COMPARATIVE ANALYSIS OF AI TECHNIQUES IN BIA

8/7/2019 COMPARATIVE ANALYSIS OF AI TECHNIQUES IN BIA

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INDEXSNO. TOPICS PAGE NO.

1. Abstract

2. Introduction

A. Intelligent System

B. Business Intelligence

C. Artificial Intelligence(AI)

3. Artificial Techniques

4. BIA and AI

5. Data Analysis

6. Conclusion

7. Referrences


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ABSTRACT

In this, comparitive analysis of Artificial

Intelligence techniques are done. Out of so manytechniques only 5 are taken for analysis i.e expert


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system, genetic algorithm, neural networks, fuzzy

logic and speech recognition and understanding

and analysis on some features are done and on the

basis of that conclusion are drawn.

INTRODUCTION

INTELLIGENT SYSTEM (IS)

Definition of an Intelligent System


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It is a system. It learns during its existence. (In other words, it senses its environment and learns, for each situation,

which action permits it to reach its objectives.) It continually acts, mentally and externally, and by acting reaches its objectives more often than pure

chance indicates (normally much oftener). It consumes energy and uses it for its internal processes, and in order to act.

What does this definition imply?

The system has to exist. An environment must exist, with which the system can interact. It must be able to receive communications from the environment, for its elaboration of the present

situation. This is an abstracted summary of the communications received by the senses. Bycommunications, in turn, we mean an interchange of matter or energy. If this communication is for thepurpose of transmitting information, it is a variation of the flow of energy or a specific structuring of

matter that the system perceives. The IS has to have an objective, it has to be able to check if its last action was favorable, if it resulted in

getting nearer to its objective, or not. To reach its objective it has to select its response. A simple way to select a response is to select one that

was favorable in a similar previous situation. It must be able to learn. Since the same response sometimes is favorable and sometimes fails, it has to

be able to recall in which situation the response was favorable, and in which it was not. Therefore itstores situations, responses, and results.

Finally, it must be able to act; to accomplish the selected response.

If any one of the above noted condition is absent the IS could not function.

Structure Of The Functioning Of An IS
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An intelligent system learns how to act so it can reach its objectives.

The easiest way to present an overall view of structure is with a representative diagram.

As you can see from this diagram, the IS is fundamentally a type ofstimulus - responsesystem. The stimulus isthe sum of the communications entering through the senses. The brain extracts information from this and

represents it as a situation.

Next, the IS selects a response rule, appropriate to the situation, and performs the response part of this rule.Here we mean by "appropriate" that performing the response permits the system to get nearer to the situationthat is its objective.The IS makes its selection ofresponse rules from those that it finds stored in its memory. In this memory, theIS has accumulated response rules that it has generated from earlier experiences and from generalizationsbased on previously elaborated response rules.
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BUSINESS INTELLIGENCE (BI)

Business intelligence refers to computer-based techniques used in spotting, digging-out and analyzingbusiness data, such as sales revenue by products and/or departments, or by associated costs and incomes.

BI technologies provide historical, current, and predictive views of business operations. Common functions ofbusiness intelligence technologies are reporting, online analytical processing, analytics, data mining, businessperformance management, benchmarking, text mining, and predictive analytics.

Business intelligence aims to support better business decision-making. Thus a BI system can be called adecision support system (DSS). Though the term business intelligence is sometimes used as a synonym forcompetitive intelligence, because they both support decision making, BI uses technologies, processes, andapplications to analyze mostly internal, structured data and business processes while competitive intelligencegathers, analyzes and disseminates information with a topical focus on company competitors. Business

intelligence understood broadly can include the subset of competitive intelligence.

History

In 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligenceas: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towardsa desired goal."

Business intelligence as it is understood today is said to have evolved from the decision support systems whichbegan in the 1960s and developed throughout the mid-80s. DSS originated in the computer-aided modelscreated to assist with decision making and planning. From DSS, data warehouses, Executive InformationSystems, OLAP and business intelligence came into focus beginning in the late 80s.

In 1989 Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella termto describe "concepts and methods to improve business decision making by using fact-based supportsystems." It was not until the late 1990s that this usage was widespread.
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Types of business intelligence tools

The key general categories of business intelligence tools are:

Spreadsheets [1] Reporting and querying software : tools that extract, sort, summarize, and present selected data OLAP : Online analytical processing Digital Dashboards Data mining Decision engineering Process mining Business performance management Local information systems

Except for spreadsheets, these tools are sold as standalone tools, suites of tools, components ofERP systems,or as components of software targeted to a specific industry. The tools are sometimes packaged into datawarehouse appliances

Applications in an enterprise

Business Intelligence can be applied to the following business purposes (MARCKM), in order to drive businessvalue.

