Soft Computing | Difference between soft computing and hard computing | Applications of soft computing | Techniques of Soft Computing

SOFT COMPUTING

Soft computing (sometimes referred to as computational intelligence, though ci does not have an agreed definition) is the use of inexact solutions to computationally hard tasks such as the solution of np-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.

Difference between hard computing and soft computing

1)hard computing, i.e., conventional computing, requires a precisely stated analytic model and often a lot of computation time. 

Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.

2) hard computing based on binary logic, crisp systems, numerical analysis and crisp software

Soft computing based on fuzzy logic, neural nets and probabilistic reasoning.

3) hard computing requires programs to be written, uses two-valued logic, is deterministic,requires exact input data, is strictly sequential, produces precise answers;

 soft computing canevolve its own programs, can use multi valued or fuzzy logic, incorporates stochastic, can deal with ambiguous and noisy data, allows parallel computations,  can yield approximate answers.

Applications

1 actuarial science actuarial science is the discipline that applies mathematical and statistical methods to evaluate risk in the insurance and finance industries.

2 agricultural engineering agricultural engineering is the engineering discipline that applies engineering science and technology to agricultural production and processing. Agricultural engineering combines the disciplines of animal biology, plant biology, and mechanical, civil, electrical and chemical engineering principles with knowledge of agricultural principles.

Healthcare

3 computer engineering computer engineering is a discipline that integrates several fields of electrical engineering and computer science required to develop computer systems. Computer engineers usually have training in electronic engineering, software design, and hardware-software integration instead of only software engineering or electronic engineering.

4 data mining data mining is a subfield of computer science which is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

5 environmental engineering environmental engineering is the integration of science and engineering principles to improve the natural environment like air, water, and/or land resources, to provide healthy water, air, and land for human habitation like house or home and for other organisms, and to remediate pollution sites

6 fault-tolerance fault-tolerance is the property that enables a system to continue operating properly in the event of the failure of some of its components. If its operating quality decreases at all, the decrease is proportional to the severity of the failure, as compared to a naïvely-designed system in which even a small failure can cause total breakdown

7 image processing i n imaging science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it. Power for the design, production, and operation of machines and tools.

8 medical diagnosis medical diagnosis refers both to the process of attempting to determine or identify a possible disease and to the opinion reached by this process. From the point of view of statistics the diagnostic procedure involves classification tests.

9  pattern recognition pattern recognition generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation.

10 process control process control is a statistics and engineering discipline that deals with architectures, mechanisms and algorithms for maintaining the output of a specific process within a desired range. . Process control enables automation, with which a small staff of operating personnel can operate a complex process from a central control room

Soft computing techniques

Neural networks

Neural networks are a computational approach which is based on a large collection of neural units loosely modeling the way the brain solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. Each individual neural unit may have a summation function which combines the values of all its inputs together. There may be a threshold function or limiting function on each connection and on the unit itself such that it must surpass it before it can propagate to other neurons.

Neural networks typically consist of multiple layers or a cube design, and the signal path traverses from front to back. Back propagation is where the forward stimulation is used to reset weights on the "front" neural units and this is sometimes done in combination with training where the correct result is known. More modern networks are a bit more free flowing in terms of stimulation and inhibition with connections interacting in a much more chaotic and complex fashion. Dynamic neural networks are the most advanced in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.

Fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, considered to be "fuzzy". By contrast, in boolean logic, the truth values of variables may only be the "crisp" values 0 or 1. Fuzzy logic has been employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific (membership) functions.

Genetic algorithm

It is known as an evolved antenna. In the field of artificial intelligence, a genetic algorithm (ga) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.

Project Management | Decision Support System | Knowledge Based Systems | Applications | Decision Management Components

DECISION SUPPORT SYSTEM

A decision support system (DSS) is a computer-based information System that supports business or organizational Decision Making activities. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. Unstructured and Semi-Structured decision problems. Decision support systems can be either fully computerized, human-powered or a combination of both.

1.     DSS tends to be aimed at the less well structured, underspecified Problem that upper level managers typically face;

2.     DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;

3.     DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an interactive mode; and

4.     DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the Decision Making  approach of the user.

KNOWLEDGE-BASED SYSTEMS

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions. The knowledge management component, like that in an expert system, provides information about the relationship among data that is too complex for a database to represent. It consists of rules that can constrain possible solution as well as alternative solutions and methods for evaluating them.

Typical information that a decision support application might gather and present includes:

·         inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),

·         comparative sales figures between one period and the next,

·         projected revenue figures based on product sales assumptions.

Applications

One example is the clinical decision support system for medical diagnosis. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based

DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Due to DSS all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decision.

