scholarly journals Chaos and complexity from quantum neural network. A study with diffusion metric in machine learning

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Sayantan Choudhury ◽  
Ankan Dutta ◽  
Debisree Ray
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Chaos ◽  
Geometric Approach ◽  
Universal Function ◽  
Stochastic Gradient Descent ◽  
Generalization Capability ◽  
Steady State Condition ◽  
Quantum Neural Network ◽  
Function Approximator

Abstract In this work, our prime objective is to study the phenomena of quantum chaos and complexity in the machine learning dynamics of Quantum Neural Network (QNN). A Parameterized Quantum Circuits (PQCs) in the hybrid quantum-classical framework is introduced as a universal function approximator to perform optimization with Stochastic Gradient Descent (SGD). We employ a statistical and differential geometric approach to study the learning theory of QNN. The evolution of parametrized unitary operators is correlated with the trajectory of parameters in the Diffusion metric. We establish the parametrized version of Quantum Complexity and Quantum Chaos in terms of physically relevant quantities, which are not only essential in determining the stability, but also essential in providing a very significant lower bound to the generalization capability of QNN. We explicitly prove that when the system executes limit cycles or oscillations in the phase space, the generalization capability of QNN is maximized. Finally, we have determined the generalization capability bound on the variance of parameters of the QNN in a steady state condition using Cauchy Schwartz Inequality.

scholarly journals Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020 ◽  
Vol 10 (6) ◽  
pp. 1999 ◽  
Author(s):  
Milica M. Badža ◽  
Marko Č. Barjaktarović
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Magnetic Resonance Images ◽  
Generalization Capability ◽  
Data Set ◽  
Fold Cross Validation

The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

scholarly journals PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING

2020 ◽  
Vol 25 (1) ◽  
pp. 40
Author(s):  
Stefanus Santosa ◽  
Suroso Suroso ◽  
Marchus Budi Utomo ◽  
Martono Martono ◽  
Mawardi Mawardi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Deep Learning ◽  
Information Needs ◽  
Chemical Properties ◽  
Stochastic Gradient Descent ◽  
Design Data ◽  
Artificial Neural

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.

IP Core Based Hardware Implementation of Multi-Layer Perceptrons on FPGAs: A Parallel Approach

2012 ◽  
Vol 433-440 ◽  
pp. 5647-5653 ◽  
Author(s):  
Xiao Jun Li ◽  
Lin Li
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Research Work ◽  
Universal Function ◽  
Multi Layer Perceptron ◽  
The Mean ◽  
Ip Cores ◽  
Artificial Neural Network Ann ◽  
Function Approximator ◽  
Mean Time

There’re many models derived from the famous bio-inspired artificial neural network (ANN). Among them, multi-layer perceptron (MLP) is widely used as a universal function approximator. With the development of EDA and recent research work, we are able to use rapid and convenient method to generate hardware implementation of MLP on FPGAs through pre-designed IP cores. In the mean time, we focus on achieving the inherent parallelism of neural networks. In this paper, we firstly propose the hardware architecture of modular IP cores. Then, a parallel MLP is devised as an example. At last, some conclusions are made.

scholarly journals Learning about reflective PPG for SpO2 determination using Machine Learning

2021 ◽  
Vol 7 (2) ◽  
pp. 33-36
Author(s):  
Idoia Badiola Aguirregomezcorta ◽  
Vladimir Blazek ◽  
Steffen Leonhardt ◽  
Christoph Hoog Antink
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Oxygen Saturation ◽  
Arterial Oxygen Saturation ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Stochastic Gradient Descent ◽  
Arterial Oxygen ◽  
Peripheral Oxygen Saturation

