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

共 40714 条记录，以下是4611-4620 订阅

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Personalized Federated learning for Histopathological Prediction of Lung Cancer 20

Personalized Federated Learning for Histopathological Predic...

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20th international computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Ayekai, Browne Judith Wenyu, Chen Sarpong Addai, Gyarteng Emmanuel Xornam, Ativi Wisdom Mawuena, Klugah Stephanie Mawuli, Cobbinah Bernard Kulevome, Delanyo Agbley, Bless Lord Turkson, Regina Esi Kuupole, Ewald Erubaar School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Cape Coast PMB University Post Office Department of Computer Science and Information Technology Cape Coast Ghana C.K. Tedam University of Tech. and Applied Sciences Navrongo Ghana

ISBN: (纸本)9798350318982

Lung cancer is a leading contributor to cancer-related fatalities worldwide, and histopathological image analysis plays a critical role in cancer detection by identifying morphological abnormalities in tissue samples. Artificial intelligence (AI) in medicine has evolved but still faces challenges, notably in maintaining data privacy and security. Federated learning (FL) has emerged as a promising solution, enabling the training of robust models without jeopardizing data privacy. However, the effectiveness of existing FL approaches often falters in non-independent and identically distributed (non-IID) scenarios, where data distributions vary across clients. Addressing this challenge, our research presents a personalized federated learning (PFL) method specifically designed for lung cancer prediction using histopathological scans. This novel framework leverages client-specific autoencoders coupled with hierarchical clustering for personalized federated learning for lung cancer prediction. Our findings demonstrate the efficacy of the proposed method for collaborative lung cancer prediction in medical heterogeneous data environments while preserving data privacy. © 2023 IEEE.

关键词： Medical imaging

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Physique- Based Human Activity Recognition Using Deep learning Approaches and Smartphone Sensors 8

Physique- Based Human Activity Recognition Using Deep Learni...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Pravesjit, Sakkayaphop Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Information Technology University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Understanding human actions via the analysis of sensor data captured by wearable sensors is the goal of the complex subject of study known as sensor-based human activity recognition (S-HAR). Human participants' characteristics are only periodically included in deep learning (DL) approaches to S-HAR. Recognizing people was challenging for these DL methods because of the variety of physical characteristics people have. To address this challenge, we introduce a physique-based S-HAR architecture that could support deep learning networks to achieve higher identification a ccuracies a nd F1-scores. The HARSense dataset, a publicly available benchmark S-HAR dataset that compiles raw sensor data acquired from smartphones, was employed to build and evaluate five DL networks. A ccording to the experiments, the five models' detection performance improves dramatically when given access to biological data. © 2023 IEEE.

关键词： Classification (of information)

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Prediction of Possible Outcomes using Big Data Analysis and Machine learning 1

Prediction of Possible Outcomes using Big Data Analysis and ...

引用

1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Nandy, Tarak Nyundo, Angella UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur Malaysia Lovely Professional University School of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350323795

Big data analytics and Machine learning (ML) are rapidly changing the way patient health is being tracked. Since Data is generated by every individual in this world anything or everything generates data and if this data is in the right hands, then it could help the employer do wonders, data is the most powerful weapon that is underestimated but data can change the face of the world. Big Data Analytics is used and is helping Healthcare evolve. By identifying these issues, we can keep potential health issues and provide solutions before the situation worsens. ML and Big Data technologies have a promising impact on healthcare industries. Big data analytics is also used to discover new treatments and medicines. This paper's main focus is to explore and analyze recent research about machine learning and big data analytics which will help identify certain diseases in their earliest possible stage or even before that, thereby providing better health services. Furthermore, the discussion on the limitations and future scopes in healthcare with machine learning and big data analysis helps to improve the way of solving the problems in this section. © 2023 IEEE.

关键词： Data Analytics

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Research on Human Activity Recognition Based on Random Forest Classifier

Research on Human Activity Recognition Based on Random Fores...

