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检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

共 107018 条记录，以下是4711-4720 订阅

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Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-Guided Radiotherapy 1st

Comparative Analysis of nnUNet and MedNeXt for Head and Neck...

引用

1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Moradi, Nikoo Ferreira, Andre Puladi, Behrus Kleesiek, Jens Fatemizadeh, Emad Luijten, Gijs Alves, Victor Egger, Jan Department of Electrical Engineering Sharif University of Technology Tehran Iran University Medicine Essen Girardetstraße 2 Essen45131 Germany Center Algoritmi/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Essen Germany Partner Site Essen Essen Germany Department of Physics TU Dortmund University Dortmund Germany University Medicine Essen Essen Germany Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

ISBN: (纸本)9783031832734

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and there- fore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid- RT MRI images. We utilized the HNTS-MRG2024 dataset, which con- sists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn. We employed two state-of-the-art models in deep learning, nnUNet and MedNeXt. For Task 1, we pretrained models on pre-RT registered and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we com- bined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input for training. Our solution for Task 1 achieved 1st place in the final test phase with an aggregated Dice Similarity Coefficient of 0.8254, and our solution for Task 2 ranked 8th with a score of 0.7005. The proposed solution is publicly available at Github Repository. © The Author(s) 2025.

关键词： Image segmentation

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Review Current Practical Skills Assessment In Machining Course Based On Psychomotor Domain

Review Current Practical Skills Assessment In Machining Cour...

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international conference on teaching and learning in computing and engineering (LaTiCE)

作者： Bin Azman, Azri Shaharoun, Awaluddin Bin Mohamed Abdullah, Morina Binti Univ Teknol Malaysia Johor Baharu Johor Malaysia

ISBN: (纸本)9781479935918

The acquisition of psychomotor skills is a key competency in engineering field. In manufacturing courses, engineering workshop practices and laboratory work provide students with practical skills (hands-on activities) thus enhance knowledge prior to their future career. This paper discuss the current levels of practical skills acquired by students' in engineering workshop machining classes with reference to engineering Accreditation Council (EAC), Malaysian Qualification Agency (MQA) and (Ministry of Education) MOE standards. The findings will be apply to teaching and learning method related accordingly to the specific taxonomy for further enhancement in machining courses.

关键词： engineering workshop practices practical skills assessment psychomotor domain

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Deep learning and IoT Based Robotics to Monitor the Traffic 2nd

Deep Learning and IoT Based Robotics to Monitor the Traffic

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2nd international conference on Cyber Warfare, Security and Space computing, SpacSec 2024

作者： Priya, V. Vishwa Pande, Soumitra S. Ilyas, Md Jayasudha, R. Ramesh, Janjhyam Venkata Naga Suganthi, D. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Ericsson Global India Private Limited Bangalore India Prestige Institute of Engineering Management and Research Indore India Dr. N. G. P. Institute of Technology Coimbatore India Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur Vaddeswaram India Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai India

ISBN: (纸本)9783031734939

In densely populated areas, managing traffic laws has become more difficult due to the rising number of cars on the road. One of the most critical components of effective traffic management and improved mobility is real-time traffic monitoring systems. Consequently, precise and dependable real-time traffic information has always been essential for cars and drivers. Many other approaches have been suggested as potential answers to the issues plaguing traffic conditions recently. A different approach is VCC or vehicle cloud computing. In addition, an advanced model called an Iot-aided robotic (IoRT), which uses cameras and Internet of Things (IoT) detector nodes to gather actual traffic information, has been created. Two deep learning methods, one based on improved LeNet-5 for real-time signs for traffic identification and the other on the Inception-V3 model for detecting and identifying traffic lights, are the critical contributions provided by this research effort. Additionally, to decrease road accidents, the ideal distance between barriers and ultrasonic sensors was determined by analyzing the timing and speed of ultrasonic waves. Data acquired by cameras and sensors is analyzed using a variety of image processing algorithms before being uploaded to the cloud and made accessible to commuters and drivers via a mobile app. According to the test findings, the suggested models are far more accurate. The expanded GTSRB (EGTSRB) dataset achieved 98.89% accuracy while the German Traffic Sign Recognition Benchmark (GTSRB) dataset achieved 98.23% accuracy using the modified LeNet-5. The second model achieved a precision of 99.7 percent using data from the Laboratory for the Intelligent and Safe Automobiles (LISA). The results of this study beat previous research on comparable traffic monitoring systems by 2.13% for traffic light recognition and detection and by 4.89% for traffic sign identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG

