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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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Unveiling the Power of Collective Intelligence: A Voting-based Approach for Dementia Classification 7

Unveiling the Power of Collective Intelligence: A Voting-bas...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Pandya, Darshanaben D. Patel, Pranav Arvindkumar Patel, Hansaben Haribhai Goswami, Akashgiri Jashvantgiri Degadwala, Sheshang Vyas, Dhairya Gujarat Visnagar India Insb Bca & Pgdca Collge Idar Gujarat India Sspc Spu Electrical Department Gujarat Visnagar India Silver Oak Univercity Silver Oak College of Computer Application Gujarat Ahmedabad India Sigma University Department of Computer Engineering Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350341485

Dementia, a debilitating neurodegenerative condition affecting millions worldwide, demands early and accurate diagnosis to facilitate timely intervention and improved patient outcomes. this study proposes an innovative approach for dementia classification that harnesses the power of collective intelligence through a voting-based methodology. Proposed method combines machine learning algorithms with the wisdom of the crowd by aggregating predictions from both experts and non-experts. By leveraging the diverse perspectives and expertise of the collective, the system achieves heightened accuracy and robustness in dementia classification. this study presents extensive experimental results demonstrating the effectiveness of this approach and showcase its potential to revolutionize medical decision-making, offering a promising avenue for advancing dementia diagnosis and personalized patient care. the findings unveil the untapped potential of collective intelligence in the healthcare domain and encourage further exploration of collaborative models in medical research and diagnostics. © 2023 IEEE.

关键词： machine learning

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Groundwater Level Prediction: A novel study on machine learning based approach with regression models for Sustainable Resource Management 11

Groundwater Level Prediction: A novel study on Machine Learn...

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11th IEEE international conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Sriram, R. Jasmeen School of Computer Application Lovely Professional University Phagwara India

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360059

—Ground water is a natural vital resource with significant implications for sustainable water management. Accurate predictions of groundwater levels is essential for informed decision-making and resource allocation. the paper presents a novel machine learning based approach for groundwater level prediction. Groundwater level prediction is a complex task due to the interplay of various environmental factors. Traditional hydrological models often struggle to capture the underlying patterns accurately. Our paper aims to develop a data-driven model that leverages historical groundwater data, meteorological information, and geospatial features to predict groundwater levels with high accuracy. We propose a multi-step approach that involves data preprocessing, feature engineering, and model development. We utilize a regression model, optimized through grid search, to capture the nonlinear relationships within the data. Our experiments on a large-scale groundwater dataset demonstrate the effectiveness of the proposed approach. the model achieves a mean squared error of 0.025 and an R-squared value of 0.85, outperforming traditional models by a significant margin. ©2023 IEEE.

关键词： Prediction models

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ChatBot Integrated with Sentiment Anlaysis: application to E-learning 4

ChatBot Integrated with Sentiment Anlaysis: Application to E...

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4th international conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： El Maazouzi, Qamar Retbi, Asmaa Bennani, Samir Mohammed v University Rime Team-Networking Modeling and E-Learning Team-Masi Laboratory Rabat Morocco

ISBN: (纸本)9798350311112

Online learning has become a popular choice for many schools and universities as well as for people with work or family obligations. However, many factors can lead the student to quit learning such as the lack of quality of the content, and the difficulty of use, the unavailability of support. these factors lead the student to become bored, demotivated, and drop out of the course. In this paper, an architecture of a chatbot integrated with sentiment analysis will be presented to determine the emotional state of the user based on the feedback collected from several e-learning platforms. In our case, a LSTM (Long Short-Term Memory) network is used with an accuracy of 91%. Integrating sentiment analysis with the chatbot in e-learning enables a more engaging and personalized learning experience for users. By understanding the emotional state of users, the chatbot can adapt its responses to provide more appropriate and effective support. In addition, by providing personalized feedback and support, the chatbot can help users identify and overcome obstacles they may encounter in their learning process. © 2023 IEEE.

关键词： Sentiment analysis

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A Recurrent Self-learning Labeler for Building Network Traffic Ground Truth 26

A Recurrent Self-learning Labeler for Building Network Traff...

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26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yang, Qingya Liu, Chang Fu, Peipei Wang, Bingxu Gou, Gaopeng Xiong, Gang University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350331684

With the increasing number of traffic category, machine learning-based methods have gradually become the mainstream way of traffic classification to support network security and management. machine learning-based methods require a large amount of high quality labelled data to learn network behavior patterns to achieve better recognition results. In the field of network traffic labeling, manual labeling can achieve more accurate labeling results, but the labeling efficiency is low and the labor cost is high. Deep packet inspection (DPI) technology can greatly improve labeling efficiency and reduce labor costs, but DPI labeling suffers from the problem of inaccurate and incomplete labeling. In this paper, we propose a recurrent self-learning framework (RSL-Labeler) for traffic labeling, which can solve the problem of inaccurate and incomplete DPI labeling. this framework consists of three components: high-quality data generation, class behavior pattern learning, and confidence filtering. Based on high-quality data labeled by multiple DPIs, we build three classifiers to learn the behavior patterns of DPI labeling intelligently from three perspectives. then, we propose the idea of confidence filtering, which combines the pseudo-labeled data and the confidence values of three learning models to filter the credible samples by combined voting. these samples are added to the self-learning model for recurrent training. Experiments show that our method is able to label application traffic with accuracy of 99%, which is at least 8% better than single DPI. © 2023 IEEE.

