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

共 40847 条记录，以下是4211-4220 订阅

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Promoting Entrepreneurial Mindset and Diversity in Power engineering Class Through Case Studies 53

Promoting Entrepreneurial Mindset and Diversity in Power Eng...

引用

53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Al-Olimat, Khalid S. Ohio Northern University Department of Electrical & Computer Engineering and Computer Science AdaOH45810 United States

ISBN: (纸本)9798350336429

Utilizing student-led case studies as an innovative educational tool can effectively introduce real-world practices into the power engineering classroom. By assigning students to draft a story around a case study, they are given ownership and agency to hone their problem-solving, self-directed learning, and team skills. These case studies are grounded in real-world situations, providing students with opportunities to enhance their domain knowledge in power engineering. The use of case studies offers several advantages, including promoting active and engaged learning, encouraging critical thinking and problem-solving, enhancing the relevance and applicability of course content, facilitating teamwork and collaboration, promoting diversity, and encouraging the use of real-world examples and data. The interactive learning strategy of case studies shifts the emphasis from teacher-centered to more student-centered activities, resulting in a more engaging and immersive learning experience. Evaluation of students' perception and attitude toward this teaching method showed that students have better understanding of the course materials and an appreciation of the method for promoting diversity and entrepreneurial mindset. © 2023 IEEE.

关键词： Case studies diversity entrepreneurial mindset student perception and attitudes

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Detection of Nuclei for Breast Cancer Detection Using Histopathology Images 7

Detection of Nuclei for Breast Cancer Detection Using Histop...

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dahake, Yash S. Shinde, Swati V. Pimpri Chinchwad College of Engineering Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350304268

Breast cancer ranks as utmost prevalent form of cancer among women on a global scale, and a pathologist must spend a lot of time studying many images under various magnifications in order to provide an accurate and prompt diagnosis. Many researchers automate this procedure in order to provide a quicker and more reliable diagnosis of these malignancies. They do this by using computer vision, machine learning, and deep learning approaches. This paper is meant for the various techniques and algorithms used for the detection of cancerous nuclei. There are the variations in shape, size and appearance and texture in the nuclei of breast cancer in histopathology images, there is a need for the automated nuclei detection. And it is the difficult field in the computer aided pathology research. In this study, For the automated identification of the nuclei of breast cancer histopathology images, Mask RCNN (Region-Based Convolution Neural Network) method is used. Various methods like Feature Pyramid Networks(FPN), Fully Convolution Networks(FCN), ROIAlign and ResNet network are used by Mask RCNN. ROIAlign is used as it increases accuracy of the nuclei detection. FCN helps to predict the results in more detail. The study also reflects that Mask RCNN algorithm is more superior compared to the other nuclei detection algorithms. © 2023 IEEE.

关键词： Breast Cancer Fast RCNN Faster RCNN Histopathology Images Mask RCNN SSD (Single-Shot Detector) YOLO(You Only Look Once)

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Research on Biological Image Time Series Change Prediction Analysis Based on DeepAR Forecasting Algorithm

Research on Biological Image Time Series Change Prediction A...

引用

2023 IEEE international conference on Image Processing and computer Applications, ICIPCA 2023

作者： Yan, Yazhe The Fu Foundation School of Engineering and Applied Science Columbia University New York10027 United States

ISBN: (纸本)9798350314670

In light of the importance and challenges of biological image time series change prediction analysis in the fields of medicine and biology, this paper proposes a biological image time series change prediction analysis method based on the DeepAR Forecasting algorithm. First, according to the features of biological images, the time series data is acquired and preprocessed to obtain information such as target values, dynamic features, and static features. Secondly, to improve the prediction accuracy and stability, a deep learning model based on a recurrent neural network, the DeepAR Forecasting algorithm, is used to fit and predict the changing trend of the time series. Thirdly, an evaluation index and optimization strategy that comprehensively consider the influence of time series periodicity, seasonality, outliers, etc., are constructed based on different application scenarios to evaluate the quality of the forecast results more accurately. Finally, a biological image time series change prediction analysis system based on the DeepAR Forecasting algorithm was built. Then, simulation and experimental verification proved the effectiveness of the proposed method in this paper. © 2023 IEEE.

关键词： Time series

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Prediction of Bushfire Area Using Machine learning Techniques 7

Prediction of Bushfire Area Using Machine Learning Technique...

