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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jing Guo, Lijun Ni, Lixin He, Xiuchao Wang, Jianhua Zhang, Rong Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications. © 2023 IEEE.

关键词： Global Features Hip joint Hip Morphology-Aware Loss Landmark Detection Local Features

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A Method for Assigning Weight to the Assessment Indicators of Computational Thinking with XGBoost 5

A Method for Assigning Weight to the Assessment Indicators o...

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5th International Workshop on Artificial Intelligence and Education, WAIE 2023

作者： Zeng, Xinyao Zhang, Wei Chen, Yi Chen, Hao Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China School of Computer Science Central China Normal University Wuhan China School of Information Technology Application Innovation Wuhan Polytechnic Wuhan China

ISBN: (纸本)9798350307061

Computational thinking (CT) assessment indicator assignment is an important strategy for optimizing assessment and an effective method for exploring differences in the importance of assessment indicators. However, the objectivity of the previous methods for assigning assessment indicators of CT is insufficient. Therefore, the paper proposed to use the XGBoost machine learning model to fit the assessment data, fully explore the data features and realize the weighting of CT assessment indicators. The CT assessment indicators are used as the features of XGBoost model training and the assessment levels are used as the training labels to obtain the feature weights through XGBoost model training. The feature weights are used as the CT assessment indicator weights to realize the weighting of assessment indicators of CT in reverse. From the analysis of the CT assessment indicator assignment results, the analysis of the importance of the assessment indicators and the comparative analysis of the assessment results before and after the indicator assignment, it is found that the CT assessment after the indicator assignment has higher robustness and credibility. © 2023 IEEE.

关键词： Assessment indicators Computational thinking Indicator weight XGBoost

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A Dual-View Fusion Network for Automatic Spinal Keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Dandan Guo, Lijun Zhang, Rong He, Xiuchao Wang, Qiang Wang, Jianhua Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

关键词： biplane X-ray images dual-view feature fusion spinal keypoint detection

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Identifying BeeBest E-application: An Analysis of User Feedback 11

Identifying BeeBest E-Application: An Analysis of User Feedb...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Sasmoko Indrianti, Yasinta Manalu, Sonya Rapinta Kurniawan, Lukas Tanto Abu Bakar, Abu Yazid Hermanus, Davy Ronald Research Interest Group in Educational Technology Bina Nusantara University Faculty of Humanities Primary Teacher Education Department Jakarta Indonesia Universitas Agung Podomoro Research Interest Group in Educational Technology Entrepreneurship Department Jakarta Indonesia School of Computer Science Research Interest Group in Educational Technology Bina Nusantara University Mobile application & Technology Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Universiti Kebangsaan Malaysia Faculty of Education Malaysia Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Bandung Indonesia

ISBN: (纸本)9798350389654

Smart education has an increasingly important role along with the development of knowledge and technology. The BeeBest application is specifically designed to make it easier for respondents to conduct self-assessments through innovative features that can improve the respondent's personal experience. User feedback analysis is an important process in the development and refinement of the BeeBest application. User feedback can provide important insights according to user needs and expectations. The research method used is the Neuroresearch Method, which consists of three stages: Exploratory, Explanatory, and Confirmatory Research. The results of the study show that User Satisfaction and Content Quality have the most significant influence on the performance of the BeeBest application, with a very high T Statistic value and a significant P Value. Personalization and Usability have the lowest influence on user feedback on the BeeBest application. © 2024 IEEE.

关键词： BeeBest application Neuroresearch Self Assessment Services

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ARF-NET:Method of spinal segmentation in deformed spine radiographs 11

ARF-NET:Method of spinal segmentation in deformed spine radi...

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11th International Conference on Information Systems and Computing technology, ISCTech 2023

作者： Lin, XiaTian Liu, Yingli Shen, Tao Gao, Ming Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China The First People's Hospital of Yunnan Province Kunming650500 China

ISBN: (纸本)9798350342406

Deformed spine radiograph spine segmentation can help doctors to analyze the disease site faster, which is a necessary part of the current development of competent healthcare. For scoliosis diagnosis, this paper designs an Attention-guided ResNet with an FPN-like Upsampling model(ARF-Net) for segmenting the cone part of the spine radiograph map. In this paper, ResNet50 is used as the backbone to propose the feature map. Then the Global-Coordinate Attention (GCA) attention mechanism is added to obtain the X-axis, Y-axis, and global features. Finally, through the SIMAMUP module, the feature map is upsampled to introduce more detailed information to prevent cone segmentation errors and to improve the cone segmentation capability. The final experimental findings demonstrate that the model described in this study has greater segmentation precision in the SpineWebl6 public dataset, with the Jaccard and Dice coefficients reaching 87.70% and 93.45%, respectively. © 2023 IEEE.

关键词： Image segmentation

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Intrusion Detection Based on Deep Belief Network and Stacking Method 3

Intrusion Detection Based on Deep Belief Network and Stackin...