1. Measurement program that creates a hierarchy ofPerformance metrics and Benchmarking that informsbusiness leaders about progress towards business goals .

2. Analytics program that builds quantitative processes for a business to arrive at optimal decisions and toperform Business Knowledge Discovery. Frequently involves: data mining, statistical analysis, Predictiveanalytics, Predictive modeling, Business process modeling

3. Reporting /Enterprise Reporting program that builds infrastructure for Strategic Reporting to serve theStrategic management of a business, NOT Operational Reporting. Frequently involves: Data visualization,

Executive information system, OLAP
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4. Collaboration /Collaboration platform program that gets different areas (both inside and outside thebusiness) to work together through Data sharing and Electronic Data Interchange.

5. Knowledge Management program to make the company data driven through strategies and practices toidentify, create, represent, distribute, and enable adoption of insights and experiences that are truebusiness knowledge. Knowledge Management leads to Learning Management and Regulatorycompliance/Compliance

Future

A 2009 Gartner paper predictedthese developments in the business intelligence market:

Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top5,000 global companies will regularly fail to make insightful decisions about significant changes in theirbusiness and markets.

By 2012, business units will control at least 40 percent of the total budget for business intelligence. By 2010, 20 percent of organizations will have an industry-specific analytic application delivered via

software as a service (SaaS) as a standard component of their business intelligence portfolio. In 2009, collaborative decision making emerged as a new product category that combines social software

with business intelligence platform capabilities. By 2012, one-third of analytic applications applied to business processes will be delivered through

coarse-grained application mashups.

A 2009 Information Managementspecial report predicted the top BI trends: "green computing, socialnetworking, data visualization, mobile, predictive analytics, composite applications, cloud computing and

multitouch."

.
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ARTIFICIAL INTELLIGENCE (AI)

Artificial intelligence (AI) refers to computer software that exhibits intelligent behavior. The term "intelligence"

is difficult to define, and has been the subject of heated debate by philosophers, educators, and psychologistsfor ages. Nevertheless, it is possible to enumerate many important characteristics of intelligent behavior.Intelligence includes the capacity to learn, maintain a large storehouse of knowledge, utilize commonsensereasoning, apply analytical abilities, discern relationships between facts, communicate ideas to others andunderstand communications from others, and perceive and make sense of the world around us. Thus, artificialintelligence systems are computer programs that exhibit one or more of these behaviors. It is the science andengineering of making intelligent machines.

The branch ofcomputer science concerned with making computers behave like humans. The term was coinedin 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes

1. games playing:programming computers to play games such as chess and checkers2. expert systems : programming computers to make decisions in real-life situations (for example,

some expert systems help doctors diagnose diseases based on symptoms)3. natural language : programming computers to understand natural human languages4. neural networks :Systems that simulate intelligence by attempting to reproduce the types of

physical connections that occur in animal brains5. robotics : programming computers to see and hearand react to other sensory stimuli
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AI is a broad discipline that promises to simulate numerous innate human skills such as automaticprogramming, case-based reasoning, neural networks, decision-making, expert systems, natural languageprocessing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysisfeatures to existing applications.AI is a science that investigates knowledge and intelligence, possibly the intelligent application of knowledge.Knowledge and Intelligence are as fundamental as the universe within which they exist, it may turn out thatthey are more fundamental.One of the aims of AI is said to be the investigation of human cognition and AI is part of Cognitive Science. AI isreally an investigation into the creation of intelligence and that there is no reason for the intelligence that iscreated to be exactly the same as human intelligence.

Importance of AI

Enterprises that utilize AI-enhanced applications are expected to become more diverse, as the needs for theability to analyze data across multiple variables, fraud detection and customer relationship managementemerge as key business drivers to gain competitive advantage.Artificial Intelligence is a branch of Science which deals with helping machines, finds solutions to complexproblems in a more human-like fashion. This generally involves borrowing characteristics from humanintelligence, and applying them as algorithms in a computer friendly way. A more or less flexible or efficient

approach can be taken depending on the requirements established, which influences how artificial theintelligent behavior appears.AI is generally associated with Computer Science, but it has many important links with other fields such asMaths, Psychology, Cognition, Biology and Philosophy, among many others. Our ability to combine knowledgefrom all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being.

