Detection of chest pathologies using autocorrelation functions

Abdikerimova, Gulzira; Shekerbek, Ainur; Tulenbayev, Murat; Beglerova, Svetlana; Zakharevich, Elena; Bekmagambetova, Gulmira; Manbetova, Zhanat; Baibulova, Makbal

doi:10.11591/ijece.v13i4.pp4526-4534

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…The rational and effective use of VR requires a significant change in curricula and training programs at the national level, and at this stage, such implementation is taking place at the level of experiments by interested scientists or educational institutions [23], [35], [36], [37]. It is important to study the requirements for the need to expand the infrastructure for the effective use of drones and virtual reality in the educational process in various fields of study, as the use of technology can vary dramatically depending on the specialty studied by students [38], [39], [40]. Further discussions and research are needed on the duration of the use of VR and drones in student education to prevent possible misuse of technology and ensure sufficient efficiency of technology use.…”

Section: Discussionmentioning

confidence: 99%

From traditional to digital: The impact of drones and virtual reality technologies on educational models in the post-epidemic era

Lu,

Dawod,

Ying

2023

Sustainable Engineering and Innovation

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Employment of various innovative tools for education in the post-epidemic era is an urgent issue of improving the quality and efficiency of education and the relevance of educational material. The study aims to investigate the impact of the use of drones and virtual reality in educational models of student learning in the post-pandemic period. The study was conducted using a quantitative method, a sociological survey, a pedagogical experiment, and mathematical processing of the results. The study involved fourth-year students (n=748) studying in the following specialties: geography and ecology (n=119), engineering, robotics (n=135), architecture and urban planning (n=120), agriculture, agronomy (n=118), information technology (IT) (n=139), journalism (n=117), and teachers (n=258) with more than 5 years of experience. For the educational process of students of each specialty, the scope of application of drones and virtual reality has been developed. Classes with the use of drones and virtual reality were held two times a week for one academic semester. Afterward, students were interviewed using an adapted questionnaire to study their attitudes toward virtual reality technologies and drones for learning. Students of all specialties positively evaluated various aspects of the use of drones and virtual reality in their educational process. The use of drones helps to improve student engagement and visual attention and has a positive impact on learning efficiency and student satisfaction with the learning process. Teachers approve of the use of drones and virtual reality in teaching but point out that further development of the methodology for training stakeholders in the educational process, infrastructure, improvement of curricula, and consideration of various aspects of technology use is needed to effectively implement drones and virtual reality in the learning process. The practical significance of this study is to present aspects of the use of drones in teaching students of certain specialties and to study the impact of the effectiveness of the use of these technologies on the opinions of students and teachers.

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Section: Discussionmentioning

confidence: 99%

From traditional to digital: The impact of drones and virtual reality technologies on educational models in the post-epidemic era

Lu,

Dawod,

Ying

2023

Sustainable Engineering and Innovation

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“…She divides them into subclasses: road images with raindrops, snowfall, and fog. After classification into road image subclasses, the AttentiveGAN submodel [19] is passed through, which detects and cleans images of noise such as snow marks, raindrops, and fog. Image classification [20] Other medical and space imaging classes go through the super-resolution generative adversarial network (SRGAN) submodel [21]- [23].…”

Section: Methodsmentioning

confidence: 99%

Noisy image enhancements using deep learning techniques

Daurenbekov,

Aitimova,

Dauitbayeva

et al. 2024

IJECE

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This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.

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“…This could entail locating additional risk variables that could be incorporated into the models, enhancing the readability of current models, or creating brand-new machine learning techniques that are especially intended to be more understandable. − Develop and test new models: the next step would be to create and test fresh models for predicting the risk of developing lung cancer after potential areas for improvement have been found [37], [38]. This might entail experimenting with new techniques for interpreting and visualising the findings, incorporating new data sources, or using fresh machine learning algorithms.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Predicting lung cancer risk using explainable artificial intelligence

Shoukat Makubhai,

Pathak,

Chandre

2024

Bulletin EEI

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Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.

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Detection of chest pathologies using autocorrelation functions

Cited by 7 publications

References 22 publications

From traditional to digital: The impact of drones and virtual reality technologies on educational models in the post-epidemic era

From traditional to digital: The impact of drones and virtual reality technologies on educational models in the post-epidemic era

Noisy image enhancements using deep learning techniques

Predicting lung cancer risk using explainable artificial intelligence

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