Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Khan, Samee U.; Haq, Ijaz Ul; Rho, Seungmin; Baik, Sung Wook; Lee, Mi Young

doi:10.3390/app9224963

Cited by 90 publications

(51 citation statements)

References 29 publications

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“…In computer vision, 2D CNN models have shown an encouraging performance on both image and video data such as facial expression analysis [52], action recognition [53], movie/video summarization [54], violence detection [55], etc. The 2D model accepts input in the two-dimension format in which pixels of images with color channels are processed simultaneously known as feature learning [56].…”

Section: One-dimensional (1d) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

et al. 2021

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The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the residential and industrial sectors use metering sensors that only measure the consumed energy but are unable to manage electricity. In this paper, we present a comparative analysis of a variety of deep features with several sequential learning models to select the optimized hybrid architecture for energy consumption prediction. The best results are achieved using convolutional long short-term memory (ConvLSTM) integrated with bidirectional long short-term memory (BiLSTM). The ConvLSTM initially extracts features from the input data to produce encoded sequences that are decoded by BiLSTM and then proceeds with a final dense layer for energy consumption prediction. The overall framework consists of preprocessing raw data, extracting features, training the sequential model, and then evaluating it. The proposed energy consumption prediction model outperforms existing models over publicly available datasets, including Household and Korean commercial building datasets.

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Section: One-dimensional (1d) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

et al. 2021

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“…Finally, the MAPE metric computes the prediction accuracy in percentage. The mathematical representation of all these metrics is depicted in Equations (16)- (19).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The simulated method in [ 12 ] performs a pivotal part in improving building constructions, and it can also accurately depict real assessments of different building designs to predict HL and CL [ 13 ]. On the other hand, most of the researchers get full advantages by applying DL models on different domains, such as movie and video summarization [ 14 , 15 ], energy forecasting [ 16 ], biological data analysis [ 17 ], violence detection [ 18 , 19 ], and action recognition [ 20 ]. In this study, we explored numerous ML and DL models for the prediction of HL and CL using an energy efficiency dataset.…”

Section: Introduction To Residential Building Energymentioning

confidence: 99%

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Sajjad

Khan

et al. 2020

Sensors

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In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

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“…A spectrogram is a 2-D representation of speech signals which is widely used in convolutional neural networks (CNNs) for extracting the salient and discriminative features in SER [2] and other signal processing applications [3], [4]. Mostly 2-D CNNs are specially designed for visual recognition tasks [5]- [7] and researchers are inspired by their performance to explore 2-D CNNs in the field of SER. Spectrograms are suitable representations of speech signals for CNNs model to extract high-level salient information to recognize emotions in speech signals.…”

Section: Introduction Of Sermentioning

confidence: 99%

Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

2020

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Emotional state recognition of a speaker is a difficult task for machine learning algorithms which plays an important role in the field of speech emotion recognition (SER). SER plays a significant role in many real-time applications such as human behavior assessment, human-robot interaction, virtual reality, and emergency centers to analyze the emotional state of speakers. Previous research in this field is mostly focused on handcrafted features and traditional convolutional neural network (CNN) models used to extract high-level features from speech spectrograms to increase the recognition accuracy and overall model cost complexity. In contrast, we introduce a novel framework for SER using a key sequence segment selection based on redial based function network (RBFN) similarity measurement in clusters. The selected sequence is converted into a spectrogram by applying the STFT algorithm and passed into the CNN model to extract the discriminative and salient features from the speech spectrogram. Furthermore, we normalize the CNN features to ensure precise recognition performance and feed them to the deep bi-directional long short-term memory (BiLSTM) to learn the temporal information for recognizing the final state of emotion. In the proposed technique, we process the key segments instead of the whole utterance to reduce the computational complexity of the overall model and normalize the CNN features before their actual processing, so that it can easily recognize the Spatio-temporal information. The proposed system is evaluated over different standard dataset including IEMOCAP, EMO-DB, and RAVDESS to improve the recognition accuracy and reduce the processing time of the model, respectively. The robustness and effectiveness of the suggested SER model is proved from the experimentations when compared to state-of-the-art SER methods with an achieve up to 72.25%, 85.57%, and 77.02% accuracy over IEMOCAP, EMO-DB, and RAVDESS dataset, respectively. INDEX TERMS Speech emotion recognition, deep bidirectional long shot term memory, key segment sequence selection, normalization of CNN features, radial-based function network (RBFN).

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Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Cited by 90 publications

References 29 publications

Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

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