Emergence of AI in business

Artificial Intelligence (AI) has been used in business applications since the early eighties. As with alltechnologies, AI initially generated much interest, but failed to live up to the hype. However, with the advent of


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web-enabled infrastructure and rapid strides made by the AI development community, the application of AItechniques in real-time business applications has picked up substantially in the recent past.Computers are fundamentally well suited to performing mechanical computations, using fixed programmedrules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, whichhumans are ill-suited to. For more complex problems, things get more difficult... Unlike humans, computershave trouble understanding specific situations, and adapting to new situations. Artificial Intelligence aims toimprove machine behavior in tackling such complex tasks.Together with this, much of AI research is allowing us to understand our intelligent behavior. Humans have aninteresting approach to problem-solving, based on abstract thought, high-level deliberative reasoning andpattern recognition. Artificial Intelligence can help us understand this process by recreating it, then potentiallyenabling us to enhance it beyond our current capabilities.

Applications of AI

The potential applications of Artificial Intelligence are abundant. They stretch from the military for autonomous

control and target identification, to the entertainment industry for computer games and robotic pets, to the bigestablishments dealing with huge amounts of information such as hospitals, banks and insurances, we can alsouse AI to predict customer behavior and detect trends.AI is a broad discipline that promises to simulate numerous innate human skills such as automaticprogramming, case-based reasoning, decision-making, expert systems, natural language processing, patternrecognition and speech recognition etc. AI technologies bring more complex data-analysis features to existingapplications.Business applications utilize the specific technologies mentioned earlier to try and make better sense ofpotentially enormous variability (for example, unknown patterns/relationships in sales data, customer buying

habits, and so on). However, within the corporate world, AI is widely used for complex problem-solving anddecision-support techniques in real-time business applications. The business applicability of AI techniques isspread across functions ranging from finance management to forecasting and product

Here are some of the applications:

game playing

You can buy machines that can play master level chess for a few hundred dollars. There is some AI in

them, but they play well against people mainly through brute force computation--looking at hundreds of

thousands of positions. To beat a world champion by brute force and known reliable heuristics requires

being able to look at 200 million positions per second.


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speech recognition

In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United

Airlines has replaced its keyboard tree for flight information by a system using speech recognition of

flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to

instruct some computers using speech, most users have gone back to the keyboard and the mouse as

still more convenient.

understanding natural language

Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either.

The computer has to be provided with an understanding of the domain the text is about, and this is

presently possible only for very limited domains.

computer vision

The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV

cameras are two dimensional. Some useful programs can work solely in two dimensions, but fullcomputer vision requires partial three-dimensional information that is not just a set of two-dimensional

views. At present there are only limited ways of representing three-dimensional information directly, and

they are not as good as what humans evidently use.

expert systems

A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in

a computer program for carrying out some task. How well this works depends on whether the intellectual

mechanisms required for the task are within the present state of AI. When this turned out not to be so,

there were many disappointing results. One of the first expert systems was

MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better

than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology

included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death,

recovery, and events occurring in time. Its interactions depended on a single patient being considered.

Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery,

etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined


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framework. In the present state of AI, this has to be true. The usefulness of current expert systems

depends on their users having common sense.

heuristic classification

One of the most feasible kinds of expert system given the present knowledge of AI is to put some

information in one of a fixed set of categories using several sources of information. An example isadvising whether to accept a proposed credit card purchase. Information is available about the owner of

the credit card, his record of payment and also about the item he is buying and about the establishment

from which he is buying it (e.g., about whether there have been previous credit card frauds at this

establishment).

The Disciplines of Artificial Intelligence

The subject of artificial intelligence spans a wide horizon. It deals with the various kinds of knowledgerepresentation schemes, different techniques of intelligent search, various methods for resolving uncertaintyof data and knowledge, different schemes for automated machine learning and many others. Among theapplication areas of AI, we have Expert systems, Game-playing, and Theorem-proving, Natural languageprocessing, Image recognition, Robotics and many others. The subject of artificial intelligence has beenenriched with a wide discipline of knowledge from Philosophy, Psychology, Cognitive Science, ComputerScience, Mathematics and Engineering. Thus in fig. , they have been referred to as the parent disciplines of AI.The fig. also reveals the subject area of AI and its application areas.


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Fig.: AI, its parent disciplines and application areas.


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AI systems can be divided into two broad categories: knowledge representation systems and machine learningsystems.

Knowledge representation systems, also known as expert systems, provide a structure for capturing andencoding the knowledge of a human expert in a particular domain. For example, the knowledge of medicaldoctors might be captured in a computerized model that can be used to help diagnose patient illnesses.

