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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024"

共 168 条记录，以下是91-100 订阅

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8th international conference on intelligent Transport Systems, INTSYS 2024

8th International Conference on Intelligent Transport System...

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8th international conference on intelligent Transport Systems, INTSYS 2024

ISBN: (纸本)9783031863691

the proceedings contain 25 papers. the special focus in this conference is on intelligent Transport Systems. the topics include: Interoperable Traceability in Supply Chains: A Use Case in Agritech;treemob: Expressive Mobility data Representation through Tree-Based Structures;From GPS Traces to Individual Emission Exposure: A data-Driven Four-Step Process;capacity Vehicle Routing Problem with Time Windows: Simulation Tool for Footprint Network Design;chain of Portable Health Folders: A Systematic Literature Review;integrating Metro Infrastructure in Circular Food Supply Chains: A Model for Decentralized Quito’s Food Bank Network Redesign;An AutoML Approach for Bike Demand Forecasting and Redistribution;adaptive Stop-Skipping Scheduling Approach Using Reinforcement learning;machine learning Approach for Labeling Undetected Planned Trips in Public Transport Operators;reinforcement learning Algorithms with Graph Convolution Networks for Traffic Signal Control;optimizing intelligent Transportation Systems with Multi-agent Reinforcement learning: A Socio-economic Impact Assessment;a Mobile Application to Secure Pedestrians Interacting with automated Vehicles;A Simulation-Based Security Benchmarking Approach for Assessing Cooperative Driving Automation (CDA) Applications;wireless Interference and Regulatory Frameworks for Frequency Allocation in V2X Communication Systems;runtime Norms Regulation Framework for Drones’ Smart Cities Applications;a data-Driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios;federated learning for Lane-Change Prediction;Evaluating Traffic Control Strategies for Autonomous Shuttle in Different AV Penetration, Using SUMO Traffic Simulation;Adaptive Video Bitrate Allocation for Remotely Operated Vehicles (ROV);experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs Against Environmental Factors;impact of Network Delays on Edge-Assisted Platooning Systems in 5G Networks: Addressing Lat

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AutoTable: Effective and Efficient automated Feature Transformation for Tabular data 25th

AutoTable: Effective and Efficient Automated Feature Transf...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Huang, Shanshan Zhu, Junpeng Zhang, Fengyan Cai, Peng Dong, Qiwen School of Data Science and Engineering East China Normal University No. 3663 Zhongshan North Road Shanghai200062 China

ISBN: (纸本)9789819605781

Mission-critical data are commonly organized as tables within relational databases, and feature transformation from these tabular data is a pivotal component of the machine learning pipeline for business intelligence. However, automating this process poses a significant challenge, primarily due to the exponential growth in the size of the search space with an increase in the number of features (i.e., columns or dimensions of the table) and transform functions. this paper presents AutoTable, an effective and efficient feature transformation framework for tabular data using reinforcement learning. Specifically, AutoTable formulates the feature transformation problem as a search process carried out on a transformation tree, which offers a more well-structured search space and facilitates fine-grained exploration, empowering domain experts to perform AFT tasks with minimal statistical and machine learning expertise. To further improve search performance, we propose merging and lazy loading mechanisms. Experimental results demonstrate that AutoTable outperforms state-of-the-art approaches in terms of both efficiency and effectiveness. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Automatic Segmentation of Human Placenta from 3D Multimodal Ultrasound data 25

Automatic Segmentation of Human Placenta from 3D Multimodal ...

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25th international conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Singh, Sonit Stevenson, Gordon Mian, Brendan Welsh, Alec Sowmya, Arcot School of Computer Science and Engineering Unsw Sydney Australia School of Womens & Children Health Unsw Sydney NSW Australia Department of Maternal-Fetal Medicine Royal Hospital for Women RandwickNSW Australia Nepean Blue Mountains Local Health District Nepean Hospital NSW Australia

ISBN: (纸本)9798350379037

Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice. Due to its safety profile, noninvasive nature and portability, ultrasound is the primary imaging modality for fetal assessment in pregnancy. Current ultrasound processing methods are either manual or semi-automatic and are therefore laborious, time-consuming and prone to errors, and automation would go a long way in addressing these challenges. In this work, we propose auto-matic three-dimensional multi-modal ultrasound segmentation of the human placenta using deep learning combined with different fusion strategies. We collected data containing B-mode and power Doppler ultrasound scans for 400 studies. We evaluated different fusion strategies and state-of-the-art image segmentation networks for placenta segmentation based on standard overlap- and boundary-based metrics. We found that multimodal information in the form of B-mode and power Doppler scans outperform any single modality. Furthermore, we found that B-mode and power Doppler input scans fused at the data level provide the best results with a mean Dice Similarity Coefficient (DSC) of 0.849. We conclude that the proposed methodology is effective in segmenting the placenta from 3D US scans in a fully automated manner and is robust to qauality variation of the datasets. © 2024 IEEE.