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2023 IEEE international conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Feng, Zhiyong Shi, Yike Zhou, Duomu Mo, Liping Jishou University The School of Computer Science and Engineering Jishou416000 China Jishou University Faculty of Computer Science and Technology Computer Science and Engineering Jishou416000 China

ISBN: (纸本)9798350399028

The current machine learning algorithms classify human activities with inaccurate accuracy, poor generalization ability of the model, and poor classification effect. Proposing to use Random Forest classifier to classify the samples. The classifier has the advantage of good generalization ability, high accuracy, and the ability to handle a large number of sample data. After comparing with BP neural networks, Naive Bayesian networks, and decision trees, the random forest classifier achieved an accuracy of 98% on the test set, much higher than the 55%, 87%, and 88% of the other algorithms. In addition, we also tested the generalization ability of the model using the K-Folder cross-validation method, which yielded an average accuracy of 95.5%, also much higher than the 49.9%, 85%, and 90% of the other classifiers. The experimental results show that the random forest classifier has significantly improved in accuracy and generalization ability compared with BP neural network by 78% and 91%, respectively. Therefore, the superiority of the random forest classifier in human activity classification is proved. © 2023 IEEE.

关键词： Decision trees

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Classification Model for Easily Confused Breast Ultrasound Image Based on Shape and Texture Features 2nd

Classification Model for Easily Confused Breast Ultrasound I...

引用

2nd international conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Wang, Jingru Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9789819909223

With the development of computer vision, in addition to natural images, medical images can also carry out edge extraction, tissue segmentation, cancer location and classification diagnosis through deep learning model to improve the diagnosis efficiency. In this paper, for the easily confused and difficult to classify samples, a breast ultrasound image classification model based on shape and texture features and improved loss function is designed. A new loss function combining lesion features is proposed. The loss function realizes a wider decision boundary between different categories in the classification task. While expanding the distance between classes, the distance within classes is reduced, and distance between lesion feature vectors of samples is included in the calculation of loss function with a certain weight. On the basis of denoising the ultrasonic image and extracting the edge information by the improved Mask R-CNN neural network, the classification model is supervised by MT-LMCL loss function proposed to achieve good training and verification effect on large-scale multi-channel public data set, and evaluation indexes are improved by 6% - 13% compared with the unmodified model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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An End-to-end learning-based 3D Reconstruction System for Digital Twins 2

An End-to-end Learning-based 3D Reconstruction System for Di...

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2nd international conference on Image Processing, computer Vision and Machine learning, ICICML 2023

作者： Du, Juan Pan, Huajie South China University of Technology School of Automation Science and Engineering Guangzhou China

ISBN: (纸本)9798350331417

In recent years, the three-dimensional (3D) reconstruction of industrial products within digital twins predominantly relies on LiDAR and stereo vision technologies. However, both of these methods come with stringent environmental prerequisites. To tackle the challenges associated with the high reconstruction standards, this research introduces a general end-to-end 3D reconstruction system based on deep learning, which directly generates 3D models from videos of industrial products. Moreover, the study refines the system's performance concerning reconstruction outcomes. To mitigate the constraints held by the reconstruction environment, a learning-based image matting approach is integrated to eliminate background noise information. Additionally, the structure from motion algorithm is employed to deduce the camera's pose information. Furthermore, a multi-metric pyramid depth map aggregation strategy along with a fresh representation method are adopted to enhance the overall effectiveness of the multi-view stereo technique. The outcomes of the experiments demonstrate a substantial enhancement in the generalization ability of 3D reconstruction and also validate the utility of each individual module in the context of multi-view stereo. © 2023 IEEE.

关键词： 3D reconstruction Artificial intelligence Digital twins Multi-view stereo

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Digital Resilience: A Review of Cutting-Edge Approaches to Cyberbullying Detection in the Social Media Landscape 1

Digital Resilience: A Review of Cutting-Edge Approaches to C...

引用

2023 1st DMIHER international conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Saim, Mohammad Rizvi, Rehma Manaal Zakir Husain College of Engineering and Technology Aligarh Muslim University Department of Computer Engineering Aligarh India

ISBN: (纸本)9798350338423

Cyberbullying, characterized as a form of harmful online conduct, involves an ongoing pattern where a bully sends a series of abusive messages to a victim with the intention of causing harm over a period. Impropriety involving blackmailing, threatening, harassment, abusing, impersonating, mocking, etc. has become a widespread problem on social media platforms. To address this persistent issue, it's vital to develop a mechanism that can accurately detect and prevent cyberbullying. In this study, we present a comparison of cutting-edge machine learning and deep learning models that are applicable in detecting bullying instances in social media networks. It also examines the current landscape of advanced techniques and algorithms capable of detecting obscene language. Various neural network architectures, natural language processing techniques, ensemble methods, and hybrid systems are considered. A strong emphasis on the feature engineering process along with feature selection algorithms to select a significant feature based on human-engineered observations are also discussed. © 2023 IEEE.