The second category of AI, machine learning systems, creates new knowledge by finding previously unknown

patterns in data. In contrast to knowledge representation approaches, which model the problem-solvingstructure of human experts, machine learning systems derive solutions by "learning" patterns in data, withlittle or no intervention by an expert. There are three main machine learning techniques: neural networks,induction algorithms, and genetic algorithms.

AI in the twenty-first century

Artificial intelligence systems provide a key component in many computer applications that serve the world ofbusiness. In fact, AI is so prevalent that many people encounter such applications on a daily basis without evenbeing aware of it.

One of the most ubiquitous uses of AI can be found in network servers that route electronic mail. Expertsystems are routinely utilized in the medical field, where they take the place of doctors in assessing the resultsof tests like mammograms or electrocardiograms. Neural networks are commonly used by credit cardcompanies, banks, and insurance firms to help detect fraud. These AI systems can, for example, monitorconsumer spending habits, detect patterns in the data, and alert the company when uncharacteristic patterns

arise. Genetic algorithms serve logistics planning functions in airports, factories, and even military operations,where they are used to help solve incredibly complex resource-allocation problems. And perhaps most familiar,many companies employ AI systems to help monitor calls in their customer service call centers. These systemscan analyze the emotional tones of callers' voices or listen for specific words, and route those calls to humansupervisors for follow-up attention.

Although computer scientists have thus far failed to create machines that can function with the complexintelligence of human beings, they have succeeded in creating a wide range of AI applications that makepeople's lives simpler and more convenient.


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ARTIFICIAL INTELLIGENCE TECHNIQUES OR TOOLS

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficultproblems in computer science. A few of the most general of these methods are discussed below:

NEURAL NETWORKS.

Neural networks simulate the human nervous system. The concepts that guide neural network research andpractice stem from studies of biological systems. These systems model the interaction between nerve cells.

Components of a neural network include neurons (sometimes called "processing elements"), input lines to theneurons (called dendrites), and output lines from the neurons (called axons).

Neural networks are composed of richly connected sets of neurons forming layers. The neural networkarchitecture consists of an input layer, which inputs data to the network; an output layer, which produces theresulting guess of the network; and a series of one or more hidden layers, which assist in propagating. This isillustrated in Figure 1.

During processing, each neuron performs a weighted sum of inputs from the neurons connecting to it; this is

called activation. The neuron chooses to fire if the sum of inputs exceeds some previously set threshold value;this is called transfer.

Inputs with high weights tend to give greater activation to a neuron than inputs with low weights. The weightof an input is analogous to the strength of a synapse in a biological system. In biological systems, learningoccurs by strengthening or weakening the synaptic connections between nerve cells. An artificial neuralnetwork simulates synaptic connection strength by increasing or decreasing the weight of input lines intoneurons.

Neural networks are trained with a series of data points. The networks guess which response should be given,and the guess is compared against the correct answer for each data point. If errors occur, the weights into the
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neurons are adjusted and the process repeats itself. This learning approach is called backpropagation, and issimilar to statistical regression.

Neural networks are used in a wide variety of business problems, including optical character recognition,financial forecasting, market demographics trend assessment, and various robotics applications.

Real life applications of ANN

The tasks to which artificial neural networks are applied tend to fall within the following broad categories: Function approximation, or regression analysis, including time series prediction and modeling. Classification, including pattern and sequence recognition, novelty detection and sequential decision making. Data processing, including filtering, clustering, blind source separation and compression.


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Application areas include system identification and control (vehicle control, process control), game-playing anddecision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, objectrecognition and more), sequence recognition (gesture, speech, handwritten text recognition), medicaldiagnosis, financial applications (automated trading systems), data mining (or knowledge discovery indatabases, "KDD"), visualization and e-mail spam filtering.

The proven success of Artificial Neural Networks (ANN) and expert systems has helped AI gain widespreadadoption in enterprise business applications. In some instances, such as fraud detection, the use of AI has

already become the most preferred method. In addition, neural networks have become a well-establishedtechnique for pattern recognition, particularly of images, data streams and complex data sources and, in turn,have emerged as a modeling backbone for a majority of data-mining tools available in the market. Some of thekey business applications of AI/ANN include fraud detection, cross-selling, customer relationship managementanalytics, demand prediction, failure prediction, and non-linear control.A majority of the enterprises adopt horizontal or vertical solutions that embed neural networks such asinsurance risk assessment or fraud-detection tools, or data-mining tools that include neural networks (forinstance, from SAS, IBM and SPSS) as one of the modeling options.

INDUCTION ALGORITHMS.

