Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics
暂无分享,去创建一个
M. Zivkovic | Milos Antonijevic | Milos Kabiljo | Rade Matić | Luka Jovanovic | R. Damaševičius | Vladimir Simić | Nebojša Bačanin | Goran Kunjadic
[1] Kunyang Wang,et al. The Fundamental Property of Human Leg During Walking: Linearity and Nonlinearity , 2023, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[2] L. Abualigah,et al. Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm , 2023, Applied Sciences.
[3] P. Spalevic,et al. Cloud-load forecasting via decomposition-aided attention recurrent neural network tuned by modified particle swarm optimization , 2023, Complex & Intelligent Systems.
[4] E. B. Tirkolaee,et al. Improving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysis , 2023, Applied Soft Computing.
[5] L. Abualigah,et al. A Sinh Cosh optimizer , 2023, Knowl. Based Syst..
[6] Abdulkareem M. Albekairy,et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice , 2023, BMC Medical Education.
[7] Changsheng Wen,et al. Crayfish optimization algorithm , 2023, Artificial Intelligence Review.
[8] Fatma A. Hashim,et al. Dimensionality reduction approach based on modified hunger games search: case study on Parkinson’s disease phonation , 2023, Neural Computing and Applications.
[9] M. Zivkovic,et al. Intrusion Detection in Healthcare 4.0 Internet of Things Systems via Metaheuristics Optimized Machine Learning , 2023, Sustainability.
[10] P. Spalevic,et al. Marine Vessel Classification and Multivariate Trajectories Forecasting Using Metaheuristics-Optimized eXtreme Gradient Boosting and Recurrent Neural Networks , 2023, Applied Sciences.
[11] S. Hofvind,et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. , 2023, The Lancet. Oncology.
[12] G. Khan,et al. Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques , 2023, Sensors.
[13] B. Cao,et al. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson’s disease , 2023, Frontiers in aging neuroscience.
[14] C. Sandor,et al. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis , 2023, Nature Medicine.
[15] Pravir Kumar,et al. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease , 2023, Ageing Research Reviews.
[16] D. Pamučar,et al. Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations , 2023, Appl. Soft Comput..
[17] R. Dixon,et al. Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation , 2023, Frontiers in Aging Neuroscience.
[18] Marina Marjanovic,et al. Addressing feature selection and extreme learning machine tuning by diversity-oriented social network search: an application for phishing websites detection , 2023, Complex & Intelligent Systems.
[19] Shaker El-Sappagh,et al. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease , 2023, Comput. Methods Programs Biomed..
[20] E. Astigarraga,et al. Review of Technological Challenges in Personalised Medicine and Early Diagnosis of Neurodegenerative Disorders , 2023, International journal of molecular sciences.
[21] V. Savinov,et al. Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression , 2023, Diagnostics.
[22] H. Reichmann,et al. The use of wearables for the diagnosis and treatment of Parkinson’s disease , 2023, Journal of Neural Transmission.
[23] Sameehan Mahajani,et al. Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders , 2022, Neural regeneration research.
[24] Marina Marjanovic,et al. Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images , 2022, Electronics.
[25] F. Horak,et al. Freezing of gait, gait initiation, and gait automaticity share a similar neural substrate in Parkinson's disease. , 2022, Human movement science.
[26] V. H. C. de Albuquerque,et al. Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data , 2022, Biomedicines.
[27] Marko Tanaskovic,et al. Feature Selection by Hybrid Artificial Bee Colony Algorithm for Intrusion Detection , 2022, 2022 International Conference on Edge Computing and Applications (ICECAA).
[28] Jui-Sheng Chou,et al. Intelligent candlestick forecast system for financial time-series analysis using metaheuristics-optimized multi-output machine learning , 2022, Appl. Soft Comput..
[29] M. Zivkovic,et al. The XGBoost Tuning by Improved Firefly Algorithm for Network Intrusion Detection , 2022, 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).
[30] Marina Marjanovic,et al. Feature Selection by Improved Sand Cat Swarm Optimizer for Intrusion Detection , 2022, 2022 International Conference on Artificial Intelligence in Everything (AIE).
[31] H. Prayogo,et al. Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristics-optimized machine learning , 2022, Journal of Building Engineering.
[32] Yu-cai Liu,et al. Diagnosis of Parkinson's disease based on SHAP value feature selection , 2022, Biocybernetics and Biomedical Engineering.