DSS are also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context the consideration of single or multiple management objectives related to the provision of goods and services that traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase
                                         
                                            Data Management Component

 The data management component performs the function of storing and maintaining the information that you want your Decision Support System to use. The data management component, therefore, consists of both the Decision Support System information and the Decision Support System database management system. The information you use in yourDecision Support System comes from one or more of three sources:

-Organizational information; you may want to use virtually any information available in the organization for your Decision Support System. What you use, of course, depends on what you need and whether it is available. You can design your Decision Support System to access this information directly from your company’s database and data warehouse. However, specific information is often copied to the Decision Support System database to save time in searching through the organization’s database and data warehouses.

-External information: some decisions require input from external sources of information. Various branches of federal government, Dow Jones, Compustat data, and the internet, to mention just a few, can provide additional information for the use with a Decision Support System.

-Personal information: you can incorporate your own insights and experience your personal information into your Decision Support System. You can design your Decision Support System so that you enter this personal information only as needed, or you can keep the information in a personal database that is accessible by the Decision Support System.

Project Management | Definition of project | Definition of Project Management | Problems in Software Projects

Project

The fundamental nature of a project is that it is a “temporary endeavor undertaken to create a unique product, service, or result

                                                  Project management

Project management is the process of the application of knowledge, skills, tools, and techniques to project activities to meet project requirements.” That is, project management is an interrelated group of processes that enables the project team to achieve a successful project. These processes manage inputs to and produce outputs from specific activities; the progression from input to output is the nucleus of project management and requires integration and iteration.

problems in Software Projects

Software projects are similar to traditional projects in the sense that the same

types of problems affect them both. However, the difference in managing these

problems lies in the approach that you take to the specific issue. For example, a

technology-related problem for a software project might be the low degree of

reuse of the software components created. However, for a car-manufacturing firm,

there is no chance of reusing a component such as a front axle

People-related problems

Process-related problems

Product-related problems

People-related problems

Low motivation: As the project manager it is your responsibility to ensure an

optimal level of motivation within the team. Lengthy projects, complex activitiesand scarce resources often decrease the motivation level in a

software development team. However, you need to lead in such a way that the

team is constantly motivated to do a good job

Problem employees: Some members of any team always create a problem.  Problem

employees raise the chances of conflicts and differences of opinions within

the development team. They lower the efficiency and productivity of other

team members and make it difficult to meet the objectives of the software

project within the specified time. You need to ensure that employees are not

allowed to create a problem for the rest of the team

Lack of stakeholder interest: For a software project to be a success, each

stakeholder needs to take an active interest in the progress of the project. Al1

stakeholders, including the customer, the management, and the software

development team, need to commit to the success of the project. For example,

if the software development team is not committed to the project, then their

contribution may not be to the optimum level

Process- related Problems

Unrealistic schedule: Assigning unrealistic deadlines for a software project is

a primary reason why software projects are delayed. Often, the marketing or

the management team commit a delivery date to the customer in the hope of

getting the project contract. However, these dates are not decided in

consultation with the development team. The rationale for assigning the

deadlines is unfounded. You need to ensure that the deadlines match the

ability of the software team to deliver the software product. 

.

·

Insufficient identification: Unidentified, partially identified, and unplanned

risks pose a threat to the success of a software project. You need to intensively

identify risks and evolve a risk management plan such that the project is

completed successfully, on time

unsuitable life cycle model selection: Different software projects require

different SDLC models. For example, a project to create banking .software is

different from software for a satellite where the concept needs to be

researched. For the former example, the Waterfall model is more applicable.

For the latter example, the Spiral model is more suitable. Selecting the correct

life cycle model is critical to the success of a software project.

Product-related Problems

Product scope changed toward the end of the project life cycle: The project

time, effort, and cost estimates for a software project can go up dramatically

when the customer changes the scope 9f the product toward the end of the

project. In such situations, you should verify the criticality of the scope

change. However, if the change request is not critical, you should retain the

original scope with a proper explanation to the customer. If the change request

is critical, you should explain the situation to the customer. Usually, a

customer gives more time and funds to a software project if proper

justification is provided. In some cases, the scope change may also be because

of a change in government policy. It may become mandatory for you to

include such change requests.

