Adaptive Neuro-Fuzzy Inference System (ANFIS) in Parkinson’s Disease (PD) Diagnosis
Abstract
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor impairments such as tremors, rigidity, and bradykinesia, along with a wide range of non-motor symptoms. Conventional diagnostic methods rely primarily on clinical evaluation, often leading to delayed or inaccurate diagnoses due to overlapping symptoms with other movement disorders. The integration of Artificial Intelligence (AI) in medical practice has opened new pathways for improving diagnostic accuracy and early detection of PD. Among these approaches, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has gained attention for its ability to combine the adaptive learning power of neural networks with the uncertainty-handling capacity of fuzzy logic. ANFIS models have been applied to diverse patient data sources, including speech analysis, gait dynamics, handwriting patterns, and biomedical signals, to differentiate PD from healthy controls and assess disease severity. This paper reviews the role of ANFIS in PD diagnosis, highlighting its advantages over conventional machine learning models, its medical implications, challenges, and future research opportunities.
Keywords
Full Text:
AbstractReferences
Boller, F., et al., Parkinson disease, dementia, and Alzheimer disease: clinicopathological correlations. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 1980. 7(4): p. 329-335.
Schapira, A.H., Parkinson's disease. Bmj, 1999. 318(7179): p. 311-314.
Duval, C., A.F. Sadikot, and M. Panisset, Bradykinesia in patients with essential tremor. Brain research, 2006. 1115(1): p. 213-216.
Di Biase, L., et al., Quantitative analysis of bradykinesia and rigidity in Parkinson’s disease. Frontiers in neurology, 2018. 9: p. 121.
Schapira, A.H., K.R. Chaudhuri, and P. Jenner, Non-motor features of Parkinson disease. Nature reviews neuroscience, 2017. 18(7): p. 435-450.
Akram, A.S., et al., Advancing the frontier: neuroimaging techniques in the early detection and management of neurodegenerative diseases. Cureus, 2024. 16(5).
Miller, D.B. and J.P. O’Callaghan, Biomarkers of Parkinson’s disease: present and future. Metabolism, 2015. 64(3): p. S40-S46.
Brooks, D.J., Imaging approaches to Parkinson disease. Journal of Nuclear Medicine, 2010. 51(4): p. 596-609.
Khokhar, M., D. Yadav, and P. Sharma, Transforming healthcare in the age of artificial intelligence: a new era of diagnostic excellence in laboratory medicine. 2025, Springer. p. 1-2.
Zhou, J., et al., Artificial intelligence-driven transformative applications in disease diagnosis technology. Medical Review, 2025(0).
Ahmadi, A. and N. RabieNezhad Ganji, AI-driven medical innovations: transforming healthcare through data intelligence. International Journal of BioLife Sciences (IJBLS), 2023. 2(2): p. 132-142.
Ahmadi, H., et al., Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 2018. 161: p. 145-172.
Ghane, M., et al., Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification. Biocybernetics and Biomedical Engineering, 2022. 42(3): p. 902-920.
Nilashi, M., et al., Predicting parkinson’s disease progression: Evaluation of ensemble methods in machine learning. Journal of healthcare engineering, 2022. 2022(1): p. 2793361.
Nilashi, M., et al., Early diagnosis of Parkinson’s disease: A combined method using deep learning and neuro-fuzzy techniques. Computational biology and chemistry, 2023. 102: p. 107788.
Nilashi, M., et al., A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 2019. 12(1): p. 13-20.
Nilashi, M., et al., Remote tracking of Parkinson's disease progression using ensembles of deep belief network and self-organizing map. Expert Systems with Applications, 2020. 159: p. 113562.
Nilashi, M., et al., A soft computing approach for diabetes disease classification. Health Informatics Journal, 2018. 24(4): p. 379-393.
Nilashi, M., et al., A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques. Biocybernetics and Biomedical Engineering, 2018. 38(1): p. 1-15.
Nilashi, M., et al., An analytical method for measuring the Parkinson’s disease progression: A case on a Parkinson’s telemonitoring dataset. Measurement, 2019. 136: p. 545-557.
Adlung, L., et al., Machine learning in clinical decision making. Med, 2021. 2(6): p. 642-665.