关键词： Wages

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Faster-YOLOv8n based lightweight road sign detection for intelligent unmanned vehicles 4

Faster-YOLOv8n based lightweight road sign detection for int...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Wu, Moda Yuan, Qingzhe Wang, Tianle Zhao, Likun Li, Bowen Han, Guohui Zhang, Fengyu Taiyuan University of Science and Technology No.66 Rangliu Road Wanbailin District Shanxi Taiyuan030024 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing lightweight road sign detection methods can hardly address the challenges of occlusion and scale difference. therefore, a Faster-YOLOv8n based lightweight road sign detection method is proposed in this paper for intelligent unmanned vehicles. Firstly, we introduce Partial Convolution (PConv) into traditional YOLOv8n. the bottleneck of the C2F module is replaced by FasterBlock, which reduces the number of parameters in the model and improves the speed. To reduce the accuracy loss due to lightweighting and scale differences in the near and far views, a Multi-scale Feature Interaction Attention (MSFIA) is proposed in the feature fusion neck network. In addition, a Spatially Reinforcement Attention (SRA) module is proposed at the detection heads of the network. SRA strengthens the response to unoccluded regions and weakens the background regions to address the occlusion problem. We conducted comprehensive comparative experiments on the TT100K dataset. the experimental results show that compared to the original YOLOv8n, the proposed Faster-YOLOv8n has a substantial improvement in both detection accuracy and inference speed. © 2025 SPIE.

关键词： Deep learning

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A review of computing offloading and caching in edge computing based on reinforcement learning 23

A review of computing offloading and caching in edge computi...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Guo, Mengke Wang, Wushuang Graduate School of Comprehensive Human Sciences University of Tsukuba Ibaraki Japan Graduate School of Science and Technology University of Tsukuba Ibaraki Japan

ISBN: (纸本)9798400700705

Edge Computing (EC) leverages edge devices to provide computational and analytical capabilities, reducing the load and response time of cloud servers and ensuring the real-time processing of tasks. However, the capacity of edge devices is limited and requires rational allocation of computational and storage resources to reduce delay, energy consumption, and cost. AS one of the research hotspots in machine learning, Reinforcement learning (RL) can optimize resource allocation strategies for EC to maximize task rewards and make edge devices more intelligent. this study comprehensively reviews the literature on offloading, caching, joint offloading and caching in EC by RL, as well as classifies and compares them. the works of the computational offloading problem are further divided into three parts according to optimization objectives: reducing delay, reducing energy consumption, and the trade-off between energy consumption and delay. EC for different application scenarios is also summarized. Lastly, this paper outlines the main directions for future research and the challenges faced in this field. CCS CONCEPTS •Computing Methodologies © 2023 ACM.

关键词： Reinforcement learning

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proceedings - 2022 4th international conference on computer Science and Technologies in Education, CSTE 2022

Proceedings - 2022 4th International Conference on Computer ...

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4th international conference on computer Science and Technologies in Education, CSTE 2022

ISBN: (纸本)9781665481885

the proceedings contain 59 papers. the topics discussed include: application of Apriori algorithm in professional elective course data mining for university;programming education in Japanese elementary schools integrated with multiple subjects: origins, practical paths and implications;the application of AES in international Chinese language smart education;COVID-19, from crisis to opportunity: assessing teaching programming languages online using statistical methods;an empirical study on the factors influencing primary school teachers' acceptance towards stem teaching;the employment of testing dojo as a collaborative learning methodology for teaching failure analysis: an experience report;impact of instructors’ content knowledge on their online intercultural English teaching instructional beliefs;and construction and application of scratch gamification instruction design framework based on growing problem-based learning.

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4th international conference on Advances in Emerging Trends and Technologies, ICAETT 2022

4th International Conference on Advances in Emerging Trends ...

