Does Birth Order Have an Effect on Intelligence

In 1874 Francis Galton revealed that firstborn kids were overrepresented as high achievers in different logical fields. There were blemishes in Galton's philosophy, for example, he didn't include female youngsters in his outcomes. Male subjects were considered a previously brought into the world regardless of whether they were the tenth kid, yet the nine more seasoned kin were female (Esping, 2003). In any case, Galton’s end that birth request connects with knowledge and scholastic achievement stays well known. Indeed, even in the most recent decade, different specialists, in both Europe and North America, have affirmed and reasserted Galton’s end. What studies have exhibited that birth request impacts knowledge as well as accomplishment? Examination by Christensen and Bjerkedal inferred that birth request smallly affects instructive accomplishment (Christensen and Bjerkedal, 2010). That end has likewise been accounted for by other related investigations. Investigation of the National Longitudinal Survey of Youth (NLSY) and the Wisconsin Longitudinal Study (WLS) show that birth request affects instructive accomplishment and insight (Retherford and Sewell, 1991 and Rodgers, Cleveland, van sanctum Oord and Rowe, 2000). Additionally, prior exploration on Norwegian male military recruits likewise showed that birth request impacts on insight (Bjerkedal et al., 2007). The juncture model guesses that initially conceived youngsters are brought up in a grown-up situated, profoundly savvy condition. Likewise, when originally conceived kids cooperate with their more youthful they embrace the job of educator. This is known as the guide impact (Zajonc& Sullaway ,2007). Are contemplates that help birth request impact on knowledge and/instructive achievement imperfect? Wichman, Rodgers and MacCallum recommend a basic defect in past examination that underpins that birth request affects insight as well as instructive fulfillment They propose that in bigger families the primary conceived is similarly savvy as the fourth-conceived kid, however they are not as canny as kids from a littler family (Wichman et al,2006). The investigations that show a connection between instructive fulfillment as well as knowledge and birth request have been censured by different specialists. In any case, as indicated by the conjunction model it is just as kids with more youthful kin approach adulthood that they at last accomplish greatest profit by showing their more youthful kin, as it commonly builds their endeavors to do well educationally (Zanjonc and Sulloway, 2007). What factors other than birth request impact knowledge or potentially accomplishment? Wichman, Rodgers and MacCallum contend that the discoveries were a consequence of contrasts between families, not inside families. They propose that the more youthful a mother is at the introduction of her first youngster will bring about lower insight scores inside the family. More youthful moms will in general be less taught, have more youngsters and lower salary. At the point when specialists controlled for mother’s age from the outset birth, the impact on birth request on knowledge was about wiped out. As they would like to think birth request seems to affect insight, yet that’s simply because bigger families don’t have the upsides of littler families. Family condition and hereditary impacts are the most significant elements and they may abrogate birth request (Wichman et al., 2006).