Peiffer-Smadja, N., et al., Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection, 2020. 26(5): p. 584-595.
Awaysheh, A., et al., Review of medical decision support and machine-learning methods. Veterinary pathology, 2019. 56(4): p. 512-525.
Jang, J.-S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993. 23(3): p. 665-685.
Akbari, E., et al., ANFIS modeling for bacteria detection based on GNR biosensor. Journal of Chemical Technology & Biotechnology, 2016. 91(6): p. 1728-1736.
Foroughi, B., et al., Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 2023. 70: p. 103158.
Iranmanesh, M., et al., Factors influencing attitude and intention to use autonomous vehicles in Vietnam: findings from PLS-SEM and ANFIS. Information Technology & People, 2024. 37(6): p. 2223-2246.
Kheirandish, A., et al., Using ANFIS technique for PEM fuel cell electric bicycle prediction model. International Journal of Environmental Science and Technology, 2019. 16(11): p. 7319-7326.
Nilashi, M., Evaluation of Security Pillars in the Industrial Internet of Things: A Fuzzy Logic Approach. Journal of Soft Computing and Decision Support Systems, 2024. 11(4): p. 1-7.
Nilashi, M., et al., Accuracy analysis of Type-2 fuzzy system in predicting parkinson’s disease using biomedical voice measures. International Journal of Fuzzy Systems, 2024. 26(4): p. 1261-1284.
Nilashi, M., et al., A hybrid method to solve data sparsity in travel recommendation agents using fuzzy logic approach. Mathematical Problems in Engineering, 2022. 2022(1): p. 7372849.
Nilashi, M., et al., Research Article A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach. 2022.
Nilashi, M., et al., Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 2019. 215: p. 767-783.
Nilashi, M., O. bin Ibrahim, and N. Ithnin, Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 2014. 41(8): p. 3879-3900.
Nilashi, M., O. bin Ibrahim, and N. Ithnin, Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 2014. 60: p. 82-101.
Nilashi, M., et al., A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 2015. 14(6): p. 542-562.
Nilashi, M., et al., Measuring country sustainability performance using ensembles of neuro-fuzzy technique. Sustainability, 2018. 10(8): p. 2707.
Nilashi, M., et al., A soft computing method for the prediction of energy performance of residential buildings. Measurement, 2017. 109: p. 268-280.
Nilashi, M., et al., A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 2015. 19(11): p. 3173-3207.
Nilashi, M., et al., Analysis of travellers’ online reviews in social networking sites using fuzzy logic approach. International Journal of Fuzzy Systems, 2019. 21(5): p. 1367-1378.
Yadegaridehkordi, E., et al., Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 2018. 66: p. 364-386.
Zogaan, W.A., et al., A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features. MethodsX, 2024. 12: p. 102553.
El-Hasnony, I.M., S.I. Barakat, and R.R. Mostafa, Optimized ANFIS model using hybrid metaheuristic algorithms for Parkinson’s disease prediction in IoT environment. IEEE Access, 2020. 8: p. 119252-119270.
Yadollahpour, A., et al., Designing and implementing an ANFIS based medical decision support system to predict chronic kidney disease progression. Frontiers in physiology, 2018. 9: p. 1753.
Kim, T.-h., et al., ANFIS Fuzzy convolutional neural network model for leaf disease detection. Frontiers in Plant Science, 2024. 15: p. 1465960.
Shabeeb, A.G., H.A. Hashim, and S.K. Gharghan, Heart disease classification based on combination of PCA/ANFIS model. Research on Biomedical Engineering, 2024. 40(3): p. 609-625.
Nilashi, M., et al., Parkinson’s disease diagnosis using Laplacian score, Gaussian process regression and self-organizing maps. Brain sciences, 2023. 13(4): p. 543.
Polat, K., Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering. International Journal of Systems Science, 2012. 43(4): p. 597-609.
Mittal, V. and R. Sharma, Machine learning approach for classification of Parkinson disease using acoustic features. Journal of Reliable Intelligent Environments, 2021. 7(3): p. 233-239.
Chen, H.-L., et al., An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert systems with applications, 2013. 40(1): p. 263-271.
Wan, S., et al., Deep multi-layer perceptron classifier for behavior analysis to estimate parkinson’s disease severity using smartphones. IEEE Access, 2018. 6: p. 36825-36833.