Induction algorithms form another approach to machine learning. In contrast to neural networks, which arehighly mathematical in nature, induction approaches tend to involve symbolic data. As the name implies, thesealgorithms work by implementing inductive reasoning approaches. Induction is a reasoning method that can becharacterized as "learning by example." Unlike rule-based deduction, induction begins with a set ofobservations and constructs rules to account for these observations. Inductive reasoning attempts to findgeneral patterns that can fully explain the observations. The system is presented with a large set of data

consisting of several input variables and one decision variable. The system constructs a decision tree byrecursively partitioning data sets based on the variables that best distinguish between the data elements. Thatis, it attempts to partition the data so that each partition contains data with the same value for a decisionvariable. It does this by selecting the input variables that do the best job of dividing the data set intohomogeneous partitions. For example, consider Figure 2, which contains the data set pertaining to decisionsthat were made on credit loan applications.


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Figure 2Artificial Intelligence & Expert Systems

An induction algorithm would infer the rules in Figure 3 to explain this data.

Figure 3

As this example illustrates, an induction algorithm is able to induce rules that identify the general patterns indata. In doing so, these algorithms can prune out irrelevant or unnecessary attributes. In the example above,salary was irrelevant in terms of explaining the loan decision of the data set.

Induction algorithms are often used for data mining applications, such as marketing problems that helpcompanies decide on the best market strategies for new product lines. Data mining is a common service

included in data warehouses, which are frequently used as decision support tools.

GENETIC ALGORITHMS.

Genetic algorithms use an evolutionary approach to solve optimization problems. These are based on Darwin'stheory of evolution, and in particular the notion of survival of the fittest. Concepts such as reproduction,natural selection, mutation, chromosome, and gene are all included in the genetic algorithm approach.

Genetic algorithms are useful in optimization problems that must select from a very large number of possiblesolutions to a problem. A classic example of this is the traveling salesperson problem. Consider a salesman


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who must visit n cities. The salesperson's problem is to find the shortest route by which to visit each of these ncities exactly once, so that the salesman will tour all the cities and return to the origin. For such a problemthere are ( n 1)! possible solutions, or ( n 1) factorial. For six cities, this would mean 5 4 3 2 1 =120 possible solutions. Suppose that the salesman must travel to 100 cities. This would involve 99! possiblesolutions. This is such an astronomical number that if the world's most powerful computer began solving sucha problem at the time that the universe had begun and worked continuously on it since, it would be less thanone percent complete today!

Obviously, for this type of problem a brute strength method of exhaustively comparing all possible solutionswill not work. This requires the use of heuristic methods, of which the genetic algorithm is a prime example.For the traveling salesperson problem, a chromosome would be one possible route through the cities, and agene would be a city in a particular sequence on the chromosome. The genetic algorithm would start with aninitial population of chromosomes (routes) and measure each according to a fitness function (the total distancetraveled in the route). Those with the best fitness functions would be selected and those with the worst wouldbe discarded. Then random pairs of surviving chromosomes would mate, a process called crossover. Thisinvolves swapping city positions between the pair of chromosomes, resulting in a pair of child chromosomes. Inaddition, some random subset of the population would be mutated, such that some portion of the sequence of

cities would be altered. The process of selection, crossover, and mutation results in a new population for thenext generation. This procedure is repeated through as many generations as necessary in order to obtain anoptimal solution.

Genetic algorithms are very effective at finding good solutions to optimization problems. Scheduling,configuration, and routing problems are good candidates for a genetic algorithm approach. Although geneticalgorithms do not guarantee the absolute best solution, they do consistently arrive at very good solutions in arelatively short period of time.

1. FUZZY LOGIC

Fuzzy logic is a version of first-order logic which allows the truth of a statement to be represented as a valuebetween 0 and 1, rather than simply True (1) or False (0). Fuzzy systems can be used for uncertain reasoningand have been widely used in modern industrial and consumer product control systems.

2. PROBABILISTIC METHODS FOR UNCERTAIN REASONING
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Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operatewith incomplete or uncertain information. AI researchers have devised a number of powerful tools to solvethese problems using methods from probability theory and economics.

Bayesian networks are a very general tool that can be used for a large number of problems:reasoning (using the Bayesian inference algorithm),learning (using the expectation-maximizationalgorithm), planning (using decision networks) and perception (using dynamic Bayesiannetworks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and

finding explanations for streams of data, helping perception systems to analyze processes thatoccur over time

A key concept from the science ofeconomics is "utility": a measure of how valuable something is to anintelligent agent. Precise mathematical tools have been developed that analyze how an agent can makechoices and plan, using decision theory, decision analysis, information value theory. These tools includemodels such as Markov decision processes, dynamic decision networks, game theory and mechanism design.