Fighting Malnutrition Essay

Ailing health has been a genuine worry over the world which is caused because of absence of fundamental nutrients and minerals in the eating routine where in lion's share of the devastated or destitute individuals experience the ill effects of micronutrient lacks. Handling lack of healthy sustenance has to be sure been a test for the administration, contributors, and the private part to focus on the formative viability and offering some benefit for cash. Approx. 2 billion individuals over the globe experience the ill effects of weakness which caused because of iron inadequacy and iodine lack which prompts mental impediment, brings about being protection from the illness, bringing down the consideration and grouping of kids in homerooms, causes demise of pregnant moms, passings because of the runs and million of individuals go daze every year. As this issue of ailing health removes nearly 3% of the country’s GDP, organizations should be a lot of cautious about this reality which thus influences the utilization example of the purchasers. Organizations like Coca-Cola and P&G have for sure invested a lot of amounts of energy in making their food and refreshments plentiful in nutrients and minerals however the issue in this respects is the way firms focus available as far as the accessibility of the item, its estimating methodologies, the method of advancing and making mindfulness among the majority and the comfort of the item. Along these lines rather than just centering upon the Product blend, much the same as Coke and P&G have managed the issue, the organizations should focus on the other promoting blend components also to effectively showcase their merchandise. What extra endeavors have been taken up by organizations to unravel this worry of unhealthiness, How has the innovative work group of the organizations concentrated on their exercises to survive and battle the monetary concern has been the inquiries in the majority of the associations watch list. Issues/Issues: Coca-Cola presented the item named Vitango, which was a powdered beverage to be blended in with water and to be expended as a drink, the issue was that exploration showed that there was a colossal enhancements in specific spots where it was presented however in specific places because of the polluting influences in the water, it invalidated the endeavors of the powdered vitango. The firm experienced difficulty in view of absence of center in the underlying phases of the new item improvement which it at a later stage approached bundling a prepared to-drink equation for its shoppers. P&G likewise came out with their own creation of Nutridelight which had a component of nutrient An, iron substance and Growth Plus was the principle fixing. Be that as it may, tragically it didn't sell well in the market as the costs were non †serious and it didn't meet the current market †going pace of the product’s cost. It later accompanied Nutristar which performed great in the market however it would target just the superior gathering clients as the item was accessible at just McDonald’s outlet, and in significant cases it is the portion of the poor which really require the item. In this manner again P&G had absence of center in their vital usage. In both the cases referenced over, the organizations needed focal point of their advertising blend factors regarding the item, value, place or special systems. Investigation: Based on the exploration conveyed, both over supported and under sustained prompts unhealthiness and henceforth destitution and absence of food has been an issue getting looked at in specific nations which has side effects of frailty, the runs, confusion, goiter, absence of coordination and loss of reflexes, scaling and splitting of lips and mouth are horrendous outcomes looked by the individuals. Organizations need to give sustained nourishments which have the chance of including esteem and giving economies of scale by bringing down upon the costs, creating quality items to improve exchange and rivalry and connecting with more up to date clients that have not been focused, there by battling the issue of lack of healthy sustenance of an enormous fragment of those misery. Therefore lack of healthy sustenance is an endless loop where in an under-supported or over-fed individual experiences different maladies and diseases which thusly builds the prerequisite of vitality into the body which should be satisfied. On the off chance that this interest s not satisfied it brings about ailing health which further lessens and reduces the invulnerability level of the kids and seniors also making them progressively inclined to such diseases. Proposals/Solutions to the Problem: To improve the nourishing degrees of food, the earlier concern ought to be to build the salary levels where by the quality and the quantitative food admission can be expanded. Firms need to concentrate on consider the accompanying perspectives in the plan of a vital improvement so as to battle ailing health and the destitution line. Organizations need to center after: ?Creating mindfulness among the general population on nourishing and medical problems: This will assist the neighborhood needy individuals with understanding the estimation of wellbeing and the advantages of having adjusted food propensities. ?Increment the salary levels there by improving the way of life: If the buying power equality of buyers is expanded, the utilization level will likewise build prompting appropriation of the best food eating strategies. ?Improve the wholesome and the wellbeing status of the general public everywhere, ?Focus on the rustic advertising and conveyance techniques: provincial showcasing needs concentrate particularly to ingrain the earnestness of the passings and shortcomings which result through lack of healthy sustenance. Making the item accessible to them at a sensible cost and at their advantageous spots will make positive feeling of belongingness in the psyches of the clients. ?Sort out and create advertise overviews to examine the general pattern of the buyers: Study the market and comprehend the changing food propensities there by advancing the procedures in the way which frees the issues from opposing and battling the issue of lack of healthy sustenance. ?Train the volunteers and make mindfulness among the provincial areas, ?Setting up creation and advertising units to take into account the objectives and goals set by organizations there by ad libbing on the sustenance status of the objective town or market thought about. End: Fighting Malnutrition has been a worry and a major issue for all the organizations whose primary goal has been to create items which are higher in supplement level with nourishing enhancements. The earlier spotlight ought to be on whether the food or the refreshment ought to be utilized as an enhancement or as a substitute which thus will help in planning the business forms in a way which suits the objective markets necessities and requirements. References: Capart. (2008). Advancement of network activities to battle lack of healthy sustenance and give salary age in the retrogressive districts of India. Recovered October 3, 2008, from Website: http://209. 85. 175. 104/search? q=cache:PWnXLkls_2sJ:capart. nic. in/plot/projectm. pdf+marketing+:+malnutrition&hl=en&ct=clnk&cd=3&gl=in Michael, J. (2007). Battling Poverty with Markets, Marketing to the malnourished. Recovered October 3, 2008, from Foreign Policy Website: http://blog. foreignpolicy. com/hub/3712