关键词： Traffic signs

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Asthma Prediction Using Fuzzification and Machine learning Based Ensemble Classifier 3rd

Asthma Prediction Using Fuzzification and Machine Learning B...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Maniar, Himanshu K. Molia, Hardik K. Popat, Kalpesh Bhagwan Mahavir University Gujarat Surat India Government Engineering College Gujarat Rajkot India Marwadi University Gujarat Rajkot India

ISBN: (纸本)9783031862984

Asthma is a chronic respiratory condition that affects millions of individuals worldwide, requiring prompt attention and accurate prediction for necessary treatments. This research integrates fuzzification and machine learning techniques to build an ensemble classifier for asthma prediction. Fuzzification handles the uncertainty and imprecision inherently present in medical data by transforming numerical attributes into fuzzy sets using appropriate membership functions. Machine learning models, including Decision Tree, Logistic Regression, and Neural Network algorithms, are utilized to build individual classifiers. To improve classification accuracy, the bagging technique is employed to combine predictions from these classifiers into an ensemble model. The implementation is carried out using Python for fuzzification and the Orange tool for machine learning. The proposed solution demonstrates the potential of combining fuzzification with machine learning techniques to predict asthma more accurately compared to conventional approaches. Tree, Naive Bayes, Logistic Regression, kNN, SVM, Neural Network and Random Forest methods are evaluated. Additionally, we used the bagging technique to build an ensemble classifier, improving accuracy by providing output based on analyzing the outputs of individual classifiers. Compared to the initial performance analysis, accuracy improved after feature selection, as it identified the most relevant features for building the model. The accuracy was further improved after fuzzification, as it handled the possibility of uncertainty by transforming numerical features into categorical ones. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Fraud Detection Using Machine learning Models and Encryption Techniques 26th

Fraud Detection Using Machine Learning Models and Encryptio...

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26th international conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Agrawal, Jigyasa Nguyen, Hien Penn State Harrisburg MiddletownPA17036 United States

ISBN: (纸本)9783031866227

In the era of increasing digital transactions and data exchange, safeguarding financial assets and data from fraudulent activities is a critical concern. Although existing algorithms have shown success, they face challenges in achieving a commendable level of precision for encrypted data. This research investigates the integration of Fully Homomorphic Encryption (FHE) into Logistic Regression and XGBoost models for credit card fraud detection, addressing a gap in existing literature. The results indicate that FHE does not significantly compromise model accuracy, with Logistic Regression and XGBoost achieving comparable performance on encrypted and plain text data. Real-world applicability is demonstrated using the ULB and Vesta datasets. The findings contribute to the discourse on privacy-preserving machine learning and offer insights for secure credit card fraud detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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Extracting the Pneumonia Signature: A Deep learning Framework for Definitive Diagnosis 3rd

Extracting the Pneumonia Signature: A Deep Learning Framewor...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Vyas, Malay Mehta, Apurva. A. Dharmsinh Desai University Computer Engineering Nadiad387001 India

ISBN: (纸本)9783031862953

Pneumonia is a significant worldwide health and wellness concern that creates considerable health issues plus fatality, highlighting the importance of promptly and accurately detecting and treating it. Despite the improvements in imaging innovation, the hand-operated evaluation of chest radiographs by radiologists remains the fundamental technique for spotting pneumonia, bringing about hold-ups in medical diagnosis together with therapy. This research suggests a pneumonia discovery technique that utilizes deep learning strategies to automate the procedure. By harnessing an extensive data source of classified chest radiographs the recommended design intends to precisely determine locations influenced by pneumonia, making it possible for better and more efficient medical diagnosis and therapy. The design uses a custom convolutional neural network (CNN) that undergoes training on various pneumonia- positive and pneumonia-negative instances from multiple healthcare organizations. Before educating the design, various pre-processing actions were taken for the chest radiographs to boost integrity and efficiency. This research study adds to the growth of an automated, coupled with a trusted, pneumonia discovery system, which has the potential to enhance individual results and boost healthcare effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diagnosis

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The Design of A Computer-supported Collaborative learning Environment that Promotes Interaction

The Design of A Computer-supported Collaborative Learning En...

