Movie trailer classification using deer hunting optimization based deep convolutional neural network in video sequences

Shambharkar, Prashant Giridhar; Doja, M. N.

doi:10.1007/s11042-020-08922-6

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…e rhythmic structure of music represents the rhythmic relationship between music and time and contains regular information such as rhythm and beat. is significant periodic information contains obvious temporal order, which can be modelled by HMM completion [12]. Tamboli et al constructed an active learning SVM model using the MFCC features of music and Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP) features and made an active learning method for uncertainty reduction improved by developing sample balance criteria and adjusting the sample balance to make the selected sample more valuable while ensuring the diversity of the sample [13].…”

Section: Status Of Researchmentioning

confidence: 99%

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Kai¹

2021

Complexity

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Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things (IoT) technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion.

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Section: Status Of Researchmentioning

confidence: 99%

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Kai¹

2021

Complexity

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“…The results present in the Table 4 indicate that the proposed HANN method has achieve better performance in terms of accuracy and precision when compared with gram layer in CNN [16]. The HANN method has achieved less accuracy (only 73.15%) compared to DCNN [17], this is because the HANN method uses the text as input data and the imbalance and skewed data are presented in the collected movie data. However, the HANN method achieved 96% of accuracy, where DCNN [17] achieved nearly 90-95% of accuracy on video data.…”

Section: Comparative Studymentioning

confidence: 95%

“…The HANN method has achieved less accuracy (only 73.15%) compared to DCNN [17], this is because the HANN method uses the text as input data and the imbalance and skewed data are presented in the collected movie data. However, the HANN method achieved 96% of accuracy, where DCNN [17] achieved nearly 90-95% of accuracy on video data. The proposed HANN method effectively pre-process the collected data by using attention layers in neural network.…”

Section: Comparative Studymentioning

confidence: 97%

“…Shambharkar and Doja [17] implemented the optimized Deep CNN (DCNN) for human action based movie classification using video sequences. The pre-processing was conducted using adaptive median filtering process and the foreground portions were extracted using the threshold based segmentation approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The performance of the HANN method is compared with the gram layer in CNN [16] and DCNN [17] in terms of accuracy and precision on text and video sequence data.…”

Section: Comparative Studymentioning

confidence: 99%

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Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

Govindaswamy¹,

Ragunathan²

2021

IJIES

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Genre Classification of movies is useful in the movie recommendation system for video streaming applications like Amazon, Netflix, etc. The existing methods used either video or audio data as input that requires more computation resources to process the data for the genre classification of movies. In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input. Twitter data related to the Telugu and English movies are collected and applied to HANN for movie’s genre classification. IMDB data are used to evaluate the performance of the proposed HANN method. The hierarchical structures of the twitter data is considered by the proposed HANN method and the most important words related to genre classification is identified by the attention mechanism, where the other neural networks such as Artificial Neural Network and Convolutional Neural Network (CNN) returns only the important weights resulting from previous words. The HANN method has the advantages of encoding the relevant information that helps to improve the performance of the recommendation system. The experimental results show that the HANN method achieve higher performance compared to other classifiers Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). The HANN method achieves accuracy of 73.15% in classification, while the existing BiLSTM method achieve the accuracy of 68% in classification.

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Hybrid optimization technique for IoT‐enabled residential buildings under cost‐effective energy management system on smart grid: A GPCHUA method

Krishnan

Jacob

2022

Intl J of Energy Research

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Summary An energy management system that uses the Internet of Things structure using hybrid system is proposed. The proposed hybrid technique is the joint execution of both Giza pyramids construction and human urbanization algorithm and is commonly named as combined Giza Pyramids Construction, Human urbanization algorithm (GPCHUA) technique. The major aim of the proposed technique is to optimally accomplish the power and resources of the distribution system. Moreover, the proposed scheme is in charge to fulfil the general supply and energy demand. At last, the efficiency of proposed scheme is portrayed on MATLAB/Simulink and evaluated with various existing techniques like Deer hunting optimization, bacterial foraging optimization, and crow search optimization. The comparison outcomes prove the superiority of the GPCHUA system and confirm their potential to solve the problem.

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Movie trailer classification using deer hunting optimization based deep convolutional neural network in video sequences

Cited by 13 publications

References 28 publications

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

Hybrid optimization technique for IoT‐enabled residential buildings under cost‐effective energy management system on smart grid: A GPCHUA method

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