A novel adaptive butterfly optimization algorithm

Emotion recognition from human faces are recently considered as growing topic for the applications in HCI (human-computer interaction) field. Therefore, a new framework is introduced in this method for emotion recognition from video. Human faces may carry huge features which increase the complexity of recognizing the emotions from the give video. Therefore, to minimize such defect, the wrapper based feature selection technique is introduced which reduce the complexity of proposed recognition framework. Initially, the frames from the input video is preprocessed. Next, the features exhibited by each emotions are extracted with geometric and local binary pattern-based feature extraction methods. Then, the features that reduce the performance of recognition technique is avoided using a feature selection algorithm. It selects the features that provides effective result on recognition process. Finally, the selected features are provided to deep belief network (DBN) for emotion recognition. The weight parameter selection of DBN is improved using an efficient Harris Hawk optimization algorithm. The performance of presented architecture is evaluated using a three different datasets they are FAMED, CK+, and MMI. The overall rate shown by proposed architecture is found better than existing methods. Furthermore, the precision, recall, and specificity are also evaluated for six different emotions (angry, disgust, fear, happy, sad, and surprise) in this proposed method. This entire emotion recognition process is implemented in Python platform.

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“…Butterflies utilize the sense receptors to identify the optimal food (best solution). 36 The fragrance in BOA is determined using Equation ( 7),…”

Section: Adaptive Butterfly Optimization Algorithmmentioning

confidence: 99%

Modified deep belief network based human emotion recognition with multiscale features from video sequences

Sreenivas¹,

Namdeo²,

Kumar

2021

Softw Pract Exp

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“…For example, [31] used BOA for the power systems. [32] used improved BOA by introducing the adaptive mechanism for updating the sensory value at each iteration in the original BOA and applied it for some benchmark functions.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

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“…However, the BOA algorithm has a potential to be further developed in terms of adaptability part pertaining to global search and sensory modality (Singh & Anand, 2019). In particular, BOA algorithm has a large improvement space for its basic parameters.…”

Section: Modified Butterfly Optimization Algorithmmentioning

confidence: 99%

“…With the value of parameter c continuing to increase, the performance of the algorithms also rises to a certain extent. In the traditional growth model, c ‐value can be represented (Singh & Anand, 2019) as:

c^{t + 1} = c^{t} + ((), 0.025 / ((), c^{t} \times italicMaxGeneration)),

where MaxGeneration is the number of iteration from the algorithm.…”

Section: Modified Butterfly Optimization Algorithmmentioning

confidence: 99%

A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

Jiang

et al. 2020

Expert Systems

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A modified adaptive butterfly optimization algorithm is established with the aim of addressing the “early search blindness” and the relatively poor adaptability of the sensory modality. A normal‐distribution‐based model and a Weibull‐distribution‐based adaptive model of sensory modalities are respectively proposed for the global search process and iteration process. Among them, the Weibull‐distribution‐based adaptive model of sensory modalities is mainly manifested as the c value, that is, the adaptive change trend based on the Weibull model. The performance of the modified butterfly optimization algorithm is validated using a 14‐benchmark test function and compared with performances of some latest algorithms. The experimental results indicate that the modified algorithm performs competitively in terms of accuracy and stability. Following the experiment, the modified algorithm is further tested by running a support‐vector‐machine prediction model based on engineering data of a pipe belt conveyor's flat‐pipe/pipe‐flat transition segment. The results of the modified algorithm are then compared with test‐run outcomes of the back‐propagation prediction model and KCV‐SVM model. The results show that the prediction error is well within 10%, demonstrating the method's competence as a reliable reference for future designs of pipe belt conveyors.

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A novel adaptive butterfly optimization algorithm

Cited by 20 publications

References 23 publications

Modified deep belief network based human emotion recognition with multiscale features from video sequences

Modified deep belief network based human emotion recognition with multiscale features from video sequences

Dynamic Butterfly Optimization Algorithm for Feature Selection

A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

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