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international conference on teaching and learning in computing and engineering (LaTiCE)

作者： Shukor, Nurbiha A. Tasir, Zaidatun Zaid, Norasykin Ahmad, Norazlina Univ Teknol Malaysia Fac Educ Educ Sci Maths & Creat Multimedia Dept Skudai Malaysia

ISBN: (纸本)9781479935918

Designing an educational online learning environment requires proper planning as it will influence students' learning experiences. For the purpose of developing a computer-supported collaborative learning (CSCL) environment, the development process should be properly designed to meet the objectives of learning. In this research, the design of a CSCL environment is explained using the Three-phase Development Model (3PD) which serves as a guideline for developing a functional learning environment. This study attempts at promoting interaction in CSCL by designing interaction during the development process of the CSCL environment rather than scaffolding interaction during the online learning process.

关键词： instructional design computer-supported collaborative learning online learning interaction

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AI Cardiologist: Arrhythmia Detection by Transformer-Based Language Model 16th

AI Cardiologist: Arrhythmia Detection by Transformer-Based ...

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16th international conference on ICT Innovations, ICT Innovations 2024

作者： Gusev, Marjan Faculty of Computer Science and Engineering Sts Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9783031861611

Wearable electrocardiogram sensors have emerged on the market but lack the technology to interpret the results with high performance. This paper explores the potential for innovation in developing a heart-based language and applying natural language processing to detect and classify dangerous arrhythmia. In the AI Cardiologist project, we propose real-time heart monitoring and alerting dangerous arrhythmia using AI-based technology using RoBERTa transformers in large language models. The focus is on analyzing the domains of medical applications and introducing new business models to exploit the technology’s results. We confirmed that foundation models for ECG records created by deep learning methods could greatly enhance precision and effectiveness of heart health monitoring and detection of atrial fibrillation, enabling better healthcare and well-being. No similar solution is available on the market outside of hospital environments, giving the project results a high potential for commercialization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Supporting In-class learning with Asynchronous and Autonomous Viewing of Near Real-time Lecture Videos

Supporting In-class Learning with Asynchronous and Autonomou...

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international conference on teaching and learning in computing and engineering (LaTiCE)

作者： Koh, Jia-Ling Chu, Te-Chin Lee, Greg C. Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781479935918

One of the reasons to have in-class quizzes or exercises is to ensure that students understand the lecture and to reinforce the most important concepts. In the past, students must rely on their class notes when working on those exercises. In this study, we embarked on a practice to record and made available the in-class lectures to the students in near real-time. Students can then re-watch any part of a lecture to clarify or reinforce learned concepts asynchronously and autonomously.

关键词： in-class exercises student-centered learning teaching strategy lecture video

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Generative AI Tool in learning an Introductory Programming: A Research Perspective 3rd

Generative AI Tool in Learning an Introductory Programming: ...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Sivasakthi, M. Meenakshi, A. Department of Computer Science and Applications FSH SRM IST Vadapalani Campus Tamilnadu Chennai600 026 India

ISBN: (纸本)9783031862953

Since generative artificial intelligence applications have become more diverse, there has been a sharp rise in interest in their application across all societal segments and fields in recent years. Program learning processes are one of these domains. ChatGPT is one of the generative Artificial Intelligence instruments utilized for this. While ChatGPT has some prospective in the process of programming, its application in the process of building programs has grown significantly. The impact of teaching programming to students through ChatGPT on their computational thinking abilities was examined in this research. A study of 160 undergraduate students who took an introductory programming course called "Problem Solving using Python" was carried out. Pretest-posttest control group participation was maintained throughout the study in accordance with the experimental design. The students were divided into experiment and control groups at random. The exploratory gathering understudies gained benefits from ChatGPT during their week after week programming rehearses, while the benchmark group didn't use it. The computational thinking scale was employed to collect research data for the "Problem Solving using Python" course. Results indicated that students in the experimental group exhibited significantly higher levels of computational thinking skills compared to their peers in the control group. Because of this, it is possible to conclude that ChatGPT, a generative AI tool, is beneficial to students and helps them learn and write programming. AI assistance can be most useful while learning and writing code, according to study findings. For academics and educators in this regard, many recommendations were given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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