An efficient learning algorithm for periodic perceptron to test XOR function and parity problem

Mallick, Chittaranjan; Bhoi, Sourav Kumar; Panda, Sanjaya Kumar; Jena, Kalyan Kumar

doi:10.1007/s42452-020-1952-8

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Data scaling and panning are used in this method; as a result, it is capable of examining both time-domain and frequency-domain components of a signal at the same time. The wavelet technique [ 6 ] may be used to split a noisy signal into numerous scales and denoise the signal while keeping its integrity. This is true regardless of the frequency content of the signal.…”

Section: Bitcoin and Gold Prices Predictionmentioning

confidence: 99%

“…Price prediction models based on neural networks, such as recurrent neural networks (RNN) [ 5 ], convolutional neural networks (CNN), multilayer perceptron (MLP) [ 6 , 7 ], and long short-term memory (LSTM) [ 8 ], have proven to be the most widely used among machine learning approaches in recent years. For the period between March 11, 2014, and March 31, 2019, Soylemez used a multilayer artificial neural network technique to estimate gold prices, utilizing parameters such as Brent oil prices, the VIX index, the Dow Jones index, and the US Dollar index [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

Zhang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.

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Section: Bitcoin and Gold Prices Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

Zhang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…They had not stated their performance time of their proposed networks. Mallick et al [ 36 ] had proposed an effective learning technique for the periodic perceptron on two real set of problems. Their technique has generated higher results with respect to simple multilayer perceptron.…”

Section: Literature Surveymentioning

confidence: 99%

Episode of COVID-19 Telepsychiatry Session Key Origination Upon Swarm-Based Metaheuristic and Neural Perceptron Blend

Dey¹,

Sarkar

Chowdhury

et al. 2021

SN COMPUT. SCI.

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Current pandemic has immensely disrupted the entire world in the field of medical science. The novel corona virus has not only brought physical sufferings but also huge psychiatric complications on the patients. Treating the psychiatric issues from remote locations can be best done through telepsychiatry. Patients can virtually consult with psychiatrists from their quarantines. However, during this COVID-19 era of excessive digital transactions, patients’ data security mechanism is a challenging issue to prevent from intruding. Efficient cryptographic algorithms are used depending on the transmission key. This paper deals with the episode of transmission key origination with the help of salp swarm algorithm and neural perceptron. Threshold cryptography provides the generation of the partial shares of the E-prescriptions, which can be restructured on the threshold set of shares. The property of lossless theory has been implemented on the proposed set of telepsychiatry shares. A mask matrix has been proposed to diffuse the E-prescription shares into the specified group of users. The transmission key validation has been carried out in this paper based on myriad statistical tests. Chi Square test, has been observed under 5% level of significance. Thus, there exists no similarity between the bit patterns of the transmission key. A correlation coefficient between the average encryption and decryption time and the functional time has been estimated as 0.92076 and 0.72340, respectively. Also, it confirms the data resistance against the opponents in terms of different mathematical and statistical methods.

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An efficient learning algorithm for periodic perceptron to test XOR function and parity problem

Cited by 2 publications

References 22 publications

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

Episode of COVID-19 Telepsychiatry Session Key Origination Upon Swarm-Based Metaheuristic and Neural Perceptron Blend

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