关键词： Image segmentation

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automated Welding Defect Recognition through Deep learning Fusion: CNN and SVM Integration 4

Automated Welding Defect Recognition through Deep Learning F...

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4th international conference on intelligent Technologies, CONIT 2024

作者： Yadav, Vishal Banerjee, Deepak Sharma, Neha Singh, Vijay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350349900

the research is briefly summarized in the abstract, which includes metrics and important conclusions from the assessment of the CNN-SVM hybrid model for welding defect classification. the model performs exceptionally well in terms of precision, recall, and F1-score metrics. It routinely outperforms 96% in terms of all defect classes, including ideal Weld, Cracks, Porosity, Undercut, and Slag Inclusion. these measures demonstrate the model's strong capability to detect welding flaws, which is an essential component for guaranteeing structural integrity in a range of industrial settings. the model's good generalization skills are attributed to the support proportions, which show a balanced representation of fault classes within the dataset. the method was able to classify various welding flaws with an amazing overall accuracy of 96.92% by utilizing a dataset of 4,880 pictures. the model's remarkably high total accuracy of 96.93% demonstrates its dependability. Additionally, the abstract makes use of macro, weighted, and micro averages to demonstrate the proficiency of the model and provide a comprehensive understanding of its performance. A more nuanced evaluation is provided by the weighted average, which considers the support proportions, while the macro average treats all classes equally. the micro average provides a thorough assessment of the model's overall performance by combining data from all classes. this study represents the significant advancement in automated welding fault identification with a robust system that satisfies the accuracy or dependability criteria of industrial settings. the abstract provides a concise overview of the work, emphasizing the model's precision, applicability, and potential impact on enhancing welding inspection protocols across diverse industrial contexts. © 2024 IEEE.

关键词： Cracks

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Multi-Branch Instance Segmentation of Cervical Cells 25

Multi-Branch Instance Segmentation of Cervical Cells

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25th international conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Shi, Yiran Yang, Xiaona Zhou, Xuefeng Zhou, Jun Ding, Bo Soochow University School of Computer Science and Technology Soochow215031 China Zaozhuang University School of Information Science and Engineering Zaozhuang277100 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Griffith University School of Information and Communication Technology Brisbane4111 Australia

ISBN: (纸本)9798350379037

Cervical cell segmentation plays an important role in intelligent cervical cancer diagnosis. However, the performance of current methods is still limited because cervical cells are overlapping, adhesion, and even visual inseparability. In addition, deep learning-based instance segmentation methods need large amounts of labeled data, leading to significant consumption of manpower and material resources. To solve these problems, we propose a multi-branch instance segmentation network for cervical cells, named MBSeg. this method selects cervical cell images and automatically annotates them at the pixel level using active learning. Next, we leverage CondInst, a one-stage instance segmentation network, and improve it by adding a new over-lapping segmentation branch. this branch generates overlapping mask maps, which are integrated into the original mask branch to enhance attention on the receptive fields of the overlapping areas. Furthermore, we employ a Nucleus Enhanced Module (NEM) to accurately locate nuclei and a Mask-Assisted Segmentation (MAS) module to minimize interference from impurities. Experiments on our dataset MS-cellSeg, the public Cx22, and ISBI2015 datasets demonstrate the superiority of our method in segmenting overlapping cervical cells. © 2024 IEEE.

关键词： Cells

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Robust Traffic Prediction Using Probabilistic Spatio-Temporal Graph Convolutional Network 25th

Robust Traffic Prediction Using Probabilistic Spatio-Tempora...

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25th international conference on engineering Applications of Neural Networks (EANN)

作者： Karim, Atkia Akila Nower, Naushin Univ Dhaka Inst Informat Technol Dhaka 1209 Bangladesh

ISBN: (纸本)9783031624940;9783031624957

Accurate traffic forecasting is crucial for the effective functioning of intelligent transportation systems (ITS). It helps in urban traffic planning, traffic management, and traffic control. Although deep neural networks have made significant progress in traffic forecasting, their effectiveness is compromised by noise and missing data due to sensor malfunctions, communication errors, and many more. While various missing data imputation techniques exist, they often apply pre-processing before prediction which creates an extra processing step. In addition, the spatio-temporal nature of traffic makes missing data handling more challenging since data can be lost at the temporal axis or on a spatial axis(missing data at multiple sensors). thus, there is a need for a robust model that can inherently handle missing data and noise in traffic forecasting. In this paper, we proposed a Robust Probabilistic Spatio-temporal Graph Convolutional Network model that can handle noisy and missing data using the proposed probabilistic adjacency matrix and node-specific learning. Two real-world datasets with noisy and missing data are used to evaluate the performance of our proposed model. Our model surpasses baseline models in accurately forecasting traffic data, even in the presence of noise and missing data. the source code of our model is available at https://***/atkia/RPSTGCN.