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Swain, Debabrata Kumar, Manish Jain, Nikhil Devnani, Chirag Pandit Deendayal Energy University Computer Science and Engineering Department Gujarat India Pandit Deendayal Energy University Electronics and Communication Engineering Department Gujarat India

ISBN: (纸本)9798350304268

Wildfires are a widespread natural disaster that affect animal and human lives around the world, and can also cause property damage. It is important for fire-prone areas to have effective measures for curbing the damage caused by fires. Predicting the spread of fires is a useful tool for this. This paper explores the use of state-of-the-art machine learning models, tools, and techniques for predicting the estimated fire area using environmental factors like weather and vegetation index as input parameters. Wildfire data of Australia from 2005 to 2020 is used in this study. The importance of different features is studied and the performance of various models is compared for different feature sets calculated using feature selection and extraction techniques. The accuracy and effectiveness of these techniques are analyzed through comparison of the results in terms of different error measures. Out of all the implemented methods, the Random Forest model has shown the most promising result with an MAE of 78.6 km2 © 2023 IEEE.

关键词： Artificial Intelligence Bushfire Climate change Environment linear regression Machine learning

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Unleashing the Potential of Reinforcement learning for Enhanced Personalized Education 53

Unleashing the Potential of Reinforcement Learning for Enhan...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Fernandes, Chelsea William Miari, Tahereh Rafatirad, Setareh Sayadi, Hossein California State University Department of Computer Engineering and Computer Science Long BeachCA United States Center for Information Systems and Technology Claremont Graduate University CA United States University of California Department of Computer Science DavisCA United States

ISBN: (纸本)9798350336429

Providing personalized content that meets the diverse needs of learners is a significant challenge for today's learning systems. Current educational platforms lack the capability to effectively support the needs of learners with varying intellectual abilities, learning pace, preferences, and academic backgrounds. To overcome this challenge, there is a pressing need for sustainable educational tools that can adapt to the individual needs of student learners. Our study proposes Reinforced-EDU, an intelligent and effective reinforcement learning-guided framework based on Q-learning technique. The framework adaptively schedules course assignments and educational activities based on students' characteristics, preferences, skills, and academic backgrounds. It considers different student-centered academic factors to prescribe a suitable learning plan that maximizes the students' overall grade and satisfaction rate while reducing dropouts. We identify four distinct learning modes tailored towards effective logical reasoning that align best with the students' preferences and characteristics. Our proposed intelligent method, Reinforced-EDU, can dynamically assign an optimal learning path with 95% accuracy based on students' preferences and pre-knowledge. © 2023 IEEE.

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Robust network based on embedded convolutional block attention module and earth mover's distance for few-shot learning 3

Robust network based on embedded convolutional block attenti...

引用

3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Zhang, Tong Xin, Min Zhang, Yinan Liao, Jiashe Xie, Qingfeng School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510668522

Deep neural networks of training require a large quantity of effectively labeled datasets, and models trained with insufficient samples will cause a variety of problems, such as overfitting and poor generalization ability. Few-shot learning has been proposed to settle the problem, and the methods introduced in some papers have achieved impressive results. However, most of the methods have poor robustness due to the under-utilization of the global information and local information of the image. For solving the above problems, an improved classification network is proposed in this paper, which introduces two modifications. On the one hand, Convolutional Block Attention Module (CBAM) is embedded in the network for extracting global features, thus the model can quickly suit various tasks. On the other hand, the image is randomly divided into tiles and represented as a set of local features, the Earth Mover's distance (EMD) is employed to calculate the distance between embedded sets of two images. Finally, the weighted sum of these two parts' classification errors, is computed as the total classification error of the proposed model. Experiments are conducted on two benchmark datasets: mini-ImageNet and tiered-ImageNet, and the results demonstrate that our proposed method achieves state-of-Theart(SOTA) results in the field of few-shot image classification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image classification

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An End-to-End Auto-Encoder Algorithm for Hardware-Impaired Transceivers Based on Meta and Joint learning

An End-to-End Auto-Encoder Algorithm for Hardware-Impaired T...

引用

2023 IEEE international Black Sea conference on Communications and Networking, BlackSeaCom 2023

作者： Elfar, Sara H. Ikki, Salama Lakehead University Faculty of Engineering Electrical and Computer Department Canada

ISBN: (纸本)9798350337822

The application of Deep learning (DL) in wireless communications has achieved remarkable success. However, there are still marked challenges impeding its use in the physical layer, largely due to the random nature of the wireless channel. This work investigates an end-to-end transceiver based on an unsupervised auto-encoder. We aim to detect a wireless signal suffering from transceiver hardware impairments as it passes through the fading channel. Furthermore, we examine the ability of this receiver to detect signals corrupted with non-Gaussian noise. We use meta and joint learning techniques to train the auto-encoder transceiver with random unknown channels and HWIs to simulate real channel conditions. We compare the results with maximum likelihood detection (MLD) in terms of bit error rate (BER). Our results demonstrate that using meta-learning to train the auto-encoder enhances system performance by reducing the BER of the received signal. © 2023 IEEE.

