IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

Abdar, Moloud; Wijayaningrum, Vivi Nur; Hussain, Sadiq; Alizadehsani, Roohallah; Pławiak, Paweł; Acharya, U. Rajendra

doi:10.1007/s10916-019-1343-0

Cited by 46 publications

(13 citation statements)

References 76 publications

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“…In this circumstance, an application of artificial intelligence (AI) techniques comes into play because of its quick performance and sufficient accuracy under some conditions. To achieve this, several prominent machinelearning-based methods (e.g., refer to [3] and its references), viz., radial basis function regression (RBFR), multiple linear regression (MLR), support vector machine (SVM) [4], random forest (RF) [5,6], k-nearest neighbors (KNN), and random tree (RT) were used and applied in engineering problems [7][8][9]. The ML field has been developing at a rapid pace and the recent breakthroughs in data storage and calculating power have made it ubiquitous across a plethora of various applications, many of which are prevalent, see [10] and the references cited therein.…”

Section: Introductionmentioning

confidence: 99%

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

2020

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Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.

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Section: Introductionmentioning

confidence: 99%

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

2020

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“…Many studies have been carried out in the literature using machine learning (ML) techniques and these studies show that ML techniques can be used effectively in different areas (Abdar et al 2019;Hammad et al 2020;Tuncer et al 2020). However, there are studies such as driver fatigue, mental fatigue, driver drowsiness estimation, path planning, which are focused on in this article and presented with ML techniques.…”

Section: Research Motivationmentioning

confidence: 99%

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

et al. 2020

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Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

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“…Recently, the techniques based on ample dataset training such as deep neural networks [20], [21], and ensemble learning methods [22] accomplished great success in different applications. However, in the small-sample-size cases, the performances are mainly decided by [23]- [25], how the designed feature reveal the characteristics of the pathological disease path and human body state dynamics. The nonlinear time series analysis provided insights for feature designing based on the dynamical system theory, such as recurrence plot [26], Poincare plot [27], Lyapunov exponents [28], detrended fluctuation analysis (DFA) [29], approximate entropy, and sample entropy-based analysis [30].…”

Section: Introductionmentioning

confidence: 99%

Classification of Neurodegenerative Diseases via Topological Motion Analysis—A Comparison Study for Multiple Gait Fluctuations

et al. 2020

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Neurodegenerative diseases are common progressive nervous system disorders that show intricate clinical patterns. The gait fluctuations reflect the physiology and pathologic alterations in the locomotor control system. Using gait fluctuations for disease state evaluation is an essential way for clinical trials and healthcare monitoring. The classification of gait fluctuations helps improve the life quality and enhance clinical diagnosis ability in neuro-degenerative patients. In this work, we firstly embed the time series of multiple gait fluctuations into the phase space. Then we use persistent homology to extract the topological signatures of barcodes. Together with a random forest classifier, we proposed a topological motion analysis (TMA) framework to analyze the gait fluctuations. Further, we proposed a comprehensive comparison study using the TMA framework in the neuro-degenerative classification tasks for stance-, stride-, and swing-based gait fluctuations. In the tasks of comparing amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) to the healthy control (HC) group, the bestachieved AUC scores were 0.9135, 0.9906 and 0.9667 respectively, which show the effectiveness of TMA framework. In summary, our study proposed a TMA framework towards gait fluctuations classification in the neuro-degenerative analysis tasks. The proposed method shows promising clinical application value in earlier interventions and state monitoring for neurodegenerative patients.

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IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

Cited by 46 publications

References 76 publications

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

Classification of Neurodegenerative Diseases via Topological Motion Analysis—A Comparison Study for Multiple Gait Fluctuations

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