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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40757 条记录，以下是4261-4270 订阅

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Implementation of Science learning Application Two Seasons Weather Change Material Based of multimedia For Deaf Students

Implementation of Science Learning Application Two Seasons W...

引用

2023 international conference on Informatics engineering, Science and Technology, INCITEST 2023

作者： Effendi, Diana Dinasti, Sri Ela Noviansyah, Beri Cancerina, Delviola Universitas Komputer Indonesia Department of Informatics Management Bandung Indonesia Universitas Komputer Indonesia Department of Information System Bandung Indonesia Politeknik Negeri Bandung Department of Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350317862

This research is a continuation of previous research, namely the Design of Multimedia Based Two Season Weather Change Science learning Application For Deaf Students Part B for Deaf. This study will discuss how to implement programs that have been made into applications that can be used by user with prototype approach. In addition, a measurement of user satisfaction with the application was carried out. To measure user satisfaction with the application, descriptive and quantitative methods were used, using a questionnaire distributed to seven respondents, namely six students of grade VI and one science teacher at SLBN Cicendo Bandung, with seven questions. From the results of the questionnaire distribution, the satisfaction level of the respondents was 95%, which means that the user is satisfied with the multimedia application, where this application can help in the process of teaching science, especially the material of two seasons weather change. The results of this research contribute to how the learning model is more interesting for deaf students and makes it easier for teachers to explain the material compared to conventional teaching methods. © 2023 IEEE.

关键词： Students

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Multiple Disease Prediction Using ML 5

Multiple Disease Prediction Using ML

引用

5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Ray, Sameer Ali, Sajid Katiyar, Alok Galgotias University School Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

Timely precise evaluation of any health concern is critical for the successful prevention and healing process of illnesses. Therefore, the development of a medical diagnostic system based on machine learning (ML) algorithms can lead to more precise diagnoses than traditional methods. We have created a disease prediction system that uses ML to predict illnesses based on data or symptoms entered into the system, providing accurate results. This disease prediction system employs Python and machine learning programming language, with its Flask interface, and utilizes hospital database to predict diseases. By utilizing this predictive disease system using machine learning, we can improve diagnosis accuracy and support effective disease prevention and treatment. © 2023 IEEE.

关键词： Forecasting

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Harnessing CNNs for Accurate Skin Disease Identification 3

Harnessing CNNs for Accurate Skin Disease Identification

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dhoundiyal, Parveen Sharma, Vikrant Vats, Satvik Singh, Karan Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Computer and System Sciences Jnu New Delhi New Delhi India

ISBN: (纸本)9798350342338

Skin disorders are common and can be brought on by several things, including viruses, bacteria, allergies, or fungi. The speed and precision of detecting skin diseases have increased because of developments in laser and photonics-based medical technologies. The expense of such a diagnostic is still considerable, though, which restricts accessibility. An automated early-stage dermatological screening system has been developed using image processing techniques to solve this issue. Effective skin disease classification requires the extraction of key information from pictures, and many detection algorithms rely on computer vision. DenseNet169 and NASNetMobile are some of the methods that were employed in the study. Graphs were used to display the results as the researchers compared the algorithms' performance based on training accuracy and confusion matrix. Keywords: Skin Disease, Medical Diagnosis, Deep learning, Transfer learning, CNNs © 2023 IEEE.

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Discovery of Novel Superconducting Materials with Deep learning 4

Discovery of Novel Superconducting Materials with Deep Learn...

引用

4th IEEE international conference on Quantum Computing and engineering, QCE 2023

作者： Burdine, Colin Blair, E.P. Baylor University Electrical and Computer Engineering Department WacoTX United States

ISBN: (纸本)9798350343236

Superconducting devices are crucial to emerging quantum technologies, contributing to innovations in the areas of computing, metrology, and communication systems. However, many of these devices are based on conventional BCS (Bardeen-Cooper-Schrieffer) superconductors, which have operating critical temperatures (Tc) that require expensive helium-based cryogenic cooling. This has motivated the exploration of unconventional non-BCS superconductors as an alternative platform for these technologies, since some of these superconductors exhibit desirable properties such as high Tc and topologically robust states. Although unconventional superconductors have been known to exist for several decades, a comprehensive theory of superconductivity in these materials has not yet been developed, making the discovery of new superconductors a challenging endeavor. In this paper, we present ongoing work in the autonomous discovery of conventional, unconventional, and exotic superconductors with deep learning methods. Using experimental data from over 36,000 superconducting materials, we demonstrate that graph neural network models trained on raw crystalline structure can be used to discover superconductors with desirable properties such as high Tc. © 2023 IEEE.

关键词： Quantum chemistry

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A Deep Reinforcement learning Perspective on Adaptive Federated Dropout 20

A Deep Reinforcement Learning Perspective on Adaptive Federa...

引用

20th IEEE international conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Sun, Tonghui Huang, Yan Xie, Zhenzhen Pang, Junjie Chen, Xingyun Cai, Zhipeng Qingdao University College of Computer Science and Technology Qingdao266000 China Kennesaw State University College of Computing and Software Engineering AtlantaGA United States Shandong University School of Computer Science and Technology Qingdao266237 China Georgia State University Department of Computer Science AtlantaGA United States

ISBN: (纸本)9798350324334

Federated learning is a distributed learning solution that achieves high-quality machine learning models while ensuring privacy and collaboration among various end devices. However, different kinds of end devices can lead to an unstable training process due to limited and dynamic communication and computation resources. Federated Dropout is an important technique for mitigating such resource bottlenecks by randomly removing a fixed percentage of activations of model components. However, most of the existing FL dropout methods cannot effectively utilize the distinguished characteristics of different clients or fail to automatically adapt to end device heterogeneity. In this paper, we thus propose Adaptive Federated Dropout with Reinforcement learning (AFD-RL). AFD-RL employs reinforcement learning technology to adapt to the clients' unbalanced computation and communication resources and provide a personalized dropout strategy for each client. By automatically selecting suitable model components for each client in each communication round, AFD-RL achieves better accuracy with fewer communication rounds. Extensive experiments validate the effectiveness of the proposed approach in improving training efficiency and inference accuracy. © 2023 IEEE.

