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

共 40628 条记录，以下是4991-5000 订阅

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Thyroid Nodule Detection and Classification Using Deep learning 1

Thyroid Nodule Detection and Classification Using Deep Learn...

引用

1st international conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Gowda, Samarth Thrishul, D. Sheth, Vaibhav B Vishwas, R. Gargi, N. Hemavathi, P. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350339727

Cells within the thyroid gland can grow abnormally, leading to the formation of radiographically identifiable lumps termed nodules. The latter is a little mass that has developed at the thyroid gland's level and necessitatesa medical examination in order to accurately diagnose the nodule's source and decide any potential treatments. Malignant nodules can be found using the Fine Needle Aspiration Biopsy (FNAB) technique. Yet, FNAB is an expensive, intrusive procedure that significantly increases patient anxiety. As a substitute, thyroid nodule ultrasound imaging is performed to classify the nodules. Even for experienced radiologists, accurately diagnosing thyroid nodules by ultrasonography is challenging due to the varied appearances of both cancerous and non-cancerous nodules. This paper suggests deep learning-based system that is capable of detecting and classifying thyroid nodules that may be able to offer radiologists unbiased assistance. © 2023 IEEE.

关键词： Convolutional neural networks

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Multimodal Data Trajectory Prediction: A Review 10

Multimodal Data Trajectory Prediction: A Review

引用

10th IEEE international conference on Cyber Security and Cloud Computing and 9th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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Diabetic Retinopathy Classification using Deep learning Techniques

Diabetic Retinopathy Classification using Deep Learning Tech...

引用

2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Surabhi, S. Prasad, G.R. B.M.S College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350313413

Diabetic retinopathy is a prevalent and debilitating eye disease, often leading to vision impairment or blindness if not diagnosed and treated promptly. In this research, we employ deep learning techniques to enhance the accuracy of diabetic retinopathy detection. Specifically, we investigate the performance of Convolutional Neural Networks (CNN) and DenseNet-201, two widely used architectures, for the multiclass classification of retinal images into five distinct diabetic retinopathy stages. Our results demonstrate a significant disparity in accuracy between the two models. While the CNN model achieves a respectable 67% accuracy, DenseNet-201 excels with an impressive 92% accuracy. The stark contrast underscores the potential of advanced deep learning architectures in addressing this critical healthcare challenge. We delve into the architectural differences and inherent features of DenseNet-201 that contribute to its superior performance. Our findings contribute to the ongoing efforts to develop efficient tools for early diagnosis and management of diabetic retinopathy, with the ultimate goal of preserving vision and improving the quality of life for affected individuals. © 2023 IEEE.

关键词： Convolutional Neural Networks Densenet201 Diabetic Retinopathy DR stages

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A Relative Analysis of Machine learning based approaches to Detect Human Pain Intensity using Facial Expressions 1

A Relative Analysis of Machine Learning based approaches to ...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Shreya Sri, A.N. Nithin, S. Vasist, Pranav Mundra, Varad Babu, Ramesh Girish, Anupama Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348057

Pain is a distressing emotional and physical experience caused by tissue damage. Detection of pain is usually through self-reporting, and is not a feasible option for a patient in a coma or with impaired communication. Another technique called "observer's report"is based on trained staff observing patients and detecting pain levels, but is subjective and error-prone. It is highly impractical to have hospital staff monitoring the patients around the clock. To solve the problem, automatic patient monitoring and alerting systems assist in continuous examination and detection of pain levels using facet reactions and improve the existing system. Pain is reliably indicated by facial expressions and is used in techniques for assessing pain by observation. The article analyzes, categorizes, and discusses methodologies in the literature produced. It provides specifics regarding the datasets, pre-processing techniques, Machine learning and Deep learning frameworks. It gives a comparative study of the same. © 2023 IEEE.

关键词： Deep learning

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RED PANDA: DISAMBIGUATING IMAGE ANOMALY DETECTION BY REMOVING NUISANCE FACTORS 11

RED PANDA: DISAMBIGUATING IMAGE ANOMALY DETECTION BY REMOVIN...

