Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353
2017 4 International Conference on “Computing for Sustainable Global Development”, 01 st - 03rd March, 2017
Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA)
th
A review of solving real domain problems in Engineering for
Computational Intelligence using Soft Computing
Nikhat Akhtar
Research Scholar-Ph.D. (Computer Science & Engineering)
Department of Computer Science & Engineering,
Babu Banarasi Das University, Lucknow, India
dr.nikhatakhtar@gmail.com
Firoj Parwej
1) Research Scholar-Ph.D. (Computer Science & Engineering)
Department of Computer Science & Engineering
Singhania University, Pacheri Bari, Jhunjhunu, Rajasthan, India
dr.firojparwej@gmail.com
Abstract-Now a day’s, well known accepted soft computing
methods likes as neural sets and fuzzy sets, adaptive evolutionary
computing and neuro fuzzy based system, and Intelligence
Computing’s etc. for carrying out numerical varying data
simulation analysis. These approaches become applied on different
engineering problems as independently. In this research paper
many introducer and researchers describe the soft computing
review technique which using for solving real domains problems
and simulate the reappearance of the intelligence, as adaptation
and learning for real times engineering problems... The
intelligence computing Computational intelligence is a universal
approximate, and it has the great function of non-linear mapping
and optimization techniques. Also we discuss a brief of intelligent
systems in Neural Network. And discuss it features and
constituents of real problems. We concentrate on solving real
domain problems in Neural Network of Intelligent System as
Intelligence computing and soft computing.
Keywords: Neural Network, Machine Learning, intelligent systems,
computational intelligence.
I.
INTRODUCTION
As review of problem solving proves to better understand for opened
problems for engineering, according to abstraction level, activity,
planning, design and reporting. We can develop quantitative outcome
which conducted a soft design of experiments to retrieve the effects
that the process variables have on the response variables. In this paper
discuss different computational, designing process modeling effort,
also discuss review their implications for the general specific model
for different engineering problems [6]. Practicing real domain
problems which are hired and retained as rewarded for solving
problems. So that specific engineers learn how to solve different
problems for workplace. Many Engineer problems are different form
the finds of problems which solve in workplace. Complex work place
problems are ill-structured due to their process as well as their goals,
different solutions technique for non-engineering standards, nonengineering [1]collaborative activity and unanticipated refer to
importance of experience of multiple form of representation of
problems. Several computational tools for analytic [26] become
matured in that facilitated problem solving which was impossible or
difficult to solve. In above figure 1 a design process of engineering
problems in real domains describe the problems of definitions,
gathering information’s, analyze and select the appropriate solution
then testing occurs for the implantations. From the different
perspective of solving computations of real domain problems,
system become characterized by its adaptability, flexibility,
Dr Yusuf Perwej
Assistant Professor, Deptt of Computer Information System
AI BAHA University, AL BAHA Kingdom of Saudi Arabia (KSA)
yusufperwej@gmail.com
Jai Pratap Dixit
2) Research Scholar-Ph.D. (Computer Science & Engineering)
Department of Computer Science & Engineering,
Ambalika Institute of Management and Technology, Lucknow
jpdixit.iiita@gmail.com
reasoning and the ability to manage imprecise and uncertain
information’s.
Fig .1 Component of Software Design Process
II.
THE DESIGN LAYOUT STRUCTURE
There are basic five-step processes usually used in a problemsolving works for design problems as well. Since design
problems are usually defined more vaguely and have a
multitude of correct answers, the process may require
backtracking and iteration.
Fig.2 Real domain engineering problem formulation
Solving a design problem is a contingent process and the
solution is subject to unforeseen complications and changes as
it develops. Until the Wright brothers actually built and tested
their early gliders, they did not know the problems and
difficulties they would face controlling a powered plane. There
are the five steps basically used solving design problems such
as: Define the problem, Gather pertinent information, Generate
Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3
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Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353
2017 4 International Conference on “Computing for Sustainable Global Development”, 01 st - 03rd March, 2017
th
Quality: The percentage of the design objectives the client
thought the team achieved the closeness of the final outcome to
client’s initial expectations [9]. Design’s feasibility in its
The first step in the design process is the problem definition. application and fabrication Client’s opinion on implementing
This definition usually contains a listing of the product or the design Client’s opinion on students’ knowledge of math,
customer requirements and specially information about product science and engineering in developing solutions Overall
functions and features among other things. In the next step, satisfaction with the design outcome
relevant information for the design of the product and itsA. Basic: Requirements The design meets the technical criteria
functional specifications is obtained. Considering cost, safety,
and the customer requirements
and other criteria for selection, the more promising alternativesB. Feasibility: The design is feasible in its application and
are selected for further analysis [27].
fabrication / assembly
C. Creativity: The design incorporates original and novel ideas,
non-intuitive approaches or innovative solutions.
