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5. The performance of various machine learning methods for Parkinson's disease recognition: a systematic review

Author

Salari, Nader, Kazeminia, Mohsen, Sagha, Hesam, Daneshkhah, Alireza, Ahmadi, Arash, and Mohammadi, Masoud

Subjects
Parkinson's disease -- Diagnosis -- Models, Machine learning -- Usage -- Health aspects, Psychology and mental health
Abstract

Parkinson's disease (PD) is a common neurodegenerative disorder that causes degeneration of dopaminergic neurons in the Nigrostriatal pathway and the discharge of Dopamine in the striatum. Machine learning algorithms have been used as a tool to predict and diagnose diseases. Some of these algorithms got the popularity due to their high recognition performance. In the recognition of PD, studies demonstrated various recognition performances and this systematic review study the performance of machine learning algorithms. This systematic review is based on the cochrane's proposed seven phases of review. After identifying the question of research and inclusion/exclusion criteria, we searched different related databases (SID, MagIran, PubMed, ProQuest, ScienceDirect, WoS, Scopus, and Google Scholar) with the help of combination of keywords. After selection of the studies we extract information and summarize the results. From 10,980 found-studies, and removing them based on inclusion/exclustion criteria, we selected 82 studies. To diagnose PD, 59 studies used clinical indicators, 2 studies used genetic characteristics, 12 used MRI, two used PET, 5 used SPECT and 2 used Laboratory markers. In most of these studies RF, SVM, LR have performed the best. The accuracies of RF, SVM, and LR are reported between 58.9%-99.42%, 65.2%-99.99%, and 43.9%-96%. The results show that the performance of RF, SVM, and LR are high for PD diagnosis. Therefore, they can be used for PD diagnosis as a help for doctors and specialist., Author(s): Nader Salari [sup.1] , Mohsen Kazeminia [sup.2] , Hesam Sagha [sup.3] , Alireza Daneshkhah [sup.4] , Arash Ahmadi [sup.5] , Masoud Mohammadi [sup.6] Author Affiliations: (1) grid.412112.5, 0000 0001 [...]

Published
2023
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6. Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Author

Batsch, Felix, Daneshkhah, Alireza, Cheah, Madeline, Kanarachos, Stratis, and Baxendale, Anthony

Subjects
Computer Science - Machine Learning, Computer Science - Robotics, Statistics - Applications, Statistics - Machine Learning
Abstract

Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices., Comment: 6 pages, 5 figures, accepted at 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, New Zealand, October 2019

Published
2019

13. Cloud Computing Security: Hardware-Based Attacks and Countermeasures

Author

Montasari, Reza, Daneshkhah, Alireza, Jahankhani, Hamid, Hosseinian-Far, Amin, Masys, Anthony J., Series Editor, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, Montasari, Reza, editor, Jahankhani, Hamid, editor, Hill, Richard, editor, and Parkinson, Simon, editor

Published
2021
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14. Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence

Author

Montasari, Reza, Carroll, Fiona, Macdonald, Stuart, Jahankhani, Hamid, Hosseinian-Far, Amin, Daneshkhah, Alireza, Masys, Anthony J., Series Editor, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, Montasari, Reza, editor, Jahankhani, Hamid, editor, Hill, Richard, editor, and Parkinson, Simon, editor

Published
2021
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17. Behavioural Analytics: A Preventative Means for the Future of Policing

Author

Daneshkhah, Alireza, Jahankhani, Hamid, Forouzan, Homan, Montasari, Reza, Hosseinian-Far, Amin, Masys, Anthony J., Series Editor, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, Jahankhani, Hamid, editor, Akhgar, Babak, editor, Cochrane, Peter, editor, and Dastbaz, Mohammad, editor

Published
2020
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18. Classification of a Pedestrian’s Behaviour Using Dual Deep Neural Networks

Author

Spooner, James, Cheah, Madeline, Palade, Vasile, Kanarachos, Stratis, Daneshkhah, Alireza, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Kapoor, Supriya, editor, and Bhatia, Rahul, editor

Published
2020
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19. Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models.

Author

Kent, Paul, Abolfathi, Soroush, Al Ali, Hannah, Sedighi, Tabassom, Chatrabgoun, Omid, and Daneshkhah, Alireza

Abstract

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Estimation of Stress-Strength model in the Generalized Linear Failure Rate Distribution

Author

Shahsanaei, Fatemeh and Daneshkhah, Alireza

Subjects
Statistics - Applications, 62
Abstract

In this paper, we study the estimation of $R=P [Y < X ]$, also so-called the stress-strength model, when both $X$ and $Y$ are two independent random variables with the generalized linear failure rate distributions, under different assumptions about their parameters. We address the maximum likelihood estimator (MLE) of $R$ and the associated asymptotic confidence interval. In addition, we compute the MLE and the corresponding Bootstrap confidence interval when the sample sizes are small. The Bayes estimates of $R$ and the associated credible intervals are also investigated. An extensive computer simulation is implemented to compare the performances of the proposed estimators. Eventually, we briefly study the estimation of this model when the data obtained from both distributions are progressively type-II censored. We present the MLE and the corresponding confidence interval under three different progressive censoring schemes. We also analysis a set of real data for illustrative purpose., Comment: 31 pages, 2 figures, preprint

Published
2013

24. Approximation Multivariate Distribution with pair copula Using the Orthonormal Polynomial and Legendre Multiwavelets basis functions

Author

Daneshkhah, Alireza, Parham, Golamali, Chatrabgoun, Omid, and Jokar, M.

