In the wake of the outbreak of the new coronavirus, the countries in the world have fought to combat the spread of infection and imposed preventive measures to compel the population to social distancing, which led to ...
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This paper presents the application of 2 selected machine learning methods for transmission quality assessment in optical networks. The proposed solution uses real data to train classification models that predict whet...
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Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (...
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Accurate detection of respiratory pauses is crucial in patients undergoing cataract surgery under sedation, as undetected pauses can lead to serious complications. Conventional monitoring techniques, such as pulse oxi...
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ISBN:
(数字)9798331532543
ISBN:
(纸本)9798331532550
Accurate detection of respiratory pauses is crucial in patients undergoing cataract surgery under sedation, as undetected pauses can lead to serious complications. Conventional monitoring techniques, such as pulse oximetry and capnography, often fail to detect respiratory pauses in a timely manner, limiting their effectiveness in high-stakes clinical settings. To address this gap, we propose an automated method leveraging tracheal sound analysis using a deep learning model based on ResNet18. Our approach incorporates signal processing techniques, noise reduction, Mel-spectrogram extraction, and data augmentation, to optimize the model's performance. The model was evaluated using multiple performance metrics, including AUC of the precision-recall curve, F1 score, etc. It achieved an AUC of 92.5% and an F1 score of 89% for the apnea class, demonstrating the system's potential for precise, real-time respiratory monitoring in clinical environments.
Urbanization and the emergence of smart cities have led to increased industrial activities, raising concerns about safety and environmental hazards. Detecting and monitoring gas leaks accurately is crucial due to the ...
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ISBN:
(数字)9798350374674
ISBN:
(纸本)9798350374681
Urbanization and the emergence of smart cities have led to increased industrial activities, raising concerns about safety and environmental hazards. Detecting and monitoring gas leaks accurately is crucial due to the significant risks they pose to public health and the environment. In this study, we propose a novel approach that combines thermal imaging with sensor data to enhance gas detection accuracy in urban and industrial settings. By employing advanced algorithms and analyzing extensive datasets, our methodology achieves an impressive accuracy rate of 99.32%, setting a new standard in gas leak detection. Our innovation lies in the utilization of a unique oversampling technique called Normal Synthetic Minority Oversampling Technique (NSMOTE), coupled with the simultaneous integration of thermal imaging and sensor data within a multimodal framework. This breakthrough not only underscores the potential of integrating diverse data sources for environmental monitoring but also represents a significant stride in mitigating the risks associated with industrial gas emissions.
This study aims to track and categorize milk quality grades using the Logistic Model Tree (LMT) algorithm, based on the analysis of 1,059 milk samples. The focus of the research is to evaluate key factors such as pH v...
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ISBN:
(数字)9798331533267
ISBN:
(纸本)9798331533274
This study aims to track and categorize milk quality grades using the Logistic Model Tree (LMT) algorithm, based on the analysis of 1,059 milk samples. The focus of the research is to evaluate key factors such as pH value, taste, smell, fat content, concentration, and color, which play a crucial role in determining the overall quality of milk. The goal is to create an efficient and standardized process for sorting cow's milk, which can save significant time and resources in the dairy industry. To identify the most suitable algorithm for this task, the research team initially tested 12 different algorithms, assessing their performance in predicting milk quality. Among these, the Logistic Model Tree (LMT) emerged as the most effective model, achieving an impressive accuracy rate of 99.76%. The LMT algorithm, known for combining the advantages of decision trees and logistic regression, demonstrated its ability to classify milk quality grades with high precision. The results of this study indicate that the LMT algorithm can be a valuable tool for automating the milk quality grading process. By leveraging key attributes such as pH, fat content, and color, it is possible to efficiently sort and grade milk, reducing the need for manual inspection and improving overall productivity in the dairy industry. This method offers a significant improvement over traditional quality assessment techniques and has the potential to be widely adopted for milk sorting and quality control applications.
the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. architecture smells (ASs) can be used to identify any quality issue...
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One common neurodevelopmental issue that needs prompt and precise diagnosis for successful treatment is attention-deficit/hyperactivity disorder (ADHD). In this study, we present a novel approach using a cost-sensitiv...
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ISBN:
(数字)9798331511272
ISBN:
(纸本)9798331511289
One common neurodevelopmental issue that needs prompt and precise diagnosis for successful treatment is attention-deficit/hyperactivity disorder (ADHD). In this study, we present a novel approach using a cost-sensitive LightGBM (Light Gradient Boosting Machine) model to enhance the prediction of ADHD. Our key contribution lies in the integration of cost-sensitive learning, which prioritizes minimizing the misclassification costs associated with false negatives and false positives, critical in medical diagnostics. Additionally, we leverage the computational efficiency and speed of LightGBM, a gradient boosting framework known for its high performance in handling complex data structures. Our results demonstrate that the cost-sensitive LightGBM model achieves a predictive accuracy of 94% and an F1-score of 94%, while significantly reducing the time required for training and inference. This makes it a viable tool for real-world ADHD screening applications. This study highlights the importance of incorporating cost-sensitive algorithms in medical predictions to achieve a balanced trade-off between accuracy and resource efficiency.
In recent decades, the advent of digital information services by YouTube, Amazon, Netflix, and many other web services of this kind have made recommendation systems more and more ubiquitous in our lives. rice field. T...
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In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a hig...
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