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A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease
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Outgoing claims: the papers this work cites, builds on, or argues with.
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A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach1 1Edited by B. Holland
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Structural learning of Bayesian networks by bacterial foraging optimization
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Phonatory Impairment in Parkinson's Disease: Evidence from Nonlinear Dynamic Analysis and Perturbation Analysis
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Advances in the treatment of Parkinson's disease
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Gene Selection for Cancer Classification using Support Vector Machines
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Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors
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Improving Parkinson's disease identification through evolutionary-based feature selection
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Biomimicry of bacterial foraging for distributed optimization and control
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Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease
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A Hybrid Least Square-Fuzzy Bacterial Foraging Strategy for Harmonic Estimation
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Multi-objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process
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Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation
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A training algorithm for optimal margin classifiers
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Speech Impairment in a Large Sample of Patients with Parkinson’s Disease
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Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
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Computer‐Aided Diagnosis of Parkinson’s Disease Using Complex‐Valued Neural Networks and mRMR Feature Selection Algorithm
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Nonlinear Models Using Dirichlet Process Mixtures
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Nonlinear Models Using Dirichlet Process Mixtures
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Advances in kernel methods: support vector learning
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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