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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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5th international conference on Applied Technologies, ICAT 2023

5th International Conference on Applied Technologies, ICAT 2...

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5th international conference on Applied Technologies, ICAT 2023

ISBN: (纸本)9783031589553

the proceedings contain 40 papers. the special focus in this conference is on Applied Technologies. the topics include: Unsupervized Techniques to Identify Patterns in Gynecologic Information;web application for Early Cataract Detection Using a Deep learning Cloud Service;Analyze and Implement a Reinforced AI Chatbot in Guayaquil to Improve Mental Health in Adolescents with the Use of the Neural Generative Models;detection of Ovarian Cancer Using Improved Deep learning Model;Navigating the Chatbot Terrain: AI-Driven Conversational Interfaces;intelligent Virtual Assistant for Elevators Powered by Facial Recognition and Voice Commands;otsu Segmentation and Deep learning Models for the Detection of Melanoma;Hybrid Residual Network and XGBoost Method for the Accurate Diagnosis of Lung Cancer;generic Framework of New Era Artificial Intelligence and Its applications;Exploring the Molecular Diversity of SARS, Ebola, MERS, and SAR-COV-2 Viruses Using Genomics Virus Classification Algorithm: ViroGen;improving Dynamic Behavior of Vehicular Ad Hoc Networks by Integrating Game theory Technique;machine learning to Investigate Determinants of Intention to Purchase Organic Food;Influence Analysis of Driving Style on the Energy Consumption of an Electric Vehicle through PID Signals Study;prediction of Academic Outcomes Using machine learning Techniques: A Survey of Findings on Higher Education;identification of Factors and Teacher Profile Associated with Student Performance Using Fuzzy Techniques and Data Mining;predictive Model for Accurate Horticultural Product Pricing Using machine learning;analysis of Driving Style and Its Influence on Fuel Consumption for the City of Quito, Ecuador: A Data-Driven Study;preventing Drug Interactions in Diabetic Patients: the Role of a Mobile Conversational Agent.

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Prediction of COVID-19 Epidemiology with Chest computerized Tomography Images Using machine learning Techniques 4

Prediction of COVID-19 Epidemiology with Chest Computerized ...

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Ramaphosa, Khokhoni Innocentia Mpho Zuva, Tranos Otunniyi, Temidayo Vaal University of Technology Faculty of Applied and Computer Sciences Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

the COVID-19 pandemic placed immense pressure healthcare systems, emphasizing the need for efficient prediction methods. this research proposes a deep learning-based solution using Convolutional Neural Networks (CNNs) to classify chest CT scan images as either COVID-19 positive or negative. A dataset of 400 normal and 399 COVID-19 CT scan images was used to evaluate CNN models, including VGG19, DenseNet201, Xception, InceptionV3, and VGG16, over 25 and 50 epochs. At 25 epochs, DenseNet201 achieved the highest accuracy of 97%, followed by VGG16 at 96%, InceptionV3 at 93%, and VGG19 and Xception at 91%. At 50 epochs, accuracy improved, with VGG16 and DenseNet201 both reaching 98%, and VGG19 at 97%. Xception and Inception V3also saw improvements, reaching 96%. VGG16 consistently achieved 100% for both Precision and Recall across all epochs, while DenseNet201 maintained 100% Precision at 25 epochs but dropped to 95% at 50 epochs. Xception showed significant improvement in Recall, rising from 85% to 97%. Enhanced preprocessing, including noise reduction and contrast adjustment, further boosted model performance. A COVID-19 Prediction Web application (CPWA) was developed using the fine-tuned VGG19 model to offer a practical tool for healthcare professionals. this study demonstrates the potential of AI-driven diagnostics to streamline COVID-19 screening and improve early diagnosis, providing scalable solutions for clinical settings. © 2024 IEEE.

关键词： COVID-19

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5th international conference on Applied Technologies, ICAT 2023

5th International Conference on Applied Technologies, ICAT 2...

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5th international conference on Applied Technologies, ICAT 2023

ISBN: (纸本)9783031589522

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A Remote Sensing Image Defogging Method Based on Improved CycleGAN Network 4

A Remote Sensing Image Defogging Method Based on Improved Cy...