关键词： Reinforcement learning

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Brain Image Segmentation for Tumor Detection Using Ensemble Machine learning Techniques 14

Brain Image Segmentation for Tumor Detection Using Ensemble ...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rajamohanreddy, Nadenlla Muneeswari, G. VIT-AP University School of Computer Science & Engineering Guntur District A. P Amaravati India

ISBN: (纸本)9798350335095

Brain tumors can develop when abnormal cells in the brain grow uncontrollably, and MRI images provide valuable information about the presence of unwanted tissue growth. Numerous academic publications have utilized machine learning and deep learning algorithms to detect malignant tumors. These algorithms can swiftly identify brain tumors in MRI scans, leading to faster prediction times and improved precision, thereby facilitating easier patient treatment. Radiologists benefit from the accelerated decision-making process enabled by these predictive models. The proposed study evaluates the performance of Rectified Linear Unit (ReLU) and Sigmoid activation function-based Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) networks for the detection of brain tumors. © 2023 IEEE.

关键词： Recurrent neural networks

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A Robust Approach for Brain Tumor Detection using Transfer learning 5

A Robust Approach for Brain Tumor Detection using Transfer L...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Mahajan, Nikita Chavan, Hariram K. J. Somaiya Institute of Technology Sion University of Mumbai Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350321425

Brain tumor is a significant health concern that requires an early diagnosis for improved treatment outcomes. Artificial Intelligence (AI) can assist to reduce the need for invasive biopsies by analyzing medical imaging data to successfully detect brain tumor. Transfer learning is an effective technique in the field of deep learning, it has developed into a promising approach for improving model performance. In this study, transfer learning model VGG16, Inception V3 and EfficientNet B2 used for brain tumor detection based on their texture, location, and shape. The Kaggle (Brain Tumor Classification - MRI) dataset was utilized for training and testing the model. The outcome of the proposed model state-of-the-art methods on result demonstrates that it is more accurate and could assist doctors make better decisions. © 2023 IEEE.

关键词： Magnetic resonance imaging

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GNSS Spoofing Detection Based Correlation and distance for Vehicle Application 7

GNSS Spoofing Detection Based Correlation and Distance for V...

引用

7th IEEE international conference on Smart Internet of Things, SmartIoT 2023

作者： Yang, Yanzhao Guo, Jun Wang, Shouwei Yang, Jiesen Liu, Hong China Automotive Innovation Corporation Department of Automotive Cybersecurity Nanjing China Software Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)9798350316575

Autonomous vehicle (AV) has been a hot topic in recent years, but some safety accidents of some self-driving cars have also aroused people's concern about the safety of AV. Among all AV security threats, GNSS spoofing attack is becoming a technology that can be used artificially and has an excellent potential threat to AV security. This paper proposes a GNSS spoofing detection method based on correlation and distance. The spoofing attack simulation experiment results suggest that a delayed attack can be detected with nearly 99.2% probability in one second alarm time. © 2023 IEEE.

关键词： Autonomous vehicles

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Deep Texture Feature Aggregation on Leaf Microscopy Images for Brazilian Plant Species Recognition 24

Deep Texture Feature Aggregation on Leaf Microscopy Images f...

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9th international conference on Machine learning Technologies (ICMLT)

作者： Scabini, Leonardo Zielinski, Kallil M. Fares, Ricardo T. Konuk, Emir Miranda, Gisele Kolb, Rosana M. Ribas, Lucas C. Bruno, Odemir M. Univ Sao Paulo Sao Carlos Inst Phys Sao Carlos Brazil Sao Paulo State Univ Inst Biosci Humanities & Exact Sci Sao Jose Do Rio Preto Brazil KTH Sch Elect Engn & Comp Sci Div Computat Sci & Technol Stockholm Sweden Sci Life Lab Stockholm Sweden Sao Paulo State Univ Fac Sci & Letters Assis Brazil

ISBN: (纸本)9798400716379

In this work, we explore various computer vision techniques, with a focus on texture recognition approaches, for the task of plant species detection. We particularly emphasize the study of a challenging dataset consisting of 50 Brazilian plant species' leaf midrib cross-sections using microscope images. The research focuses on a recent method named Random Encoding of Aggregated Deep Activation Maps (RADAM) that leverages deep features from pre-trained Convolutional Neural Networks (CNNs) for improved plant species identification. This method demonstrates significant advancement over traditional texture analysis and deep learning approaches, showcasing the potential of combining deep feature engineering with texture analysis for accurate plant species recognition.

关键词： computer Vision Texture Analysis Plant Sciences Deep learning

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Utilizing Machine learning in Medical Diagnosis: Systematic Review and Empirical Analysis 24

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

引用

24th international Arab conference on Information Technology, ACIT 2023

作者： Alazaidah, Raed Hassan, Mohammad Al-Rbabah, Lara Samara, Ghassan Yusof, Marina Al-Sherideh, Ala'a Saeb Zarqa University Ai Faculty of Information Technology Department of Data Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan The Hashemite University Faculty of Engineering Department of Computer Engineering Zarqa Jordan Selanger Malaysia Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350384307

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection. © 2023 IEEE.

关键词： Classification Feature Selection Machine learning Medical Diagnosis Prediction

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