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4th international conference on Advances in Emerging Trends and Technologies, ICAETT 2022

ISBN: (纸本)9783031259418

the proceedings contain 54 papers. the special focus in this conference is on Advances in Emerging Trends and Technologies. the topics include: Micro-enterprise Management Towards Scenario Building for Decision Making;analysis of Business Efficiency Considering the Influence of the Particular Events on Sales Increase Period 2016–2020;income from Ordinary Activities and Its Tax Impact on Companies in the Automotive Sector in Ecuador;proof of Concepts of Corda Blockchain Technology Applied on the Supply Chain Area;spread Virus: Usability Evaluation on a Mobile Augmented Reality Videogame;neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results;security Mechanisms and Log Correlation Systems;identification of Corn Leaves Diseases Images Using MobileNet Architecture in SmartPhones;experimental Assessment of Photovoltaic Systems Using One-Axis Tracking and Positioning Strategies in Equatorial Regions;Evaluation of the Reliability of a LiDAR Sensor through a Geometric Model in applications to Autonomous Driving;Design of Optimal Controllers Applying Reinforcement learning on an Inverted Pendulum Using Co-simulation NX/Simulink;socio-spatial Segregation Using Computational Algorithms: Case Study in Ambato, Ecuador;expert System with Facial Recognition Implemented in Human-machine Conversation Services for the Automation of Multi-platform Remote Processes in the Identification of People Reported Missing;radar Probability of Detection in Multipath Environments;Low Detection Symbol Algorithm for MIMO Systems with Big Number of Antennas;preface;Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and machine learning Techniques;deployable Networks. An Alternative for Communications in Critical Environments;application for the Study of Underwater Wireless Sensor Networks: Case Study;learning Performance Indicators a Statistical Analysis on the Subject of Natural Sciences During the COVID-19 Pandemic

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Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance 4

Research on intelligent customs declaration generation in Gu...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Han, Chengguo Wang, Bin School of Management Guangdong University of Science and Technology Guangdong Dongguan523083 China Shenzhen Taizhou Technology Co. Ltd. Guangdong Shenzhen518031 China

ISBN: (纸本)9781510671232

Due to the uniqueness of "one country, two systems" and "two customs territories" in Guangdong (GD) and Hong Kong (HK), cross-border road cargo clearance between the two regions requires the creation of several documents. these include the Road Cargo Manifest, Vehicle Declaration, Customs Declaration, HK Seamless, HK Bundle, and HK Customs Declaration, as well as additional documents such as Packing List, Contract, and more. the use of Artificial intelligence (AI) can streamline the process of generating these documents and ensure accuracy and efficiency in cross-border road cargo clearance operations. this paper focuses on four aspects: text scanning and recognition, extraction of commodity classification declaration elements, conversion of customs declaration documents, and intelligent commodity classification. these areas of research aim to address the time-consuming and labour-intensive process of document creation and verification. By leveraging AI technologies, such as optical character recognition (OCR), data extraction algorithms, and machine learning (ML), it is possible to automate and optimize the production and review of these documents, thereby reducing time and manpower costs in cross-border road cargo clearance operations between GD and HK. Based on big data analysis of customs declaration documents from both regions, AI technologies such as feature extraction and machine learning can be utilized to achieve automation in the identification of documents like road cargo manifests and customs declarations. this research applied to the China international Trade Single Window (Shenzhen) and significantly improved the efficiency and accuracy of customs declaration in GD and HK. © 2023 SPIE.

关键词： international trade

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proceedings of the 4th international conference on Neural Computing for Advanced applications, NCAA 2023

Proceedings of the 4th International Conference on Neural Co...

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proceedings of the 4th international conference on Neural Computing for Advanced applications, NCAA 2023

ISBN: (纸本)9789819958467

the proceedings contain 84 papers. the special focus in this conference is on Neural Computing for Advanced applications. the topics include: Self-Attention-Based Reconstruction for Planetary Magnetic Field;semi-supervised Multi-class Classification Methods Based on Laplacian Vector Projection;Integrating EMD, LMD and TCN Methods for COVID-19 Forecasting;LOS Guidance Law for Unmanned Surface Vehicle Path Following with Unknown Time-Varying Sideslip Compensation;application of Bat Algorithm to Reduce Power Loss in Electrical Power Systems;an Enhanced Subregion Dominance Relation for Evolutionary Many-Objective Optimization;a Stacked Autoencoder Based Meta-learning Model for Global Optimization;optimization Design of Photovoltaic Power Generation System Under Complex Lighting Conditions;analysis of the Impact of Regional Customer Charging on the Grid Under the Aggregator Model;image Reconstruction and Recognition of Optical Flow Based on Local Feature Extraction Mechanism of Visual Cortex;indicators Directed Multi-strategy Artificial Bee Colony Algorithm;Energy-Efficient Cellular Offloading Optimization for UAV-Aided Networks;artificial Bee Colony Based on Adaptive Search Strategies and Elite Selection Mechanism;minimization of Active Power Loss Using Enhanced Particle Swarm Optimization;preference Weight Vector Adjustment Strategy Based Dynamic Multiobjective Optimization;complementary Environmental Selection for Evolutionary Many-Objective Optimization;an Investigation on the Effects of Exemplars Selection on Convergence and Diversity in Large-Scale Particle Swarm Optimizer;A LSTM Assisted Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization;an Adaptive Brain Storm Optimization Based on Hierarchical learning for Community Detection;optimization Method of Multi-body Integrated Energy System Considering Air Compression Energy Storage;conditional Diffusion Model-Based Data Augmentation for Alzheimer’s Prediction;Sequential Seeding Initialization for

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