 Subjects -> SOCIOLOGY (Total: 553 journals)
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- Content-Based Music Recommendation Using Non-Stationary Bayesian
Reinforcement Learning-
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Abstract: This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.
Keywords: Environmental Science and Technologies; Environment & Agriculture; Sustainable Development
Citation: International Journal of Social Ecology and Sustainable Development (IJSESD), Volume: 13, Issue: 9 (2022) Pages: 0-0
PubDate: 2022-01-04T05:00:00Z
DOI: 10.4018/IJSESD.292048
Issue No: Vol. 13, No. 9 (2022)
- Content-Based Music Recommendation Using Non-Stationary Bayesian
Reinforcement Learning-
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Abstract: This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.
Keywords: Environmental Science and Technologies; Environment & Agriculture; Sustainable Development
Citation: International Journal of Social Ecology and Sustainable Development (IJSESD), Volume: 13, Issue: 9 (2022) Pages: 0-0
PubDate: 2022-01-04T05:00:00Z
DOI: 10.4018/IJSESD.292051
Issue No: Vol. 13, No. 9 (2022)
- Diagnosing Brain Tumors Using a Super Resolution Generative Adversarial
Network Model-
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Authors: Gupta; Ashray, Shukla, Shubham, Chaurasia, Sandeep
Pages: 1 - 18
Abstract: Аutоmаted deteсtiоn оf tumоrs in MRIs is inсredibly vital as it рrоvides details аbоut аnomalous tissues that are imроrtаnt fоr рlаnning further pathways of treаtment. It is an imрrасtiсаl method requiring massive аmоunt оf knоwledge. Henсe, trustworthy аnd аutоmаtiс сlаssifiсаtiоn sсhemes and рrоgrаmmes аre сruсiаl to put an end to the deаth rаte оf humаns. Sо, deteсtiоn methods аre developed that wоuld not only save the time of the radiologist but also help in асquiring а tested ассurасy. Manual detection of MRI tumor соuld be а соmрliсаted tаsk due tо the соmрlexity аnd vаriаnсe оf tumоrs. In this paper, the authors рrороse both mасhine leаrning and deep learning-based generative adversarial network (GAN) аlgоrithms tо overcome the challenges оf conventional сlаssifiers where tumоrs were deteсted in brаin MRIs using mасhine leаrning аlgоrithms only. Making use of SR-GAN increases the accuracy of the proposed method to more than 98%.
Keywords: Environmental Science and Technologies; Environment & Agriculture; Sustainable Development
Citation: International Journal of Social Ecology and Sustainable Development (IJSESD), Volume: 13, Issue: 9 (2022) Pages: 1-18
PubDate: 2022-01-04T05:00:00Z
DOI: 10.4018/IJSESD.314158
Issue No: Vol. 13, No. 9 (2022)
- Comparative Analysis of Artificial Neural Networks and Deep Neural
Networks for Detection of Dementia-
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Authors: Bansal; Deepika, Khanna, Kavita, Chhikara, Rita, Dua, Rakesh Kumar, Malhotra, Rajeev
Pages: 1 - 18
Abstract: Dementia is a neurocognitive brain disease that emerged as a worldwide health challenge. Machine learning and deep learning have been effectively applied for the detection of dementia using magnetic resonance imaging. In this work, the performance of both machine learning and deep learning frameworks along with artificial neural networks are assessed for detecting dementia and normal subjects using MRI images. The first-order and second-order hand-crafted features are used as input for machine learning and artificial neural networks. And automatic feature extraction is used in the last framework with the pre-trained networks. The outcomes show that the framework using the deep neural networks performs better contrasted with the first two methodologies used in terms of various performance measures.
Keywords: Environmental Science and Technologies; Environment & Agriculture; Sustainable Development
Citation: International Journal of Social Ecology and Sustainable Development (IJSESD), Volume: 13, Issue: 9 (2022) Pages: 1-18
PubDate: 2022-01-04T05:00:00Z
DOI: 10.4018/IJSESD.313966
Issue No: Vol. 13, No. 9 (2022)
- Comparison of Garbage Classification Frameworks Using Transfer Learning
and CNN-
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Authors: Gourisaria; Mahendra Kumar, Agrawal, Rakshit, Singh, Vinayak, Sahni, Manoj, Raja, Linesh
Pages: 1 - 23
Abstract: With the never-ending increase in the population, garbage and other waste materials have become one of the major hurdles in forming a healthy environment. The proliferation in the development of such schemes and integration of technology brings up the concept of smart waste management based on its biodegradability. These proposed models can be found useful to the smart waste development program and other likely schemes which require the classification of garbage based on their images. The experiment uncovers the reasons behind the working of VGG19 and A9 architecture CNN-based models which were found to provide the best results in accurately detecting the type of garbage. Experimental evaluation was based on 27 models including out of which A9 and VGG19 models were found to be the most efficient ones with 92.24% and 86.35% accuracy, respectively, which are further compared in detail for understanding these models better.
Keywords: Environmental Science and Technologies; Environment & Agriculture; Sustainable Development
Citation: International Journal of Social Ecology and Sustainable Development (IJSESD), Volume: 13, Issue: 9 (2022) Pages: 1-23
PubDate: 2022-01-04T05:00:00Z
DOI: 10.4018/IJSESD.313973
Issue No: Vol. 13, No. 9 (2022)
- Content-Based Music Recommendation Using Non-Stationary Bayesian
Reinforcement Learning-
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Authors: Bharadwaj; Brijgopal, Selvanambi, Ramani, Karuppiah, Marimuthu, Poonia, Ramesh Chandra
Pages: 1 - 18
Abstract: This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.
Keywords: Environmental Science and Technologies; Environment & Agriculture; Sustainable Development
Citation: International Journal of Social Ecology and Sustainable Development (IJSESD), Volume: 13, Issue: 9 (2022) Pages: 1-18
PubDate: 2022-01-04T05:00:00Z
DOI: 10.4018/IJSESD.292053
Issue No: Vol. 13, No. 9 (2022)
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