III.
DECISIONS FOR THE DESIGN PROCESS
multiple solutions, Analyze and select a solution, Test and
implement the solution
After analyzing the alternative solution that you required to
decide that which design solution is best [13]. You will refine
and develop the solution in detail during the different stages of
the design process of particular problems. In different level to
determine the each solution against the design criteria as
requirement and also design a alternative solution.
Fig.3. Typical Design Process Model
These attributes can include factors such as safety,
manufacturing considerations, the ease of fabrication and
assembly, cost, portability, compliance with government
regulations, etc. You then assign to each attribute or criteria a
value factor related to the relative importance of that attribute.
For example, suppose you decide that safety is twice as
important to the success of your design as cost. You would
assign a value factor of 20 for safety and a value factor of 10
for cost. You assign value factors on a basis of 0 to 100,
representing relative importance of each criterion to the
decision. Next you evaluate each design alternative against the
stated criteria [24].
Each design-related activity received two codes. The first is
level of abstraction where we identify three levels. Concept
design (C) addresses a problem or sub-problem with
preliminary ideas, strategies, and/or approaches.
V.
COMPUTATIONAL INTELLIGENCE
Natural Computing, Quantum computing, Soft Computing are
defines as computational intelligence. The development of
digital computers made possible the invention of human
engineered systems that show intelligent behavior or features.
In other words, AI builds up an intelligent system by studying
first the structure of the problem (typically in formal logical
terms), then formal reasoning procedures are applied within
that structure. Alternatively, non-symbolic and bottom-up
approaches (in which the structure is discovered and resulted
for man unordered source) to intelligent systems are also
known.
A. Natural Computing: Natural computing, also called Natural
computation, is a terminology introduced to encompass three
classes of methods:
1) Those that take inspiration from nature for the development
of novel problem-solving techniques;
2) Those that are based on the use of computers to synthesize
natural phenomena; and
3) Those that employ natural materials (e.g., molecules) to
compute. The main fields of research that compose these three
branches are artificial neural networks, evolutionary
B. Quantum computing : Quantum computing is the area of
study focused on developing computer technology based on the
principles of quantum theory, which explains the nature and
behavior of energy and matter on the quantum (atomic and
subatomic) level.
C. Soft Computing: proposed a new approach for Machine
Intelligence; separating Hard Computing techniques based
Artificial Intelligence from Soft Computing techniques based
Computational Intelligence . Hard computing is oriented
towards the analysis and design of physical processes and
systems, and has the characteristics precision, formality, and
category.
IV.
METRICES FOR CLIENT SPECIFICATION
There are the following matrices for client specification
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A review of solving real domain problems in Engineering for Computational Intelligence using Soft Computing
possible. Many engineering programs are incorporating design
experiences throughout their curricula. Design problems are the
most complex and ill-structured of all kinds of problems [9],
and there are different kinds of design problems [25].
A. Problem-Based Learning Environments: For engineering
faculty who are committed to problem solving but do not have
the support to develop PBL programs, they can with minimal
support design, develop, and implement problem based
learning environments.
Fig.4. Computational intelligence vs. Artificial Intelligence
VI.
KNOWLEDGE AND SKILL FOR SOLVING
APPROCHESS
There are the following stages by which knowledge and skill
become measure for the solving real domain approaches.
1. Instructional time, laboratory activities using safe, ethical
practices and conduction of different engineering fields:
i. Safe practices demonstrations during laboratory activities and
engineering fields
ii. Choice make among the legal disposal of material,
conversions of resources, recycling of materials etc.
2. Expectations of knowledge, tools and technology:
i. discuss concept out line , essential knowledge skills relevant
to real engineering design problems
ii. Selections of appropriate soft computing model
iii. Integration of technique of soft computing to develop
solution for real domain engineering problems.
iv. Justify appropriate model
v. Integrate and apply relevant tools or Martials
3. Expectations representation of graphics, written documents
and presentations tools:
a. Sketching a communication model.
b. Study of specification and procedure
iii. Prepare a specific model
iv. Analysis of appropriate formats.
4. Expectations of development and practices of engineering
professions:
i. Build of appropriate team of professional engineers’
ii. Emphasizing guidelines for ethical
iii. Interactions among engineers
5. Expectations of design practices and processes and creation
of justifiable solutions.
6. Expectations of managing engineering design projects
VII. DIFFERENT KIND OF PROBLEMS
There are different kinds of problems related with real domain
engineering problems.
Another implication of this study is to engage engineering
students in solving as many different kinds of problems as
Fig.5. Problem-Based Learning Environments
We are working on design architectures for scaling the
development of story problems [26] and troubleshooting
problems [22]. We describe an example of a problem-based
learning environment to introduce undergraduates to the range
of nuclear applications [13].
