Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map

Nilashi, Mehrbakhsh; Ahmadi, Hossein; Sheikhtaheri, Abbas; Naemi, Roya; Alotaibi, Reem; Alarood, Ala Abdulsalam; Munshi, Asmaa; Rashid, Tarik A.; Zhao, Jing

doi:10.1016/j.eswa.2020.113562

Cited by 54 publications

(38 citation statements)

References 70 publications

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“…Under the test of different classifiers, the proposed feature set still can maintain a certain accuracy, which shows that the proposed feature set is discriminative in debris removal prediction. Used other types of deep neural networks [44]- [45] or some advanced classifiers [46] may improve classification performance, but this is beyond the scope of this paper. In addition to verifying the classification performance of the proposed feature set, in the second stage of the experiment, this paper integrates the established debris removal predicted model into the EDM machine for verifying the effectiveness of the proposed intelligent system in improving machining efficiency.…”

Section: Resultsmentioning

confidence: 99%

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

Lee

Lai

2021

IEEE Access

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Electrical discharge machining (EDM) can effectively solve the shortcomings of traditional machining processes that cannot process special materials, so it is widely used on workpieces with strong hardness materials, such as titanium alloys and tool steels to produce various molds and dies. However, the operating procedures of EDM are quite complicated and low machining productivity. To improve machining efficiency, this study develops an intelligent system that adaptively controls debris removal operations instead of using preset debris removal parameters. A feature extraction method is proposed in this study to effectively identify the machining states from streaming images of the machining curve for evaluating the appropriate time of the debris removal operation. Then, the extracted features feed into the artificial neural network model to establish a debris removal predicted model. The preliminary experimental result shows that the established predicted model can achieve an accuracy of 96.93% for a testing dataset containing 750 machining curve images. To further verify the effectiveness of the proposed intelligent system in improving EDM efficiency, we integrate the debris removal predicted model into the EDM machine and test it on the manufacturing site. Compared with the preset debris removal parameter, the proposed intelligent system can save nearly 38.60% of machining time for the machining depth of 6.45mm under specific EDM conditions. INDEX TERMS Artificial intelligence, debris removal operation, electric discharge machining, intelligent system, machining efficiency.

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Section: Resultsmentioning

confidence: 99%

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

Lee

Lai

2021

IEEE Access

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“…In-depth learning not only assists in the selection and extraction of characteristics, but also has the potential to measure predictive target audiences and provide proactive predictions to help clinicians greatly. Machine learning cannot replace doctors, but it can help streamline routine tasks, increase forecast accuracy, and simplify solving atypical problems [12].…”

Section: Discussionmentioning

confidence: 99%

“…Two parallel hyperplanes are constructed on both sides of the hyperplane separating the classes. The separating hyperplane is the hyperplane that maximizes the distance to two parallel hyperplanes [12]. The algorithm works under the assumption that the greater the difference or distance between these parallel hyperplanes, the smaller the average error of the classifier will be.…”

Section: Conditional Probability For Each Class For Each Value Of Xmentioning

confidence: 99%

Using machine learning methods in problems with large amounts of data

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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This article explores the use of artificial intelligence in medicine, in particular in radiology, pathology, drug development. The usefulness of robotic assistants in the medical field is revealed. Machine learning in medical science, as well as routing in hospitals. It also discusses such machine learning methods as classification methods, regression restoration methods, clustering methods. As a result, based on what is considered in this article, it is concluded that manual processing becomes more complicated and impossible with a large amount of data, there is a need for automatic processing that can transform modern medicine. And also, conclusions were made about how accurately the deep learning mechanisms can provide a more accurate result in the processing and classification of images compared to the results obtained at the human level. It became clear that deep learning not only aids in the selection and extraction of characteristics, but also has the potential to measure predictive target audiences and provide proactive predictions to help clinicians go a long way

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“…Among the classifiers, LDA obtained the best performance. In [161], a new DL model for tracking PD progression was developed by hybridizing clustering and DBN. The progression of the disease was being evaluated based on UPDRS.…”

Section: ) Ml-based Approaches In Pd Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map

Cited by 54 publications

References 70 publications

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

Using machine learning methods in problems with large amounts of data

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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