The proceedings contain 24 papers. The topics discussed include: autonomous floating garbage collection device using computer vision and robotics;coordinated optimization of scheduling and path planning for delivery r...
ISBN:
(纸本)9798400718304
The proceedings contain 24 papers. The topics discussed include: autonomous floating garbage collection device using computer vision and robotics;coordinated optimization of scheduling and path planning for delivery robots in intelligent manufacturing workshops;a preliminary study of the stressed and drowsy driving prediction models using semi-supervised learning;axial capacity prediction of concrete externally strengthened by low-cost natural fiber reinforced polymer composite utilizing machine learning intelligence;enhancing brain tumor diagnosis: a cutting-edge ensemble deep learning approach;a comparative study of sentiment analysis on twitter and reddit using deep learning techniques;generative artificial intelligence for future education: current research status, hot spots, and research trends;methodological considerations for anonymizing tabular data using generative adversarial networks;and data migration in large scale storage systems with varying file sizes.
Nowadays, the surveillance system performing as an indispensable part for modern industrial Internet has attracted much attention. With the continuous progress of microprocessor technology, sensor technology, particul...
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With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with th...
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作者：
Mota, BrunoFaria, PedroRamos, CarlosPolytech Porto
LASI Intelligent Syst Associate Lab GECAD Res Grp Intelligent Engn & Comp Adv Innovat Rua Dr Antonio Bernardino Almeida 431 P-4200072 Porto Portugal
Predictive Maintenance (PdM) through Machine Learning (ML) has become an essential strategy for industries to reduce redundant maintenance activities. Even so, many companies lack the technical knowledge to implement ...
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ISBN:
(纸本)9783031820724;9783031820731
Predictive Maintenance (PdM) through Machine Learning (ML) has become an essential strategy for industries to reduce redundant maintenance activities. Even so, many companies lack the technical knowledge to implement these PdM systems, and more often than not, do not trust ML models for their lack of transparency and interpretability. To mitigate these issues, the present paper explores and implements an Automated Machine Learning (AutoML) and Explainable Artificial Intelligence (XAI) framework designated as MLJAR. The framework is evaluated for its AutoML and XAI capabilities in a widely used synthetic PdM dataset in the literature. Promising results were found as the framework was able to outperform most works in the literature by up to 26.6% in recall score, with the only work surpassing MLJAR by up to 3.2%, yet having the drawbacks of 58.3% worse precision score, and no AutoML or XAI capabilities. Overall, the MLJAR framework was able to, on average, provide 13.2% and 2.2% better scores in recall and accuracy, respectively.
This paper presents an adaptive fractional order sliding mode projective synchronization scheme for different hyperjerk systems with uncertainty and disturbances. A fractional order sliding mode controller with three ...
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The Automatic Voltage Regulator (AVR) plays a unique role in the functioning of the electrical grid. The AVR's complexity stems from the fact that its transfer function is of a higher order. Therefore, the analysi...
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The attacks BW-DDoS represent known onslaughts of packets forwarded by large numbers of participating websites that interfere with a valid stream of traffic through congested networks. While such attacks often rely on...
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The protection mode of digitalization effectively transforms various national traditional cultures into digital products, providing a way of thinking for the protection of national traditional cultures. This mode stil...
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The paper provides an overview of advancements in bird detection and recognition systems using Machine Learning and Artificial Intelligence (AI). It highlights the increasing adoption of wind farms amid rising electri...
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ISBN:
(纸本)9783031820724;9783031820731
The paper provides an overview of advancements in bird detection and recognition systems using Machine Learning and Artificial Intelligence (AI). It highlights the increasing adoption of wind farms amid rising electricity demand, underscoring their environmental impact on avian species. To address these ecological challenges, the development of bird recognition solutions is crucial. The paper analyzes various techniques, including radar systems, sound recognition, Convolutional Neural Networks (CNNs), electromagnetic detection, YOLOv5, and color segmentation, discussing their features, computational costs, and constraints. It concludes that while deep learning models offer superior results, they need a balance between accuracy and speed, alongside training with large and representative datasets. Ultimately, the paper aims to contribute to efforts aimed at mitigating the adverse effects of wind farms on bird populations through advanced technologies. Additionally, through this paper we intend to shed light over the state of the art on bird detection systems and provide insights that intends to solve some of these drawbacks.
With the rapid development of smart grids, intelligent power measurement and controlsystems have become a key technology for achieving efficient energy management and energy conservation and emission reduction. Tradi...
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