Jain, S. and S. Shetty. Improving accuracy in noninvasive telemonitoring of progression of Parkinson'S Disease using two-step predictive model. in 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA). 2016. IEEE.
Benayad, N., et al., Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease. International Journal of Electrical & Computer Engineering (2088-8708), 2022. 12(2).
Behroozi, M. and A. Sami, A multiple-classifier framework for Parkinson’s disease detection based on various vocal tests. International journal of telemedicine and applications, 2016. 2016.
Sharma, S.R., B. Singh, and M. Kaur, Classification of Parkinson disease using binary Rao optimization algorithms. Expert Systems, 2021. 38(4): p. e12674.
Das, R., A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 2010. 37(2): p. 1568-1572.
Uppalapati, B., S. Srinivasa Rao, and P. Srinivasa Rao, Application of ANN Combined with Machine Learning for Early Recognition of Parkinson’s Disease, in Intelligent System Design. 2023, Springer. p. 39-49.
Afonso, L.C., et al., A recurrence plot-based approach for Parkinson’s disease identification. Future Generation Computer Systems, 2019. 94: p. 282-292.
Anand, A., et al. Neurodegenerative Disorder of Ageing using Neural Networks. in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). 2022. IEEE.
Pereira, C.R., et al., Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. Artificial intelligence in medicine, 2018. 87: p. 67-77.
Shinde, S., et al., Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage: Clinical, 2019. 22: p. 101748.
Manap, H.H., N.M. Tahir, and A.I.M. Yassin. Statistical analysis of parkinson disease gait classification using Artificial Neural Network. in 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2011. IEEE.
Eskidere, Ö., F. Erta?, and C. Hanilçi, A comparison of regression methods for remote tracking of Parkinson’s disease progression. Expert Systems with Applications, 2012. 39(5): p. 5523-5528.
Babu, G.S. and S. Suresh, Parkinson’s disease prediction using gene expression–A projection based learning meta-cognitive neural classifier approach. Expert Systems with Applications, 2013. 40(5): p. 1519-1529.
Hariharan, M., K. Polat, and R. Sindhu, A new hybrid intelligent system for accurate detection of Parkinson's disease. Computer methods and programs in biomedicine, 2014. 113(3): p. 904-913.
Khan, M.M., S.K. Chalup, and A. Mendes. Parkinson’s disease data classification using evolvable wavelet neural networks. in Australasian Conference on Artificial Life and Computational Intelligence. 2016. Springer.
Muniz, A., et al., Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait. Journal of biomechanics, 2010. 43(4): p. 720-726.
Buza, K. and N.Á. Varga, Parkinsonet: estimation of updrs score using hubness-aware feedforward neural networks. Applied Artificial Intelligence, 2016. 30(6): p. 541-555.
Al-Fatlawi, A.H., M.H. Jabardi, and S.H. Ling. Efficient diagnosis system for Parkinson's disease using deep belief network. in 2016 IEEE Congress on Evolutionary Computation (CEC). 2016. IEEE.
Borzì, L., et al., Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor. Artificial Intelligence in Medicine, 2023. 135: p. 102459.
Grover, S., et al., Predicting severity of Parkinson’s disease using deep learning. Procedia computer science, 2018. 132: p. 1788-1794.
Daher, A., et al. Adaptive Neuro-Fuzzy Inference System As New Real-Time Approach For Parkinson Seizures Prediction. in 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). 2021. IEEE.
Li, D.-C., C.-W. Liu, and S.C. Hu, A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artificial Intelligence in Medicine, 2011. 52(1): p. 45-52.
Bellino, G.M., et al. Fuzzy logic as a control strategy to command a deep brain stimulator in patients with parkinson disease. in Latin American Conference on Biomedical Engineering. 2019. Springer.
Aversano, L., et al. Fuzzy neural networks to detect parkinson disease. in 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2020. IEEE.
Guo, P.-F., P. Bhattacharya, and N. Kharma. Advances in detecting Parkinson’s disease. in International Conference on Medical Biometrics. 2010. Springer.
Parziale, A., et al., Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues. Artificial Intelligence in Medicine, 2021. 111: p. 101984.