3. ROBOTICS

In the area of robotics, computers are now widely used in assembly plants, but they are capable only of verylimited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still moveand handle objects clumsily.

4. NATURAL-LANGUAGE

Its processing offers the greatest potential rewards because it would allow people to interact with computerswithout needing any specialized knowledge. You could simply walk up to a computer and talk to it.Unfortunately, programming computers to understand natural languages has proved to be more difficult than
http://en.wikipedia.org/wiki/Probabilityhttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Bayesian_networkhttp://en.wikipedia.org/wiki/Bayesian_inferencehttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Expectation-maximization_algorithmhttp://en.wikipedia.org/wiki/Expectation-maximization_algorithmhttp://en.wikipedia.org/wiki/Automated_planning_and_schedulinghttp://en.wikipedia.org/wiki/Decision_networkhttp://en.wikipedia.org/wiki/Machine_perceptionhttp://en.wikipedia.org/wiki/Dynamic_Bayesian_networkhttp://en.wikipedia.org/wiki/Dynamic_Bayesian_networkhttp://en.wikipedia.org/wiki/Machine_perceptionhttp://en.wikipedia.org/wiki/Economichttp://en.wikipedia.org/wiki/Utilityhttp://en.wikipedia.org/wiki/Decision_theoryhttp://en.wikipedia.org/wiki/Decision_analysishttp://en.wikipedia.org/wiki/Applied_information_economicshttp://en.wikipedia.org/wiki/Markov_decision_processhttp://en.wikipedia.org/wiki/Decision_networkhttp://en.wikipedia.org/wiki/Game_theoryhttp://en.wikipedia.org/wiki/Mechanism_designhttp://en.wikipedia.org/wiki/Probabilityhttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Bayesian_networkhttp://en.wikipedia.org/wiki/Bayesian_inferencehttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Expectation-maximization_algorithmhttp://en.wikipedia.org/wiki/Expectation-maximization_algorithmhttp://en.wikipedia.org/wiki/Automated_planning_and_schedulinghttp://en.wikipedia.org/wiki/Decision_networkhttp://en.wikipedia.org/wiki/Machine_perceptionhttp://en.wikipedia.org/wiki/Dynamic_Bayesian_networkhttp://en.wikipedia.org/wiki/Dynamic_Bayesian_networkhttp://en.wikipedia.org/wiki/Machine_perceptionhttp://en.wikipedia.org/wiki/Economichttp://en.wikipedia.org/wiki/Utilityhttp://en.wikipedia.org/wiki/Decision_theoryhttp://en.wikipedia.org/wiki/Decision_analysishttp://en.wikipedia.org/wiki/Applied_information_economicshttp://en.wikipedia.org/wiki/Markov_decision_processhttp://en.wikipedia.org/wiki/Decision_networkhttp://en.wikipedia.org/wiki/Game_theoryhttp://en.wikipedia.org/wiki/Mechanism_design


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originally thought. Some rudimentary translation systems that translate from one human language to anotherare in existence, but they are not nearly as good as human translators. There are also voice recognitionsystems that can convert spoken sounds into written words, but they do not understandwhat they are writing;they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly. Thereare several programming languages that are known as AI languages because they are used almost exclusivelyfor AI applications. The two most common are LISP and Prolog.

5. EXPERT SYSTEMS

In the early 1980s, expert systems were believed to represent the future of artificial intelligence and ofcomputers in general. To date, however, they have not lived up to expectations. Many expert systems helphuman experts in such fields as medicine and engineering, but they are very expensive to produce and arehelpful only in special situations.

6. SPEECH RECOGNITION AND UNDERSTANDING

It attempts to allow computers to recognize words or phrases of human speech i.e translation of humanvoice into individual words and sentences understandable by a computer.

BIA AND AI
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Business applications utilize the specific technologies mentioned earlier to try and make better sense of

potentially enormous variability (for example, unknown patterns/relationships in sales data, customer

buying habits, and so on). However, within the corporate world, AI is widely used for complex problem-

solving and decision-support techniques in real-time business applications. The business applicability of AI

techniques is spread across functions ranging from finance management to forecasting and production.

In the fiercely competitive and dynamic market scenario, decision-making has become fairly complex and

latency is inherent in many processes. In addition, the amount of data to be analyzed has increased

substantially. AI technologies help enterprises reduce latency in making business decisions, minimize fraudand enhance revenue opportunities.

Artificial Intelligence Helps Business Strategy

Increase profits with artificial intelligence

Artificial Intelligence (AI) applications can offer tremendous help for business strategies. Predicting outcomes

allows the AI program to improve business intelligence.

