Sep 29, 2021
Fuzzy Set in rosa lila - von SuperTanya
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Fuzzy Set in rosa lila - von SuperTanya
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Classification of cancer patterns from gene expression data is a difficult task in computational biology and artificial intelligence due to the sufficient number of training samples is often difficult, expensive, and hard to gather. Although, the classification results obtained by the conventional classifiers trained with insufficient training samples are generally low. However, unlabeled samples are relatively low-cost and easy to gather, whereas conventional classifiers do not utilize these unlabeled samples to train the model. In this context, a self-training-based model semi-supervised ordered weighted average fuzzy-rough nearest neighbour classifier for cancer pattern classification from gene expression data is proposed. The experiments are carried out on eight publicly available real-life gene expression cancer datasets. The performance of the proposed method is compared with four other methods (two supervised and two semi-supervised) in terms of percentage accuracy, precision, recall, macro averaged F1 measure, micro averaged F1 measure and kappa. The dominance of the proposed method is justified by the experimental results.
A Study of Student’s Academic Performance Using Artificial Intelligence: A Fuzzy Logic Approach | Chapter 11 | Innovations in Science and Technology Vol. 7
Student academic performance evaluation consists of various components, each of which is dependent on the amount of imprecise judgments resulting from human (teacher/tutor) interpretation. In this Book chapter, we investigate the applicability of fuzzy logic and fuzzy expert systems to the problem of allocating new students to homogeneous groups of defined maximum capacity, and we examine the effects of such allocations on students' academic performance. The book chapter also introduces a Dynamic Fuzzy Expert System model based on a Fuzzy set and Regression analysis that is capable of dealing with imprecision and missing data that is usually inherited in student academic performance evaluation. Using the fuzzy C-Means clustering algorithm, this model automatically converts crisp sets to fuzzy sets. Author(S) Details Ramjeet Singh Yadav Computer Application, Department of Business Management and Entrepreneurship, Dr. Rammanohar Lohia Avadh University, Hawai Patti, Prayagraj Road, Ayodhya-224001, (Uttar Pradesh), India. Jyotirmay Patel Shri Ram Murti Smarak College of Engineering, Technology & Research Ram Murti Puram, 13 KM Bareilly-Nainital Highway, Bhojipura, Bareilly-243202, UP, India. View Book:- https://stm.bookpi.org/IST-V7/article/view/6068
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