Classification and Detection of Plant Leaf Diseases Using Various Deep Learning Techniques and Convolutional Neural Network

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

Multimedia Tools and Applications ◽

10.1007/s11042-020-09428-x ◽

2020 ◽

Vol 79 (47-48) ◽

pp. 36063-36075 ◽

Cited By ~ 1

Author(s):

Valentina Franzoni ◽

Giulio Biondi ◽

Alfredo Milani

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Critical Question ◽

Domain Specific ◽

Visual Knowledge ◽

Learning Techniques ◽

Proposed Model ◽

Political Demonstrations ◽

Fine Tune

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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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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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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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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Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

Energies ◽

10.3390/en13092242 ◽

2020 ◽

Vol 13 (9) ◽

pp. 2242 ◽

Cited By ~ 1

Author(s):

Alejandro J. del Real ◽

Fernando Dorado ◽

Jaime Durán

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Energy Demand ◽

Demand Forecasting ◽

Demand Forecast ◽

Learning Techniques ◽

Energy Demand Forecasting ◽

Artificial Neural

This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.

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A Deep Convolutional Neural Network to Detect the Existence of Geospatial Elements in High-Resolution Aerial Imagery

Proceedings ◽

10.3390/proceedings2019019017 ◽

2019 ◽

Vol 19 (1) ◽

pp. 17 ◽

Cited By ~ 3

Author(s):

Calimanut-Ionut Cira ◽

Ramón Alcarria ◽

Miguel-Ángel Manso-Callejo ◽

Francisco Serradilla

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Recognition ◽

High Resolution ◽

Convolutional Neural Network ◽

Road Network ◽

Deep Convolutional Neural Network ◽

Aerial Imagery ◽

Learning Techniques

This paper tackles the problem of object recognition in high-resolution aerial imagery and addresses the application of Deep Learning techniques to solve a challenge related to detecting the existence of geospatial elements (road network) in the available cartographic support. This challenge is addressed by building a convolutional neural network (CNN) trained to detect roads in high resolution aerial orthophotos divided in tiles (256 × 256 pixels) using manually labelled data.

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