Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
My presentation gives a brief overview about soft computing and it's concepts. Such as..Neural networks, Machine learning, Artificial Intelligence etc...
My presentation gives a brief overview about soft computing and it's concepts. Such as..Neural networks, Machine learning, Artificial Intelligence etc...
New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on...ijasuc
There is a vast amount of researched literature available on Route Finding and Link Establishment in
MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”,
“cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the
several aspects that go into effective route establishment. When we look at practical implementations of
MANETs, we have to take into account various factors in totality, not in isolation. The several factors that
decide and influence the routing have to be considered as a whole in the difficult task of finding the best
solution in route finding and optimization. The inputs to the system are manifold and apparently unrelated.
Most of the parameters are imprecise or non-crisp in nature. The uncertainty and imprecision lead to think
that intelligent routing techniques are essential and important in evolving robust and dependable solutions
to route finding. The obvious method by which this can be achieved is the deployment of soft computing
techniques such as Neural Nets, Fuzzy Logic and Genetic algorithms. Neural Networks help us to solve the
complex problem of transforming the inputs to outputs without apriori knowledge of what the relationship
is between inputs and outputs. Fuzzy Logic helps us to deal with imprecise and ill-conditioned data.
Genetic Algorithms help us to select the best possible solution from the solution space in an optimal sense.
Our paper presented here below seeks to explore new horizons in this direction. The results of our
experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a
large extent. There is of course considerable room for further refinements.
Army Study: Ontology-based Adaptive Systems of Cyber DefenseRDECOM
The U.S. Army Research Laboratory is part of the U.S. Army Research, Development and Engineering Command, which has the mission to ensure decisive overmatch for unified land operations to empower the Army, the joint warfighter and our nation. RDECOM is a major subordinate command of the U.S. Army Materiel Command.
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Computation of Neural Network using C# with Respect to BioinformaticsSarvesh Kumar
Neural network is the emerging field in the era of globalization which is fully based on the concept of soft-computing technique and bioinformatics. In the competitive market of new development process, Bioinformatics play the vital role to give the process of integration aspect as multidisciplinary subject like- biological Science, medicine science, computer science, engineering, chemical science, physical science as well as mathematical science who gives the experiences of artificial activities of human behaviour in the form of software. Now a days neural Network and its multidimensional approach give the idea for solving bioinformatics problems to handle imprecision, uncertainty in large and complex search spaces. This paper gives the emphasis on multidimensional approaches of neural network with soft computing paradigm using C# in bioinformatics with integrative research methodology. The overall process of multidimensional approaches of bioinformatics neurons can also be understood with the help of flow chart and diagram is the major concerned.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Invited talk at Tsinghua University on "Applications of Deep Neural Network". As the tech. lead of deep learning task force at NIO USA INC, I was invited to give this colloquium talk on general applications of deep neural network.
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on...ijasuc
There is a vast amount of researched literature available on Route Finding and Link Establishment in
MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”,
“cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the
several aspects that go into effective route establishment. When we look at practical implementations of
MANETs, we have to take into account various factors in totality, not in isolation. The several factors that
decide and influence the routing have to be considered as a whole in the difficult task of finding the best
solution in route finding and optimization. The inputs to the system are manifold and apparently unrelated.
Most of the parameters are imprecise or non-crisp in nature. The uncertainty and imprecision lead to think
that intelligent routing techniques are essential and important in evolving robust and dependable solutions
to route finding. The obvious method by which this can be achieved is the deployment of soft computing
techniques such as Neural Nets, Fuzzy Logic and Genetic algorithms. Neural Networks help us to solve the
complex problem of transforming the inputs to outputs without apriori knowledge of what the relationship
is between inputs and outputs. Fuzzy Logic helps us to deal with imprecise and ill-conditioned data.
Genetic Algorithms help us to select the best possible solution from the solution space in an optimal sense.
Our paper presented here below seeks to explore new horizons in this direction. The results of our
experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a
large extent. There is of course considerable room for further refinements.
Army Study: Ontology-based Adaptive Systems of Cyber DefenseRDECOM
The U.S. Army Research Laboratory is part of the U.S. Army Research, Development and Engineering Command, which has the mission to ensure decisive overmatch for unified land operations to empower the Army, the joint warfighter and our nation. RDECOM is a major subordinate command of the U.S. Army Materiel Command.
