The feedback artificial tree (FAT) algorithm

Li, Q. Q.; He, Z.C.; Li, Eric

doi:10.1007/s00500-020-04758-2

Cited by 36 publications

(24 citation statements)

References 36 publications

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“…In this case, the training process of the DMN classifier is carried out using the proposed FASSO approach. This approach is newly designed by the integration of FAT [ 22 ] and SSOA [ 23 ]. Figure 1 shows the schematic representation of the proposed FASSO-based DMN for human emotion recognition using EEG signals.…”

Section: Proposed Methodsmentioning

confidence: 99%

Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals

Bhanumathi

Jayadevappa

Tunga³

2022

International Journal of Telemedicine and Applications

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Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively.

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Section: Proposed Methodsmentioning

confidence: 99%

Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals

Bhanumathi

Jayadevappa

Tunga³

2022

International Journal of Telemedicine and Applications

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“…Once matching is done, the cluster centroid is updated then the cluster information based on features is passed through image classification, which is performed using the deep neuro fuzzy network 28 . However, training of network classifier is performed using developed FSOA, which is obtained by integrating Feedback Artificial Tree (FAT) 29 Algorithm, and Social Optimization Algorithm (SOA) 30 . Furthermore, dynamic classification is done using weight bounding decision based on error condition.…”

Section: Proposed Fsoa‐based Deep Neuro Fuzzy Optimizer For Increment...mentioning

confidence: 99%

“…The Deep Neuro optimizer is trained using the proposed FSOA, which is derived by integrating the features of FAT 29 Algorithm and SOA 30 . SOA is a population‐based meta‐heuristic that inspires the social behavior of human‐beings and imitates the two concepts of justice, such as equality of opportunity and principle of community.…”

Section: Proposed Fsoa‐based Deep Neuro Fuzzy Optimizer For Increment...mentioning

confidence: 99%

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Nadendla

Srikrishna

Rao

2022

Concurrency and Computation

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The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

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“…The sentiment is classified by training the Deep RNN classifier with the help of a developed RFATO algorithm. The RFATO algorithm is developed by combining the FAT algorithm (Li et al, 2020) and ROA (Binu and Kariyappa, 2018). In the developed RFATO algorithm, the self-evolution operator of the FAT algorithm is included in the position update equation of the ROA to provide an effective update of the position of the overtaker.…”

Section: Training Deep Recurrent Neural Network For the Classification Of Sentimentsmentioning

confidence: 99%

Optimal deep recurrent neural network for sentiment grade classification

Jasti

2021

IJWIS

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Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.

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The feedback artificial tree (FAT) algorithm

Cited by 36 publications

References 36 publications

Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals

Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Optimal deep recurrent neural network for sentiment grade classification

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