Survey of machine learning methods for big data applications

Vinothini, A.; Priya, S. Baghavathi

doi:10.1109/iccids.2017.8272638

Cited by 10 publications

(20 citation statements)

References 18 publications

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“…An interval-valued AQI (air quality index) forecasting proposed in [10] used a learning approach consisting of a double decomposition and optimal combination jointly. An algorithm that is a hybrid forecaster has been proposed in [11]. The method which is proposed that is a hybrid forecaster is based on the modified grasshopper optimization algorithm (MGOA) and locally weighted support vector regression (LWSVR).…”

Section: Related Workmentioning

confidence: 99%

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Ashwini¹,

Nagaveni²,

Singh³

2022

Intelligent Automation &Amp; Soft Computing

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Section: Related Workmentioning

confidence: 99%

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Ashwini¹,

Nagaveni²,

Singh³

2022

Intelligent Automation &Amp; Soft Computing

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“…Therefore, the signal strength of smartphones [20] is significant for receiving and sending information. If the space between sender and receiver is higher, the path loss [21] would occuron the floors and walls due to the attenuation focuses. Hence, for accurate floor planning, devices with higher signal strength and lower path loss between the floors and walls are considered in the proposed work.…”

Section: Deep Recurrent Multilayer Perceptive Neural Learning (Increase Positioning Accuracy)mentioning

confidence: 99%

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

Pichaimani¹,

Manjula²

2022

Intelligent Automation &Amp; Soft Computing

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The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset are through two processing stages dimensionality reduction and position estimation. Initially, the essential elements selection using the Gaussian Distributive Feature Embedding technique is the novel framework. The feature reduction process aims to reduce the time consumption and overhead for estimating the location of various devices. In the next stage, employ Deep Recurrent multilayer Perceptive Neural Learning to evaluate the device position with dimensionality reduced features. The proposed Deep-learning approach accurately learns the quality and the signal strength data with multiple layers by applying Deming Regressive Trilateral Positioning Model. As a result, the GDFE-DRPNL framework increases the positioning accuracy and minimizes the error rate. The experimental assessments with various factors such as positioning accuracy minimized by 70% and 60%, computation time minimized by 45% and 55% as well as overhead by 11% and 23% compared with PFRL and two-dimensional localization algorithm. Through the experiment and after analyzing the data, verify that the proposed GDFE-DRPNL algorithm in this paper is better than the previous methods.

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“…Vinothini et al [15] construct LSTM based DL networks to forecast the closing prices of the stock and relate the predictive accuracy of the ML model using the LSTM models. Further, we increase the prediction models by incorporating a sentimental analysis model on twitters information for correlating the public sentiments of stock price with the market sentiments.…”

Section: Related Workmentioning

confidence: 99%

Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis

Singamaneni¹,

Akeji²,

Mithun³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In recent times, stock price prediction helps to determine the future stock prices of any financial exchange. Accurate forecasting of stock prices can result in huge profits to the investors. The prediction of stock market is a tedious process which involves different factors such as politics, economic growth, interest rate, etc. The recent development of social networking sites enables the investors to discuss the stock market details such as profit, future stock prices, etc. The proper identification of sentiments posted by the investors in social media can be utilized for predicting the upcoming stock prices. With this motivation, this paper focuses on the design of effective stock price prediction using dragonfly algorithm (DFA) based deep belief network (DBN) model. The DFA-DBN technique aims to properly determine the sentiments of the investors from Twitter data and forecast future stock prices. From Twitter data, the DFA-DBN technique attempts to accurately determine the sentiments of investors, as well as predict future stock prices. For accurate stock price prediction, the proposed DFA-DBN model includes the development of a DBN model. The proposed DFA-DBN model involves the design of DBN model for accurate prediction of stock prices. Besides, the hyperparameter tuning of the DBN technique is performed by utilize of DFA and thereby boosts the overall prediction performance. For validating the supremacy of the DFA-DBN model, a comprehensive experimental analysis takes place and the results demonstrate the accurate prediction of stock prices. A predicted DFA-DBN algorithm with a higher accuracy of 94.97 percent is available. On the basis of the data in the tables and figures above, the DFA-DBN approach has been demonstrated to be an effective instrument for anticipating stock price fluctuations.

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Survey of machine learning methods for big data applications

Cited by 10 publications

References 18 publications

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis

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