引用

11th international conference on learning Representations, ICLR 2023

作者： Cohen, Niv Kahana, Jonathan Hoshen, Yedid School of Computer Science and Engineering The Hebrew University of Jerusalem Israel

Anomaly detection methods strive to discover patterns that differ from the norm in a meaningful way. This goal is ambiguous as different human operators may find different attributes meaningful. An image differing from the norm by an attribute such as pose may be considered anomalous by some operators while others may consider the attribute irrelevant. Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute when detecting anomalies. Our approach aims to learn representations which do not contain information regarding such nuisance attributes. Anomaly scoring is performed using a density-based approach. Importantly, our approach does not require specifying the attributes where anomalies could appear, which is typically impossible in anomaly detection, but only attributes to ignore. An empirical investigation is presented verifying the effectiveness of our approach. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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Hyper-Parameter Tuning with Grid and Randomized Search Techniques for Predictive Models of Hotel Booking

Hyper-Parameter Tuning with Grid and Randomized Search Techn...

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2023 IEEE international conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Yadav, Abhay Raj Shekhar, Sushant Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Computer Science Engineering Dehradun India Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350305111

The tourism and hospitality industries rely heavily on the hotel industry, making it crucial for hotels to optimize their business strategies. Predicting hotel bookings is one strategy that can enable hotel management to make informed decisions about pricing, marketing, and resource allocation. In this paper, we explore the use of machine learning algorithms to predict hotel bookings, focusing on two types of hotels, leisure and business hotels. We use a dataset from Kaggle containing information on hotel bookings, including 32 features such as hotel type, arrival date, departure date, and booking date. We pre-process the data by converting categorical variables into numerical variables, imputing missing values, and performing feature scaling. We evaluate various machine learning algorithms on the dataset, including Gradient Boosting Classifier, Random Forest Classifier, and Logistic Regression Classifier, using accuracy as the evaluation metric. Our results show that these three algorithms perform well on the given dataset, with the Gradient Boosting Classifier achieving the highest accuracy of 86.5%, followed by Random Forest Classifier with an accuracy of 85.2%, and Logistic Regression Classifier with an accuracy of 80.7%. Our findings demonstrate the potential of machine learning techniques in predicting hotel bookings and can be useful to hotel management in optimizing their business strategies. © 2023 IEEE.

关键词： learning systems

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Enhancing Offensive Bengali Social Media Meme Detection: A Weighted Ensemble Architecture for Predicting Type and Target Classes 26

Enhancing Offensive Bengali Social Media Meme Detection: A W...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md. Maruful Roy, Nidita Sheila, Tania Sultana Ibrahim Khan, Muhammad Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chattagram4349 Bangladesh Bangladesh Army International University of Science & Technology Department of Computer Science and Engineering Cumilla3501 Bangladesh

ISBN: (纸本)9798350359015

This paper investigates the classification of offensive memes using a weighted ensemble approach of multimodal models that integrates visual and textual information. A meticulously curated dataset, Multi-modal Offensive Memes Bengali Dataset (MoMBD), containing 3662 offensive Bengali memes, is created to facilitate work in this domain. A three-level hierarchical classification system is followed to accurately categorize the fine-grained categorization of offensive memes. In this paper, BERT, as text classifier, is combined with transfer learning model, as image classifier, to create a multimodal unit. Then, a weighted ensemble is performed on the three best-performing multimodal models. The proposed weighted ensemble technique is effective in achieving high accuracy and f1-scores for both coarse-grained and fine-grained classifications. The proposed method achieves an impressive weighted f1-score of 71.84% in fine-grained classification. Overall, this research contributes valuable insights and techniques for detecting offensive Bengali memes, type and their target. © 2023 IEEE.

关键词： coarse-grained fine-grained Low-resource languages Multimodal Offensive memes Three level dataset weighted ensemble

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Development of Intraoral Clinical Image Dataset for Deep learning Caries Detection 2

Development of Intraoral Clinical Image Dataset for Deep Lea...