[33] R. Barker,et al. Patient Experience in Early-Stage Parkinson’s Disease: Using a Mixed Methods Analysis to Identify Which Concepts Are Cardinal for Clinical Trial Outcome Assessment , 2022, Neurology and Therapy.
[34] Marko Tanaskovic,et al. Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection , 2022, Mathematics.
[35] E. Minikel,et al. Disease stages and therapeutic hypotheses in two decades of neurodegenerative disease clinical trials , 2022, Scientific Reports.
[36] Marina Marjanovic,et al. The AdaBoost Approach Tuned by Firefly Metaheuristics for Fraud Detection , 2022, 2022 IEEE World Conference on Applied Intelligence and Computing (AIC).
[37] Yudong Zhang,et al. A Hybrid Framework for Lung Cancer Classification , 2022, Electronics.
[38] Riti Kushwaha,et al. An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction , 2022, Frontiers in Public Health.
[39] I. J. Pomeraniec,et al. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms , 2022, npj Digital Medicine.
[40] S. Hindocha,et al. The Role of Artificial Intelligence in Early Cancer Diagnosis , 2022, Cancers.
[41] A. Gandomi,et al. Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization , 2022, Computer Methods in Applied Mechanics and Engineering.
[42] M. Nadimi-Shahraki,et al. DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization , 2022, Expert Syst. Appl..
[43] S. Lehéricy,et al. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease , 2022, Frontiers in Neurology.
[44] María Gómez-Vela,et al. Listening to families with a person with neurodegenerative disease talk about their quality of life: integrating quantitative and qualitative approaches , 2022, Health and Quality of Life Outcomes.
[45] N. Carbonara,et al. Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making , 2022, Technovation.
[46] Jing Zhang. Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease , 2022, npj Parkinson's Disease.
[47] A. Schrag,et al. Caregiver Burden and Quality of Life in Late Stage Parkinson’s Disease , 2022, Brain sciences.
[48] J. Suri,et al. Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review , 2022, Diagnostics.
[49] P. Rajpurkar,et al. AI in health and medicine , 2022, Nature Medicine.
[50] B. M. Bidgoli,et al. CMML: Combined metaheuristic-machine learning for adaptable routing in clustered wireless sensor networks , 2022, Appl. Soft Comput..
[51] Zheyu Xu,et al. Longitudinal Healthcare Utilization and Costs in Parkinson's Disease: Pre-Diagnosis to 9 Years After. , 2021, Journal of Parkinson's disease.
[52] M. Hallett,et al. Addressing the Challenges of Clinical Research for Freezing of Gait in Parkinson's Disease , 2021, Movement disorders : official journal of the Movement Disorder Society.
[53] Boyan Fang,et al. The quality of life in patients with Parkinson's disease: Focus on gender difference , 2021, Brain and behavior.
[54] N. Aydin,et al. Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases , 2021, Comput. Biol. Chem..
[55] R. Maskeliūnas,et al. A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns , 2021, Int. J. Appl. Math. Comput. Sci..
[56] A. Gandomi,et al. Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer , 2021, Expert Syst. Appl..
[57] M. Mohammed,et al. Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals , 2021, Diagnostics.
[58] M. Knaflitz,et al. Atypical Gait Cycles in Parkinson’s Disease , 2021, Sensors.
[59] Chinmay Chakraborty,et al. Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System , 2021, Wireless Personal Communications.
[60] N. Cuenca,et al. Current and future therapeutic strategies for the treatment of retinal neurodegenerative diseases , 2021, Neural regeneration research.
[61] O. Hansson. Biomarkers for neurodegenerative diseases , 2021, Nature Medicine.
[62] Nebojsa Bacanin,et al. Feature Selection by Firefly Algorithm with Improved Initialization Strategy , 2021, ECBS.
[63] M. Zivkovic,et al. Optimizing Convolutional Neural Network by Hybridized Elephant Herding Optimization Algorithm for Magnetic Resonance Image Classification of Glioma Brain Tumor Grade , 2021, 2021 Zooming Innovation in Consumer Technologies Conference (ZINC).
[64] S. Reich,et al. The inconsistency and instability of Parkinson's disease motor subtypes. , 2021, Parkinsonism & related disorders.
[65] Amit Dua,et al. Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: a survey, current challenges and future directions , 2021, Journal of Ambient Intelligence and Humanized Computing.
