertaining to international business could be realized effectively in case of Royal Mail to predict distinct inferences for the international business action of the organization (Helm Gritsch, 2014). The essential elements in literature pertaining to the international business that can be considered with respect to the observed event in case of Royal mail would refer to the competitive advantage and strategic direction of an enterprise in the international business environment, frameworks for analyzing the international business environment as well as the challenges and opportunities that can be vested in the method of acquisitions to expand international trade. Literature Review: The reflection on literature pertaining to international business would provide a notable contribution to the determination of strategic approaches for business organizations in foreign business environments. According to Jia, et al, prior to the illustration of theoretical models, it is imperative to consider the generic definitions of international business which rely on the expansion of business activities beyond the national boundaries of the organization. Therefore organizations which engage in the transaction of goods and services across national boundaries with the location of central management in a single country could be encompassed in the definitions of international business (Jia, et al., 2014). According to Leung Morris, the other dimensions of international business are profoundly indicative of the equation of international business with the big enterprises that have established operating units in the external jurisdictions. Furthermore, international business has also been associated with the institutional arrangements that engage in direction of economic activity towards managerial applications in foreign jurisdictions without formal depiction of ownership control. Such examples can be perceived in the joint ventures of organizations with locally owned businesses as well as with the governments in international jurisdictions (Leung Morris, 2015). International business activities are associated with a broader scope that reflects on the challenges as well as opportunities in the international business environment. Some of the cognizable factors that could be accounted as major influences on the international business initiatives of an organization include cultural differences, foreign exchange markets, and legal infrastructures in the foreign jurisdictions and the variations in inflation rates. International business has formidable implications towards the evolving changes in the international business environment as compared to the domestic environment which are often associated with traits of contradiction and ambiguity. Managers with the skills for identification of potential pitfalls in the international business environment could be able to cater the changes in priorities of the business environment thereby obtaining feasible competitive advantage over the peers in the industry which are unable to cope with the changing p riorities of international business environments (Lord, 2014). Prior to the evaluation of opportunities that can be accessed from the international business actions of an enterprise the estimation of the setbacks could provide a critical perspective. The identification of the setbacks that can be associated with the domain of international business would help in providing viable improvements in the strategic direction of an enterprise in the foreign market environment. An organization faces political and legal conflicts in international business with the notable implications towards disparities in the domestic and international political structure and the implications of legal regulations. The complexities of managing political and legal setbacks in business environment could be increased with the involvement of the company in various international business jurisdictions. Furthermore, the disparities in the political and legal environments of the provinces could also be accounted as notable influences on the strategic direction in international jurisdictions. As per Oparaocha, cultural differences could be observed as the next formidable impact on international business actions since cultural diversity is an imperative requirement for addressing the evolving trends in international markets (Oparaocha, 2015). The limitations on international barriers and the increasing movement of people across international boundaries have led to the concerns for increasing variability of different cultural and ethnic sections in the demographics of various geographical jurisdictions. Therefore, organizations which prefer engagement in international trade are required to tailor their strategies and business operations especially marketing to the cultural framework profoundly observed in the international market environment. The analysis of the demographics as well as reflection on cultural dimensions such as long term orientation could be helpful for drawing valid implications pertaining to the strategic approach for international business (Peng, et al., 2017). Economic conflicts also serve as profound impacts on international business actions since they could impinge detrimental outcomes on the financial returns acquired by the organization from international business operations. The prominent factors that can be associated with the economic aspects of international business relate to the fluctuations in exchange rates as well as inflation rates. Variability of currency exchange rates creates potential setbacks for conversion of currency leading to potential ambiguities in the financial standing of an organization in the international business environment. As per Sandberg, marketing infrastructure variation in international business environments leads to cognizable setbacks for marketing activities of an organization. Marketing infrastructure refers to the accessibility and nature of marketing facilities in a particular country which direct towards the varying efficiencies of marketing media in different market environments (Sandberg, 2014). It has been observed that the negative implications pertaining to international trade have a considerable impact on international business action of an organization. The use of specific models such as PESTEL analysis could be considered as viable additions to improve the outcomes of market research and plausible identification of the issues. On the contrary, observation of the viable opportunities that can be accessed from the international business actions could also be assumed as driving forces for organizations to engage in international trade. As per Young Makhija, international trade provides the foremost promising outcome of sustainability since the access to foreign markets allows organization to acquire feasible outcomes such as substantial improvement in the market share of the organization as well as the expansion of the scope of international experience (Young Makhija, 2014). The observable growth in international markets of developing countries could be perceived as a viable opportunity for neglecting economic and marketing problems and entering the markets to obtain competitive advantage. Diversification has become an imperative constituent of the domain of international trade implying profound indications towards the consideration of foreign markets as a viable solution for mitigating the risks for an organization through the concerns of variable demand. The beneficial aspects of diversification lead to acquisition of comprehensive insights into the variability of production processes and other business operations. Another cognizable element that should be included in the discussion pertaining to efficient perspectives for international business is the acquisition of competitive advantage and precise identification of strategies. Competitive advantage can be illustrated as the edge obtained by organizations over competitors which is generally acquired through the provision of