scholarly journals Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

2022 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Tan Nghia Duong ◽  
Nguyen Nam Doan ◽  
Truong Giang Do ◽  
Manh Hoang Tran ◽  
Duc Minh Nguyen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Matrix Factorization ◽  
Feature Vector ◽  
Side Information ◽  
Digital Transformation ◽  
Unstructured Data ◽  
Training Process ◽  
Huge Amount ◽  
Convolutional Autoencoder

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

scholarly journals Performance Evaluation of Fine-tuned Faster R-CNN on specific MS COCO Objects

2019 ◽  
Vol 9 (4) ◽  
pp. 2548
Author(s):  
Garima Devnani ◽  
Ayush Jaiswal ◽  
Roshni John ◽  
Rajat Chaurasia ◽  
Neha Tirpude
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Fine Tuning ◽  
Training Process

<span lang="EN-US">Fine-tuning of a model is a method that is most often required to cater to the users’ explicit requirements. But the question remains whether the model is accurate enough to be used for a certain application. This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. The evaluation is based on the training process which provides us with intermediate models after every 1000 iterations. While 1000 iterations are not substantial enough over the range of 490k iterations, the groups are sized with 100k iterations each. Now, the intention was to compare the recorded metrics to evaluate the model in terms of accuracy. The training model used the set of specific categories chosen from the Microsoft Common Objects in Context (MS COCO) dataset while allowing the users to use their externally available images to test the model’s accuracy. Our trained model ensured that all the objects are detected that are present in the image to depict the effect of precision.</span>

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

2021 ◽  
Vol 5 (2) ◽  
pp. 312-318
Author(s):  
Rima Dias Ramadhani ◽  
Afandi Nur Aziz Thohari ◽  
Condro Kartiko ◽  
Apri Junaidi ◽  
Tri Ginanjar Laksana ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computer Science ◽  
No Value ◽  
Convolutional Neural Networks ◽  
Training Process ◽  
Indonesian Government ◽  
Speed Up ◽  
Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

scholarly journals Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 972 ◽  
Author(s):  
Xingchen Liu ◽  
Qicai Zhou ◽  
Jiong Zhao ◽  
Hehong Shen ◽  
Xiaolei Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Industrial Applications ◽  
Experimental Result ◽  
Noisy Environment ◽  
Experimental Conditions ◽  
Global Average ◽  
Noisy Input ◽  
Convolutional Autoencoder

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.

scholarly journals Self-Paced Convolutional Neural Network for PolSAR Images Classification

2019 ◽  
Vol 11 (4) ◽  
pp. 424 ◽  
Author(s):  
Changzhe Jiao ◽  
Xinlin Wang ◽  
Shuiping Gou ◽  
Wenshuai Chen ◽  
Debo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Synthetic Aperture ◽  
Training Process ◽  
Polarimetric Synthetic Aperture Radar ◽  
Learning Mechanism ◽  
3 Dimensional ◽  
Spatial Features ◽  
Rgb Values

Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset.

scholarly journals Classifying Informatory Tweets during Disaster Using Deep Learning

2020 ◽  
Vol 32 ◽  
pp. 03025
Author(s):  
Pradip Bhere ◽  
Anand Upadhyay ◽  
Ketan Chaudhari ◽  
Tushar Ghorpade
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Natural Disaster ◽  
Feature Vector ◽  
Sound Image ◽  
Life Saving ◽  
Disaster Data ◽  
Educational Information

Micro blogging platforms like Twitter generate a wealth of information during a disaster. Data can be in the form of sound, image, text, video etc. by way of tweets. Tweets produced during a disaster are not always educational. Information tweets can provide useful information about affected people, infrastructure damage, civilized organizations etc. Studies show that when it comes to sharing emergency information during a natural disaster, time is everything. Research on Twitter use during hurricanes, floods and floods provide potentially life-saving data on how information is disseminated in emergencies. The proposed system outlines how to distinguish sensitive and non-useful tweets during a disaster. The proposed method is based on the use of Word2Vec and the Convolutional Neural Network (CNN). Word2vec provides a feature vector and CNN is used to classify tweets.

scholarly journals Implementing Convolutional Neural Networks for Simple Image Classification

2019 ◽  
Vol 9 (2) ◽  
pp. 3616-3619
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
High Volume ◽  
Classification Systems ◽  
Learning Tools ◽  
Huge Amount ◽  
Recognition Systems

In recent years, huge amounts of data in form of images has been efficiently created and accumulated at extraordinary rates. This huge amount of data that has high volume and velocity has presented us with the problem of coming up with practical and effective ways to classify it for analysis. Existing classification systems can never fulfil the demand and the difficulties of accurately classifying such data. In this paper, we built a Convolutional Neural Network (CNN) which is one of the most powerful and popular machine learning tools used in image recognition systems for classifying images from one of the widely used image datasets CIFAR-10. This paper also gives a thorough overview of the working of our CNN architecture with its parameters and difficulties.

scholarly journals Implementation of Deep Learning for Slump Optimization Based on Concrete Quality Using Convolutional Neural Network in PT. SELO PROGO SAKTI

2020 ◽  
Vol 5 (2) ◽  
pp. 83-88
Author(s):  
Hedi Pandowo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Experimental Design ◽  
Convolutional Neural Network ◽  
Processing Unit ◽  
Training Process ◽  
Average Accuracy ◽  
Graphical Processing ◽  
Intelligence Science

Deep Learning is part of the scientific field of Machine Learning and Machine Learning is part of Artificial Intelligence science. Deep Learning has extraordinary capabilities by using a hardware Graphical Processing Unit (GPU) so that the artificial requirement network can run faster than using a Personal Computer Unit (CPU). Especially in terms of object classification in images using existing methods in the Convolutional Neural Network (CNN). The method used in this research is Preprocessing and Processing of Input Data, Training Process in which CNN is trained to obtain high accuracy from the classification carried out and the Testing Process which is a classification process using weights and bias from the results of the training process. This type of research is a pre experimental design (pre experimental design). The results of the object image classification test with different levels of confusion in the Concrete database with the Mix Design K-125, K-150, K-250 and K-300 produce an average accuracy value. This is also relevant to measuring the failure rate of concrete or slump

scholarly journals An Unconventional Approach for Analyzing the Mechanical Properties of Natural Fiber Composite Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Govindaraj Ramkumar ◽  
Satyajeet Sahoo ◽  
G. Anitha ◽  
S. Ramesh ◽  
P. Nirmala ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Convolutional Neural Network ◽  
Natural Fiber ◽  
Fiber Composite ◽  
Ground Truth ◽  
Fiber Composites ◽  
Natural Fiber Composites ◽  
Training Process ◽  
Natural Fiber Composite

Over the past few years, natural fiber composites have been a strategy of rapid growth. The computational methods have become a significant tool for many researchers to design and analyze the mechanical properties of these composites. The mechanical properties such as rigidity, effects, bending, and tensile testing are carried out on natural fiber composites. The natural fiber composites were modeled by using some of the computation techniques. The developed convolutional neural network (CNN) is used to accurately predict the mechanical properties of these composites. The ground-truth information is used for the training process attained from the finite element analyses below the plane stress statement. After completion of the training process, the developed design is authorized using the invisible data through the training. The optimum microstructural model is identified by a developed model embedded with a genetic algorithm (GA) optimizer. The optimizer converges to conformations with highly enhanced properties. The GA optimizer is used to improve the mechanical properties to have the soft elements in the area adjacent to the tip of the crack.

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