A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

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Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1770 ◽

2020 ◽

Vol 4 (2) ◽

pp. 329-335

Author(s):

Rusydi Umar ◽

Imam Riadi ◽

Purwono

Keyword(s):

Social Media ◽

Gradient Descent ◽

Classification Model ◽

Stochastic Gradient Descent ◽

Support Vector ◽

Svm Algorithm ◽

Vector Machines ◽

Performance Patterns ◽

A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

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Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026816500048 ◽

2016 ◽

Vol 15 (01) ◽

pp. 1650004 ◽

Cited By ~ 3

Author(s):

Hedieh Sajedi ◽

Mehran Bahador

Keyword(s):

Support Vector Machines ◽

Recognition Rate ◽

Nearest Neighbors ◽

Polynomial Kernel ◽

Support Vector ◽

K Nearest Neighbors ◽

New Approach ◽

Number Recognition ◽

Vector Machines

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

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Imputation And Classification Of Missing Data Using Least Square Support Vector Machines – A New Approach In Dementia Diagnosis

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE ◽

10.14569/ijarai.2012.010404 ◽

2012 ◽

Vol 1 (4) ◽

Cited By ~ 1

Author(s):

T R ◽

A.R.Nadira Banu ◽

V.Thavavel

Keyword(s):

Support Vector Machines ◽

Missing Data ◽

Least Square ◽

Support Vector ◽

Dementia Diagnosis ◽

New Approach ◽

Vector Machines

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Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽

10.3390/sci2030062 ◽

2020 ◽

Vol 2 (3) ◽

pp. 62

Author(s):

Jian Zhang ◽

Rahul Soangra ◽

Thurmon E. Lockhart

Keyword(s):

Older Adults ◽

Support Vector Machines ◽

Daily Life ◽

Human Motion ◽

Function Parameter ◽

Support Vector ◽

Activities Of Daily Life ◽

Svm Algorithm ◽

Vector Machines ◽

Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

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Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽

10.3390/sci2030060 ◽

2020 ◽

Vol 2 (3) ◽

pp. 60

Author(s):

Jian Zhang ◽

Rahul Soangra ◽

Thurmon E. Lockhart

Keyword(s):

Older Adults ◽

Support Vector Machines ◽

Daily Life ◽

Human Motion ◽

Function Parameter ◽

Support Vector ◽

Activities Of Daily Life ◽

Svm Algorithm ◽

Vector Machines ◽

Two Parameters

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Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Sensors ◽

10.3390/s19173720 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3720 ◽

Cited By ~ 3

Author(s):

Chengyin Liu ◽

Zhaoshuo Jiang ◽

Xiangxiang Su ◽

Samuel Benzoni ◽

Alec Maxwell

Keyword(s):

Fall Detection ◽

Support Vector ◽

Accelerometer Signal ◽

Svm Algorithm ◽

Study Results ◽

Benchmark Database ◽

Vector Machines ◽

Floor Vibration ◽

Nonfatal Injuries ◽

Peak Value

Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.

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A new approach for ICD rhythm classification based on support vector machines

10.1109/iembs.2009.5334794 ◽

2009 ◽

Cited By ~ 2

Author(s):

B. Kamousi ◽

A. Tewfik ◽

B. Lin ◽

A. Al-Ahmad ◽

H. Hsia ◽

...

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

New Approach ◽

Vector Machines

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FEATURE SELECTION FOR SUPPORT VECTOR MACHINES USING GENETIC ALGORITHMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213004001818 ◽

2004 ◽

Vol 13 (04) ◽

pp. 791-800 ◽

Cited By ~ 26

Author(s):

HOLGER FRÖHLICH ◽

OLIVIER CHAPELLE ◽

BERNHARD SCHÖLKOPF

Keyword(s):

Genetic Algorithms ◽

Feature Selection ◽

Support Vector Machines ◽

Cross Validation ◽

Support Vector ◽

Generalization Error ◽

New Approach ◽

Vector Machines ◽

Selection For ◽

Natural Way

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.

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Perbandingan Algoritma k-Nearest Neighbors (k-NN) dan Support Vector Machines (SVM) untuk Klasifikasi Pengenalan Citra Wajah

Jurnal ICT : Information Communication & Technology ◽

10.36054/jict-ikmi.v20i1.354 ◽

2021 ◽

Vol 20 (1) ◽

pp. 186-191

Author(s):

Parasian DP Silitonga ◽

Romanus Damanik

Keyword(s):

Image Recognition ◽

Machine Learning Algorithms ◽

Public Image ◽

Support Vector ◽

Facial Image ◽

K Nearest Neighbors ◽

Svm Algorithm ◽

Vector Machines ◽

Significant Research ◽

The Moment

Abstract- The study of face recognition is one of the areas of computer vision that requires significant research at the moment. Numerous researchers have conducted studies on facial image recognition using a variety of techniques or methods to achieve the highest level of accuracy possible when recognizing a person's face from existing images. However, recognizing the image of a human face is not easy for a computer. As a result, several approaches were taken to resolve this issue. This study compares two (two) machine learning algorithms for facial image recognition to determine which algorithm has the highest level of accuracy, precision, recall, and AUC. The comparison is carried out in the following steps: image acquisition, preprocessing, feature extraction, face classification, training, and testing. Based on the stages and experiments conducted on public image datasets, it is concluded that the SVM algorithm, on average, has a higher level of accuracy, precision, and recall than the k-NN algorithm when the dataset proportion is 90:10. While the k-NN algorithm has the highest similarity in terms of accuracy, precision, and recall at 80%: 20% and 70%: 30% of 99.20. However, for the highest AUC percentage level, the k-NN algorithm outperforms SVM at a dataset proportion of 80%: 20% at 100%.

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