FDSMO: Frequent DNA Sequence Mining Using FBSB and Optimization

Lakshmanna, Kuruva; Khare, Neelu

doi:10.22266/ijies2016.1231.17

Cited by 21 publications

(15 citation statements)

References 26 publications

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“…The system uses 3D animated simulations that allow the learner to view from the front, side, back, and 360 degrees, so that movements that are difficult to understand and master can be learned effectively and repeatedly. There is no need to spend a lot of energy and time listening to lectures, breaking the traditional learning model, without any space or time constraints [29][30][31][32].…”

Section: Discussionmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

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Nowadays, people are paying greater attention to the concept of childcare, and the healthy development of children is humanity’s hope for the future. The newborn stage is the most critical stage of a child’s development, and learning and mastering neonatal care is the first important lesson for parents. To meet the needs of today’s learners and to break the traditional teaching model, this paper develops a set of interactive newborn nursing knowledge system that composes the content of trivial nursing knowledge, reconstructs the knowledge structure system, and provides new teaching methods such as text browsing, 3D animation presentation, picture presentation, video presentation, interactive games, and tests for newborns. Text browsing, 3D animation, image demonstration, video demonstration, and interactive games and tests, among other new teaching methods, give a new teaching mode and method for neonatal nursing education. The study included 158 infants from local hospital. The study was divided into two groups, each with 80 cases: a control group (conventional care) and a study group (quality care management). The outcomes were contrasted and compared. The results revealed that the study group had a lower incidence of nursing defects than the control group, and the difference was statistically significant ( P 0.05 ); the study group had a lower incidence of diseases than the control group, and the difference was statistically significant ( P < 0.05 ). The study goes over the complete process of incorporating the instructional display into the interactive newborn care knowledge system. Multimedia techniques and applications were researched throughout the design and production process in order to blend intrinsic knowledge material with digital technology to produce new educational ideas.

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Section: Discussionmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

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“…Some of the issues raised include the need for end-to-end deep learning models, improved run time, lower computing costs, and flexibility. The authors proposed contemporary technologies like edge computing, fog computing and cloud computing [26][27][28], federated learning, the GAN method, and IoT [7] as problem-solving technologies. As we have seen, there are a variety of approaches utilized in medical imaging, particularly on MRI pictures of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Shelatkar

Garg

Shorfuzzaman

et al. 2022

Computational and Mathematical Methods in Medicine

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Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can diagnose brain tumors by employing magnetic resonance imaging (MRI) as a supporting technology. We propose transfer learning approaches for a deep learning model to detect malignant tumors, such as glioblastoma, using MRI scans in this study. This paper presents a deep learning-based approach for brain tumor identification and classification using the state-of-the-art object detection framework YOLO (You Only Look Once). The YOLOv5 is a novel object detection deep learning technique that requires limited computational architecture than its competing models. The study used the Brats 2021 dataset from the RSNA-MICCAI brain tumor radio genomic classification. The dataset has images annotated from RSNA-MICCAI brain tumor radio genomic competition dataset using the make sense an AI online tool for labeling dataset. The preprocessed data is then divided into testing and training for the model. The YOLOv5 model provides a precision of 88 percent. Finally, our model is tested across the whole dataset, and it is concluded that it is able to detect brain tumors successfully.

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“…Among the supervised algorithms, methods based on image processing [ 7 ] and optimization [ 8 ] are used to process retinal images. The optimization algorithm proposed in the literature [ 9 – 11 ] is a good reference idea in the direction of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation of Retinal Blood Vessels Based on Dual-Attention Multiscale Feature Fusion

Gao

Huang

Gao

et al. 2022

Computational and Mathematical Methods in Medicine

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Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods.

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FDSMO: Frequent DNA Sequence Mining Using FBSB and Optimization

Cited by 21 publications

References 26 publications

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Image Segmentation of Retinal Blood Vessels Based on Dual-Attention Multiscale Feature Fusion

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