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3rd International Conference on computer Communication and Network Security, CCNS 2022

作者： Wang, Jia Bao Miao, Xiang Hua Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510660113

After data preprocessing, the feature dimension of NSL-KDD dataset increases from 42 dimensions to 122 dimensions. High dimensional data will make it more difficult for the model to learn the characteristics of the data, and there will be a lot of redundant data in the data set. Therefore, this paper uses the deep belief network to reduce the dimension of the characteristics of the intrusion detection data set after data preprocessing, and uses the stacking algorithm as the classifier to construct the intrusion detection model. Through comparative experiments, it is proved that the model has good performance in the four evaluation indexes of accuracy, precision, recall and F1 score, and effectively improves the performance of intrusion detection model. © 2022 SPIE.

关键词： Intrusion detection

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Security Test Case Prioritization through Ant Colony Optimization Algorithm

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computer Systems Science & Engineering 2023年第12期47卷 3165-3195页

作者： Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Mohd Waris Khan Mohd Faizan Alka Agrawal Dhirendra Pandey Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Computer Application Integral UniversityLucknowUttar Pradesh226026India Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknowUttar Pradesh226025India

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software *** of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test *** effectively applying test case prioritization,both the time and cost required for developing secure software can be *** paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic *** performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional *** has been applied to a Mobile Payment Wallet application to validate the proposed *** results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD *** ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering *** findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

关键词： Confidentiality integrity authentication non-repudiation resilience authorization Ant Colony Optimization algorithm

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Self-Assessment for Personal Growth: Evaluating the Effectiveness of the BeeBest Mobile application 11

Self-Assessment for Personal Growth: Evaluating the Effectiv...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Indrianti, Yasinta Sasmoko Manalu, Sonya Rapinta Kurniawan, Lukas Tanto Abu Bakar, Abu Yazid Hermanus, Davy Ronald Universitas Agung Podomoro Research Interest Group in Educational Technology Entrepreneurship Department Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Faculty of Humanities Primary Teacher Education Department Jakarta Indonesia School of Computer Science Research Interest Group in Educational Technology Bina Nusantara University Mobile application & Technology Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Universiti Kebangsaan Malaysia Faculty of Education Malaysia Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Bandung Indonesia

ISBN: (纸本)9798350389654

Self-assessment in personal growth is an important element in evaluating the effectiveness of mobile applications such as BeeBest, which are designed to support the concept of smart education by helping students with time management and self-development. This study evaluates the relationship between multiple intelligences, entrepreneurial awareness, and students' academic performance through the use of the BeeBest application. Using the SEM-PLS method, the results show that Intrapersonal Intelligence (coefficient 0.55) and Logic Intelligence (coefficient 0.45) have a significant influence on academic achievement. Entrepreneurial Mindfulness also shows a positive contribution (coefficient 0.35), reflecting the importance of focus, creativity, and stress resilience in supporting academic success. This model explains 60% of the variability in academic performance, reinforcing the importance of a personalized approach in smart education. The BeeBest application is proven to be effective in supporting self-management and academic growth through features that monitor student potential and provide learning recommendations based on intelligence and entrepreneurial profiles. © 2024 IEEE.

关键词： BeeBest application Entrepreneurial Mindfulness P4AI Self Assessment Personal Growth

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Utilizing Machine Learning to Develop an Improved Real-Time Data Analysis System 3

Utilizing Machine Learning to Develop an Improved Real-Time ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khandari, Harsimrat Sahoo, Girija Shankar Raja Praveen, K.N. Shree, Ritu Umamaheswari, B. Yashwant, V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records © 2023 IEEE.

关键词： Statistics

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Improving the Quality of P4AI Mobile Apps through User Interface Results and User Experience Surveys 11

Improving the Quality of P4AI Mobile Apps through User Inter...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Indrianti, Yasinta Sasmoko Manalu, Sonya Rapinta Kurniawan, Lukas Agung Podomoro University Research Interest Group in Education Technology Entrepreneurship Department Jakarta Indonesia Bina Nusantara University Faculty of Humanities Research Interest Group in Education Technology Primary Teacher Education Department Jakarta Indonesia Bina Nusantara University Mobile Application & Technology School of Computer Science Research Interest Group in Education Technology Jakarta Indonesia Bina Nusantara University Computer Science School of Computer Science Research Interest Group in Education Technology Jakarta Indonesia

ISBN: (纸本)9798350305968

The Profiling For Aptitude Inventory (P4AI) application is an aptitude test developed to determine the capacity of students' interests and talents. The aptitude profile will help students in setting goals and chosen academic fields so that they can support and predict student success in the future. Profiling For Aptitude Inventory (P4AI) application was created using Flutter framework as the frontend and Flask framework as the backend. This research aims to analyze the results of the user interface and user experience to improve application quality. Based on the analysis results of the UI/UX evaluation survey of the P4AI application, it can be concluded that users tend to agree on the attractiveness of the appearance of the P4AI application. However, there are several indicators and items that need to be improved and developed to increase user satisfaction with the appearance and flow of the application. Therefore, changes have been made to the application to improve the quality of application use. © 2023 IEEE.

关键词： User interfaces

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