Salem witch trials Essay -- essays research papers

Salem Witch Trials: Casting a spell on the individuals Today, seeing a witch is practically insignificant. Our Halloween occasion denotes a festival wherein many will embellish themselves with pointy dark caps and long wiry hair, and most will hold onto them as funny and bubbly. Indeed, even the contemporary black magic strict gatherings framing are being acknowledged with less analysis. All the more as of late, the Blair Witch film furor has carried more interest than dread to these dull and otherworldly figures. Along these lines, it turns into no big surprise that when our ages watch motion pictures like the Crucible, a to some degree exact delineation of the Salem Witch Trials, we are irritated and befuddled by the shamefulness and the commotion that happened in 1692. For most, our egocentric perspective on the past nearly prevents us from seeing what a difficulty was fermenting in that Puritan way of life. Around then, witches were undeniably in excess of a nonexclusive ensemble for an easygoing special festival, or an endured religion, or another type of Hollywood interest, they were crafted by a terrible, vindictive, concealed force. In the seventeenth century, nearly everybody, even those with the best of trainings, where under the conviction that black magic was underhanded and the control of the fiend. Black magic had once, before the Middle Ages had been acknowledged as the forces of medication and great deeds; be that as it may, the congregation of that time had announced the art as crafted by the fallen angel and the activities of apostates. From that point on witches were significantly feared. They accepted that they had unique powers that permitted them to make hurt those that they had squabbles with; they could understand minds, tell the future, raise apparitions of the dead and power the blessed to perform unholy acts. There was just a single method to spare somebody who offered their spirit to the fallen angel for the endowments of black magic , to execute them (Dickinson 4). Individuals were marked witches for irrelevant accidents. On the off chance that the farmer’s sheep all passed on from an infection in the water, at that point the neighbor who battled with him a week ago probably do magic. In our current reality where individuals are sure of black magic, nothing is unintentional. Importantly, numerous individuals were unreasonably sentenced to death. In the start of the century the objectives for black magic were â€Å"the poor, the old, the intellectually sick, the impolite and quarrelsome†, however as the century attracted to an end those blamed were picked â€Å"more [democratically],† even those as youthful as fou... ...ent speculations of what the young ladies were burdened with. A few specialists proposed that they were experiencing ergot harming from ruined rye grain. Others believed that young ladies were getting a charge out of the consideration that they would have never gotten in any case being youthful females. Also, others felt that the reason for their manifestations are from a mainstream mental turmoil from the 1970’s called clinical agitation or widespread panic, alluding to a condition experienced by a gathering of individuals who, through proposal, perception, or other mental procedures, create comparative feelings of trepidation, hallucinations, strange practices, or physical side effects. (Trask 1 and Plotnik 520) The Salem black magic hallucination turned into the way to what is currently known as the way to Enlightenment. In spite of the fact that the preliminaries in New England didn't end there, Salem denoted the start of and end to the appalling treachery. Witch-chasin g is as yet a scourge that plagues today in different structures. Individuals are made to languish over their convictions. Strict and political mistreatment has recolored consistently from that point forward. Maybe, the best thing picked up from the preliminaries was the understanding that the larger part isn't generally the voice of equity.

Intelligent Agent - Free Essay 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). [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. 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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//www.projectory.de/kaariboga. , 2004. [39] Pakistan. Map of Pakistan. URL: www.mideastweb.org/pakistan.htm., 2004. [40] https://dms.irb.hr/tutorial/tut_dtrees.php Appendix A Appendix B An agent program for the ID3 algorithm under kaariboga agent framework package org.kaariboga.agents; import java.lang.*; import java.io.*; 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).

