Machine learning prediction of interaction energies in rigid water clusters

2018 ◽  
Vol 20 (35) ◽  
pp. 22987-22996 ◽  
Author(s):  
Samik Bose ◽  
Diksha Dhawan ◽  
Sutanu Nandi ◽  
Ram Rup Sarkar ◽  
Debashree Ghosh
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Water Clusters ◽  
High Accuracy ◽  
Support Vector ◽  
Interaction Energies ◽  
Many Body ◽  
New Machine

A new machine learning based approach combining support vector regression (SVR) and many body expansion (MBE) that can predict the interaction energies of water clusters with high accuracy (for decamers: 2.78% of QM estimates).

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel Adinyira ◽  
Emmanuel Akoi-Gyebi Adjei ◽  
Kofi Agyekum ◽  
Frank Desmond Kofi Fugar
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cash Flow ◽  
Predictive Accuracy ◽  
Model Development ◽  
Construction Project ◽  
Support Vector ◽  
Sensitivity Index ◽  
Content Type ◽  
Hyperparameter Selection

PurposeKnowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.Design/methodology/approachThe study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.FindingsThe developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.Originality/valueThe developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

scholarly journals Support Vector Regression Method for Regional Economic Mid- and Long-Term Predictions Based on Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lingyu Dong
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Support Vector Regression ◽  
Wireless Network ◽  
Regression Method ◽  
Network Communication ◽  
Support Vector ◽  
Network Coverage ◽  
Data Packets

In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.

scholarly journals A novel method for total chlorine detection using machine learning with electrode arrays

RSC Advances ◽  
2019 ◽  
Vol 9 (59) ◽  
pp. 34196-34206
Author(s):  
Zhe Li ◽  
Shunhao Huang ◽  
Juan Chen
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cyclic Voltammetry ◽  
Support Vector Regression ◽  
Principal Component ◽  
Measurement Model ◽  
Support Vector ◽  
Electrode Arrays ◽  
Soft Measurement ◽  
Novel Method

Establish soft measurement model of total chlorine: cyclic voltammetry curves, principal component analysis and support vector regression.

Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model

2012 ◽  
Vol 468-471 ◽  
pp. 579-582
Author(s):  
Wei Sun ◽  
Le Shen
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Empirical Analysis ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
High Accuracy ◽  
Support Vector ◽  
Type Selection

Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the Support Vector Regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.

scholarly journals 24-hour cloud cover calculation using ground-based imager with machine learning

2021 ◽  
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cloud Cover ◽  
Image Data ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Learning Methods ◽  
Human Eye ◽  
Machine Learning Methods

Abstract. In this study, image data features and machine learning methods were used to calculate 24-h continuous cloud cover from image data obtained by a camera-based imager on the ground. The image data features were the time (Julian day and hour), solar zenith angle, and statistical characteristics of the red-blue ratio, blue–red difference, and luminance. These features were determined from the red, green, and blue brightness of images subjected to a pre-processing process involving masking removal and distortion correction. The collected image data were divided into training, validation, and test sets and were used to optimize and evaluate the accuracy of each machine learning method. The cloud cover calculated by each machine learning method was verified with human-eye observation data from a manned observatory. Supervised machine learning models suitable for nowcasting, namely, support vector regression, random forest, gradient boosting machine, k-nearest neighbor, artificial neural network, and multiple linear regression methods, were employed and their results were compared. The best learning results were obtained by the support vector regression model, which had an accuracy, recall, and precision of 0.94, 0.70, and 0.76, respectively. Further, bias, root mean square error, and correlation coefficient values of 0.04 tenth, 1.45 tenths, and 0.93, respectively, were obtained for the cloud cover calculated using the test set. When the difference between the calculated and observed cloud cover was allowed to range between 0, 1, and 2 tenths, high agreement of approximately 42 %, 79 %, and 91 %, respectively, were obtained. The proposed system involving a ground-based imager and machine learning methods is expected to be suitable for application as an automated system to replace human-eye observations.

scholarly journals Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 539
Author(s):  
Farzin Piltan ◽  
Rafia Nishat Toma ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
Hyun-Kyun Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Support Vector Regression ◽  
Support Vector ◽  
Signal Estimation ◽  
Digital Twin ◽  
Crack Type ◽  
Type Size ◽  
Ae Signal ◽  
Strict Feedback

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.

scholarly journals Comparative Study of Forecasting Models for Forex Predictions with the Impact of Different Currencies

2022 ◽  
Vol 12 (1) ◽  
pp. 11-22
Author(s):  
Satria Wiro Agung ◽  
◽  
Kelvin Supranata Wangkasa Rianto ◽  
Antoni Wibowo
Keyword(s):  
Machine Learning ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Short Term Memory ◽  
Political Stability ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

- Foreign Exchange (Forex) is the exchange / trading of currencies from different countries with the aim of making profit. Exchange rates on Forex markets are always changing and it is hard to predict. Many factors affect exchange rates of certain currency pairs like inflation rates, interest rates, government debt, term of trade, political stability of certain countries, recession and many more. Uncertainty in Forex prediction can be reduced with the help of technology by using machine learning. There are many machine learning methods that can be used when predicting Forex. The methods used in this paper are Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR). XGBOOST, and ARIMA. The outcome of this paper will be comparison results that show how other major currency pairs have influenced the performance and accuracy of different methods. From the results, it was proven that XGBoost outperformed other models by 0.36% compared to ARIMA model, 4.4% compared to GRU model, 8% compared to LSTM model, 9.74% compared to SVR model. Keywords— Forex Forecasting, Long Short Term Memory, Gated Recurrent Unit, Support Vector Regression, ARIMA, Extreme Gradient Boosting

scholarly journals Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

2021 ◽  
Author(s):  
Ewerthon Dyego de Araújo Batista ◽  
Wellington Candeia de Araújo ◽  
Romeryto Vieira Lira ◽  
Laryssa Izabel de Araújo Batista
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

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