关键词： Traffic forecasting Graph Convolutional network Robustness Missing data Probabilistic adjacency matrix Graph convolutional network

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TPMARL-MSS thought-Perception Multi-Agent Reinforcement learning Meeting Scheduling System 25

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Jinsong Mao, Xiaoxuan Li, Lu Wu, Hanqian Joint Graduate School Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China

ISBN: (纸本)9798350330014

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the thought-Perception Multi-Agent Reinforcement learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability. © 2023 IEEE.

关键词： Decision making

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HEADS: Hybrid Ensemble Anomaly Detection System for Internet-of-things Networks 25th

HEADS: Hybrid Ensemble Anomaly Detection System for Internet...

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25th international conference on engineering Applications of Neural Networks (EANN)

作者： Ahmad, Zeeshan Petrovski, Andrei Arifeen, Murshedul Khan, Adnan Shahid Shah, Syed Aziz Robert Gordon Univ Natl Subsea Ctr Aberdeen Scotland Univ Malaysia Sarawak Fac CS & IT Kota Samarahan Sarawak Malaysia Coventry Univ Res Ctr Intelligent Healthcare Coventry W Midlands England

ISBN: (纸本)9783031624940;9783031624957

the rapid expansion of Internet-of-things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due to the presence of sensitive nature of data exchanged within IoT networks. To address these concerns, network-based anomaly detection systems play a crucial role in ensuring the security of IoT networks through continuous network traffic monitoring. However, despite significant efforts from researchers, these detection systems still suffer from lower accuracy in detecting new anomalies and often generate high false alarms. To this end, this study proposes an efficient Hybrid Ensemble learning-based Anomaly Detection System (HEADS) to secure an IoT network from all types of anomalies. the proposed solution is based on a novel hybrid approach to improve the voting strategy for ensemble learning. the ensemble prediction is assisted by a Random Forest-based model obtained through the best F1 score for each label through dataset subset selection. the efficiency of HEADS is evaluated using the publicly available CICIoT2023 dataset. the evaluation results demonstrate an F1 score of 99.75% and a false alarm rate of 0.038%. these observations signify an average 4% improvement in the F1 score while a reduction of 0.7% in the false alarm rate comparing other anomaly detection-based strategies.

关键词： Anomaly Detection System Ensemble-based learning Gradient Boosting Machine Internet-of-things Machine learning

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based learning Approach 25

Real-Time Object Detection and Robotic Manipulation for Agri...

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25th IEEE international conference on Industrial Technology, ICIT 2024

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George Imperial College The Department Of Physics London United Kingdom University Of Sheffield The Department Of Automatic Control And Systems Engineering SheffieldS1 3JD United Kingdom University Of Manchester The Department Of Electrical And Electronic Engineering Manchester United Kingdom University Of Southampton The Department Of Electronics And Computer Science Southampton United Kingdom Fudan University The Department Of Economics Shanghai200433 China AmesIA50011 United States

ISBN: (纸本)9798350340266

the optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. this study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. the You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. the proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. © 2024 IEEE.

关键词： Crops

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Leveraging Large Language Models for automated Chinese Essay Scoring 25th

Leveraging Large Language Models for Automated Chinese Essay...

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25th international conference on Artificial Intelligence in Education (AIED)

作者： Feng, Haiyue Du, Sixuan Zhu, Gaoxia Zou, Yan Poh Boon Phua Feng, Yuhong Zhong, Haoming Shen, Zhiqi Liu, Siyuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Hohai Univ Business Sch Nanjing Jiangsu Peoples R China Webank Shenzhen Peoples R China Woodlands Secondary Sch Singapore Singapore

ISBN: (纸本)9783031643019;9783031643026

automated Essay Scoring (AES) plays a crucial role in offering immediate feedback, reducing the workload of educators in grading essays, and improving students' learning experiences. With strong generalization capabilities, large language models (LLMs) offer a new perspective in AES. While previous research has primarily focused on employing deep learning architectures and models like BERT for feature extraction and scoring, the potential of LLMs in Chinese AES remains largely unexplored. In this paper, we explored the capabilities of LLMs in the realm of Chinese AES. We investigated the effectiveness of the application of well-established LLMs in Chinese AES, e.g., the GPT-series by OpenAI and Qwen-1.8B by Alibaba Cloud. We constructed a Chinese essay dataset with carefully developed rubrics, based on which we acquired grades from human raters. then we fed in prompts to LLMs, specifically GPT-4, fine-tuned GPT-3.5 and Qwen to get grades, where different strategies were adopted for prompt generations and model fine-tuning. the comparisons between the grades provided by LLMs and human raters suggest that the strategies to generate prompts have a remarkable impact on the grade agreement between LLMs and human raters. When model fine-tuning was adopted, the consistency between LLMs' scores and human scores was further improved. Comparative experimental results demonstrate that fine-tuned GPT-3.5 and Qwen outperform BERT in QWK score. these results highlight the substantial potential of LLMs in Chinese AES and pave the way for further research in the integration of LLMs within Chinese AES, employing varied strategies for prompt generation and model fine-tuning.

关键词： automated Chinese Essay Scoring Large Language Models GPT

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