Convolutional Neural Network

Deep Learning ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)

Infotek : Jurnal Informatika dan Teknologi ◽

10.29408/jit.v4i2.3582 ◽

2021 ◽

Vol 4 (2) ◽

pp. 217-227

Author(s):

Muhammad Saiful ◽

◽

Lalu Muhammad Samsu ◽

Fathurrahman Fathurrahman ◽

◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Network Architecture ◽

Infection Detection ◽

Learning Techniques ◽

The development of the world's technology is growing rapidly, especially in the field of health in the form of detection tools of various objects, including disease objects. The technology in point is part of artificial intelligence that is able to recognize a set of imagery and classify automatically with deep learning techniques. One of the deep learning networks widely used is convolutional neural network with computer vision technology. One of the problems with computer vision that is still developing is object detection as a useful technology to recognize objects in the image as if humans knew the object of the image. In this case, a computer machine is trained in learning using artificial neural networks. One of the sub types of artificial neural networks that are able to handle computer vision problems is by using deep learning techniques with convolutional neural network algorithms. The purpose of this research is to find out how to design the system, the network architecture used for COVID-19 infection detection. The system cannot perform detection of other objects. The results of COVID-19 infection detection with convolutional neural network algorithm show unlimited accuracy value that ranges from 60-99%.

Convolutional Neural Network for Image Recognition

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39061 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1619-1624

Author(s):

Ankith I

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Pattern Recognition ◽

Image Recognition ◽

Computational Models ◽

Simplified Approach ◽

Abstract: Recent developments in the field of machine learning have changed the way it operates for ever, especially with the rise of Artificial Neural Networks (ANN). There is no doubt that these biologically inspired computational models are capable of performing far better than previous forms of artificial intelligence in common machine learning tasks as compared to their previous versions. There are several different forms of artificial neural networks (ANNs), but one of the most impressive is the convolutional neural network (CNN). CNN's have been extensively used for solving difficult pattern recognition tasks using images. With their simple yet precise architecture, they offer a simplified approach to getting started with ANNs. The goal of this paper is to provide a brief introduction to CNN. It discusses the latest papers and newly formed techniques in order to develop these absolutely brilliant models of image recognition. This introduction assumes that you already have a basic understanding of ANNs and machine learning. Keywords: Pattern recognition, artificial neural networks, machine learning, image analysing.

EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

Vestnik komp iuternykh i informatsionnykh tekhnologii ◽

10.14489/vkit.2021.01.pp.044-051 ◽

2021 ◽

pp. 44-51

Author(s):

E. Yu. Shchetinin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Recurrent Neural Network ◽

Deep Neural Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Audio Recordings ◽

Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

Jurnal Ilmu Komputer ◽

10.33060/jik/2020/vol9.iss1.144 ◽

2020 ◽

Vol 9 (1) ◽

pp. 7-10

Author(s):

Hendry Fonda

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

18Th Century ◽

The Public ◽

Artificial Neural ◽

The Difference ◽

Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning ABSTRAK Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

Convolutional Neural Network for Iris Recognition

10.21203/rs.3.rs-244624/v1 ◽

2021 ◽

Author(s):

Wael Alnahari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Iris Recognition ◽

Recognition System ◽

High Accuracy ◽

Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

Applications of Artificial Neural Networks for Nonlinear Data - Advances in Computational Intelligence and Robotics ◽

Fundamental Categories of Artificial Neural Networks

10.4018/978-1-7998-4042-8.ch003 ◽

2021 ◽

pp. 30-64

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch001 ◽

2022 ◽

pp. 1-30

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

10.1007/978-3-030-30484-3 ◽

2019 ◽

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

Global NEST Journal ◽

10.30955/gnj.003923 ◽

2021 ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Water Quality ◽

Deep Learning ◽

Anomaly Detection ◽

Quality Assessment ◽

Water Quality Assessment ◽

Machine Learning Algorithms ◽

Quality Analysis

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>