A Global Model of β−-Decay Half-Lives Using Neural Networks

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more re- cently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of β−-decay halflives of the class of nuclei that decay 100% by β− mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg- Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates gen- erated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the β−-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.

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Statistical Global Modeling of β−-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines

HNPS Proceedings ◽

10.12681/hnps.2602 ◽

2020 ◽

Vol 16 ◽

pp. 243

Author(s):

N. Costiris ◽

E. Mavrommatis ◽

K. A. Gernoth ◽

J. W. Clark ◽

H. Li

Keyword(s):

Neural Networks ◽

Large Scale ◽

Feedforward Neural Networks ◽

Predictive Performance ◽

Support Vector ◽

Β Decay ◽

Statistical Framework ◽

Nuclear Models ◽

Conventional Models ◽

R Process

In this work, the beta-decay halflives problem is dealt as a nonlinear optimiza- tion problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SV Ms) for each class with even-odd character in Z and N to global model the systemat- ics of nuclei that decay 100% by the β−-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from tradi- tional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of β-decay systematics and can complement the latter.

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Nuclear-data input to the classical r-process: The case of β-decay properties

10.1063/1.4984852 ◽

2017 ◽

Cited By ~ 1

Author(s):

Karl-Ludwig Kratz ◽

Khalil Farouqi ◽

Bernd Pfeiffer

Keyword(s):

Nuclear Data ◽

Data Input ◽

Β Decay ◽

Decay Properties ◽

R Process

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β− -Decay Half-lives Using the ANN Model: Input for the R-Process

HNPS Proceedings ◽

10.12681/hnps.2537 ◽

2019 ◽

Vol 18 ◽

pp. 43

Author(s):

N. J. Costiris ◽

E. Mavrommatis

Keyword(s):

Nuclear Physics ◽

Nuclear Astrophysics ◽

Predictive Performance ◽

Element Distribution ◽

Ann Model ◽

Β Decay ◽

Conventional Models ◽

R Process ◽

Artificial Neural Network Ann ◽

Fully Connected

Full understanding of nucleosynthesis via the r-process continues to be a major challenge for nuclear astrophysics. Apart from issues within astrophysical modeling, there remain significant uncertainties in the nuclear physics input, notably involving the β- decay halflives of neutron-rich nuclei. Both the element distribution on the r-process path and the time scale of the r-process are highly sensitive to β− lifetimes. Since the majority of nuclides that lie on the r-process path will not be experimentally accessible in the foreseeable future, it is important to provide accurate predictions from reliable models. Toward this end, a statistical global model of the β−-decay halflife systema- tics has been developed to estimate the lifetimes of nuclides relevant to the r-process, in the form of a fully-connected, multilayer feedforward Artificial Neural Network (ANN) trained to predict the halflives of ground states that decay 100% by the β− mode. In predictive performance, the model can match or even surpass that of conventional models of β-decay systematics. Results are presented for nuclides situated on the r-ladders N=50, 82 and 126 where abundances peak, as well as for others that affect abundances between peaks. Also reported are results for halflives of interesting neutron-rich nuclides on or towards the r-process path that have been recently measured. Comparison with results from experiment and conventional models is favorable.

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An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.404 ◽

2017 ◽

Vol 7 (9) ◽

pp. 13

Author(s):

Bhargavi Munnaluri ◽

K. Ganesh Reddy

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Fossil Fuels ◽

Renewable Energy Sources ◽

Support Vector ◽

Hybrid Forecasting ◽

Artificial Neural ◽

Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed. In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

10.31221/osf.io/cjv7p ◽

2019 ◽

Author(s):

Chem Int

Keyword(s):

Neural Networks ◽

Water Quality ◽

Wastewater Treatment ◽

Artificial Neural Networks ◽

Performance Modeling ◽

Predictive Performance ◽

Mathematical Tool ◽

Effluent Water ◽

Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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Ozone Concentration Prediction using Artificial Neural Networks

Revista de Chimie ◽

10.37358/rc.17.10.5860 ◽

2017 ◽

Vol 68 (10) ◽

pp. 2224-2227 ◽

Cited By ~ 2

Author(s):

Camelia Gavrila

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Ozone Concentration ◽

Meteorological Data ◽

Global Solar Radiation ◽

Time Data ◽

Marquardt Algorithm ◽

Sensitivity Tests ◽

Artificial Neural ◽

The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

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Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancers ◽

10.3390/cancers13092133 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2133

Author(s):

Francisco O. Cortés-Ibañez ◽

Sunil Belur Nagaraj ◽

Ludo Cornelissen ◽

Gerjan J. Navis ◽

Bert van der Vegt ◽

...

Keyword(s):

Cancer Incidence ◽

Binary Classification ◽

Predictive Performance ◽

Population Based ◽

Support Vector ◽

Clinical Variables ◽

Incident Cancer ◽

History Of ◽

Diagnosis Of Cancer ◽

Auc Value

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

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The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽

10.3390/biom11040500 ◽

2021 ◽

Vol 11 (4) ◽

pp. 500

Author(s):

László Keresztes ◽

Evelin Szögi ◽

Bálint Varga ◽

Viktor Farkas ◽

András Perczel ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector ◽

User Needs ◽

Computational Technique ◽

Linear Support Vector Machine ◽

Β Sheets ◽

Artificial Neural ◽

Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽

10.1093/sleep/zsab072.413 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A164-A164

Author(s):

Pahnwat Taweesedt ◽

JungYoon Kim ◽

Jaehyun Park ◽

Jangwoon Park ◽

Munish Sharma ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Deep Neural Networks ◽

Support Vector ◽

Learning Models ◽

Obstructive Sleep ◽

Screening Questionnaires ◽

Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

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