B. Meaningful Collaboration: In order to address ABET
requirements that engineers become able to function on multidisciplinary teams, team work, and collaborative learning have
become staples of engineering classrooms [12]. It is also
important to avoid bias or marginalization that
underrepresented students often experience when participating
in team related activities.
C. Frame work: As a methodological framework, we employed
a modified analytic induction process, a qualitative research
methodology that uses a systematic set of procedures to
develop an inductively derived grounded theory [10]. In order
to systematically identify our assumptions and derive new
aspects of problem solving informed by data, we utilized two
approaches:
(a) The development of a case-based reasoning (CBR) library
indexing semi-structured interviews with engineers based on
our assumptions and existing problem-solving research
followed by
(b) A grounded theory approach to elicit new perspectives on
workplace problem solving that are informed by the CBR
indexes but go beyond them.
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Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353
2017 4 International Conference on “Computing for Sustainable Global Development”, 01 st - 03rd March, 2017
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D. Structured Problems: A recurring characteristic of
workplace problems is that they are ill-structured. Initially,
some problems appeared fairly well structured, however, as
constraints and unanticipated problems (described later)
became apparent, the problems became more ill-structured. For
example, one problem looked very well-structured: measuring
the flow of a certain pipe and the temperature coming out of a
large container.
E. Well-Structured Problems: Within large projects, numerous
well-structured problems are solved, such as “what is the load
strength for material x” and how big the radius of machinery to
clear a path is. Engineering students learn to solve wellstructured problems in university courses; however, they rarely
experience well-structured problems in everyday contexts.
F. Structured Problems with Have Multiple, Often Conflicting
Goals : For example, the primary goal of one project was to
“find a solution that will meet the purpose and needs statement
that we include in our environmental impact statement that has
a level of public and community support along the corridor that
is politically acceptable and ultimately that we can afford.”
Accommodating the goals and expectations of each of those
factors is a complex undertaking. Sub-goals can often conflict
with the primary goal, so the engineer must determine which
goals have higher priority.
G. Structured Problems Are Solved in Many Different Ways: In
textbook problems, there is a preferred solution path or method.
In many ill-structured problems, the problem solvers never
know which solution method is optimal or even how to
evaluate the efficacy of different solutions.
H. Success Is Rarely Measured by Engineering Standards:
Engineering classroom problems often assume that engineering
problems are solved using only engineering criteria as the
criteria of success.
VII NON ENGINEERING’S CONSTRAINTS
Engineering education programs treat problems as engineeringonly problems. However, workplace problem constraints, like
standards, usually had little to do with engineering. Rather they
most often related to time (“We had a very aggressive schedule
from start to finish”) and budgets (“Dollars, it is always
dollars.”). When the clients are other companies, the constraints
are determined by cost, functionality, and the requirements that
new solutions have to work together with elements already in
place, such as overall corporate brand, jobs, tasks, and tools
that are already in place. The list of criteria is developed by the
design team. The design team is made up of people from
various engineering backgrounds that have expertise pertinent
to the problem. For example, if you were designing a critical
life support system, you would not include the criterion of
"must be minimum cost," because cost is not an important
factor in evaluating this design.
Fig. 6. Non Engineering design
VIII. DISTRIBUTION OF KNOWLEDGE SOLVING
AMONG TEAM MEMBER
There are the following basic facts related with knowledge
solving problems among team members:
Traditional conceptions of learning have focused on knowledge
that is acquired by individuals. Early theories of cognition
focused on information processing and knowledge in the heads
of individuals. According to newer perspectives, learning is less
a solitary act of individuals but rather is distributed among
people, their tools and communication media, history, and the
artifacts they create.
A. Establish Criteria for Success
Criteria for success are the specifications a design solution
must meet or the attributes it must possess to be considered
successful. You should include criteria in the problem
statement to provide direction toward the solution. At this point
in the design process, the criteria are preliminary. As the design
solution develops, you will most likely find that the initial
criteria need to be redefined or modified.
Fig.7. Engineering Successful design
The following is a list of preliminary criteria for a better
mousetrap design. This list would be included in the problem
definition statement. • The design must be low cost. • The
design should be safe, particularly with small children. • The
design should not be detrimental to the environment. • The
design should be aesthetically pleasing. • The design should be
simple to operate, with minimum human effort. • The design
must be disposable (you don't reuse the trap). • The design
should not cause undue pain and suffering for the mouse.
B. Pertinent Information collection:
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A review of solving real domain problems in Engineering for Computational Intelligence using Soft Computing
other; no one person is in possession of all the information
needed to make a decision.
