scholarly journals Application of Machine Learning-Based Electrochemical Deposition Models to CMP Modeling

2020 ◽  
pp. 39-48
Author(s):  
Ruben Ghulghazaryan ◽  
Davit Piliposyan ◽  
Misak Shoyan ◽  
Hayk Nersisyan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Integrated Circuits ◽  
Electrochemical Deposition ◽  
Surface Profile ◽  
Superior Performance ◽  
Learning Approaches ◽  
Ic Manufacturing ◽  
Surface Profiles ◽  
Cmp Modeling

Chemical mechanical polishing/planarization (CMP) is the primary process used for modern integrated circuits (IC) manufacturing. Modeling of the post-CMP surface profile is critical for detecting planarity hotspots prior to manufacturing and avoiding fatal failures of chips. Electrochemical deposition (ECD) is a key process for the void-free filling of interconnection wires and vias in modern chips. Large surface topography variations generated after ECD affect the post-CMP surface profile. In this paper, several machine learning approaches are used to model surface profiles after ECD that are used as input to CMP models. Different combinations of deep neural networks, long-shortterm-memory (LSTM) recurrent networks, convolutional neural networks and XGBoost algorithms are investigated and compared. The model based on the XGBoost library showed superior performance and accuracy

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

Computerised Decision Support for Women’s Health Informatics

2012 ◽  
pp. 1404-1416 ◽  
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Computerised Decision Support for Women's Health Informatics

2009 ◽  
pp. 302-314
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

scholarly journals Behavioural analysis of single-cell aneural ciliate,Stentor roeselii, using machine learning approaches

2019 ◽  
Author(s):  
Kiều Mi Trịnh ◽  
Matthew T. Wayland ◽  
Sudhakaran Prabakaran
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Neural Circuits ◽  
Neural Systems ◽  
Learning Approaches ◽  
Learning Behaviour ◽  
Decision Making Processes ◽  
Feed Forward Neural Networks ◽  
Unicellular Organisms ◽  
Fully Functioning

AbstractThere is still a significant gap between our understanding of neural circuits and the behaviours they compute – i.e. the computations performed by these neural networks (Carandini 2012). Learning, behaviour, and memory formation, what used to only be associated with animals with neural systems, have been observed in many unicellular aneural species, namely Physarum, Paramecium, and Stentor (Tang & Marshall 2018). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed inStentor roeselii(Jennings 1902) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidant reactions. Although we were able to observe all responses described in literature and in a previous study (Dexter et al. 2019, manuscript in preparation), they do not conform to any particular learning model. We then investigated whether models based on machine learning approaches, including decision tree, random forest, and feed-forward neural networks could infer and predict the behavior ofS. roeselii. Our results showed that an artificial neural network with multiple ‘computational’ neurons is inefficient at modelling the single-celled ciliate’s avoidant reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

scholarly journals Diagnosis of Brain Diseases using Neural Networks

2019 ◽  
Vol 9 (2S) ◽  
pp. 402-407
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Neurological Diseases ◽  
Brain Diseases ◽  
Learning Approaches ◽  
Learning Practices ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
The Brain

Intensification in the occurrence of brain diseases and the need for the initial diagnosis for ailments like Tumor, Alzheimer’s, Epilepsy and Parkinson’s has riveted the attention of researchers. Machine learning practices, specifically deep learning, is considered as a beneficial diagnostic tool. Deep learning approaches to neuroimaging will assist computer-aided analysis of neurological diseases. Feature extraction of neuroimages carried out using Artificial Neural Networks leads to better diagnoses. In this study, all the brain diseases are revisited to consolidate the methodologies carried out by various authors in the literature.

scholarly journals Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

10.2196/23938 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e23938
Author(s):  
Ruairi O'Driscoll ◽  
Jake Turicchi ◽  
Mark Hopkins ◽  
Cristiana Duarte ◽  
Graham W Horgan ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Neural Networks ◽  
Random Forest ◽  
Energy Expenditure ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Sets ◽  
Out Of Sample

Background Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities. Methods Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations. Results The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks. Conclusions Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms.

scholarly journals Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

2019 ◽  
Vol 16 (11) ◽  
pp. 1992 ◽  
Author(s):  
Gebreab K. Zewdie ◽  
David J. Lary ◽  
Estelle Levetin ◽  
Gemechu F. Garuma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Land Surface ◽  
Deep Neural Networks ◽  
Airborne Pollen ◽  
Training Data ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Ambrosia Pollen ◽  
Extreme Gradient Boosting

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

scholarly journals A study of deep learning approaches for medication and adverse drug event extraction from clinical text

2019 ◽  
Vol 27 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Qiang Wei ◽  
Zongcheng Ji ◽  
Zhiheng Li ◽  
Jingcheng Du ◽  
Jingqi Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Joint Model ◽  
Machine Learning Algorithms ◽  
Entity Recognition ◽  
Superior Performance ◽  
Drug Event ◽  
Support Vector ◽  
Learning Approaches

AbstractObjectiveThis article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task.Materials and MethodsThe clinical corpus used in this study was from the MIMIC-III database and the organizers annotated 303 documents for training and 202 for testing. Our system consists of 2 components: a named entity recognition (NER) and a relation classification (RC) component. For each component, we implemented deep learning-based approaches (eg, BI-LSTM-CRF) and compared them with traditional machine learning approaches, namely, conditional random fields for NER and support vector machines for RC, respectively. In addition, we developed a deep learning-based joint model that recognizes ADEs and their relations to medications in 1 step using a sequence labeling approach. To further improve the performance, we also investigated different ensemble approaches to generating optimal performance by combining outputs from multiple approaches.ResultsOur best-performing systems achieved F1 scores of 93.45% for NER, 96.30% for RC, and 89.05% for end-to-end evaluation, which ranked #2, #1, and #1 among all participants, respectively. Additional evaluations show that the deep learning-based approaches did outperform traditional machine learning algorithms in both NER and RC. The joint model that simultaneously recognizes ADEs and their relations to medications also achieved the best performance on RC, indicating its promise for relation extraction.ConclusionIn this study, we developed deep learning approaches for extracting medications and their attributes such as ADEs, and demonstrated its superior performance compared with traditional machine learning algorithms, indicating its uses in broader NER and RC tasks in the medical domain.

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