[66] Bogdan Ionescu,et al. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques , 2021, Artif. Intell. Medicine.
[67] M. Javaid,et al. Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. , 2021, Journal of oral biology and craniofacial research.
[68] Marcin Woźniak,et al. Red fox optimization algorithm , 2021, Expert Syst. Appl..
[69] H. Ghadiri,et al. Model predictive control optimisation using the metaheuristic optimisation for blood pressure control , 2021, IET systems biology.
[70] S. Hofvind,et al. Can artificial intelligence reduce the interval cancer rate in mammography screening? , 2021, European Radiology.
[71] V. Rajinikanth,et al. Artificial Intelligence and Machine Learning in Emergency Medicine , 2021, Biocybernetics and Biomedical Engineering.
[72] Angelo Antonini,et al. Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease , 2020, Sensors.
[73] Ahmed M. Alaa,et al. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 , 2020, Machine Learning.
[74] B. Alatas,et al. Comparative Assessment Of Light-based Intelligent Search And Optimization Algorithms , 2020 .
[75] B. Alatas,et al. Chaos based optics inspired optimization algorithms as global solution search approach , 2020 .
[76] Serhat Simsek,et al. Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis , 2020, Annals of Operations Research.
[77] Jong Seung Kim,et al. Fluorescent Diagnostic Probes in Neurodegenerative Diseases , 2020, Advanced materials.
[78] J. Jang,et al. Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model , 2020, International journal of molecular sciences.
[79] Kevin Smith,et al. Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. , 2020, The Lancet. Digital health.
[80] E. Balaji,et al. Supervised machine learning based gait classification system for early detection and stage classification of Parkinson's disease , 2020, Appl. Soft Comput..
[81] B. Bloem,et al. Personalized Care Management for Persons with Parkinson’s Disease , 2020, Journal of Parkinson's disease.
[82] Xin-She Yang,et al. Firefly Algorithm , 2020, Swarm Intelligence Algorithms.
[83] Milan Tuba,et al. Glioma Brain Tumor Grade Classification from MRI Using Convolutional Neural Networks Designed by Modified FA , 2020 .
[84] R. Albin,et al. Current and projected future economic burden of Parkinson’s disease in the U.S. , 2020, npj Parkinson's Disease.
[85] C. Kobylecki. Update on the diagnosis and management of Parkinson's disease. , 2020, Clinical medicine.
[86] Arturo Hernández Aguirre,et al. COLSHADE for Real-World Single-Objective Constrained optimization Problems , 2020, 2020 IEEE Congress on Evolutionary Computation (CEC).
[87] Surendrabikram Thapa,et al. Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).
[88] Safial Islam Ayon,et al. Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery , 2020, SN Computer Science.
[89] Vincenzo Di Lazzaro,et al. Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring , 2020, Sensors.
[90] Milan Tuba,et al. Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm , 2020, 2020 International Wireless Communications and Mobile Computing (IWCMC).
[91] Pierluigi Ritrovato,et al. Trends in IoT based solutions for health care: Moving AI to the edge , 2020, Pattern Recognition Letters.
[92] Milan Tuba,et al. Enhanced Grey Wolf Algorithm for Energy Efficient Wireless Sensor Networks , 2020, 2020 Zooming Innovation in Consumer Technologies Conference (ZINC).
[93] Jun Liu,et al. Freezing of gait in Parkinson’s disease: pathophysiology, risk factors and treatments , 2020, Translational Neurodegeneration.
[94] Zewen Li,et al. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[95] Qian Fan,et al. A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems , 2020, Soft Computing.
[96] Di Zhao,et al. A review of the application of deep learning in medical image classification and segmentation , 2020, Annals of translational medicine.
[97] M. Okun,et al. Diagnosis and Treatment of Parkinson Disease: A Review. , 2020, JAMA.
[98] Mohd Javaid,et al. Artificial Intelligence (AI) applications in orthopaedics: An innovative technology to embrace. , 2020, Journal of clinical orthopaedics and trauma.
[99] Zehra Karapinar Senturk,et al. Early diagnosis of Parkinson's disease using machine learning algorithms. , 2020, Medical hypotheses.
[100] Wei Wang,et al. A Novel Image Classification Approach via Dense-MobileNet Models , 2020, Mob. Inf. Syst..
[101] Hugh Chen,et al. From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.