Castelli, M., L. Vanneschi, and S. Silva, Prediction of the unified Parkinson’s disease rating scale assessment using a genetic programming system with geometric semantic genetic operators. Expert Systems with Applications, 2014. 41(10): p. 4608-4616.
Avci, D. and A. Dogantekin, An expert diagnosis system for parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson’s disease, 2016. 2016.
Pelzer, E.A., et al., Clustering of Parkinson subtypes reveals strong influence of DRD2 polymorphism and gender. Scientific reports, 2022. 12(1): p. 1-6.
Shalaby, M., N.A. Belal, and Y. Omar, Data clustering improves Siamese neural networks classification of Parkinson’s disease. Complexity, 2021. 2021.
Sherly Puspha Annabel, L., S. Sreenidhi, and N. Vishali, A Novel Diagnosis System for Parkinson’s Disease Using K-means Clustering and Decision Tree, in Communication and Intelligent Systems. 2021, Springer. p. 607-615.
Nilashi, M., et al., Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. Journal of healthcare engineering, 2022. 2022.
Mabrouk, R., Principal Component Analysis versus Subject’s Residual Profile Analysis for Neuroinflammation Investigation in Parkinson Patients: A PET Brain Imaging Study. Journal of imaging, 2022. 8(3): p. 56.
Kiran, G.U. and D. Vasumathi, Predicting Parkinson’s Disease using Extreme Learning Measure and Principal Component Analysis based Mini SOM. Annals of the Romanian Society for Cell Biology, 2021: p. 16099-16111.
Wang, Y., et al., Principal component analysis of routine blood test results with Parkinson's disease: A case-control study. Experimental Gerontology, 2021. 144: p. 111188.
Xu, Z. and Z. Zhu. Handwritten dynamics classification of Parkinson’s disease through support vector machine and principal component analysis. in Journal of Physics: Conference Series. 2021. IOP Publishing.
Rao, D.V., et al. Diagnosis of Parkinson's Disease using Principal Component Analysis and Machine Learning algorithms with Vocal Features. in 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). 2022. IEEE.
Lakshmi, T., et al., An Ensemble Model to Detect Parkinson’s Disease Using MRI Images, in Intelligent System Design. 2023, Springer. p. 465-473.
Masud, M., et al., CROWD: crow search and deep learning based feature extractor for classification of Parkinson’s disease. ACM Transactions on Internet Technology (TOIT), 2021. 21(3): p. 1-18.
Gunduz, H., Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access, 2019. 7: p. 115540-115551.
Bhakar, S. and S.S. Verma, Parkinson’s Disease Detection Through Deep Learning Model, in ICT Systems and Sustainability. 2023, Springer. p. 95-103.
Singh, K.R. and S. Dash, Early detection of neurological diseases using machine learning and deep learning techniques: A review. Artificial Intelligence for Neurological Disorders, 2023: p. 1-24.
Johri, A. and A. Tripathi. Parkinson Disease Detection Using Deep Neural Networks. in 2019 Twelfth International Conference on Contemporary Computing (IC3). 2019. IEEE.
Kose, U., et al., Diagnosing parkinson by using deep autoencoder neural network, in Deep Learning for Medical Decision Support Systems. 2021, Springer. p. 73-93.
Nagasubramanian, G. and M. Sankayya, Multi-variate vocal data analysis for detection of Parkinson disease using deep learning. Neural Computing and Applications, 2021. 33(10): p. 4849-4864.
Nilashi, M. and R.A. Abumalloh, Parkinson's Disease Facts and Figures. Journal of Soft Computing and Decision Support Systems, 2023. 10(5): p. 1-6.
Nilashi, M., O. Ibrahim, and A. Ahani, Accuracy improvement for predicting Parkinson’s disease progression. Scientific reports, 2016. 6(1): p. 34181.
Rezayi, S., et al., Vulnerability of Parkinson’s Patients to COVID?19 and Its Consequences and Effects on Them: A Systematic Review. Parkinson’s Disease, 2023. 2023(1): p. 6272982.
Goldman, J.G., et al., Cognitive impairment in Parkinson’s disease: a report from a multidisciplinary symposium on unmet needs and future directions to maintain cognitive health. npj Parkinson's Disease, 2018. 4(1): p. 19.
