An Introduction to Computational Intelligence Paradigms

Konar, Amit; Jain, L. C.

doi:10.1007/978-94-010-0678-1_1

Cited by 16 publications

(11 citation statements)

References 36 publications

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“…is expressed as in (15), then we obtain the same ordering of (y, v) in both (15) and (17). So, for each positionwise element of (y, v) in (17), we have a corresponding position-wise element in μ(y, v), representing the secondary distribution of the former element.…”

Section: B3 Combining Primary and Secondary Membershipsmentioning

confidence: 98%

Abductive reasoning with type 2 fuzzy sets

Datta

Konar

Chowdhury

et al. 2009

2009 IEEE International Conference on Fuzzy Systems

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In fuzzy abduction, one needs to evaluate the membership distribution of the premise (antecedent clause), when the membership distribution of the consequent clause, and the fuzzy implication relations between the antecedent and the consequent clauses are provided. The paper formulates and solves the problem of fuzzy abduction by using type-2 fuzzy sets. It presumes background knowledge about the primary and the secondary antecedent to consequent implication relations to uniquely determine the type-2 fuzzy set corresponding to the antecedent clause, when the same for the consequent clause is provided. The proposed methodology of abduction would serve many interesting applications on predictions, forecasting, and diagnosis, where the environmental factor can be modeled with type-2 secondary distributions.

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Section: B3 Combining Primary and Secondary Membershipsmentioning

confidence: 98%

Abductive reasoning with type 2 fuzzy sets

Datta

Konar

Chowdhury

et al. 2009

2009 IEEE International Conference on Fuzzy Systems

Self Cite

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“…In fact, there has been a constant demand on IT professionals for improved methodologies, design, and applications. Accordingly, the researchers are responding to this demand through computational intelligence, particularly focusing on neural networks (Haykin, 1994;Wang & Takefuji, 1993), genetic algorithms (Davis, 1987;Deb, 2001;Goldberg, 1989), evolutionary programming (Diego & Duc Truong, 2007;Konar & Jain, 2001), artificial immune systems (Dasgupta, 1980;De Castro & Timmis, 2002), and fuzzy systems (Zadeh, 1965;Zimmermann, 1999).…”

Section: Angappa "Guna" Gunasekaran Phd University Of Massachusetts Dartmouthmentioning

confidence: 99%

Handbook of Computational Intelligence in Manufacturing and Production Management

Laha¹,

Mandal²

2008

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Earlier.methods.for.solving.manufacturing.scheduling.problems.by.classical.optimization.techniques. such.as.linear.programming.and.branch.and.bound.reveal.serious.limitations..More.advanced.heuristics. as well as various efficient optimization methods based on the evolutionary computing paradigm such as genetic algorithms, simulated annealing, and artificial immune system are required. This chapter briefly discusses the overview of these emerging heuristics and metaheuristics, and their applications to scheduling problems. The artificial immune system is discussed at length to add to the growing research interests..

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“…Artificial Intelligence (AI) can be categorized here as the field is dealing with a wide range of utilization and layouts of numerous algorithms for interpreting and attaining knowledge from data. And the AI concept is firmly related to many fields like pattern recognition, probability theory, statistics, machine learning, and numerous procedures like fuzzy models, neural networks which are collectively known as “Computational Intelligence” Vinod and Anand ( 2021 ), Engelbrecht ( 2007 ), Konar ( 2006 ), Duda et al ( 2012 ), Webb ( 2003 ), Friedman et al ( 2001 ). Multiple complicated usages engaged with AI strategies like classification, regression, predictions and also optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

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An Introduction to Computational Intelligence Paradigms

Cited by 16 publications

References 36 publications

Abductive reasoning with type 2 fuzzy sets

Abductive reasoning with type 2 fuzzy sets

Handbook of Computational Intelligence in Manufacturing and Production Management

Machine Learning in Drug Discovery: A Review

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