Robust front-end for audio, visual and audio–visual speech classification

Terissi, Lucas D.; Sad, Gonzalo D.; Gómez, Juan Carlos

doi:10.1007/s10772-018-9504-y

Cited by 9 publications

(4 citation statements)

References 49 publications

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“…During the development of every tree, another variety mechanism is presented, because on each node, a process in RF selects a tiny random subclass of features and searches for the optimal split using just this subclass (Terissi et al , 2018). The combination of the two variety mechanisms (bootstrapping with selecting across every single node just from a subclass of features) results in improved ensemble construction and resilient performance for a data mining predicting approach (Granitto et al , 2007; Terissi et al , 2018).…”

Section: Methodsmentioning

confidence: 99%

Predicting daily precision improvement of Jakarta Islamic Index in Indonesia’s Islamic stock market using big data mining

Ledhem,

Moussaoui

2023

JM2

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Purpose This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market. Design/methodology/approach This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared. Findings The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique. Practical implications This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility. Originality/value This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

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Section: Methodsmentioning

confidence: 99%

Predicting daily precision improvement of Jakarta Islamic Index in Indonesia’s Islamic stock market using big data mining

Ledhem,

Moussaoui

2023

JM2

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“…Another diversity process is introduced during the growth of each tree; on each node, the method picks out a small random subclass of attributes and uses only this subclass to search for the best split (Terissi et al , 2018).…”

Section: Methodsmentioning

confidence: 99%

“…The mixture of the two processes of diversity (bootstrapping with picking on every single node only from a subclass of characteristics) generates better building of the ensembles with robust performance for a prediction technique in data mining (Granitto et al , 2007; Terissi et al , 2018).…”

Section: Methodsmentioning

confidence: 99%

Data mining techniques for predicting the financial performance of Islamic banking in Indonesia

Ledhem¹

2021

JM2

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Purpose The purpose of this paper is to apply various data mining techniques for predicting the financial performance of Islamic banking in Indonesia through the main exogenous determinants of profitability by choosing the best data mining technique based on the criteria of the highest accuracy score of testing and training. Design/methodology/approach This paper used data mining techniques to predict the financial performance of Islamic banking by applying all of LASSO regression, random forest (RF), artificial neural networks and k-nearest neighbor (KNN) over monthly data sets of all the full-fledged Islamic banks working in Indonesia from January 2011 until March 2020. This study used return on assets as a real measurement of financial performance, whereas the capital adequacy ratio, asset quality and liquidity management were used as exogenous determinants of financial performance. Findings The experimental results showed that the optimal task for predicting the financial performance of Islamic banking in Indonesia is the KNN technique, which affords the best-predicting accuracy, and gives the optimal knowledge from the financial performance of Islamic banking determinants in Indonesia. As well, the RF provides closer values to the optimal accuracy of the KNN, which makes it another robust technique in predicting the financial performance of Islamic banking. Research limitations/implications This paper restricted modeling the financial performance of Islamic banking to profitability through the main determinants of return of assets in Indonesia. Future research could consider enlarging the modeling of financial performance using other models such as CAMELS and Z-Score to predict the financial performance of Islamic banking under data mining techniques. Practical implications Owing to the lack of using data mining techniques in the Islamic banking sector, this paper would fill the literature gap by providing new effective techniques for predicting financial performance in the Islamic banking sector using data mining approaches, which can be efficient tools in business and management modeling for financial researchers and decision-makers in the Islamic banking sector. Originality/value According to the author’s knowledge, this paper is the first that provides data mining techniques for predicting the financial performance of the Islamic banking sector in Indonesia.

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“…Accordingly, few studies have reported that superior performance is achieved when hearing and vision are combined in several groups. [12, 13].…”

Section: Introductionmentioning

confidence: 99%

Multimodal audiovisual speech recognition architecture using a three‐feature multi‐fusion method for noise‐robust systems

Jeon,

Lee,

Yeo

et al. 2024

ETRI Journal

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Exposure to varied noisy environments impairs the recognition performance of artificial intelligence‐based speech recognition technologies. Degraded‐performance services can be utilized as limited systems that assure good performance in certain environments, but impair the general quality of speech recognition services. This study introduces an audiovisual speech recognition (AVSR) model robust to various noise settings, mimicking human dialogue recognition elements. The model converts word embeddings and log‐Mel spectrograms into feature vectors for audio recognition. A dense spatial–temporal convolutional neural network model extracts features from log‐Mel spectrograms, transformed for visual‐based recognition. This approach exhibits improved aural and visual recognition capabilities. We assess the signal‐to‐noise ratio in nine synthesized noise environments, with the proposed model exhibiting lower average error rates. The error rate for the AVSR model using a three‐feature multi‐fusion method is 1.711%, compared to the general 3.939% rate. This model is applicable in noise‐affected environments owing to its enhanced stability and recognition rate.

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Robust front-end for audio, visual and audio–visual speech classification

Cited by 9 publications

References 49 publications

Predicting daily precision improvement of Jakarta Islamic Index in Indonesia’s Islamic stock market using big data mining

Predicting daily precision improvement of Jakarta Islamic Index in Indonesia’s Islamic stock market using big data mining

Data mining techniques for predicting the financial performance of Islamic banking in Indonesia

Multimodal audiovisual speech recognition architecture using a three‐feature multi‐fusion method for noise‐robust systems

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