Abstract Reflective Photoplethysmography (PPG) sensors are less obtrusive than transmissive sensors, but they present patient-dependent variations in the so-called “Ratio of Modulation” (R). Thus, the conventionally employed calibration curves for determining peripheral oxygen saturation ( SpO2) may report inaccurate values. In this paper, we study the possibility of overcoming these limitations through Machine Learning (ML). For that, we show the results of applying several algorithms and feature combinations to PPG data from a human hypoxia study. The study was performed on ten healthy subjects. Their target oxygen saturation was reduced in five steps from 98- 100% to 70-77% through an oral mask. Blood Gas Analysis (BGA) was performed five times for each saturation level to measure the arterial oxygen saturation. PPG data were acquired from a reflective pulse oximeter placed in the subjects’ ear canals. PPG signals were pre-processed, and several features in the frequency and temporal domain were calculated. For the ML algorithms’ input, we explored different combinations of the features. We trained and validated the algorithms with the data from seven patients, and we tested them on three. Finally, we performed leaveone- out cross-validation to ensure the universality of the methods. The results show a good agreement of the predictions with the BGA values for Linear Regression, k- Nearest Neighbors, Stochastic Gradient Descent, and Neural Network for all input feature combinations with an average RMSE in the range of 3%. However, the performance of the Linear Regression was not beaten by the Neural Network, even for overfitting with 2000 hidden layers. The combination of R calculated with a Fast-Fourier Transform and ACRMS.red/ACRMS.irsignificantly improved the results, reducing the RMSE by 25%. This work demonstrates that a straight-forward Linear Regression is capable of determining SpO2with reflective PPG independently of the subject if the ratio ACRMS.red/ACRMS.ir is considered simultaneously with the Ratio of Modulation.

scholarly journals Quantum Neural Network Based Machine Translator for Hindi to English

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ravi Narayan ◽  
V. P. Singh ◽  
S. Chakraverty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Translation ◽  
Translation System ◽  
Quantum Neural Network ◽  
Individual Word ◽  
Part Of Speech ◽  
Machine Translation System ◽  
Simulation And Evaluation

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

An efficient speaker recognition using quantum neural network

2018 ◽  
Vol 32 (31) ◽  
pp. 1850384 ◽  
Author(s):  
Rupinderdeep Kaur ◽  
R. K. Sharma ◽  
Parteek Kumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Speaker Recognition ◽  
Bias Reduction ◽  
Speech Signals ◽  
Statistical Features ◽  
Quantum Neural Network ◽  
Inductive Bias

Speaker recognition is the technique to identify the identity of a person from statistical features obtained from speech signals. Many speaker recognition techniques have been designed and implemented so far to efficiently recognize the speaker. From the existing review, it is found that the existing speaker recognition techniques suffer from the over-fitting issues. Therefore, to overcome the over-fitting issue in this paper, we design, a novel ensemble-based quantum neural network. It selects one base model (i.e. expert) for each query, and concentrates on inductive bias reduction. A set of quantum neural networks are trained by considering different kinds of quantum features and are afterwards used to recognize the speaker. In the end, ensembling is used to combine these classification results. Extensive experiments have been carried out by considering the proposed technique and existing competitive machine learning-based speaker recognition techniques on speaker recognition data. It is observed that the proposed technique outperforms existing speaker recognition techniques in terms of accuracy and sensitivity by 1.371% and 1.291%, respectively.

scholarly journals Quantum Computing Aided Machine Learning Through Quantum State Fidelity

2021 ◽  
Author(s):  
Samuel A. Stein
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Computing ◽  
Deep Learning ◽  
Network Architecture ◽  
Rapid Development ◽  
Original Data ◽  
Quantum Circuit ◽  
Data Generation ◽  
Quantum Neural Network

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

scholarly journals Learning And Predicting Individual Preferences In Multicriteria Decision Making With Neural Networks Vs. Utility Functions

2016 ◽  
Vol 15 (1) ◽  
pp. 15-20
Author(s):  
Dat-Dao Nguyen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Utility Theory ◽  
Individual Preference ◽  
Predictive Ability ◽  
Multicriteria Decision Making ◽  
Universal Function ◽  
Multi Attribute Utility Theory ◽  
Function Approximator ◽  
And Behavior

This paper reports an empirical investigation into the performance of neural network technique vs. traditional utility theory-based method in capturing and predicting individual preference in multi-criteria decision making. As a universal function approximator, a neural network can assess individual utility function without imposing strong assumptions on functional form and behavior of the underlying data.  Results of this study show that in all cases, the predictive ability of neural network technique was comparable to the multi-attribute utility theory-based models. 

scholarly journals Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yao-Yi Kuo ◽  
Shu-Tien Huang ◽  
Hung-Wen Chiu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Real World ◽  
Clinical Situation ◽  
Stochastic Gradient Descent ◽  
Real World Data ◽  
World Data ◽  
Hourly Data ◽  
Artificial Neural

Abstract Purpose Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation. Materials and methods The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation. Results The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided. Conclusions Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

Sign in / Sign up

Export Citation Format

Share Document