关键词： Fading channels

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Predictive Analysis in Education: Using Artificial Intelligence Models to Identify learning Difficulties Early 10

Predictive Analysis in Education: Using Artificial Intellige...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Ravichandran, Kathiravan Virgin, B. Anita Tiwari, Anil Javheri, S.B. Fatma, Gulnaz Lourens, Melanie Karunya Institute of Technology and Sciences Department of English Coimbatore641114 India Rnb Global University Department of Commerce and Management Bikaner India Walchand Institute of Technology Department of Civil Engineering Maharashtra Solapur India Jazan University Department of English Jazan Saudi Arabia Durban University of Technology Faculty of Management Sciences South Africa

ISBN: (纸本)9798350382471

This empirical study investigates the revolutionary possibilities of integrating AI and predictive analysis to detect learning challenges at an early stage in the ever-changing educational landscape. The study uses a variety of data sources, machine learning algorithms, as well as an iterative procedure to create predictive models. It is based on a strict technical methodology. The study highlights the detrimental effects of delayed identification on students' academic alongside socioemotional well-being, emphasizing the urgent need for early detection. The function of artificial intelligence (AI) in education is examined, highlighting its capacity to personalize learning, expedite administrative duties, and assist with language learning. Carefully considered are data privacy issues, ethical issues, and integrating predictive analysis into the classroom. A future where AI-driven predictive analysis promotes interdisciplinary collaboration, continuous improvement, as well as the integration of cutting-edge technologies is envisioned by the paper, announcing a more efficient personalized educational experience, despite obstacles like bias mitigation and infrastructure disparities. In order to fully reap the benefits of AI-driven predictive analysis in education, the conclusion highlights the significance of moral principles, teacher empowerment, as well as broad technological integration. © 2023 IEEE.

关键词： Artificial Intelligence in Education Early Detection of learning Difficulties Ethical AI Implementation Inclusive learning Environment Predictive Analysis

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A Rainfall Prediction System using Machine learning (ML) and Internet of Things (IoT) 8

A Rainfall Prediction System using Machine Learning (ML) and...

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8th international conference on Computing in engineering and Technology, ICCET 2023

作者： Paul, Parthib Basak, Santanu Khan, Angshuman Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur303807 India Department of Electronics and Communication Engineering University of Engineering and Management Rajasthan Jaipur303807 India

ISBN: (纸本)9781839539237

One of the most pressing scientific and technical issues on a global scale is the difficulty of accurately predicting rainfall. Serious effects can result from significant changes in rainfall patterns, such as crop destruction from erratic heavy rain and danger to human life from catastrophic floods. It needs more accurate and dependable rainfall forecast techniques to get over these obstacles. This work presents an Internet of Things (IoT) and machine learning (ML)-based rainfall forecast system. As a note, this model used a modest amount of computer resources to predict rain, which is one of its benefits. In order to provide a more thorough evaluation, the performance of the proposed strategy is compared with that of many well-used techniques. The comparison shows that the random forest model outperforms the other ML models, which classify rainfall events with a higher accuracy of 94.68% and a low MSE. © The Institution of engineering & Technology 2023.

关键词： Internet of things

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18th EAI international conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TRIDENTCOM 2023

18th EAI International Conference on Tools for Design, Imple...

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18th EAI international conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TRIDENTCOM 2023

ISBN: (纸本)9783031513985

The proceedings contain 9 papers. The special focus in this conference is on Tools for Design, Implementation and Verification of Emerging Information Technologies. The topics include: A Survey on Edge Intelligence for Music Composition: Principles, Applications, and Privacy Implications;AI-Driven Sentiment Analysis for Music Composition;Fault Diagnosis with BERT Bi-LSTM-assisted Knowledge Graph Aided by Attention Mechanism for Hydro-Power Plants;zero-Knowledge with Robust learning: Mitigating Backdoor Attacks in Federated learning for Enhanced Security and Privacy;PPAPAFL: A Novel Approach to Privacy Protection and Anti-poisoning Attacks in Federated learning;towards Retentive Proactive Defense Against DeepFakes;a Fast and Accurate Non-interactive Privacy-Preserving Neural Network Inference Framework.

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