Emotional sounds of crowds: spectrogram-based analysis using deep learning

AbstractCrowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. A critical question concerning the innovative concept of crowd emotions is whether the emotional content of crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations. In this work, we present a technique based on the generation of sound spectrograms from fragments of fixed length, extracted from original audio clips recorded in high-attendance events, where the crowd acts as a collective individual. Transfer learning techniques are used on a convolutional neural network, pre-trained on low-level features using the well-known ImageNet extensive dataset of visual knowledge. The original sound clips are filtered and normalized in amplitude for a correct spectrogram generation, on which we fine-tune the domain-specific features. Experiments held on the finally trained Convolutional Neural Network show promising performances of the proposed model to classify the emotions of the crowd.

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An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

Electronics ◽

10.3390/electronics9091514 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1514

Author(s):

Ali Aljofey ◽

Qingshan Jiang ◽

Qiang Qu ◽

Mingqing Huang ◽

Jean-Pierre Niyigena

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Third Party ◽

Learning Models ◽

Phone Calls ◽

Learning Techniques ◽

Proposed Model ◽

Phishing Detection

Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

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Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽

10.3390/s21082648 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2648

Author(s):

Muhammad Aamir ◽

Tariq Ali ◽

Muhammad Irfan ◽

Ahmad Shaf ◽

Muhammad Zeeshan Azam ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Natural Disasters ◽

Deep Convolutional Neural Network ◽

Multispectral Images ◽

Learning Techniques ◽

Proposed Model ◽

Disaster Intensity ◽

And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

Journal of Artificial Intelligence and Capsule Networks - September 2019 ◽

10.36548/jaicn.2019.2.004 ◽

2019 ◽

Vol 2019 (2) ◽

pp. 83-94 ◽

Cited By ~ 9

Author(s):

Dr. I. Jeena Jacob

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Recognition System ◽

Noise Removal ◽

Personal Identification ◽

Biometric Recognition ◽

Learning Techniques ◽

Frame Work ◽

The Stability

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

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A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Nature Communications ◽

10.1038/s41467-019-13749-3 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Jin-Woong Lee ◽

Woon Bae Park ◽

Jin Hee Lee ◽

Satendra Pal Singh ◽

Kee-Sun Sohn

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Inorganic Compounds ◽

Phase Identification ◽

X Ray Diffraction ◽

X Ray ◽

Learning Techniques ◽

Learning Technique ◽

Xrd Patterns

AbstractHere we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

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An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

Applied Sciences ◽

10.3390/app112110301 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10301

Author(s):

Muhammad Shoaib Farooq ◽

Attique Ur Rehman ◽

Muhammad Idrees ◽

Muhammad Ahsan Raza ◽

Jehad Ali ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real World ◽

Early Stage ◽

Binary Classification ◽

X Rays ◽

X Ray ◽

Proposed Model ◽

Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

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A Multiple Regression Convolutional Neural Network for Estimating Multi-parameters Based on Overall Data in the Inverse Heat Transfer Problem

Journal of Thermal Science and Engineering Applications ◽

10.1115/1.4051841 ◽

2021 ◽

pp. 1-31

Author(s):

Feiding Zhu ◽

Jincheng Chen ◽

Yuge Han

Keyword(s):

Neural Network ◽

Heat Transfer ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Problem ◽

Sparse Data ◽

Heat Transfer Problem ◽

Inverse Heat Transfer ◽

Inverse Heat Transfer Problem ◽

Proposed Model

Abstract The inverse heat transfer problem (IHTP) is a central task for estimating parameters in heat transfer. It is ill-posedness that is characterised by instability and non-uniqueness of the solution. Recently, novel algorithms using deep learning and neural networks for application of various sparse data in the inverse heat transfer problem. In order to overcome the optimization problem of input nodes under sparse data, we try to use the overall data of the target as the basis for inversion. In this work, we used an improved convolutional neural network (CNN) to estimate multi-parameters in the inverse heat transfer problem. Computational fluid dynamics (CFD) and deep learning are fused to provide datasets for training of the proposed model. The proposed model was verified by experiments with a cubic cavity. Additionally, the improved CNN model was used to predict the different parameters of the more complex armored vehicle model. The results showed that the model has good prediction accuracy for estimating multi-parameters on different datasets. These attempts of introducing convolutional neural network to the IHTP in the present study were successful and it was significant for the study of the inverse heat transfer problem of estimating multi-parameters.

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A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

Applied Sciences ◽

10.3390/app11219948 ◽

2021 ◽

Vol 11 (21) ◽

pp. 9948

Author(s):

Amira Echtioui ◽

Ayoub Mlaouah ◽

Wassim Zouch ◽

Mohamed Ghorbel ◽

Chokri Mhiri ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Signal To Noise Ratio ◽

Eeg Signals ◽

Learning Techniques ◽

Eeg Data ◽

Spatial And Temporal Characteristics

Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.

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Batik Classification using Deep Convolutional Network Transfer Learning

Jurnal Ilmu Komputer dan Informasi ◽

10.21609/jiki.v11i2.507 ◽

2018 ◽

Vol 11 (2) ◽

pp. 59 ◽

Cited By ~ 5

Author(s):

Yohanes Gultom ◽

Aniati Murni Arymurthy ◽

Rian Josua Masikome

Keyword(s):

Neural Network ◽

Deep Learning ◽

Cultural Heritage ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Data Representation ◽

Convolutional Network ◽

Proposed Model ◽

Feature Extractor ◽

Research Task

Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset.

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Person Re-Identification Using Convolutional Neural Network and Autoencoder Embedded on Frameworks Based on Siamese and Triplet Networks

10.21203/rs.3.rs-117671/v1 ◽

2020 ◽

Author(s):

Fábia Isabella Pires Enembreck ◽

Erikson Freitas de Morais ◽

Marcella Scoczynski Ribeiro Martins

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

State Of The Art ◽

Identification Problem ◽

Reference Image ◽

Learning Techniques ◽

Art Methods

Abstract The person re-identification problem addresses the task of identify if a person being watched by security cameras in surveillance environments has ever been in the scene. This problem is considered challenging, since the images obtained by cameras are subject to many variations, such as lighting, perspective and occlusions. This work aims to develop two robust approaches based on deep learning techniques for person re-identification, considering these variations. The first approach uses a Siamese neural network composed by two identical subnets. This model receives two input images that may or may not be from the same person. The second approach consists of a triplet neural network, with three identical subnets, which receives a reference image from a certain person, a second image from the same person and another image from a different person. Both approaches have identical subnets, composed by a convolutional neural network which extracts general characteristics from each image and an autoencoder model, responsible for addressing high variations that input images may undergo. To compare the developed networks, three datasets were used, and the accuracy and the CMC curve metrics were applied for the analysis. The experiments showed an improvement in the results with the use of the autoencoder in the subnets. Besides, Triplet Neural Network presented promising results in comparison with Siamese Neural Network and state-of-the-art methods.

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