[102] Mufti Mahmud,et al. Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective , 2019, BI.
[103] Agata Blasiak,et al. CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence , 2019, SLAS technology.
[104] Edward D Lemaire,et al. Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review , 2019, Sensors.
[105] V. Bianchi,et al. Effect of nutrition on neurodegenerative diseases. A systematic review , 2019, Nutritional neuroscience.
[106] Milan Tuba,et al. Task Scheduling in Cloud Computing Environment by Grey Wolf Optimizer , 2019, 2019 27th Telecommunications Forum (TELFOR).
[107] N. Zerhouni,et al. A CNN-based methodology for breast cancer diagnosis using thermal images , 2019, Comput. methods Biomech. Biomed. Eng. Imaging Vis..
[108] W. Fang,et al. Dietary habits, lifestyle factors and neurodegenerative diseases , 2019, Neural regeneration research.
[109] S. Hasselbalch,et al. Ageing as a risk factor for neurodegenerative disease , 2019, Nature Reviews Neurology.
[110] A. Sappa,et al. Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[111] Yulong Wang,et al. cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks , 2019, Swarm Evol. Comput..
[112] Milica Đurić-Jovičić,et al. Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review , 2019, Clinical Neurology and Neurosurgery.
[113] Paolo Bonato,et al. Gait impairments in Parkinson's disease , 2019, The Lancet Neurology.
[114] J. Volkmann,et al. Freezing of gait in Parkinson’s disease reflects a sudden derangement of locomotor network dynamics , 2019, Brain : a journal of neurology.
[115] Minming Zhang,et al. Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease. , 2019, ACS chemical neuroscience.
[116] Meet P. Vadera,et al. Multiclass Diagnosis of Neurodegenerative Diseases: A Neuroimaging Machine-Learning-Based Approach , 2019, Industrial & Engineering Chemistry Research.
[117] Marko Beko,et al. Dynamic Search Tree Growth Algorithm for Global Optimization , 2019, DoCEIS.
[118] Samira Yeasmin. Benefits of Artificial Intelligence in Medicine , 2019, 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS).
[119] B. Bloem,et al. The cost-effectiveness of specialized nursing interventions for people with Parkinson’s disease: the NICE-PD study protocol for a randomized controlled clinical trial , 2019, Trials.
[120] Kok-Swee Sim,et al. Convolutional neural network improvement for breast cancer classification , 2019, Expert Syst. Appl..
[121] Yacine Amirat,et al. Data-Driven Based Approach to Aid Parkinson’s Disease Diagnosis , 2019, Sensors.
[122] R. Bustos,et al. The Genetic Diagnosis of Neurodegenerative Diseases and Therapeutic Perspectives , 2018, Brain sciences.
[123] U. Rajendra Acharya,et al. Parkinson's disease: Cause factors, measurable indicators, and early diagnosis , 2018, Comput. Biol. Medicine.
[124] M. Katsuno,et al. Preclinical progression of neurodegenerative diseases , 2018, Nagoya journal of medical science.
[125] Rahib H Abiyev,et al. Deep Convolutional Neural Networks for Chest Diseases Detection , 2018, Journal of healthcare engineering.
[126] 김명재,et al. Genetic Algorithm , 2018, Handbook of Machine Learning.
[127] Richard K. G. Do,et al. Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.
[128] R. Migliaccio,et al. Social Cognition Dysfunctions in Neurodegenerative Diseases: Neuroanatomical Correlates and Clinical Implications , 2018, Behavioural neurology.
[129] David M. Gaba,et al. Human Error in Dynamic Medical Domains 1 , 2018 .
[130] Hossam Faris,et al. Grey wolf optimizer: a review of recent variants and applications , 2017, Neural Computing and Applications.
[131] Marcin Wozniak,et al. Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism , 2017, Symmetry.
[132] T. Sikora,et al. Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[133] Tareq Abed Mohammed,et al. Understanding of a convolutional neural network , 2017, 2017 International Conference on Engineering and Technology (ICET).
[134] Majdi Maabreh,et al. Parameters optimization of deep learning models using Particle swarm optimization , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).
[135] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[136] Kiyoharu Aizawa,et al. Efficient Optimization of Convolutional Neural Networks Using Particle Swarm Optimization , 2017, 2017 IEEE Third International Conference on Multimedia Big Data (BigMM).
