Machine Learning for Modeling and Control of Industrial Clarifier Process

Rajalakshmi, M.; Saravanan, Vadivel; Arunprasad, V; Romero, Castañeda; Khalaf, Osamah Ibrahim; Karthik, C.

doi:10.32604/iasc.2022.021696

Cited by 51 publications

(29 citation statements)

References 11 publications

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“…The authors in [ 38 ] implemented the study of various deep learning models for eye disease detection where several optimizations were performed. In [ 39 ], the authors did some benchmark experiments on it using some state-of-the-art deep neural networks. In [ 3 – 41 ], the authors used various models and algorithms for machine learning and deep learning.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Khan

Tafshir

Alam

et al. 2022

Computational Intelligence and Neuroscience

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It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Khan

Tafshir

Alam

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…In order to make the recommendation of songs more in line with the user's taste, it is necessary to extract the spectrogram of songs and the audio sequences contained in them and then classify them [26,27]. e traditional recommendation algorithm classifies songs after processing pictures and audio sequence information, and there is a technical bottleneck in integrating the classified data into the recommendation model.…”

Section: Application Of Deep Learning In Recommendation Systemmentioning

confidence: 99%

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Yang

2022

Security and Communication Networks

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With the development of information technology, various cloud music services are gradually emerging, which has fully changed and enriched people’s music life. How to propose the songs that consumers anticipate from the enormous song data is one of the key goals of the music recommendation system. This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other metrics as evaluation criteria. In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed. The music features obtained by audio processing are fully utilized, and the depth content information in music audio data is combined with other data for recommendation, which improves the tag quality and avoids the problem of low coverage. The results show that this model can extract the effective feature representation of songs in different classification criteria and achieve a good classification effect simultaneously.

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“…Comparing the predicted increase rate of the sports industry from 2016 to 2018 with the growth rate of the sports industry from 2019 to 2021, the prediction accuracy rate is obtained. According to the 2016-2018 sports industry development potential prediction index 1.72, after the linear function index conversion operation [19], it can be concluded that the predicted growth rate for 2016-2018 is 18.3%. The actual growth rate of China's sports industry from 2019 to 2021 can be calculated directly from the growth data.…”

Section: Algorithm Operation and Datamentioning

confidence: 99%

Forecast Analysis of the Overall Structure Characteristics and Development Potential of the Sports Industry Based on Wireless Communication Networks

Zhang

Jing

2022

Wireless Communications and Mobile Computing

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With the continuous development of China’s social economy, China’s sports industry has also continued to develop nowadays and has been increasingly noticed. Although the sports industry has achieved great improvement in recent years, compared with the sports industry in developed countries, the development of China’s sports industry is still in a relatively weak state. The consumption of China’s sports industry does not account for a high proportion of GDP. However, with the improvement of people’s income level and quality of life, as well as the improvement of the quality of the national group, people consume increasingly sports products. The sports market continues to expand and becomes more diversified, providing good conditions for the development of China’s sports industry. Forecasting the improvement potential of China’s sports industry has also become a hot research topic in recent years. Based on the characteristics of the structure of China’s sports industry and the status quo of the industry’s development, this paper was aimed at researching the prediction of the improvement potential of the sports industry based on radio communication networks. The article combines the time series prediction algorithm and SVM regression algorithm under the radio spectrum prediction technology to forecast and analyze the development potential of China’s sports industry. The conclusion is that the prediction accuracy rate of China’s sports industry development potential based on radio communication spectrum prediction technology is 90%, which shows that the prediction of the development potential of sports industry based on radio communication network is relatively accurate and effective.

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Machine Learning for Modeling and Control of Industrial Clarifier Process

Cited by 51 publications

References 11 publications

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Forecast Analysis of the Overall Structure Characteristics and Development Potential of the Sports Industry Based on Wireless Communication Networks

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