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Computation of Neural Network using C# with Respect to BioinformaticsSarvesh Kumar
Neural network is the emerging field in the era of globalization which is fully based on the concept of soft-computing technique and bioinformatics. In the competitive market of new development process, Bioinformatics play the vital role to give the process of integration aspect as multidisciplinary subject like- biological Science, medicine science, computer science, engineering, chemical science, physical science as well as mathematical science who gives the experiences of artificial activities of human behaviour in the form of software. Now a days neural Network and its multidimensional approach give the idea for solving bioinformatics problems to handle imprecision, uncertainty in large and complex search spaces. This paper gives the emphasis on multidimensional approaches of neural network with soft computing paradigm using C# in bioinformatics with integrative research methodology. The overall process of multidimensional approaches of bioinformatics neurons can also be understood with the help of flow chart and diagram is the major concerned.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Invited talk at Tsinghua University on "Applications of Deep Neural Network". As the tech. lead of deep learning task force at NIO USA INC, I was invited to give this colloquium talk on general applications of deep neural network.
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Gisvdlsigsbs zkdvdkod kaisvdv kshvdkd h kya hua hai kya hua hai kya hua hai kya hua h tere ko kuch bolna nahi kuch bhi ho raha h kya hai na dhund ke bta do ki nahi kuch puchungiii ho gya h kya hai ki h kya kr rhe h h h h ki mil jayega aur kya ho raha hai ki aap recharge ho jayega aur ek baat ka gussa h kya kar rahe hain aap bataiye ho gai thi na kal ke sath se ho gai hai aap bataiye ho ke aap hi nhi h kya kar rahe hain to aap bataiye hai aur aap hi ho jayegaa se baat karte hain aap ko free h kya aap bataiye hai aap ko bhi free nhi ho skta h tu bhi kuch share kar ye
This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
1. What is Soft-Computing?
Soft-Computing is a collection of techniques spanning many fields that fall under various
categories in Computational Intelligence. Soft-Computing has three main branches : Fuzzy
Logic, Evolutionary Computation, and Neural Networks. A number of other Soft-Computing
techniques do not fall neatly under any of these three branches. These would include Bayesian
Networks, Support-Vector Machines, Neuro-Fuzzy Systems and most hybrid systems, wavelet
theory, theory of fractals, chaos theory, to name a few.
Soft computing refers to a collection of computational techniques in computer science, machine
learning and some engineering disciplines, which study, model, and analyze very complex
phenomena: those for which more conventional methods have not yielded low cost, analytic, and
complete solutions. Soft Computing uses soft techniques contrasting it with classical artificial
intelligence hard computing techniques. Hard computing is bound by a Computer Science
concept called NP-Complete, which means, in layman's terms, that there is a direct connection
between the size of a problem and the amount of resources needed to solve the problem (there
are problems so large that it would take the lifetime of the Universe to solve them, even at super
computing speeds). Soft computing aids to surmount NP-complete problems by using inexact
methods to give useful but inexact answers to intractable problems.
There is no hard and fast rule that would classify any single technique under “soft-computing”.
However, there are some characteristics of soft-computing techniques which, taken together,
serve to sketch the boundaries of the field.
Soft-computing, as opposed to “hard computing”, is rarely prescriptive in its solution to a
problem. Solutions are not programmed for each and every possible situation. Instead, the
problem or task at hand is represented in such a way that the “state” of the system can somehow
be measured and compared to some desired state. The quality of the system’s state is the basis
for adapting the system’s parameters, which slowly converge towards the solution. This is the
basic approach employed by genetic algorithms and neural networks.
Soft-computing is often robust under noisy input environments and has high tolerance for
imprecision in the data on which it operates. Lotfi Zadeh, founder of Fuzzy Logic, says of
Computing with Words (CW) : “Computing, in its usual sense, is centered on manipulation of
numbers and symbols. In contrast, CW is a methodology in which objects of computation are
words and propositions drawn from natural langauage … There are two major imperatives for
computing with words. First computing with words is a necessity when the available information
is too imprecise to justify the use of numbers; and second, when there is tolerance for
imprecision which can be exploited to achieve tractability, robustness, low solution cost and
better rapport with reality”.
Soft Computing became a formal Computer Science area of study in the early 1990's.[1] Earlier
computational approaches could model and precisely analyze only relatively simple systems.
More complex systems arising in biology, medicine, the humanities, management sciences, and
2. similar fields often remained intractable to conventional mathematical and analytical methods.
That said, it should be pointed out that simplicity and complexity of systems are relative, and
many conventional mathematical models have been both challenging and very productive.
Components of soft computing include:
• Neural networks (NN)
• Fuzzy systems (FS)
• Evolutionary computation (EC), including:
o Evolutionary algorithms
o Harmony search
• Swarm intelligence
• Ideas about probability including:
o Bayesian network
o Chaos theory
Generally speaking, soft computing techniques resemble biological processes more closely than
traditional techniques, which are largely based on formal logical systems, such as sentential logic
and predicate logic, or rely heavily on computer-aided numerical analysis (as in finite element
analysis). Soft computing techniques are intended to complement each other.
Unlike hard computing schemes, which strive for exactness and full truth, soft computing
techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a
particular problem. Another common contrast comes from the observation that inductive
reasoning plays a larger role in soft computing than in hard computing.