RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)

Ananthi, M.; Vijayakumar, K.

doi:10.1007/s12652-020-01892-5

Cited by 67 publications

(30 citation statements)

References 17 publications

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“…This comparative analysis is provided in Table 3. The parameter values and other details of these established state-of-the-art classifiers (Ananthi and Vijayakumar, 2021; Kadam et al, 2019) are as follows:…”

Section: Experiments and Resultsmentioning

confidence: 99%

Breast cancer diagnosis using multiple activation deep neural network

Vijayakumar

Kadam

Sharma³

2021

Concurrent Engineering

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Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

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Section: Experiments and Resultsmentioning

confidence: 99%

Breast cancer diagnosis using multiple activation deep neural network

Vijayakumar

Kadam

Sharma³

2021

Concurrent Engineering

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“…Likewise, important slide is limited utilizing Chinese remainder theorem (CRT) in light of mystery sharing. The de-duplication rate of the plan is around 24% which is more than that of different plans (Ananthi and Vijayakumar, 2020).…”

Section: Related Workmentioning

confidence: 94%

An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

Praveena

Bharathi

2021

Concurrent Engineering

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Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.

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“…Reading candlestick charts allows traders to understand impacts of trends called visual features rather than reading numerical raw data directly. Some recent investigations manifest that analyzing visual characteristics of candlestick charts to predict stock market is still a hot topic, for examples, Hu et al [8] summarize the historical financial data as images using candlestick charts and adopt the convolutional autoencoder for feature learning from the image data, Fengqian and Chao [25] apply K-line theory to characterize candlesticks as a generalization of price movements over a time period and then propose the deep reinforcement learning based system to reach adaptive control in the unknown environment, and Ananthi and Vijayakumar [26] proposed a system that generates signals on the candlesticks to predict market price movement by using regression and candlestick pattern detection. Furthermore, a novel variation of conventional candlesticks, RGBSticks, is introduced in [27] to predict the daily stock price of a company by using an autoencoder based on deep neural network.…”

Section: Analysis Of Candlestick Chartsmentioning

confidence: 99%

DPP: Deep predictor for price movement from candlestick charts

Hung

Chen

2021

PLoS ONE

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Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

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RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)

Cited by 67 publications

References 17 publications

Breast cancer diagnosis using multiple activation deep neural network

Breast cancer diagnosis using multiple activation deep neural network

An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

DPP: Deep predictor for price movement from candlestick charts

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