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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7123 条记录，以下是161-170 订阅

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Can Cooperative Multi-Agent Reinforcement learning Boost Automatic Web Testing? An Exploratory Study 24

Can Cooperative Multi-Agent Reinforcement Learning Boost Aut...

引用

39th ACM/IEEE international conference on automated Software engineering (ASE)

作者： Fan, Yujia Wang, Sinan Fei, Zebang Qin, Yao Li, Huaxuan Liu, Yepang Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Reinforcement learning (RL)-based web GUI testing techniques have attracted significant attention in both academia and industry due to their ability to facilitate automatic and intelligent exploration of websites under test. Yet, the existing approaches that leverage a single RL agent often struggle to comprehensively explore the vast state space of large-scale websites with complex structures and dynamic content. Observing this phenomenon and recognizing the benefit of multiple agents, we explore the use of Multi-Agent RL (MARL) algorithms for automatic web GUI testing, aiming to improve test efficiency and coverage. However, how to share information among different agents to avoid redundant actions and achieve effective cooperation is a non-trivial problem. To address the challenge, we propose the first MARL-based web GUI testing system, MARG, which coordinates multiple testing agents to efficiently explore a website under test. To share testing experience among different agents, we have designed two data sharing schemes: one centralized scheme with a shared Q-table to facilitate efficient communication, and another distributed scheme with data exchange to decrease the overhead of maintaining Q-tables. We have evaluated MARG on nine popular real-world websites. When configuring with five agents, MARG achieves an average increase of 4.34 and 3.89 times in the number of explored states, as well as a corresponding increase of 4.03 and 3.76 times in the number of detected failures, respectively, when compared to two state-of-the-art approaches. Additionally, compared to independently running the same number of agents, MARG can explore 36.42% more unique web states. these results demonstrate the usefulness of MARL in enhancing the efficiency and performance of web GUI testing tasks.

关键词： Web Testing Multi-Agent Reinforcement learning Automatic GUI Testing Information Sharing

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Hybrid Physics-Inspired Machine learning Framework for Predictive Maintenance of Forklift Chains: Leveraging Sensor data Characteristics 11

Hybrid Physics-Inspired Machine Learning Framework for Predi...

引用

11th international conference on Control, Mechatronics and Automation (ICCMA)

作者： Akcatepe, Osman Moeckel, Michael Univ Appl Sci Aschaffenburg Lab Hybrid Modelling Aschaffenburg Germany

ISBN: (纸本)9798350315684

Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. the discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.

关键词： Physics-inspired machine learning Feature engineering data-driven modeling Forklifts Predictive Maintenance Remaining Useful Life (RUL) Industry 4.0

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DiverSim: A Customizable Simulation Tool to Generate Diverse Vulnerable Road User datasets 11

DiverSim: A Customizable Simulation Tool to Generate Diverse...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Ander Iñiguez de Gordoa, Jon Hormaetxea, Martín Nieto, Marcos Vélez, Gorka Mujika, Andoni Mikeletegi 57 Donostia-San Sebastián20009 Spain Donostia-San Sebastián Spain

ISBN: (纸本)9789897587450

this work presents DiverSim, a highly customizable simulation tool designed for the generation of diverse synthetic datasets of vulnerable road users to address key challenges in pedestrian detection for Advanced Driver Assistance Systems (ADAS). Although recent Deep learning models have advanced pedestrian detection, their performance still depends on the diversity and inclusivity of training data. DiverSim, developed on Unreal Engine 5, allows users to control various environmental conditions and pedestrian characteristics, including age, gender, ethnicity and mobility aids. the tool features a highly customizable virtual fisheye camera and a Python API for easy configuration and automated data annotation in the ASAM OpenLABEL format. Our experiments demonstrate DiverSim’s capability to evaluate pedestrian detection models across diverse user profiles, revealing potential biases in current state-of-the-art models. By making both the simulator and Python API open source, DiverSim aims to contribute to fairer and more effective AI solutions in the field of transportation safety. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Deep learning

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A Self-supervised Task-agnostic Embedding for EEG Signals 11

A Self-supervised Task-agnostic Embedding for EEG Signals

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Partovi, Andi Burkitt, Anthony N. Grayden, David Univ Melbourne Dept Biomed Engn Melbourne Vic Australia

ISBN: (纸本)9781665462921

Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. the success of a BCI system is largely driven by the accuracy of the BCI decoder. this accuracy, in turn, may be limited by the amount of labelled training data available for supervised machine learning algorithms. the success of deep learning algorithms in other computer science areas has not reached the field of BCI decoding due to this lack of abundant labelled data. We use a novel deep learning architecture trained in a selfsupervised manner to learn a common vector representation (embedding) of EEG signals that can be used in different BCI tasks. the vector representation is trained using EEG recordings without using any task labels. We validate our embedder using two separate BCI tasks: seizure detection and motor imagery, and assess its usefulness through distance similarity metrics in a clustering approach. the derived embeddings were successful in distinguishing binary classes in both tasks.

关键词： Self-supervised learning Embedding Transfer learning Representation learning

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Cloud Based Predictive Maintenance System 11

Cloud Based Predictive Maintenance System

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Saini, Nikesh Yadav, Anup Lal Rahman, Ataur Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350350357

Maintenance is a critical aspect of modern industrial operations, as it ensures the reliability and longevity of equipment while minimising unplanned downtime. Traditional, schedule-based maintenance approaches are often inefficient and costly and fail to harness the full potential of data-driven insights. In response, cloud-based predictive maintenance systems have emerged as a transformative solution, leveraging the power of the cloud, data analytics, and machine learning to enable proactive maintenance strategies. this research paper explores the architecture, advantages, challenges, and potential applications of cloud-based predictive maintenance systems. We delve into the underlying technology, the role of the cloud in data storage and processing, and the use of machine learning algorithms for predictive analytics. We discuss how real-time monitoring and analysis of sensor data can help anticipate equipment failures, reduce operational costs, and increase overall efficiency. © 2024 IEEE.