Kerstens, V.S. and A. Varrone, Dopamine transporter imaging in neurodegenerative movement disorders: PET vs. SPECT. Clinical and Translational Imaging, 2020. 8(5): p. 349-356.
Nicastro, N., et al., Dopaminergic imaging in degenerative parkinsonisms, an established clinical diagnostic tool. Journal of neurochemistry, 2023. 164(3): p. 346-363.
Walia, N., H. Singh, and A. Sharma, ANFIS: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 2015. 123(13).
Abraham, A., Adaptation of fuzzy inference system using neural learning, in Fuzzy Systems Engineering: Theory and Practice. 2005, Springer. p. 53-83.
Al-Hmouz, A., et al., Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE transactions on learning technologies, 2011. 5(3): p. 226-237.
Rules, A.F.I.T., ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on systems, man, and cybernetics, 1993. 23(3): p. 665.
Karaboga, D. and E. Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 2019. 52(4): p. 2263-2293.
Zadeh, L.A., Is there a need for fuzzy logic? Information sciences, 2008. 178(13): p. 2751-2779.
Licata, G., Fuzzy logic, knowledge and natural language. Fuzzy Inference Syst.-Theory Appl., 2012.
Zadeh, L.A., The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy sets and systems, 1983. 11(1-3): p. 199-227.
Al-Mahasneh, M., et al., Application of hybrid neural fuzzy system (ANFIS) in food processing and technology. Food Engineering Reviews, 2016. 8(3): p. 351-366.
Novák, V. and S. Lehmke, Logical structure of fuzzy IF-THEN rules. FUZZY sets and systems, 2006. 157(15): p. 2003-2029.
Rajab, S., Handling interpretability issues in ANFIS using rule base simplification and constrained learning. Fuzzy Sets and Systems, 2019. 368: p. 36-58.
Letanneux, A., et al., From micrographia to Parkinson's disease dysgraphia. Movement Disorders, 2014. 29(12): p. 1467-1475.
Babini, M.H., et al., Decoding of heart–brain relation by complexity-based analysis of heart rate variability (HRV) and electroencephalogram (EEG) signals. Fractals, 2022. 30(07): p. 2250190.
Segura-Aguilar, J., A new mechanism for protection of dopaminergic neurons mediated by astrocytes. Neural Regeneration Research, 2015. 10(8): p. 1225-1227.
Alghamdi, A., et al., Analysis of social data for accuracy improvement of collaborative filtering in MOOCs using text mining and deep learning techniques. Discover Computing, 2025. 28(1): p. 1-21.
Farokhi, M., et al., A Multi-Criteria Recommender System for Tourism Using Fuzzy Approach. Journal of Soft Computing & Decision Support Systems, 2016. 3(4).
Mardani, A., et al., A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 2020. 275: p. 122942.
Nilashi, M., et al., A combined method for diabetes mellitus diagnosis using deep learning, singular value decomposition, and self-organizing map approaches. Diagnostics, 2023. 13(10): p. 1821.
Nilashi, M., et al., Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques. Technology in Society, 2022. 71: p. 102118.
Nilashi, M., et al., A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques. Journal of Soft Computing & Decision Support Systems, 2016. 3(5).
Nilashi, M., et al., Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Information and Engineering, 2017. 9(3): p. 345-357.
Nilashi, M., et al., Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 2015. 293: p. 235-250.
Rokach, L. and O. Maimon, Clustering methods, in Data mining and knowledge discovery handbook. 2005, Springer. p. 321-352.
Omran, M.G., A.P. Engelbrecht, and A. Salman, An overview of clustering methods. Intelligent Data Analysis, 2007. 11(6): p. 583-605.
Saxena, A., et al., A review of clustering techniques and developments. Neurocomputing, 2017. 267: p. 664-681.
Abumalloh, R.A., et al., Parkinson’s disease diagnosis using deep learning: A bibliometric analysis and literature review. Ageing research reviews, 2024. 96: p. 102285.
LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444.
Mathew, A., P. Amudha, and S. Sivakumari. Deep learning techniques: an overview. in International conference on advanced machine learning technologies and applications. 2020. Springer.
Deng, L. and D. Yu, Deep learning: methods and applications. Foundations and trends® in signal processing, 2014. 7(3–4): p. 197-387.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.