Twin Hyper-Ellipsoidal Support Vector Machine for Binary Classification

Ebrahimpour, Zeinab; Wan, Wanggen; Khoojine, Arash Sioofy; Hou, Long

doi:10.1109/access.2020.2990611

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…This technique is mathematically complex and has heavy calculations. [ 8 ] Given that this technique performs well for binary classes[ 9 ] and patient safety and IoT are mainly associated with the binary class of fall detection, this technique can be a good candidate for the purpose. The second most used technique is KNN, applied by researchers because of its good performance.…”

Section: Discussionmentioning

confidence: 99%

“…[ 7 ] Wearable sensors using analytical algorithms have been introduced as part of the IoT for detecting and managing the daily activities of the elderly. [ 8 ] Recent progress in IoT technology can facilitate the design of proper health care systems for the elderly. [ 9 ] With the evolution of the IoT, reliable devices and sensors have emerged, used to meet the sophisticated needs of the elderly population.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

Sattari¹,

Dorri²,

Zabolinezhad³

2023

Advanced Biomedical Research

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The elderly population is projected to increase from 8.5% in 2015 to 12% in 2030 and 16% in 2050. This growing demographic is chronically vulnerable to various age-related diseases and injuries like falling, leading to long-term pain, disability, or death. Thus, there is a need to use the potential of novel technologies to support the elderly regarding patient safety matters in particular. Internet of Things (IoT) has recently been introduced to improve the lifestyle of the elderly. This study aimed to evaluate the studies that have researched the use of the IoT for elderly patients’ safety through performance metrics, accuracy, sensitivity, and specificity. We conducted a systematic review on the research question. To do this, we searched PubMed, EMBASE, Web of Science, Scopus, Google Scholar, and Science Direct databases by combining the related keywords. A data extraction form was used for data gathering through which English, full-text articles on the use of the IoT for the safety of elderly patients were included. The support vector machine technique has the most frequency of use compared to other techniques. Motion sensors were the most widely used type. The United States with four studies had the highest frequencies. The performance of IoT to ensure the elderly's safety was relatively good. It, however, needs to reach a stage of maturity for universal use.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

Sattari¹,

Dorri²,

Zabolinezhad³

2023

Advanced Biomedical Research

View full text Add to dashboard Cite

show abstract

“…At present, machine learning has been used in various fields and industries. For example, machine learning has been used to diagnose and treat diseases [ 28 ], image processing [ 29 ], classification [ 30 ], and more. Support vector regression can be used in many areas, such as dynamic response prediction of magnetorheological elastomer base isolator [ 31 ], thermal spring back of hot press forming [ 32 ], text classification [ 33 ], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Erfanian

Zhou

Razzaq

et al. 2022

Entropy

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Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.

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“…ese models have the advantages of simplicity and practicality, but they do not have weak performance on nonlinear problems. In recent years, the support vector machine (SVM) [10][11][12] model based on the VC-dimension theory and the minimum structural risk principle has provided a good idea for dealing with nonlinear classification problems [13]. For SVM, the essence of its classification accuracy can be attributed to the kernel function type and parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Financial Risk Early-Warning Model Based on Kernel Principal Component Analysis in Public Hospitals

2021

Mathematical Problems in Engineering

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Public hospitals are facing the dual pressure of coping with external medical market competition and performing public health duties. Due to the influence of various risk factors, public hospitals are facing increasing financial risks. How to effectively prevent and control financial risks and maintain the normal operation and sustainable development of the hospital is a very important topic that needs to be studied in the development of public hospitals. Because the traditional principal component analysis method only pays attention to the global structural features and ignores the local structural features, a financial risk early-warning model based on improved kernel principal component analysis in public hospitals is proposed to improve the ability of risk assessment. The core ideas of the method in this paper for financial risk forecasting are as follows: the nonlinear features of the financial data are firstly extracted under different conditions, and then the feature matrix and the optimal feature vector are calculated to construct the distance statistics so as to determines the threshold by kernel density estimation; finally the Fisher discriminant analysis is used for similarity measurement to identify the risk types. Through experiments on the financial data of a number of public hospitals and listed companies, the experimental results verify the feasibility and effectiveness of the method used in this paper for financial risk analysis. This further shows that this research has a certain display significance.

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Twin Hyper-Ellipsoidal Support Vector Machine for Binary Classification

Cited by 8 publications

References 24 publications

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Financial Risk Early-Warning Model Based on Kernel Principal Component Analysis in Public Hospitals

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