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Zhong, Mianqing Wang, Xue Wang, Jun Kang, Junmei Xi'an China College of Resources Environment and History Culture Xianyang Normal University Xianyang China Second Monitoring and Application Center China Earthquake Administration Xi'an China

ISBN: (纸本)9798350326444

this article presents a methodology for dehazing remote sensing images using deep learning and multi-temporal remote sensing data. the CycleGAN network has been selected as the foundation. We created a sample set of satellite images that are roughly aligned, depicting both smoggy and non-smoggy conditions, using multi-temporal remote sensing resources. In the proposed defogging algorithm, the perceptual loss of cyclic consistency is added, and the trained VGG16 network is utilized to input the original and generated images simultaneously. this allows for the features of a certain layer to be obtained and the loss to be calculated. the structure is beneficial for minimizing the reliance on paired training data, preserving image details, and enhancing clarity. the experimental results on GF1, GF2, and GF7 satellite images demonstrate that the proposed method produces high-quality, clear images. the system is designed to process remote sensing images without requiring extensive processing of complex data and tags. © 2023 IEEE.

关键词： Image enhancement

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application of machine learning algorithm in intrusion detection technology 6

Application of machine learning algorithm in intrusion detec...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Zhang, Tianyu Song, Ming Zhang, Qiang Li, Suwen China Academy of Railway Sciences Corporation Limited Standards & Metrology Research Institute Beijing China

ISBN: (纸本)9781510668546

Intrusion Detection (ID), as an effective network security protection technology, is an important means to ensure network security. In order to solve the problem that traditional intrusion detection technology is difficult to adapt to the current complex and changeable new network attacks, this paper uses the Random Forest(RF) algorithm of machine learning algorithm to solve this problem. Random forest algorithm has good feature learning and classification ability, which can effectively improve the accuracy of intrusion detection system. However, when the characteristic values of the sample data are high, its time complexity will also be high. In this paper, K-means clustering algorithm and One-Rule algorithm are introduced to improve the random forest algorithm, improve its modeling and classification speed, and further improve the detection efficiency by means of hierarchical division. In this paper, the proposed KOne-RF algorithm is verified by using NSL-KDD data set. Experimental results show that compared with the traditional RF algorithm, KOne-RF algorithm has improved modeling time, detection classification and detection accuracy, and detection ability for different types of attack behavior, which verifies the reliability of KOne-RF algorithm. © 2023 SPIE.

关键词： Intrusion detection

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proceedings - 2023 international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

Proceedings - 2023 International Conference on Industrial Io...

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4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

ISBN: (纸本)9798350341690

the proceedings contain 80 papers. the topics discussed include: assessment and evaluation model for road transport safety production supervision and inspection institutions;design and implementation of college students’ innovation and entrepreneurship experience system based on multidimensional dynamic innovation model;design and analysis of decentralized recommend system platform;automatic classification and analysis of spam based on machine learning;construction of real mesostructure model and numerical analysis of uniaxial compression for fiber reinforced permeable concrete;process design of intermediate cylinder parts of brake cylinder;and study on the joint probability and return period of extreme precipitation in Baoji area based on copula function.

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machine-learning-Based Methods for Optimizing Smartphone Performance in Fitness Activity Recognition 4

Machine-Learning-Based Methods for Optimizing Smartphone Per...

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4th international conference on computer Science and Blockchain, CCSB 2024

作者： Zhang, Xun Lin, Can Han, Xiaoyang Yuan, Junqing Yu, Zengyi Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350356502

With the expanding application areas of IoT, smart fitness has become an area of growing interest. IoT based fitness trackers are already playing an important role in the smart fitness domain. this paper presents a hierarchical Human Activity Recognition (HAR) framework based on smartphone sensors and an IoT fitness activity assistance system, aiming to build a tool to support fitness training without the requirement of a personal trainer. To begin with, we propose a novel hierarchical recognition framework and creatively introduce labels related to fitness activities. We use the SMOTE algorithm for data augmentation and evaluate the performance of six ML classifiers for fitness activity recognition with different feature subsets, and the experimental results clearly demonstrate the significant advantage of the hierarchical recognition strategy in terms of accuracy. After that we focus on the recognition performance of transitional postures. Comprehensive analyses reveal that SVM and RF models excel in recognizing Continuous Activities for real-time fitness tracking, while RF and KNN are effective for Counted Activities, ideal for transitional posture recognition. Additionally, we conduct ablation experiments with time-domain, frequency-domain, and time-frequency-domain features, which show that the model exhibits superior performance when time-frequency-domain features are used simultaneously. Finally, we delve into classifier performance in specific activities by introducing confusion matrices in order to find the most accurate classifier for a specific activity. the results highlight that specific classifier selection tailored to the activity type can significantly enhance the precision and reliability of fitness tracking systems. © 2024 IEEE.