IX. IMPLICATIONS FOR ENGINEERING EDUCATORS
There are the following implications for engineering educators
such as: Transfer workplace, Problem Based Learning, Problem
Structured. The study of the parameters [12]of workplace real domain
problem solving. We can recognize the successful efforts to achieve
the specific goals that may have made by expert engineering
programs.
Fig.8. Pertinent Information Design
Which type of information may be collected for the pertinent
information such as - Is the real problem and have statement
accurate? - Is there really a need for a new solution or has the
problem already been solved? And what are the existing
solutions to the problem? (Such as many aesthetics, safety and
environmental issues etc….)?
C. Requirement of extensive collaboration: Very few engineers
engage in solitary problem solving. In the overwhelming
majority of workplace problems, engineers must collaborate
with a variety of personnel in order to identify and solve the
problem. Collaborations are most successful when the roles and
relationships are well defined, and (like any good system), they
share a common goal.
D. Experiential knowledge based on primarily engineer rely:
Research has confirmed that experience is the most common
determinant of expertise, and that the recall of historical
information is the most frequent strategy for solving problems
[26]. Many For example, found that troubleshooters base their
diagnosis on their beliefs about the cause once a discrepant
symptom is found.
E. Unanticipated problems encountered: Most everyday
problems are dynamic; that is, the conditions change over time.
Most of the problems the engineers talked about were large
scale, in which a set of problems (some of which were
unanticipated) occurred. What is interesting is that the
unanticipated problems that arose were a combination of
engineering and non-engineering problems, as described by an
electrical engineer.
F. Multiple problems representation: Representing the problem
space is an essential part of all problem solving [27]. Experts
are able to represent problems in multiple ways, whereas
novices are typically restricted to a single form of problem
representations [30]. Representing problems to others directs
further interpretation of information about the problem,
simulates the behavior of the system based on knowledge about
the properties of the system, and triggers particular solution
schemas [11].
G. Communication skills: In Engineering Curricula:
Communication is the “sine qua non” of cognition [23].
Individuals may have mental representations derived from
experience or observations, but that knowledge is often useless
unless it is shared. Components of distributed systems, such as
workplace problem solving teams, must trust and rely on each
Workplace Transfer: Assumptions that underlying with goals
of engineering programs that become faster to transfer work
place in modern engineering contexts. The professional real
domain engineering programs that’s clearest goal is learning for
future work
Learning based problem: Once a solution for preparing
engineering graduates to become better problem with
workplace solvers is converting their curricula to learning based learning (LBP).
Structured problem: Many learning based problems represent
the innovations in history of solving real domain problems.
X. MULTIPLE SOLUTION GENERETOR
We consider a multiple solution generator for begins to the
design process with new ideas which solve the specific
problems. Rules of the systematic applications to solve
particular real domain technical problems. Concentrate and
focus on the problem as Selection of appropriate solution and
analysis procedure and Analysis of solution designs
A. Selection of appropriate solution and analysis procedure:
Once you've conceived alternative solutions to your design
problem, you need to analyze those solutions and then decide
which solution is best suited for implementation. Analysis is
the evaluation of the proposed designs.
B. Analysis of solution designs: When we start to implement a
solution for particular design for specific problems, we start a
tentative modal approach with a alternative solution based on
selection criteria. Engineers can perform several types of
analytic technique which required for the different level of
problems.
Fig. 9. Solution for process design
Prepare a list of requirement that to be considered as functional
analysis, liabilities, industry process design, manufacturability
and testability with specific parameters.
XI. CONCLUSION
Computational Intelligence engineering problems and their
applications easily solve by soft computing approaches. Their
experimental result desire to achieve an appropriate or
equivalent outputs with better performance rather than
Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3
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Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353
2017 4 International Conference on “Computing for Sustainable Global Development”, 01 st - 03rd March, 2017
th
traditional methods. Current scenario uses multiple soft
algorithms in field of engineering, mainly use of real data due
to their nature as incomplete, inconsistent and noisy etc. due to
lack of incomplete information ruling the phenomena under
consideration. Here we describe the different approaches which
identify the workplace attributes with different engineering real
problems which make them ill structured, complex and
ambiguous. Computational problems of real domain solve by
the appropriate constraints, distributed knowledge and also
collaborative activities in multiple form of problem solution
representation. We discussed much implication for real domain
engineering education, including with conceptualizing the
concepts of solving real domain different kind of as problem
based learning and its environments, engineering real domain
problems solved by extending implication for precipitate
research among the different field of computational intelligence
which validate to the effective approaches, techniques.
ACKNOWLEDGMENTS
I acknowledge my great gratitude and immense to different
researchers for their encouragement, inspiration and insightful
research.
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