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2nd international conference on computer System, Information Technology, and Electrical engineering, COSITE 2023

作者： Fitria, Maya Oktiana, Maulisa Rahayu, Handika Saddami, Khairun Habibie, Hafidh Elma, Yasmina Janura, Subhan Novita, Rizki Putri, Rizkika Sari, Maulidia Indah Department of Electrical and Computer Engineering Universitas Syiah Kuala Indonesia Faculty of Dentistry Universitas Syiah Kuala Indonesia Banda Aceh Indonesia Indonesia

ISBN: (纸本)9798350343069

A substantial percentage of people around the world suffer from dental caries, which is one of the chronic oral diseases that can affect anyone at any time. Children can get dental caries, and nearly all adults will eventually experience it. Untreated dental decay may be a sign of cardiovascular problems and cognitive decline if it is left untreated. The conventional examination of caries conducted by dentists has been deemed inefficient and time-consuming. Thus, this study introduces a novel dataset of dental clinical images designed for the purpose of caries classification using deep learning techniques. The dataset consists of two classes: caries and caries-free. To assess the effectiveness of a deep learning model, its performance on the dataset needs to be evaluated. The ResNet-50 architecture is employed and trained on the dataset with three different learning rates: 10-4, 10-5, and 10-6 . The training results indicate that the model trained with a learning rate of 10-4 achieved the highest accuracy of 0.7952 and a loss value of 0.4225. However, there is a notable discrepancy between the training and validation accuracy, suggesting difficulties in the model’s ability to learn from the dataset due to its significant heterogeneity. Conversely, the model trained with a learning rate of 10-6 exhibits the most favorable learning curve. This suggests that employing a lower learning rate yields more desirable learning curves. Additionally, the testing process’s confusion matrices reveal the impact of loss values obtained during training on the testing results. © 2023 IEEE.

关键词： Classification (of information)

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UNet Segmentation based Effective Skin Lesion Detection using Deep learning 2

UNet Segmentation based Effective Skin Lesion Detection usin...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Dubey, Arun Kumar Jain, Achin Panwar, Arvind Kumar, Manish Taneja, Harsh Lamba, Puneet Singh Bharati Vidyapeeth's College of Engineering New Delhi India School of Computing Science Engineering Galgotias University India University Dehradun India Sri Guru Tegh Bahadur Khalsa College University of Delhi Department of Computer Science Delhi110007 India

ISBN: (纸本)9798350369960

Skin cancer is a common and possibly fatal condition. Effective therapy depends on early discovery and precise diagnosis. This study proposes a two-step, segmentation-and classification-based method for skin lesion analysis. The U-Net architecture, a semantic segmentation model based on deep learning, is used in the initial stage to segment skin lesions. On the test set, the suggested method achieves a promising segmentation accuracy of 94.88%. Precise segmentation helps separate the skin lesions from the surrounding environment and facilitates further classification. Using a Support Vector Machine (SVM) classifier, the segmented lesions are classified into benign and melanoma categories in the second stage. The classification results show a 78% accuracy rate, indicating that the suggested method has the capacity to differentiate between benign and malignant skin lesions. © 2023 IEEE.

关键词： Deep learning Segmentation Skin cancer UNet

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Modeling and Simulation of High Frequency and High Precision Multi Axis Cooperative Control Based on Reinforcement learning 3

Modeling and Simulation of High Frequency and High Precision...

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3rd international conference on computer, Control and Robotics, ICCCR 2023

作者： Wang, Haiyu Fu, Zhuang Yan, Jun Hua, Zuohao Guan, Yisheng School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Electromechanical Engineering Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665492126

Reinforcement learning has attracted much attention in the field of mechanical control, but it has rarely been applied in projects requiring high precision and short control cycle. In order to realize the high frequency and high precision control of multi axis cooperative complex mechanism, this paper decouples and virtualizes the control system of a structure consisting of four valves, infers commands and send control signals at 500Hz, and controls the displacement precisely. The author modified the Proximal Policy Optimization algorithm and made some improvements in the updating rules and training environment, such as batch advantage normalization, attenuation of learning rate, strategy entropy reward, speed control reward, and addition of noise. Finally, the performance of the algorithm exceeded that of PID, ray-PPO in the simulation. © 2023 IEEE.

关键词： Reinforcement learning

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