A Study on the Real-Time Music Genre Classification Model Using Convolutional Neural Network-based Genre Subclass Models

2021 ◽  
Vol 27 (7) ◽  
pp. 490-496
Author(s):  
Won Jun Lee ◽  
Hong Seong Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Classification Model ◽  
The Real ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

scholarly journals Musical Genre Classification using Convolutional Neural Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 228-230
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Musical Genre ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

Music has likewise been separated into Genres and sub sorts on the premise on music. To show that, we contrast the outcomes acquired and a Convolutional Neural Network (CNN). Experiments were conducted on Marsyas databases with distinct characteristics for genre classification. The proposed CNN results in better accuracy in music genre classification.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

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