·

Research-oriented software development: Many software projects digress

from the original scope because of the nature of the software product or

technology used. When a totally new kind of software is developed 

or a new technology is used, the software development team can lose focus of the

objectives by getting into a research-oriented approach. It becomes your

responsibility as the project manager to maintain the focus on the objective

Mobile Communication | CDMA (Code-Division Multiple Access) | Architecture | Advantages

Code-Division Multiple Access

CDMA (Code-Division Multiple Access) refers to any of several protocols used in second-generation (2G) and third-generation 3G wireless communications. CDMA employs analog-to-digital conversion (ADC) in combination with spread spectrum technology. Audio input is first digitized into binary elements. The frequency of the transmitted signal is then made to vary according to a defined pattern (code), so it can be intercepted only by a receiver whose frequency response is programmed with the same code, so it follows exactly along with the transmitter frequency. There are trillions of possible frequency-sequencing codes, which enhances privacy and makes cloning difficult.

CDMA Architecture elements

Mobile Station (MS):

The MS is the mobile subscriber equipment, which can originate and receive calls and communicate with the BTS.

Base Transceiver Station (BTS):

The BTS transmits and receives radio signals, realizing communication between the radio system and the mobile station

.

Base Station Controller (BSC):

The BSC implements the following functions:

• Base Transceiver Station (BTS) control and management 
• call connection and disconnection 
• mobility management 
• stable and reliable radio link provision for the upper-layer services by soft/hard handoff 
• power control
•  radio resource management.

Packet Control Function (PCF):

The PCF implements the R-P connection management. Because of the shortage of radio resources, some radio channels should be released when subscribers do not send or receive data, but the PPP connection is maintained continuously. The PCF can shield radio mobility for the upper-layer services via handoff.

Packet Data Service Node (PDSN):

The PDSN implements the switching of packet data services of mobile subscribers. One PDSN can be connected to multiple PCFs. It provides the interface between the radio network and the packet data network

.

Home Agent (HA):

The agent locates at the place where the Mobile Node opens its account; receive the registration information from MN, Similar as HLR in mobile network. Broadcast the accessible information of MN. Setup the tunnel between FA&HA. Transfer the data from other computer to the MN via the tunnel.

Mobile Switching Center (MSC): 

The MSC implements the service switching between the calling and called subscribers. One MSC is connected with multiple BSCs. The MSC can also be connected to the PSTN, ISDN or other MSCs. It provides the interface between the radio network and PSTN.

Visitor Location Register (VLR):

It is a dynamic database, stores the temporary information (all data necessary to set up call connections) of the roaming subscribers in the local MSC area.

VLR is used to store the subscriber information of all the MSs in its local area, which can be used to establish the incoming/outgoing call connections, to support basic services, supplementary services and mobility management.

Home Location Register (HLR):

It is a database for mobile subscriber management, the HLR (Home Location Register) is responsible for storing subscription information (telecom service subscription information and subscriber status), MS location information, MDN, IMSI (MIN), etc. The AC (Authentication Center) is physically combined with the HLR. It is a functional entity of the HLR, specially dedicated to the security management of the CDMA system. It stores the authentication information. It also prevents unauthorized subscribers from accessing the system and prevents the radio interface data from being stolen.

Advantages

• Frequency  reuse
• Large coverage
• High spectrum capacity
• High privacy
• Soft handoff
• Good voice quality
• Perfect power control

OR(Operation Research) | Simulation | Types of Models | Advantages | Disadvantages

Simulation

Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviors/functions of the selected physical or abstract system or process.

Type of models 

Active models

Active models that attempt to reproduce living anatomy or physiology are recent developments. The famous "Harvey mannequin" was developed at the University of Miami and is able to recreate many of the physical findings of the cardiology examination, 

Interactive models

More recently, interactive models have been developed that respond to actions taken by a student or physician. Until recently, these simulations were two dimensional computer programs that acted more like a textbook than a patient. 

Computer simulators

Simulators have been proposed as an ideal tool for assessment of students for clinical skills. For patients, "cyber therapy" can be used for sessions simulating traumatic experiences, from fear of heights to social anxiety.

Programmed patients and simulated clinical situations, including mock disaster drills, have been used extensively for education and evaluation. These "lifelike" simulations are expensive, and lack reproducibility. A fully functional "3D" simulator would be the most specific tool available for teaching and measurement of clinical skills. 

Advantages

1. Simulation is best suited to analyze complex and large practical problems when it is not possible to solve them through a mathematical method.
2. Simulation is flexible, hence changes in the system variables can be made to select the best solution among the various alternatives.
3. In simulation, the experiments are carried out with the model without disturbing the system.
4. Policy decisions can be made much faster by knowing the options well in advance and by reducing the risk of experimenting in the real system

Disadvantages

1. Simulation does not generate optimal solutions.
2. It may take a long time to develop a good simulation model.
3. In certain cases simulation models can be very expensive.
4. The decision-maker must provide all information (depending on the model) about the constraints and conditions for examination, as simulation does not give the answers by itself.