Artificial Intelligence (AI), in the most general sense, is a branch of computing that either mimics humanreactions or performs calculations or functions as a substitute for human activity. AI has been around, at least

in theory, since 1950. Artificially intelligent computer applications are useful in many different areas ofbusiness, as well as for entertainment, health, the financial sector, and national defense. Due to the fact thatAI is used for many different purposes, there are many different measures of success, depending on the typeof AI. In the area of business intelligence, AI offers many benefits, and success is measured by profit and loss,rather than by the ability of the program to appear human.

AI Business Intelligence Applications

One area in which AI programming is highly useful is the area of business intelligence. This aspect of business

planning refers to the ability to use information to gain a competitive edge over competitors. Data mining is anessential tool in business intelligence, due to the fact that trends are a very important aspect of improvingmarket share.


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Data mining analyzes trends, whether they are pricing trends, sales trends, or the number of accidents in aparticular workplace. Any data gathered for the AI program can be used to predict future developments. Thiscan be of great benefit to a small company in need of a competitive edge, or a larger business with an edge tomaintain.

Data Mining and Business Strategies

There are many different uses for data mining in the creation of business strategies. By using an AI program to

analyze business data, the owner can gain a wider understanding of their business environment, in order tomake better and faster business intelligence decisions. All companies using AI to analyze patterns pertainingto their business are working to maximize profit, and minimize loss.

A retail business may use data mining to discover which products sell more on different days of the week,in order to maximize sales.

Website owners use data mining to determine which advertisements are more effective, based on thenumber of clicks.

Casino owners can use data mining to find patterns in the choice of slot machines, in order to change

placement of the machines, and ensure that more traffic goes to more expensive machines.

AI Applications and Business

In today's economy, businesses need to use every tool at their disposal in order to beat the competition. Whileartificially intelligent computer programs require an initial investment, they can be well worth the cost in long-term benefits.

Artificial Intelligence in Financial services

AI has found a home in financial services and is recognized as a valuable addition to numerous businessapplications. Sophisticated technologies encompassing neural networks and business rules along with AI-basedtechniques are yielding positive results in transaction-oriented scenarios for financial services. AI has beenwidely adopted in such areas of risk management, compliance, and securities trading and monitoring, with anextension into customer relationship management (CRM). Tangible benefits of AI adoption include reduced riskof fraud, increased revenues from existing customers due to newer opportunities, avoidance of fines stemmingfrom non-compliance and averted securities trade exceptions that could result in delayed settlement, if not

detected.In the field of Finance, artificial intelligence has long been used. Some applications of Artificial Intelligence are


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Credit authorization screening Mortgage risk assessment Project management and bidding strategy Financial and economic forecasting Risk rating of exchange-traded, fixed income investments Detection of regularities in security price movements Prediction of default and bankruptcy Security/and or Asset Portfolio Management

Artificial intelligence types used in finance include neural networks, fuzzy logic, genetic algorithms, expertsystems and intelligent agents. They are often used in combination with each other.

Artificial Intelligence in Marketing

Advances in artificial intelligence (AI) eventually could turbo-boost customer analytics to give companiesspeedier insights into individual buying patterns and a host of other consumer habits.

Artificial intelligence functions are made possible by computerized neural networks that simulate the sametypes of connections that are made in the human brain to generate thought. Currently, the technology is usedmostly to analyze data for genetics, pharmaceutical and other scientific research. It's seeing little use in CRMright now, though it has tremendous potential in the futureAI-enhanced analytics programs also provide survival modeling capabilities -- suggesting changes to productsbased on use. For example, customer patterns are analyzed to learn ways to extend the life of light bulbs or tohelp decide the correct dosage for medications.High-tech data mining can give companies a precise view of how particular segments of the customer basereact to a product or service and propose changes consistent with those findings. In addition to further

exploring customers" buying patterns, analytics could help companies react much more quickly to themarketplace.

