Modern software systems generate a large amount of logs during runtime, which reflect the system's operational status. The reliability of system services relies on automatic log anomaly detection. Researchers have...
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Underwater fish detection is important for underwater ecological conservation and underwater species monitoring. In this study, an underwater fish recognition method, MT-YOLO, is proposed based on the YOLOv8s model an...
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This study comprehensively examines the ability to forecast the stability of smart grids using sophisticated deep learning and machine learning models. We investigate different approaches, such as Bidirectional Gated ...
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Wildfire in the forest leads to a huge amount of financial and human losses. That is it causes damage to the forest and the life of firefighters. To reduce the amount of such damage, unmanned aerial vehicles are among...
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Road marking detection area avails research with the application of computer vision and machine learning in the identification and analysis of various types of road markings such as lane markers, crosswalks, and road ...
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We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by ...
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Vehicular networks rely on periodic broadcast of each vehicle's state information to track its surrounding vehicles and therefore, to predict potential collisions. However, in a scenario of high vehicle density, a...
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Understanding biodiversity, monitoring endangered species, and estimating the possible effect of climate change on particular regions all rely on animal species identification. Closed-circuit television (CCTV) cameras...
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This paper explores the transformative potential of Explainable Artificial Intelligence (XAI) in the context of coffee quality assessment, an area traditionally governed by subjective evaluation. By applying machine l...
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ISBN:
(纸本)9798350381771;9798350381764
This paper explores the transformative potential of Explainable Artificial Intelligence (XAI) in the context of coffee quality assessment, an area traditionally governed by subjective evaluation. By applying machine learning models, specifically a Random Forest Classifier enhanced by SHAP (SHapley Additive exPlanations) values, we identified crucial determinants of coffee quality, such as Category Two defects and high-altitude growth conditions. Our study demonstrates that machine learning can not only match but potentially exceed the accuracy of human experts in predicting coffee quality. More importantly, XAI has provided these models with a layer of transparency, making their complex predictions accessible and actionable for stakeholders in the coffee industry. This integration of AI into coffee quality assessment promises to standardize and optimize the evaluation process, offering a reliable guide for improving practices across the production chain. The findings underscore the broader impact of AI in agriculture, suggesting that such technology could be a harbinger of increased efficiency, sustainability, and trust in food production systems worldwide.
The Flexible Job Shop Scheduling Problem (FJSP) with tight time is a significant challenge in both academic and industrial fields of production scheduling. This paper addresses the FJSP with tight time using a multi-a...
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