Enhanced Vaccine Recommender System to prevent COVID-19 based on Clustering and Classification

Adday, Bahaulddin Nabhan; Shaban, Faris Ali Jasim; Jawad, Mohammed Rasool; Jaleel, Refed Adnan; Zahra, Musadaq Mahir Abdel

doi:10.1109/iceet53442.2021.9659629

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…In the COVID-19 era, recommender systems also show their viability of facilitating human beings in overcoming the challenge. Research [16] proposed a method for medical staff recommendation under the scenario of limited resources, and research [17] provides a method for recommending vaccines.…”

Section: Recommender Systemsmentioning

confidence: 99%

MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork Recommendation

Wang,

Maeda,

Kawagoe

2024

Electronics

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Recommender systems help users obtain the content they need from massive amounts of information. Artwork recommender systems is a topic that has attracted attention. However, existing art recommender systems rarely consider user preferences and multimodal information at the same time, while utilizing all the information has the potential to help make better personalized recommendations. To better apply recommender systems to the artwork-recommendation scenario, we propose a new neural topic modeling (NTM)-based multimodal artwork recommender system (MultArtRec), that can take all the information into account at the same time and extract effective features representing user preferences from multimodal content. Also, to improve MultArtRec’s performance on monomodal feature extraction, we add a novel topic loss term to the conventional NTM loss. The first two experiments in this study compare the performances of different models with different monomodal inputs. The results show that MultArtRec can improve the performance with image modality inputs by up to 174.8% compared to the second-best model and improve the performance with text modality inputs by up to 10.7% compared to the second-best model. The third experiment is conducted to compare the performance of MultArtRec with monomodal inputs and multimodal inputs. The results show that the performance of MultArtRec with multimodal inputs can be improved by up to 15.9% compared to monomodal inputs. The last experiment preliminarily tests the versatility of MultArtRec on a fashion recommendation scenario that considers clothing image content and user preferences. The results show that MultArtRec outperforms the other methods across all the metrics.

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Section: Recommender Systemsmentioning

confidence: 99%

MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork Recommendation

Wang,

Maeda,

Kawagoe

2024

Electronics

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“…A variety of methods, including DM, decision trees, rule depend mining, neural networks, clustering of fuzzy, and ML, will be used by banks and credit card firms during COVID-19 in an effort to catch fraudsters red-handed. Based on previous activity, the technique attempts to determine a customer's regular usage pattern ( Ashtiani and Raahemi, 2021 , Khan et al, 2021 , Adday et al, 2021 ). The purpose of this research is to suggest a mechanism for detecting such fraud transactions in such an uncontrolled pandemic situation.…”

Section: Introductionmentioning

confidence: 99%

Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

Al-Ghamdi

et al. 2023

Information Processing & Management

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“…Different modalities of medical imaging are utilized to aid in the detection and analysis of disorders in the human body in the modern global environment. 2 The number of patients treated each year is expected to increase, thereby increasing the amount of medical data collected. Furthermore, a medical image is larger than a normal image; thus, considerable data storage capabilities are necessary for medical imaging.…”

Section: Introductionmentioning

confidence: 99%

“…The computed tomography (CT) scan has been proven to be effective in detecting COVID-19. Different modalities of medical imaging are utilized to aid in the detection and analysis of disorders in the human body in the modern global environment 2 …”

Section: Introductionmentioning

confidence: 99%

(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Abbas

2022

J. Electron. Imag.

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Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space.

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Enhanced Vaccine Recommender System to prevent COVID-19 based on Clustering and Classification

Cited by 10 publications

References 22 publications

MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork Recommendation

MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork Recommendation

Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

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