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

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.

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

2020 ◽  
Vol 15 ◽  
pp. 210
Author(s):  
N. Costiris ◽  
E. Mavrommatis ◽  
K. A. Gernoth ◽  
J. W. Clark
Keyword(s):  
Neural Networks ◽  
Predictive Performance ◽  
Nuclear Data ◽  
Global Model ◽  
Support Vector ◽  
Β Decay ◽  
Global Models ◽  
Marquardt Algorithm ◽  
Feed Forward Network ◽  
R Process

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.

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

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.

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

scholarly journals iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

2021 ◽  
Vol 22 (16) ◽  
pp. 8958
Author(s):  
Phasit Charoenkwan ◽  
Chanin Nantasenamat ◽  
Md. Mehedi Hasan ◽  
Mohammad Ali Moni ◽  
Pietro Lio’ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
De Novo ◽  
Predictive Performance ◽  
Support Vector ◽  
Sufficient Information ◽  
Self Assessment ◽  
Accurate Identification ◽  
Bitter Peptides ◽  
Accurate Performance

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

THE APPLICATION OF FEEDFORWARD NEURAL NETWORKS IN VLSI FABRICATION PROCESS OPTIMIZATION

2001 ◽  
Vol 01 (01) ◽  
pp. 83-90 ◽  
Author(s):  
WANG XIANGDONG ◽  
WANG SHOUJUE
Keyword(s):  
Neural Networks ◽  
Manufacturing Process ◽  
Large Scale ◽  
Maximum Yield ◽  
Feedforward Neural Networks ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Manufacturing Process Control ◽  
Wafer Probing ◽  
Process Learning

In this paper, we present a neural-based manufacturing process control system for semiconductor factories to improve the die yield. A model based on neural networks is proposed to simulate Very Large-Scale Integrated (VLSI) manufacturing process. Learning from the historical processing lists with Radial Basis Function (RBF), we simulate the functional relationship between the wafer probing parameters and the die yield. Then we use a gradient-descent method to search a set of 'optimal' parameters that lead to the maximum yield of the model. At last, we adjust the specification in the practical semiconductor manufacturing process. The average die yield increased from 51.7% to 57.5% after the system had been applied in Huajing Corporation.

scholarly journals Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

2020 ◽  
Author(s):  
Alisson Hayasi da Costa ◽  
Renato Augusto C. dos Santos ◽  
Ricardo Cerri
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Feedforward Neural Networks ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Large Application

AbstractPIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

Chinese Semantic Word-Formation Analysis Using FKP-MCO Classifier Based on Layered and Weighted GED

2013 ◽  
Vol 284-287 ◽  
pp. 3044-3050 ◽  
Author(s):  
Guang Xia Gao ◽  
Zhi Wang Zhang ◽  
Shi Yong Kang
Keyword(s):  
Vector Space ◽  
Large Scale ◽  
Semantic Analysis ◽  
Feature Vector ◽  
Class Imbalance ◽  
Weighted Graph ◽  
Predictive Performance ◽  
Word Formation ◽  
Support Vector ◽  
Feature Vector Space

For Chinese information processing, automatic classification based on a large-scale database for different patterns of semantic word-formation can remarkably improve the identification for the unregistered word, automatic lexicography, semantic analysis, and other applications. However, owing to noise, anomalies, nonlinear characteristics, class-imbalance, and other uncertainties in word-formation data, the predictive performance of multi-criteria optimization classifier (MCOC) and other traditional data mining approaches will rapidly degenerate. In this paper we put forward an novel MCOC with fuzzification, kernel, and penalty factors (FKP-MCOC) based on layered and weighted graph edit distance (GED): firstly the layered and weighted GEDs between each semantic word-formation graph and prototype graphs are calculated and used for the dissimilarity measure, then the normalized GEDs are embedded into a new feature vector space, and FKP-MCO classifier based on the feature vector space is built for predicting the patterns of semantic word-formation. Our experimental results of Chinese word-formation analysis and comparison with support vector machine (SVM) show that our proposed approach can increase the separation of different patterns, the predictive performance of semantic pattern of a new compound word.

scholarly journals Technical note: Evaluation of three machine learning models for surface ocean CO<sub>2</sub> mapping

Ocean Science ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Jiye Zeng ◽  
Tsuneo Matsunaga ◽  
Nobuko Saigusa ◽  
Tomoko Shirai ◽  
Shin-ichiro Nakaoka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feedforward Neural Networks ◽  
Technical Note ◽  
Support Vector ◽  
Co2 Uptake ◽  
Self Organizing Maps ◽  
Surface Ocean ◽  
Machine Learning Model ◽  
Machine Learning Models

Abstract. Reconstructing surface ocean CO2 from scarce measurements plays an important role in estimating oceanic CO2 uptake. There are varying degrees of differences among the 14 models included in the Surface Ocean CO2 Mapping (SOCOM) inter-comparison initiative, in which five models used neural networks. This investigation evaluates two neural networks used in SOCOM, self-organizing maps and feedforward neural networks, and introduces a machine learning model called a support vector machine for ocean CO2 mapping. The technique note provides a practical guide to selecting the models.

scholarly journals IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3188 ◽  
Author(s):  
Vitor Hugo Bezerra ◽  
Victor Guilherme Turrisi da Costa ◽  
Sylvio Barbon Junior ◽  
Rodrigo Sanches Miani ◽  
Bruno Bogaz Zarpelão
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Detection System ◽  
Predictive Performance ◽  
Support Vector ◽  
Local Outlier Factor ◽  
Training Activities ◽  
Iot Devices ◽  
One Class Classification ◽  
Local Outlier

Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device’s behaviour, the approach extracts features from the device’s CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device’s energy consumption, and CPU and memory utilisation.

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