Artificial Intelligence in HR

It is widely believed that the role of managers is becoming a key determinant for enterprises' competitivenessin today's knowledge economy era. Owing to fast development of information technologies (ITs), corporationsare employed to enhance the capability of human resource management, which is called human resource

information system (HRIS). Recently, due to promising results of artificial neural networks (ANNs) and fuzzytheory in engineering, they have also become candidates for HRIS. The artificial intelligence (AT) field can playa role in this, especially; in assuring that the fuzzy neural network has the characteristics and functions of


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training, learning, and simulation to make an optimal and accurate judgment according to the human thinkingmodel. The main purposes of the study are to discuss the appointment of managers in enterprises throughfuzzy neural network, to construct a new model for evaluation of managerial talent, and accordingly to developa decision support system in human resource selection. Therefore, the research methods of reviewingliterature, in-depth interview, questionnaire survey, and fuzzy neural network are used in the study. The fuzzyneural network is used to train the concrete database, based on 191 questionnaires from experts, for gettingthe best network model in different training conditions. In order to let decision-makers adjust weighted valuesand obtain decisive results of each phase's scores, we adopted the simple additive weighting (SAW) and fuzzy

analytic hierarchy process (FAHP) methods in the study. Finally, the human resource selection system of Javauser interface has been constructed by FNN in the study.

Artificial Intelligence in Manufacturing

As the manufacturing industry becomes increasingly competitive, sophisticated technology has emerged toimprove productivity. Artificial Intelligence in manufacturing can be applied to a variety of systems. It canrecognize patterns, plus perform time consuming and mentally challenging tasks. Artificial Intelligence canoptimize your production schedule and production runs. In order for organizations to meet ever increasingcustomer demands, and to be able to survive in an environment where change is inevitable, it is crucial thatthey offer more reliable delivery dates and control their costs by analyzing them on a continual basis. Forbusinesses, being capable of delivering high quality goods at low costs and short delivery times is akin tooperating in a whirlpool environment like the Devil's Triangle, and this is no easy task for any organization.Managing so that production takes place at the right time, on the right equipment, and using the right toolswill minimize any deviations in delivery dates promised to the customer. Utilizing equipment, personnel andtools to their maximal efficiency will no doubt improve any organization's competitive strength. In return,proper utilization of these capabilities will result in lower costs for the organization

Advantages

View your best product runs and the corresponding settings. Increase efficiency and quality by using optimal settings from past production.


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Artificial Intelligence can optimize your schedule beyond normal human capabilities. Increase productivity by eliminating downtime due to unpredictable changes in the schedule

Conclusion

It is difficult for business to see general relevance from AI. This is probably one of the reasons for the

compartmentalization of AI into things like Knowledge Based Systems, Neural Networks, and GeneticAlgorithms etc. Some of these separate sub topics have been shown to be very useful in solving certaindifficult business and industrial problems and consequently funding bodies influence research directions byencouraging work on these more application based areas. This can have a positive effect for business benefitand has lead to some very useful systems that have found their way into the heart of business activity.Business should not lose sight of where AI could go because there are many potential benefits to current andnew businesses of future research. The idea of robotic domestic workers is still far fetched but companies aremaking progress even here. There is already a Robot Vacuum Cleaner marketed by Electrolux and doubtlessimproved systems with better functionality will follow. .

DATA ANALYSIS


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CONCLUSION

FEATURES NEURALNETWORK

S

FUZZYLOGIC

GENETICALGORITH

M

EXPERTSYSTEM

SPEECHRECOGNITION AND

UNDERSTANDING

USER FRIENDLY high --- high High High

SPEED high High High high High

COST --- --- --- High ---

PATERNRECOGNITION

high medium --- medium ---

HANDLINGUNCERTAINITY

medium High --- High ---

DECISIONMAKING

medium high Medium High ---

FLEXIBILITY low high Medium Medium low

FAULTTOLERANCE

low high high Medium ---

GENERALIZATION(FORECASTING)

High --- --- Low ---

USAGE high medium High high ---


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1. All the tools are user friendly except the fuzzy logic . All of them are easy to use by the end user.

2. All the analysed tools have high speed.

3. Among all the expert system are the costliest one.

4. Neural networks rate high in pattern recognition while fuzzy logic and expert sytem are rated

medium in recognizing the pattern.

5. Handling uncertainity is sufficiently high in fuzzy logic and expert system and is medium in neural

network.

6. Decision making ranks high in fuzzy logic and expert system and medium in neural network and

genetic algorithm.

7. Fuzzy logic ranks sufficiently high in flexibility while the low level of flexibility is in neural network

and speech recognition. Other two are medium in relation to flexibility.

8. Fault tolerance ability is high in fuzzy logic and genetic algorithm and lowest in neural network.

9. Neural network rate high in generalization while expert system rate low.

10. Usability of neural network, expert system and genetic algorithm are high while fuzzy logics are

use less.


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REFERRENCES

1. Genetic algorithm, neural network and fuzzy logic by S. rajshekar

2. Decision support system and Intelligent system by Efraim Turban, E. Aronson

3.Wikipedia

4. Management information system by Aman Jindal

COMPARATIVE ANALYSIS OF AI TECHNIQUES IN BIA

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