[137] Amirreza Mahbod,et al. Skin Lesion Classification Using Hybrid Deep Neural Networks , 2017, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[138] Dennis W Dickson,et al. Pathology of Neurodegenerative Diseases. , 2017, Cold Spring Harbor perspectives in biology.
[139] Ravi Sankar,et al. Gait and tremor assessment for patients with Parkinson's disease using wearable sensors , 2016, ICT Express.
[140] Carlos D. Castillo,et al. An All-In-One Convolutional Neural Network for Face Analysis , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).
[141] Andrew Lewis,et al. The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..
[142] A. Mínguez-Castellanos,et al. Diagnosis of Neurodegenerative Diseases: The Clinical Approach. , 2016, Current Alzheimer research.
[143] Seyedali Mirjalili,et al. SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..
[144] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[145] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[146] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[147] M. Warrens. Five ways to look at Cohen's kappa , 2015 .
[148] Stephen Balaban,et al. Deep learning and face recognition: the state of the art , 2015, Defense + Security Symposium.
[149] Ali Husseinzadeh Kashan,et al. A new metaheuristic for optimization: Optics inspired optimization (OIO) , 2015, Comput. Oper. Res..
[150] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[151] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[152] A. Kaveh,et al. A new meta-heuristic method: Ray Optimization , 2012 .
[153] F. Pagan,et al. Improving outcomes through early diagnosis of Parkinson's disease. , 2012, The American journal of managed care.
[154] D. Murman,et al. Early treatment of Parkinson's disease: opportunities for managed care. , 2012, The American journal of managed care.
[155] Yuhui Shi,et al. Brain Storm Optimization Algorithm , 2011, ICSI.
[156] W. Ondo,et al. Long‐term outcome of early versus delayed rasagiline treatment in early Parkinson's disease , 2009, Movement disorders : official journal of the Movement Disorder Society.
[157] Jeffrey M. Hausdorff,et al. Rhythmic auditory stimulation modulates gait variability in Parkinson's disease , 2007, The European journal of neuroscience.
[158] Jeffrey M. Hausdorff,et al. Dual tasking, gait rhythmicity, and Parkinson's disease: Which aspects of gait are attention demanding? , 2005, The European journal of neuroscience.
[159] Jeffrey M. Hausdorff,et al. Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease , 2005, Movement disorders : official journal of the Movement Disorder Society.
[160] P. Mal,et al. Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.
[161] M. Morris,et al. The biomechanics and motor control of gait in Parkinson disease. , 2001, Clinical biomechanics.
[162] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[163] S. Shapiro,et al. An Approximate Analysis of Variance Test for Normality , 1972 .
[164] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[165] A. R. Javed,et al. Parkinson’s Disease Management via Wearable Sensors: A Systematic Review , 2022, IEEE Access.
[166] Chioma Virginia Anikwe,et al. Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect , 2022, Expert Systems with Applications.
[167] M. Zivkovic,et al. COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification , 2022, Computers, Materials & Continua.
[168] Nebojsa Bacanin,et al. Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction , 2021, Lecture Notes in Networks and Systems.
[169] Jyoti R. Munavalli,et al. Real-Time Capacity Management and Patient Flow Optimization in Hospitals Using AI Methods , 2020 .
[170] Fahad Algarni,et al. A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS , 2020, IEEE Access.
[171] Yang Wang,et al. Data Driven Intelligent Diagnostics for Parkinson’s Disease , 2019, IEEE Access.
[172] Xin Yao,et al. Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization , 2018 .
[173] Jiri Matas,et al. Visual Heart Rate Estimation with Convolutional Neural Network , 2018, BMVC.
[174] Rita Chhikara,et al. Diabetic Retinopathy: Present and Past , 2018 .
[175] G. Kovacs. Concepts and classification of neurodegenerative diseases. , 2017, Handbook of clinical neurology.
[176] M. Gioulis,et al. Gait analysis and clinical correlations in early Parkinson's disease. , 2017, Functional neurology.
[177] Tome Eftimov,et al. DISADVANTAGES OF STATISTICAL COMPARISON OF STOCHASTIC OPTIMIZATION ALGORITHMS , 2016 .
[178] Joseph Jankovic,et al. Gait disorders. , 2015, Neurologic clinics.
[179] D. Karaboga,et al. On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..
[180] M. Stern. Parkinson's disease: early diagnosis and management. , 1993, The Journal of family practice.