higher value on the products to customers either through lower prices or provision of sophisticated services that can validated the higher pricing of products and services (Zhang, van Doorn Leeflang, 2014). The implications of competitive advantage can be realized by a comprehensive interpretation of the core competencies, strategy and business level strategy alongside the apprehension of the interdependencies among them. The core competences could be implemented for obtaining promising outcomes from a strategy through the provision of viable inputs into the strategy design. Core competencies could be identified as the unique traits, capabilities and resources of an organization that facilitate competitive advantage over rivals. The strategy implications are largely dependent on the core competency in order to obtain integration and coordination among the various sets of actions that can be implemented in various functional domains of an organizations business framework. The subsequent element which contributes to the competitive advantage of an organization reflects on the business level strategy that is specifically directed towards the particular business functions. The business level strategy is associated with the initiatives for providing value to customers through exploitation of core competences in particular product markets (Young Makhija, 2014). The global strategy can be observed from a distinct perspective which classifies specific planned actions as well as contingency measures which largely reflect on the necessity to evaluate the dynamics of internal and external changes as well as obligations of organizations to learn consistently. Therefore the relation between competitive advantage and the learning process of an organization could be perceived as a profound highlight in the domain of international business management. The acquisition of competitive advantage can be validated through the competitive positioning, core competences and global strategy which allow distinct opportunities in international market environments. Competitive positioning is defined on the grounds of configuration of core competences specific to the industry to the maximum possible value for accomplishing competitive advantage. The value of core competences in an organization could be realized efficiently in international business initiatives thr ough maintaining an open system with specific implications towards cooperation with environment and the utilization of core competences (Peng, et al., 2017). The impact of global strategy can be observed in the promising outcomes such as accomplishment of a global scale and the flexible configuration and the coordination of international business activities. Therefore the provision of international strategy opportunities could be realized through the identification of international opportunities, exploring the capabilities and resources and utilization of core competences in order obtain feasible outcomes related to strategic competitiveness (Young Makhija, 2014). Critique of the Event: As observed in the case of Royal Mail, the organization is considering the methods of acquisition to enter foreign markets. The rationale for engaging in international business could be perceived in case of Royal Mail as the organization is not capable of sustaining its conventional business opportunities in the industry of delivery services. As per Zhang, van Doorn Leeflang, the senior management of the organization is inclined towards the realization of strategic approaches to improve the investments in overseas acquisitions prudently. Therefore a critical evaluation of the organizations international business action should be obtained in order to propose feasible recommendations for improving competence in international business assignment. The market entry modes are also associated with the strategic direction of an organization in international business assignments (Zhang, van Doorn Leeflang, 2014). Acquisitions and mergers as well as joint ventures have proved to be legible approaches for entering foreign markets. Therefore literature pertaining to international business management has included acquisitions as a flexible option for international market entry as well as referring to the potential advantages and disadvantages that are associated with the market entry mode of acquisition. The foremost advantage that can be drawn from acquisitions is the flexible distribution of financial resources and skill competences. The benefits of acquisition in different industries are also accountable for the diversity implications since the fall in sales in a particular industry would not affect the performance of the other partners in different industries. The acquisition of companies in similar industry is also profoundly indicative of the reduction in competition as well as reduction in costs of publicity and advertisement. On the contrary, acquisitions can also be critically associated with setbacks that could lead to long term consequences for Royal Mail in its international market expansion initiatives through acquisitions (Sandberg, 2014). The difference in objectives of acquisitions among the acquired companies could lead to potential conflicts that would require strategic management thereby reflecting on the mandatory requirement of resources and costs. The lack of precise standards for pricing of the acquisitions could lead to ambiguities pertaining to the investments made by Royal Mail in its overseas acquisition approach that lead to profound implications towards overpricing of specific acquisitions by the enterprise. The integration of employees of the Royal Mail and the acquired firms could be assumed as a formidable limitation of acquisitions that lead to conflicts pertaining to the adaptability to new cultures, management and employees (Leung Morris, 2015). In case of Royal Mail, the proposed initiatives for acquisition of overseas firms by GSL, the companys international arm, could be critiqued on the grounds of the numerous acquisitions that could lead to variable concerns for management and the improvement in uncertainty in the moderation of business activities of the organization due to absence of integration. Furthermore, the variability of the financial consequences obtained from the acquisitions could also imply critical gaps in the preferences for acquisition. The foremost pitfall that can be perceived in the case of Royal Mails expansion in international markets through acquisitions is reflective of the excessive managerial focus on acquisition. The comprehensive direction of Royal Mails management towards acquisitions could lead to inhibitions pertaining to internal development and negligence for the slump in letter delivery volumes in the domestic market. Furthermore, the recommendations for improved strategic approach in case of international business action of Royal Mail would have to be dependent on the analysis of international business environment which is characterized by profound competition from dedicated parcel service providers such as Amazon (Helm Gritsch, 2014). Conclusion: The report presented a cognizable illustration of concepts pertaining to international business actions by referring to the recent instance of international business acquisitions by Royal Mail, the dedicated Universal Parcel Service Provider in UK. References Ahammad, M.F., Tarba, S.Y., Liu, Y. and Glaister, K.W., 2016. Knowledge transfer and cross-border acquisition performance: The impact of cultural distance and employee retention.International Business Review,25(1), pp.66-75. Bury, R. 2017. Royal Mail relies on international business as letter deliveries fall. [online] The Telegraph. Available at: [Accessed 19 May 2017]. Cheng, J.L., Birkinshaw, J., Lessard, D.R. and Thomas, D.C., 2014. Advancing interdisciplinary research: Insights from the JIBS special issue.Journal of International Business Studies,45(6), pp.643-648. Darendeli, I.S. and Hill, T.L., 2016. 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