Subjects
Statistics - Computation
Abstract

In this paper, we concentrate on new methodologies for copulas introduced and developed by Joe, Cooke, Bedford, Kurowica, Daneshkhah and others on the new class of graphical models called vines as a way of constructing higher dimensional distributions. We develop the approximation method presented by Bedford et al (2012) at which they show that any $n$-dimensional copula density can be approximated arbitrarily well pointwise using a finite parameter set of 2-dimensional copulas in a vine or pair-copula construction. Our constructive approach involves the use of minimum information copulas that can be specified to any required degree of precision based on the available data or experts' judgements. By using this method, we are able to use a fixed finite dimensional family of copulas to be employed in a vine construction, with the promise of a uniform level of approximation. The basic idea behind this method is to use a two-dimensional ordinary polynomial series to approximate any log-density of a bivariate copula function by truncating the series at an appropriate point. We present an alternative approximation of the multivariate distribution of interest by considering orthonormal polynomial and Legendre multiwavelets as the basis functions. We show the derived approximations are more precise and computationally faster with better properties than the one proposed by Bedford et al. (2012). We then apply our method to modelling a dataset of Norwegian financial data that was previously analysed in the series of papers, and finally compare our results by them., Comment: 31 pages, 7 figures. arXiv admin note: text overlap with arXiv:1206.2700 by other authors

Published
2012

28. Crime Data Mining, Threat Analysis and Prediction

Author

Farsi, Maryam, Daneshkhah, Alireza, Far, Amin Hosseinian, Chatrabgoun, Omid, Montasari, Reza, Masys, Anthony J., Series Editor, Bichler, Gisela, Editorial Board Member, Johnson, Chris, Editorial Board Member, Karampelas, Panagiotis, Editorial Board Member, Leuprecht, Christian, Editorial Board Member, Morse, Edward C., Editorial Board Member, Skillicorn, David, Editorial Board Member, Yamagata, Yoshiki, Editorial Board Member, and Jahankhani, Hamid, editor

Published
2018
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34. Estimation in causal graphical models

Author

Daneshkhah, Alireza

Subjects
519.542, QA Mathematics
Abstract

Pearl (2000), Spirtes et al (1993) and Lauritzen (2001) set up a new framework to encode the causal relationships between the random variables by a causal Bayesian network. The estimation of the conditional probabilities in a Bayesian network has received considerable attention by several investigators (e. g., Jordan (1998), Geiger and Heckerman (1997), Ileckerman et al (1995)), but, this issue has not been studied in a causal Bayesian network. In this thesis, we define the multicausal essential graph on the equivalence class of Bayesian networks in which each member of this class manifests a sort of strong type of invariance under (causal) manipulation called hypercausality. We then characterise the families of prior distributions on the parameters of the Bayesian networks which are consistent with hypercausality and show that their unmanipulated uncertain Bayesian networks must demonstrate the independence assumptions. As a result, such prior distributions satisfy a generalisation of the Geiger and lieckerman condition. In particular, when the corresponding essential graph is undirected, the mentioned class of prior distributions will reduce to the Hyper-Dirichlet family (see Chapter 6). In tile second part of this thesis, we will calculate certain local sensitivity measures and through them we are able to provide the solutions for the following questions: Is the network structure that is learned from data robust with respect to changes of the directionality of some specific arrows? Is the local conditional distributions associated with the specified node robust with respect to the changes to its prior distribution or with respect to the changes to the local conditional distribution of another node? Most importantly, is the posterior distribution associated with the parameters of any node robust with respect to the changes to the prior distribution associated with the parameters of one specific node? Finally, are the quantities mentioned above robust with respect to the changes in the independence assumptions described in Chapter 3? Most of the local sensitivity measures (particularly, local measures of the overall posteriors sensitivity), developed in the last decade, tend to diverge to infinity as the sample size becomes very large (Gustafson (1994) and Gustafson et al (1996)). This is in contrast to our knowledge that, starting from different priors, posteriors tend to agree as the data accumulate. Here we define a now class of metrics with more satisfactory asymptotic behaviour. The advantage of the corresponding local sensitivity measures is boundedness for large sample size.

Published
2004

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