ArticleComputational intelligence and neuroscience2021
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Intelligent demand-side energy management via optimized ANFIS-gene expression programming in hybrid renewable-grid systems.Scientific reports · 2025Article
- Efficient lung cancer detection using computational intelligence and ensemble learning.PloS one · 2024Article
- Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery.Scientific reports · 2023Article
- Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters.Sensors (Basel, Switzerland) · 2023Article
- Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.New generation computing · 2023Article
- Retracted: Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences.Computational intelligence and neuroscience · 2023Article
- Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation.Computational intelligence and neuroscience · 2022Article
- Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder.Computational intelligence and neuroscience · 2022Article
- PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer.BioMed research international · 2022Article
- Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.BioMed research international · 2022Article
- Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.Computational and mathematical methods in medicine · 2022Article
- Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder.Computational and mathematical methods in medicine · 2022Article
- Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer's Disease-Based Neurodegenerative Disorders.Computational and mathematical methods in medicine · 2022Article
- Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.BioMed research international · 2022Article
- Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.BioMed research international · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.