关键词： Active learning

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FSRNet: Facial Structural-aware Representation learning for Facial Expression Recognition via Swin Transformers 4

FSRNet: Facial Structural-aware Representation Learning for ...

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4th international conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： He, Lulu Liu, Xionghua Liu, Tingting Liu, Hai He, Dingxin Wuhan Technology and Business University School of Computer Science and Automation Wuhan China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798331534622

Dynamic facial expression recognition (DFER) is an essential upstream task in the areas of human-machine interaction, driving safety, education and health aids. However, the inherent uncertainty between variations significantly impacts the effectiveness of DFER in practical applications, particularly due to the similar facial changes associated with different facial expressions. To address this challenge, we introduced a novel relationship-driven approach based on the Transformer architecture that can learn the relationships between facial expressions and facial variations. Specifically, we designed several facial variation tokens to explicitly encode the variation status of key facial regions. In addition, we developed a novel module to model the interactions between facial variation tokens and help the model better focus on the specific facial variations of facial expressions. Experimental results on two challenging benchmark DFER datasets show that our proposed FSRNet achieves state-of-the-art performance. © 2024 IEEE.

关键词： Human robot interaction

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Revolutionizing the Malaysian Used Car Market: A machine learning Approach to Transparent Pricing 4

Revolutionizing the Malaysian Used Car Market: A Machine Lea...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Luo, Scott Lai Yong Kanaan, Abdulkarim M. Jamal Ayyasamy, Ramesh Kumar Meng, Goh Chuan Ooi, Boon-Yaik Tyng, Chai Meei Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350318432

In Malaysia, the used car market frequently lacks transparency and fair pricing, making it difficult for buyers and sellers to make wise decisions. In-depth research on the creation of a machine learning-based pricing prediction model created exclusively for the Malaysian used car market is presented in this paper. the main goal of this project is to develop a reliable and open model that predicts used car pricing based on essential variables including age, condition, location, and the availability of comparable models on the market. Numerous techniques, like the neural network and regression model, are used to accomplish this purpose. the scope of the project also encompasses the identification of challenges and limitations in the current used car market in Malaysia, along with proposed solutions to improve transparency and fairness for all stakeholders. the methodology involves data collection, preprocessing, feature selection, model training, and evaluation. the results demonstrate that the developed model provides precise and transparent pricing information, empowering buyers and sellers to make informed decisions regarding their transactions. this project holds significant potential for enhancing transparency and fairness in the Malaysian used car market, while also serving as a valuable reference for similar initiatives in other countries and markets. © 2023 IEEE.

关键词： Transparency

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the application of Virtual Reality in Enhancing Medical Education: Benefits, Challenges, and Outlook for the Future 10th

The Application of Virtual Reality in Enhancing Medical Ed...

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10th EAI international conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI international conference on Wearables in Healthcare, HealthWear 2023

作者： Barro, Seydou Golo Nikiema, Delwêndé Serge Lebian Wilfried Kantagba, Yves Staccini, Pascal Université Nazi BONI de Bobo Dioulasso Bobo Dioulasso Burkina Faso Bobo Dioulasso Burkina Faso RETINES - Université Côte d’Azur Nice France Koudougou Burkina Faso

ISBN: (纸本)9783031719103

this article discusses the revolution of medical education through immersive learning experiences in Virtual Reality (VR). It highlights the advantages, challenges, and future advancements of VR in medical education. the benefits of VR include immersive and interactive scenarios that help students understand complex medical concepts and empower personalized and self-directed learning. However, implementing VR in medical education is difficult due to technical challenges and potential physiological side effects. Despite these challenges, the growth of the VR industry offers hope for transforming medical education by closing gaps, increasing accessibility, and encouraging collaboration. the article emphasizes the need for collaboration among educators, researchers, and institutions to fully harness the transformative potential of VR in medical education. It concludes by stating that VR not only transforms medical education technologically but also changes how medical knowledge is acquired, internalized, and applied, creating a dynamic and immersive learning experience for future healthcare professionals. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Virtual environments

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