Quantum Neural Network Based Machine Translator for Hindi to English

Narayan, Ravi; Singh, Varinder; Chakraverty, Snehashish

doi:10.1155/2014/485737

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…In the recent past, various models of QNN have been used in other research areas such as, prediction of weather, disease diagnosis, voice recognition, and machine translation. 34 – 38 In such manner, more states can be expressed in a hidden layer neural cell in comparison to traditional sigmoid function in which only two states may be expressed.…”

Section: Methodsmentioning

confidence: 99%

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Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach

Narain¹,

Saxena²,

Goyal³

2016

PPA

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PurposeCurrently cardiovascular diseases (CVDs) are the main cause of death worldwide. Disease risk estimates can be used as prognostic information and support for treating CVDs. The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough. In this paper, a novel CVD prediction system based on machine learning is proposed.MethodsThis study has been conducted with the data of 689 patients showing symptoms of CVD. Furthermore, the dataset of 5,209 CVD patients of the famous Framingham study has been used for validation purposes. Each patient’s parameters have been analyzed by physicians in order to make a diagnosis. The proposed system uses the quantum neural network for machine learning. This system learns and recognizes the pattern of CVD. The proposed system has been experimentally evaluated and compared with FRS.ResultsDuring testing, patients’ data in combination with the doctors’ diagnosis (predictions) are used for evaluation and validation. The proposed system achieved 98.57% accuracy in predicting the CVD risk. The CVD risk predictions by the proposed system, using the dataset of the Framingham study, confirmed the potential risk of death, deaths which actually occurred and had been recorded as due to myocardial infarction and coronary heart disease in the dataset of the Framingham study. The accuracy of the proposed system is significantly higher than FRS and other existing approaches.ConclusionThe proposed system will serve as an excellent tool for a medical practitioner in predicting the risk of CVD. This system will be serving as an aid to medical practitioners for planning better medication and treatment strategies. An early diagnosis may be effectively made by using this system. An overall accuracy of 98.57% has been achieved in predicting the risk level. The accuracy is considerably higher compared to the other existing approaches. Thus, this system must be used instead of the well-known FRS.

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Section: Methodsmentioning

confidence: 99%

“…The weights between input and hidden layers are denoted by W ij and the weights between hidden and output layers are denoted by W kj . 37 – 41 …”

Section: Methodsmentioning

confidence: 99%

Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach

Narain¹,

Saxena²,

Goyal³

2016

PPA

View full text Add to dashboard Cite

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“…The initial weights are small random numbers and t denotes Target value. First apply the inputs to the network and calculate the output [29,30,31].…”

Section: Algorithm For Training the Quantum Neural Networkmentioning

confidence: 99%

A Novel Heart Disease Prediction System Based on Quantum Neural Network Using Clinical Parameters

Narain

Saxena

Goyal

2017

ARRB

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Aims: The diagnosis of Heart disease at earliest possible stage is very crucial to increase the chance of successful treatment and to reduce the mortality rate. The interpretation of cardiovascular disease is time-consuming and requires analysis by an expert physician. Thus there is a need of expert system which may provide quick and accurate prediction of Heart disease at early possible stage, without the help of physician. Place and Duration of Study: The study was carried out during 2010 to 2013 in the vicinity of Yamuna Nagar, Haryana, India. Methodology: The data used for this study consists of clinical values (Diabetes Mellitus, Low Density Lipoprotein, Triglycerides and High Density Lipoprotein) and has been collected from various Hospitals of 689 patients, who have symptoms of heart disease. All these cases are analyzed after careful scrutiny with the help of the Physicians. For training and evaluation purpose we have carefully predicted the level of heart disease by taking the help of Cardiologist/ Physician. The data consists of patients' record with doctor's predictions/ diagnosis.

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“…Fortunately, the bidirectional transmission mechanism can make up for the above defect [20]. Moreover, the quantum computing can contribute to improving the global optimization ability and computational performance of the recurrent neural network [21][22][23] because of its high speed and parallelism [24,25]. Based on the complementary advantages of bidirectional transmission mechanism and quantum computing, a novel recurrent neural network called quantum gene chain coding bidirectional neural network (QGCCBNN) is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

Cheng

Tang

et al. 2020

Preprint

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In classical recurrent neural networks, the pre- and post-relationships of time series tend to be neglected so that long-term overall memory is generally inaccessible; meanwhile, the weights are transferred and updated mainly by the gradient descent method, which leads to their low prediction accuracy and high computation cost in the application of residual useful life (RUL) prediction of rotating machinery (RM). In view of this, a quantum gene chain coding bidirectional neural network (QGCCBNN) is proposed to predict RUL of RM in this paper. In our proposed QGCCBNN, the quantum bidirectional transmission mechanism is designed to establish the pre- and post-relationships of time series for readjusting the weight parameters according to the feedback from the output layer, so that higher consistency between the input information and the overall memory of the network can be realized, thus endowing QGCCBNN with better nonlinear approximation ability. Moreover, in order to improve the global optimization ability and convergence speed, the quantum gene chain coding instead of gradient descent method is constructed to transmit and update data, in which the qubit probability amplitude real number coding is adopted and the cosine and sinusoidal qubit probability amplitudes corresponding to the minimum loss function are compared with those of the current time by the phase selection matrix for the directional parallel updating of the weight parameters. On this basis, a new RUL prediction method for RM is proposed, and higher prediction accuracy as well as desirable efficiency can be obtained due to the advantages of QGCCBNN in nonlinear approximation ability and convergence speed. The experimental example for RUL prediction of a double-row roller bearing demonstrates the effectiveness of our proposed method.

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Quantum Neural Network Based Machine Translator for Hindi to English

Cited by 13 publications

References 11 publications

Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach

Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach

A Novel Heart Disease Prediction System Based on Quantum Neural Network Using Clinical Parameters

Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

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