关键词： data Analytics

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Pedestrian Detection Using Hybrid DWT and HOG in Multiple Scenarios 11

Pedestrian Detection Using Hybrid DWT and HOG in Multiple Sc...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Chhabra, Rishu Khullar, Vikas Singh, Saravjeet Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

With the advent of smart transportation, technology plays a key role in improving the safety of people on roads. Pedestrian detection techniques have various applications in driving assistance systems, intelligent vehicles, surveillance systems, etc. the driving assistance systems and intelligent vehicle technology aim to improve the safety of pedestrians by preventing them from collisions with vehicles on the road. Different pedestrian detection machine learning and deep learning techniques have been proposed in the literature. the key to the accuracy of these techniques is the image preprocessing feature extraction phase. In this paper, we have considered three different datasets representing three different scenarios and applied Discrete Wavelet Transform (DWT), Histogram of Oriented Gradients (HOG) to extract features and hybrid DWT -HOG that combines the features of DWT and HOG for pedestrian detection. An experimental analysis has been done using these feature extraction techniques on the three datasets and it is evident that the maximum accuracy is generated by using Logistic Regression and MLP with HOG features and hybrid DWT-HOG features with input images in extracted/ pixelated form. For most of the machine learning algorithms, the hybrid technique with DWT-HOG features performs better than HOG and DWT feature extraction techniques. © 2024 IEEE.

关键词： Feature extraction

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Integrating Photonics and Fiber Bragg Grating Sensors with Deep Reinforcement learning for Advanced Robotic Systems 18

Integrating Photonics and Fiber Bragg Grating Sensors with D...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jaideep, R. Manjula, C. Seema, V. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Mechatronics Engineering Bangalore India

ISBN: (纸本)9789380544519

this research introduces the Photonics-Enhanced Embedded Robotic Intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DRL) for advanced robotics applications. Addressing the limitations of current AI models in dynamic and complex environments, PEERIM leverages the rapid data transmission capabilities of photonics to process high-frequency sensor data, enabling real-time decision-making and enhanced automation. the methodology encompasses the development and integration of a variant of the Proximal Policy Optimization (PPO) algorithm, tailored to manage the continuous data stream and execute precise, adaptive control. the proposed model has been empirically evaluated, demonstrating a significant improvement in handling real-time sensor data with an average reward of 99.02 and a low average loss of 0.099, indicating robust performance and learning stability. these findings suggest that PEERIM provides a substantial advantage over existing AI-driven robotic systems, offering a scalable solution for a variety of challenging applications. the study's contributions lay the groundwork for future advancements in autonomous systems, promising a new era of precision and reliability in robotics. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Autonomous Systems Deep Reinforcement learning Fiber Bragg Grating Sensors Photonics-Enhanced Robotics Proximal Policy Optimization Real-Time Decision Making

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Accuracy Prediction for Music therapy data using Kappa Model 18

Accuracy Prediction for Music Therapy Data using Kappa Model

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. University of Mumbai Terna Engineering College Department of Computer Engineering Mumbai India Vidyavardhini's College of Engg. & Tech Mumbai India

ISBN: (纸本)9789380544519

this research explores the therapeutic potential of music, specifically Indian classical ragas, for individuals suffering from diabetes, hypertension, and thyroid disorders. the proposed system uses a LGBM classifier model which analyses a dataset comprising 417 data entries of patients. Kappa model is used to check the performance of a classification model. Findings gave good results that classical ragas have measurable effects on sugar levels, hypertension, and thyroid function. Machine learning model plays an important role to show the improvements in health. Finally, Kappa score of 0.7319 indicates that model is substantially moderate which is a good level of agreement. And Machine learning model also gives accuracy of 86.90%. thus, Music therapy acts an alternative medicine which is a unique and novel approach. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cohen's Kappa Kappa Coefficient Kappa Statistic Machine learning Music therapy Predictive Analytics

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Advanced Neural Network Approaches for Distinguishing Real from Synthetic in GAN-generated data Authenticity Challenges 18

Advanced Neural Network Approaches for Distinguishing Real f...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jayaraj, N. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9789380544519

Amid the transformative advancements of Generative Adversarial Networks (GANs) in machine learning, a pertinent challenge arises: discerning real instances from synthetic ones. this research introduces a novel neural network model meticulously tailored to differentiate between genuine tasks and those artfully crafted by GANs. the paper elaborates on the unique architectural design and optimization techniques employed, offering a comprehensive insight into the model's development and testing phases. Empirical evaluations reveal an unparalleled accuracy rate, underscoring the model's practicality and efficacy. Notwithstanding its high precision and recall balance, the study identifies potential areas of refinement, ensuring its adaptability to future GAN sophistications. As the realm of artificial data generation continues to evolve, this research stands as a beacon, advancing the understanding and tools essential for maintaining authenticity and integrity in data-driven domains. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： data Authenticity Generative Adversarial Networks Neural Network Precision Recall Synthetic data Detection

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Machine learning in Particle Physics 11th

Machine Learning in Particle Physics

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11th international conference on Big data Analytics in Astronomy, Science, and engineering (BDA)

作者： Purohit, Milind, V Univ South Carolina Dept Phys & Astron Columbia SC 29208 USA

ISBN: (纸本)9783031585012;9783031585029

this note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. With the aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machine learning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.

关键词： particle physics machine learning statistics simulation optimal transport physics

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