Contemporary manufacturing system design centres on the Industry 4.0 paradigm. A versatile platform secondary the efficient optimisation of manufacturing-related activities, such as predictive keep, is necessary for t...
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Purpose: Watermarking is one of the techniques used to protect multimedia data, and images in particular, from malicious attacks by inserting a signature into these images. However, traditional watermarking schemes en...
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The classification of electrical cardiac signals is a crucial technique in this field because it is significantly dependent on the early identification of patients with heart disease. Many researchers have dedicated t...
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The primary objective of this research work is to harness the advanced capabilities of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLMs), to develop a comprehensive system ...
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ISBN:
(纸本)9781959025498
The primary objective of this research work is to harness the advanced capabilities of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLMs), to develop a comprehensive system for detecting and understanding the causes of oil spills. Our approach involves utilizing deep learning algorithms to detect oil spill incidents from images, extracting relevant factors from these images, and feeding these factors into LLMs to determine the causality of the incidents. This research is motivated by the increasing frequency and environmental impact of oil spill events globally, and the lack of existing mechanisms to accurately monitor and explain these incidents. By enabling rapid detection and causality analysis, this system aims to enhance environmental protection efforts and prevent future oil spills through informed decision-making and timely intervention. The methodology of this study involves several critical steps. We began by utilizing an industrial dataset comprising labeled images of oil spills. Initial preprocessing steps included resizing and normalization of the images, followed by extensive data augmentation to enhance the dataset's robustness. We then employed advanced deep learning models, where images are considered as a grid of cells, with bounding boxes. We trained the Convolutional Neural Networks (CNNs) model to identify oil spill incidents by extracting key features from each image. These factors were then fed into a Large Language Model (LLM), to analyze and determine the underlying causes of the oil spills. The study demonstrates the effectiveness of integrating deep learning and LLMs in environmental monitoring and analysis. Our approach achieved a considerable increase in the accuracy of oil spill detection compared to traditional methods. Additionally, we attained a better accuracy rate in identifying contributory factors to oil spills. These results underscore the ecological importance of promptly identifying a
This research evaluates the effectiveness of machine learning models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Neural Networks (NN), in detecting fraudulent credit card trans...
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ISBN:
(数字)9798331508616
ISBN:
(纸本)9798331508623
This research evaluates the effectiveness of machine learning models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Neural Networks (NN), in detecting fraudulent credit card transactions. Fraud detection is challenging due to the highly imbalanced nature of the data, with fraudulent transactions making up only a tiny fraction of the total. To address this, RandomOverSampler balances the dataset, ensuring a fair representation of both classes. The study employs key evaluation metrics, including accuracy, precision, recall, F1-score, and AUC, to assess model performance comprehensively. Among the models tested, Random Forest demonstrated outstanding results, achieving 99.99 accuracy, 99.99 precision, 100 recall, 99.99 F1-score, and 100 AUC. Gradient Boosting also performed well, with 99.52 accuracy and 99.95 AUC, but RF’s perfect recall and balanced performance across all metrics make it the most reliable model. This research highlights the significance of robust models in practical fraud detection systems, ensuring precise and comprehensive transaction monitoring.
Secure data aggregation and effective data transmission are necessary to enhance sensor network durability and performance. Secure data collection from the duty-cycled sensor nodes using a moving sink and evenly distr...
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In the last decades, Artificial Intelligence (AI) approaches have been fruitfully employed in many tasks;for instance, Deep Learning (DL)-based methods have shown great ability in extracting meaningful features from i...
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Blockchain technology spread very quickly during the last few years and has become one of the most popular trends among the research and developers community. In particular, the Ethereum blockchain is one of the most ...
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Aquaculture production has been shown to represent approximately 46% of the total fish production in 2018 from FAO. This percentage shows how aquaculture contributes to global fish production, and how the demand is in...
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Lead generation refers to the identification of potential topics (the ‘leads’) of importance for journalists to report on. In this article we present $$\mu $$ XL, a new lead generation tool based on a microservice a...
Lead generation refers to the identification of potential topics (the ‘leads’) of importance for journalists to report on. In this article we present $$\mu $$ XL, a new lead generation tool based on a microservice architecture that includes a component of explainable AI. $$\mu $$ XL collects and stores historical and real-time data from web sources, like Google Trends, and generates current and future leads. Leads are produced by a novel engine for hypothetical reasoning based on temporal logical rules, which can identify propositions that may hold depending on the outcomes of future events. This engine also supports additional features that are relevant for lead generation, such as user-defined predicates (allowing useful custom atomic propositions to be defined as Java functions) and negation (needed to specify and reason about leads characterized by the absence of specific properties). Our microservice architecture is designed using state-of-the-art methods and tools for API design and implementation, namely API patterns and the Jolie programming language. Thus, our development provides an additional validation of their usefulness in a new application domain (journalism). We also carry out an empirical evaluation of our tool.
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