关键词： Hybrid systems

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Machine learning Classification Model for Identifying Internet Addiction among University Students 2

Machine Learning Classification Model for Identifying Intern...

引用

2nd international conference on computer Technologies, ICCTech 2023

作者： Theng, Tan Hui Kit, Mun Hou Handayani, Dini Department of Mechatronic and Biomedical Engineering Faculty of Engineering and Science Universiti Tunku Abdul Rahman Bandar Sungai Long Selangor Malaysia Computer Science Department Kulliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9781665455824

In this era of globalization, Internet addiction is a concerning issue, especially among university students as they are required to use the internet for academic purposes. However, things might go wrong when they are addicted to the Internet as the Internet does not only provide knowledge but also entertainment such as music, videos, games, social media, etc. Internet addiction was exposed to the public when Young introduced Internet addiction in her study as well as an assessment for Internet addiction known as Young’s Internet addiction test (IAT) which is a questionnaire. Nonetheless, there are some issues associated with the questionnaire regarding the integrity and literacy of the participants as well as the experience of the specialist which might introduce inconsistencies in the assessment of one’s Internet addiction level. Hence, the machine learning algorithm is introduced to replace the conventional assessment method for Internet addiction. In this study, three machine learning models are developed and compared. The three models include convolutional neural network (CNN), K-nearest neighbours (KNN), and logistic regression (LR). The low Alpha power band of the EEG data is transformed into spectrograms and utilized as the input for the machine learning models. The spectrograms are presented as images and fed into the CNN model. On the other hand, as KNN and LR could not take in images as the input data, the magnitude of each frequency in every time segment of each spectrogram is computed and fed into the KNN and LR. The results show that CNN gives the best performance in terms of overall accuracy, precision, recall, and F1-score, while KNN gives the most consistent performance. © 2023 IEEE.

关键词： Convolutional neural networks

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A Review of Intrusion Detection Research Based on Deep learning 2

A Review of Intrusion Detection Research Based on Deep Learn...

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2nd international conference on Signal Processing, computer Networks and Communications, SPCNC 2023

作者： Deng, Miaolei Kan, Yupei Xu, Haihang Sun, Chuanchuan School of Information Science & Engineering Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9798400716430

In recent years, the network security situation has become critical. A key component of network security is intrusion detection. With the continuous development of deep learning technology, more and more deep learning methods are being used in intrusion detection systems. The article describes the latest research on deep learning-based network intrusion detection based on an extensive literature review. First, a brief overview of the current security landscape of networks and various intrusion detection systems is given. Next, we introduce datasets and evaluation metrics commonly used in deep learning-based intrusion detection systems. Next, deep learning models commonly used in network intrusion detection systems are summarized. Finally, seven intrusion detection models are discussed and summarized and future research directions are suggested. © 2023 ACM.

关键词： Intrusion detection

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Machine learning and Artificial Intelligence Algorithms that Minimize Power Consumption in Internet of Things Gadgets

Machine Learning and Artificial Intelligence Algorithms that...

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Berin Shalu, S. Vergin Raja Sarobin, M. VIT Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

The exponential growth of IoT gadgets has enabled previously unimaginable levels of connectedness and convenience, but it has also introduced a significant new difficulty: battery life. In this study, we investigate how artificial intelligence (AI) and machine learning may be used to cut down on power usage in Internet of Things (IoT) devices. With an emphasis on data-driven insights and fictitious data situations, we provide a systematic technique for data collecting, algorithm creation, and rigorous testing. Our results show that by combining Q-learning with supervised learning and statistical analysis, considerable gains in energy efficiency may be achieved. There is strong statistical evidence that data transmission operations may reduce their energy footprint by 25%. In addition to the technical details, we explore the practical ramifications of our study, such as increased battery life, reduced environmental impact, and reduced expenses. Insights into the revolutionary potential of machine learning and AI in transforming the IoT landscape towards a more sustainable and user-centric future are provided in this research paper, which may be used as a practical guide by industry experts, academics, and policymakers. We also provide an overview of promising future avenues for improving IoT power management and scalability, such as the use of sophisticated reinforcement learning models, the incorporation of energy harvesting, and real-time adaption. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Battery Life Extension Internet of Things (IoT) Machine learning Power Consumption Q-learning Real-world Applications Reinforcement learning Models Scalability Statistical Analysis Supervised learning

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