Biology Prefixes and Suffixes diplo-

The prefix (diplo-) means double, twice as many or twice as much. It is derived from the Greek diploos meaning double. Words Beginning With: (Diplo-) Diplobacilli (diplo-bacilli): This is the name given to rod-shaped bacteria that remain in pairs following cell division. They divide by binary fission and are joined end to end. Diplobacteria (diplo-bacteria): Diplobacteria is the general term for bacteria cells that are joined in pairs. Diplobiont (diplo-biont): A diplobiont is an organism, such as a plant or fungus, that has both haploid and diploid generations in its life cyle. Diploblastic (diplo-blastic): This term refers to organisms that have body tissues that are derived from two germ layers: the endoderm and ectoderm. Examples include cnidarians: jellyfish, sea anemones, and hydras. Diplocardia (diplo-cardia): Diplocardia is a condition in which the right and left halves of the heart are separated by a fissure or groove. Diplocardiac (diplo-cardiac): Mammals and birds are examples of diplocardiac organisms. They have two separate circulatory pathways for blood: pulmonary and systemic circuits. Diplocephalus (diplo-cephalus): Diplocephalus is a condition in which a fetus or conjoined twins develop two heads. Diplochory (diplo-chory): Diplochory is a method by which plants disperse seeds. This method involves two or more distinct mechanisms. Diplococcemia (diplo-cocc-emia): This condition is characterized by the presence of diplococci bacteria in the blood. Diplococci (diplo-cocci): Spherical or oval-shaped bacteria that remain in pairs following cell division are called diplococci cells. Diplocoria (diplo-coria): Diplocoria is a condition that is characterized by the occurrence of two pupils in one iris. It may result from eye injury, surgery, or it may be congenital. Diploe (diploe): Diploe is the layer of spongy bone between the inner and outer bone layers of the skull. Diploid (diplo-id): A cell that contains two sets of chromosomes is a diploid cell. In humans, somatic or body cells are diploid. Sex cells are haploid and contain one set of chromosomes. Diplogenic (diplo-genic): This term means producing two substances or having the nature of two bodies. Diplogenesis (diplo-genesis): The double formation of a substance, as seen in a double fetus or a fetus with double parts, is known as diplogenesis. Diplograph (diplo-graph): A diplograph is an instrument that can produce double writing, such as embossed writing and normal writing at the same time. Diplohaplont (diplo-haplont): A diplohaplont is an organism, such as algae, with a life cycle that alternates between fully developed haploid and diploid forms. Diplokaryon (diplo-karyon): This term refers to a cell nucleus with double the diploid number of chromosomes. This nucleus is polyploid meaning that it contains more than two sets of homologous chromosomes. Diplont (diplo-nt): A diplont organism has two sets of chromosomes in its somatic cells. Its gametes have a single set of chromosomes and are haploid. Diplopia (diplo-pia): This condition, also known as double vision, is characterized by seeing a single object as two images. Diplopia can occur in one eye or both eyes. Diplosome (diplo-some): A diplosome is a pair of centrioles, in eukaryotic cell division, that aids in spindle apparatus formation and organization in mitosis and meiosis. Diplosomes are not found in plant cells. Diplozoon (diplo-zoon): A diplozoon is a parasitic flatworm that fuses together with another of its kind and the two exist in pairs.

Strategies for Improving Student’s Content Area Reading to...

