scholarly journals Predicting of Banking Stability Using Machine Learning Technique of Random Forests

2021 ◽  
Vol 6 (1) ◽  
pp. 1-8
Author(s):  
Agus Afiantara ◽  
Bagus Mahawan ◽  
Eka Budiarto
Keyword(s):  
Machine Learning ◽  
Global Financial Crisis ◽  
Banking Industry ◽  
Principal Component ◽  
Economic Indicator ◽  
Absolute Error ◽  
Numerical Illustration ◽  
Money Markets ◽  
Machine Learning Model ◽  
Banking Stability

The purpose of this research is to create a predicting model of banking stability in Indonesia. Authors use a small set of explanatory indicators primarily related to the banking industry and some relevant economic variables. Among the indicators candidate to be used in this study are the indicator of banking industry, the money markets, capital markets and creditors, and the macro-economic indicator. The source of data in this research is obtained from CEIC and SEKI (Indonesian Economic and Financial Statistics) that published by Central Bank of Indonesia from 2004 and 2011. Principal Component Analysis is used to avoid the multi-collinearity problem when construct the model. Authors train the model using Random Forest Regression with data over the 2004-2007 period, and made predictions of global financial crisis that happened in 2008/9. Python 2.7.10 and scikit-learn version 0.20.0 module has been exploited for simulations and evaluation of the model. Numerical illustration is provided to demonstrate the efficiency of proposed model. As the result nine most components analysis obtained as input for the machine learning model with explained variance ratio around 97%, accuracy around 89%, and precision 91% and mean absolute error around 11%.

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

scholarly journals A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

2019 ◽  
Author(s):  
Ramin Mohammadi ◽  
Amanda Jayne Centi ◽  
Mursal Atif ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Absolute Error ◽  
Learning Model ◽  
Step Count ◽  
Entropy Measure ◽  
Daily Step ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

AbstractIt is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individual’s level of activity and thereby improving adherence to fitness tracker.

scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

scholarly journals Development of accurate classification of heavenly bodies using novel machine learning techniques

2021 ◽  
Vol 25 (10) ◽  
pp. 7213-7228
Author(s):  
Michał Wierzbiński ◽  
Paweł Pławiak ◽  
Mohamed Hammad ◽  
U. Rajendra Acharya
Keyword(s):  
Machine Learning ◽  
Outer Space ◽  
Principal Component ◽  
Sloan Digital Sky Survey ◽  
Machine Learning Techniques ◽  
Evolutionary System ◽  
Stellar Spectra ◽  
Learning Techniques ◽  
Machine Learning Model

AbstractThe heavenly bodies are objects that swim in the outer space. The classification of these objects is a challenging task for astronomers. This article presents a novel methodology that enables an efficient and accurate classification of cosmic objects (3 classes) based on evolutionary optimization of classifiers. This research collected the data from Sloan Digital Sky Survey database. In this work, we are proposing to develop a novel machine learning model to classify stellar spectra of stars, quasars and galaxies. First, the input data are normalized and then subjected to principal component analysis to reduce the dimensionality. Then, the genetic algorithm is implemented on the data which helps to find the optimal parameters for the classifiers. We have used 21 classifiers to develop an accurate and robust classification with fivefold cross-validation strategy. Our developed model has achieved an improvement in the accuracy using nineteen out of twenty-one models. We have obtained the highest classification accuracy of 99.16%, precision of 98.78%, recall of 98.08% and F1-score of 98.32% using evolutionary system based on voting classifier. The developed machine learning prototype can help the astronomers to make accurate classification of heavenly bodies in the sky. Proposed evolutionary system can be used in other areas where accurate classification of many classes is required.

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

scholarly journals Machine learning applied to the prediction of citrus production

2017 ◽  
Vol 15 (2) ◽  
pp. e0205 ◽  
Author(s):  
Irene Díaz ◽  
Silvia M. Mazza ◽  
Elías F. Combarro ◽  
Laura I. Giménez ◽  
José E. Gaiad
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Piecewise Linear ◽  
Regression Tree ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Absolute Error ◽  
Linear Functions ◽  
Machine Learning Techniques ◽  
Citrus Production

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

scholarly journals High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3324
Author(s):  
Yun Zhang ◽  
Jiwei Yin ◽  
Shuhu Yang ◽  
Wanting Meng ◽  
Yanling Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Microwave Radiometer ◽  
Principal Component ◽  
Absolute Error ◽  
Sea Surface ◽  
Support Vector ◽  
High Wind ◽  
High Wind Speed ◽  
Inversion Model

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy.

scholarly journals A Novel Approach to Enhance the Generalization Capability of the Hourly Solar Diffuse Horizontal Irradiance Models on Diverse Climates

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4868
Author(s):  
Raghuram Kalyanam ◽  
Sabine Hoffmann
Keyword(s):  
Machine Learning ◽  
Diffuse Radiation ◽  
Absolute Error ◽  
Training Data ◽  
Learning Approaches ◽  
Simulation Tools ◽  
Energy Applications ◽  
Novel Approach ◽  
Machine Learning Model ◽  
Different Climates

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m2, but also generalized very well on European climates with MAE in the range of 20 to 27 W/m2. Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly.

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