Before a student can even begin to understand how to read expository content within a text book, they must first begin to read meaningfully and they reasons behind why they read. Reading is not just for entertainment, it is also used to acquire information. Reading any form of text opens its audience, the reader, to the world without them having to buy a plane ticket or putting them in dangerous situations to gain firsthand experience (content within storybooks or novels). Reading opens one’s â€Å"cognitive eye†. Once a tolerance for reading is achieved, students can gather information from every text that they read, whether fact or fiction. Reading in content area is basically about â€Å"students interacting with text before, during, and after†¦show more content†¦For example, this strategy could b used to teach a history or literature class, where students are put into groups, assigned a character or historical figure, and using text and other resources, research the person they are demonstrating and make direct quotes from what they have done or said. They present it to their fellow classmates; there each student takes an active role in gathering information to be displayed. †¢ Introducing conflicting thought: Teacher presents students with a scenario that goes contrary to what they have previously learnt, ask them to research the following to discredit either what was learnt or what was now presented to them. For example, this strategy could be used in Science, where the teacher states the sky is not blue, or that colours that we see are not their actual colours, this could be done while teaching the topic of light. Introducing such scenarios causes disequilibrium and students seek the need (through research) to discredit it. †¢ Project based assessment: here students are given topics to review and present. The teacher provides a hotlist (a list of resource books) where they are to gather information and organize it to present orally or otherwise. This strategy could be used in teaching Studies-Studies under any scheme of work, for instance, in teaching about theShow MoreRelatedA Digital World Of Information And Communication1625 Words   |  7 Pagesdigital world of information and communication, it is imperative for us to begin thinking about reading and literacy in a new way. Our students must be proficient in what scholars describe as â€Å"new literacies.† This relatively new perspective in literacy instruction acknowledges and investigates the literacy practices that are borne out of digital technology (Houtman, 2013). In today’s world, being a proficient learner requires more than the traditional literacy skills of reading and writing. StudentsRead MoreLiteracy Is The Foundatio n Of Every Student s Learning Essay1956 Words   |  8 PagesImportance of Literacy Literacy is the foundation of every student’s learning, and learning to read English is a particularly challenging task. The OECD Programme for International Assessment of Adult Competencies defines literacy as: the ability to identify, understand, interpret, create, communicate and compute, using printed and written materials associated with varying contexts. Literacy involves a continuum of learning in enabling individuals to achieve their goalsRead More English As A Second Language Education Essay3495 Words   |  14 Pagesdevelop their understanding of mathematics. Students then effectively use mathematical tools, charts, patterns and other strategies, as well as their prior learning experiences to make connections to solve related problems. The majority are able to transfer their manipulative exploration to solving problems with pencil and paper. Students use multiple solutions and strategies when they solve problems. They express their mathematical thinking through drawing, writing, and speaking. Students socializeRead MoreIntegrated Planning Matrix4838 Words   |  19 PagesLearning Area/s Broad objective/s Lesson Objective/s Key learning opportunities Evaluation/assessment Resources (Use if you wish) Literacy, Critical and creative thinking, Aboriginal and Torres Strait Islander histories and cultures, Asia and Australia ´s engagement with Asia. 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Direct and explicit instruction helps enhance a struggling adolescents reading ability and, therefore, helps them to succeed in the literacy classroom.  Struggling adolescent readers also need to be actively engaged and motivated in daily literacy activities. Strategies thatRead MoreHow Can Modern Technology Be Used to Aid Learning in Schools2354 Words   |  10 Pageshold of reference materials students had to search for books for a long time or had to get a membership in a library which may or may not be near their homes. However internet has made information gathering an extremely easy task. Students can now easily access and know the various developments taking place at their area of interest at the simple click of a button. Getting reference materials is not meant for only a few students. Now everyone can search and find reference materials in cyberspace and

Thomas Paine, Common Sense free essay sample

A letter from eight white clergymen The clergymen’s letter suggests that the racial problem in Birmingham, Alabama, needs to be resolved in court peacefully. The exigency of his argument is to try to solve the racial issue with an innovative and constructive approach. The letter was written to the editor of a Birmingham’s newspaper. Based on that, the audience of this letter was the newspaper’s readers, all the city’s citizens. The fact that the writer of this letter is a religious person, and he also represents a group of religious, the constraints are the following: based on his religious beliefs and background, he is opposed to the usage of violence in order to resolve the community’s racial problems; and another aspect, is the fact that he is white, and this also influence how he views the racial issue. The issue of this letter is to resolve the racial problems in court, and, in the meantime, the laws should be obeyed in peacefully manners. We will write a custom essay sample on Thomas Paine Common Sense or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Therefore, the author of this letter is using the newspaper medium to convince the local citizens for the necessity to do not follow the outsider’s leader suggestions, which is King’s suggestions. He criticizes the fact that a foreigner leadership is influencing in part the local Negros’ approach to solve this problem. The position of his argument is to solve Birmingham’s racial problems peacefully. In addition to that, the resolution can be done locally through usage of negotiations done between local whites and Negros. He and the other religious, who he represents, are against the outsiders’ influence. He suggests in several parts of the letter how important is for the community to solve its own problems, without external help. The author supports his claim by providing explanation of the importance to solve the racial issue peacefully. He uses emotional aspects, specific supports, such as when he mentions that he understands the â€Å"natural impatience† of the ones who are dealing directly with the problem ( ¶ Clergymen 5). The appeal for people’s emotions is a warrant that he uses to persuade his readers. A warrant can be found when the author states â€Å"hatred and violence have no sanction in our religious and political traditions† is an approach to back-up his warrant ( ¶ Clergymen 5). A fallacy in the author’s argumentation is the fact that he does not provide data or supportive arguments to the aspect that Negros are receiving and being influenced by outsiders. He mentions more than once that locals have more knowledge than outsiders. The rebuttal for this argument is that outsiders may have more experience with racial issues, than the locals, which may bring more solutions to help the local Negros. Clergymen’s letter uses an ethical approach throughout his argument. He demonstrates to understand the issue and how it is affecting people’s lives. He does try to convince the readers of the necessity to solve the racial problems by following the principles of law, order, and common sense. Despite the fact that, it was clear to verify his belief that the outsider leadership is making the issue worst, he failures to provide any major approach to manipulate the audience opinions. Letter from Birmingham Jail In response to the clergymen’s letter, Matin Luter King writes a letter to reply what it was said about â€Å"unwise and untimely† activities, and â€Å"outside agitor†. He also demonstrated his believes of just and unjust laws; and his disappointment with the white moderates, white churches, and its leadership. The exigence of this argument is based on the demonstrations of segregation against black people. The audience of this letter is the clergymen and Birmingham’s population. The author of this letter is constrained by the discrimination, and other demonstrations of segregation against Negros. He is also constrained by the clergymen’s opinion against his nonviolent camping in defense of the Negros rights. The issue of this letter is in defense of Negros, and against several facts that have contributed to social tension between whites and Negros, specifically in Birmingham. Unjust treatment in court, brutality against Negros’ lives, and unsolved bombings of Negros’ homes are some examples of what has contributed to this social tension and segregation. While the clergymen’s letter defends the resolution of these social tensions â€Å"peacefully† and without the interruption of â€Å"outsiders†, King’s letter argues the necessity of a peaceful campaign where the mistreated people can scream in the streets in their own defense, and in defense of their rights. The claim of King’s argument is the â€Å"superficial kind of social analysis that deals merely with effects and does not grapple with underlying causes† ( ¶ King 5) of segregation demonstration of the city’s white power structure. King provides several aspects to support his claim. For example, he talks about the brutal facts that are constantly happing against Negros, such as the following: how rude and hate-filled the policemen act against them by cursing, kicking, and even killing. In fact, King’s letter has sub claims, where he not just talks about the severity of how Negros are being mistreated, but he also mentions about just and unjust laws, the country’s antireligious laws, his disappointment with white churches and its leadership, and cruelties of slavery. He also provides several warrants all throughout his letter. One example of a warrant can be found, when he appeals to human motives, in regards to Negros’ children incomprehension about mistreatment. The following expresses this incomprehension: â€Å"when you have to concoct an answer of a five-year-old son who is asking, ‘Daddy, why do white people treat colored people so mean? ’† ( ¶ King 14). Examples of backing also can be found all throughout King’s letter. For example, when he discusses about just and unjust laws, he explains the difference between them, as well as he supports his definitions with examples of philosophers’ descriptions. King uses rebuttal against the clergymen’s arguments in several parts of his letter. For instance, a rebuttal can be found when he mentions that he is not an outsider agitator. He states that everyone who lives within the country’s bounds can never be considered an outsider, and since he lives in the United States he is not an outsider. King’s letter use an ethical approach to try to convenience its readers that he is not trying to foster a violent resolution for the segregation crisis in Birmingham. Instead, he is trying to convince the readers of the necessity to promote a peaceful â€Å"war† by comparing Socrates with his necessary non-violent tension in order to create a creative analysis of the segregation problem. When comparing these two letters, one can verify that King’s letter uses several approaches, examples, and theories to demonstrate the necessity of the segregation resolution. Clergymen’s letter is more focus on the repetition of information. It mentions more than one time about the necessity to keep â€Å"outsiders† off the city’s social conflicts in order to observe the principals of law, order, and common sense.