In the construction of 'Emerging engineering Education' and 'First-Class Course', 'Mechanical Principle' is oriented to the requirements of talent training, the application of innovative thinki...
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With the rapid development of information technology, the importance of introducing advanced big data technology for classroom teaching management and resource optimization in university education was investigated. Th...
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With the rapid and continuous development of Unmanned Aerial Vehicle(UAV) and communication technology, UAV cluster have become a preferred solution for long-distance mission execution. Additionally, Unmanned Aerial V...
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The pursuit of improved computing architecture drives continuous innovation to enhance user application performance. One such advancement involves the implementation of floating-point accelerators. However, identifyin...
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This study presents a deep learning model created for enabling comprehensive wildfire control by seamlessly combining satellite images, weather data and terrain details. Current systems face challenges in comprehensiv...
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ISBN:
(纸本)9798350386356;9798350386349
This study presents a deep learning model created for enabling comprehensive wildfire control by seamlessly combining satellite images, weather data and terrain details. Current systems face challenges in comprehensively analyzing these factors due to limitations in data integration, dynamic fire behavior prediction, and post-fire ecological impact evaluation. By improving detection and accurate assessment of impact, the system addresses all aspects of wildfire management from forecasting to post event analysis. The model integrates soil quality examination and vegetation regrowth simulation Using image analysis and state of the art deep learning methods. This holistic approach of Image analysis employs Convolutional Neural Networks (CNN) for predicting wildfire risk and Recurrent Neural Networks (RNN) for assessing soil and hydrological effects. This adaptable approach, which aims to transform the way fire control is done, can be readily adjusted to changing conditions and takes correlations between different aspects into account. It surpasses conventional techniques by including soil quality analysis, vegetation regrowth modeling, and vegetation damage evaluation. The adaptable nature of this method proves invaluable, in lessening the impact of wildfires with a focus, on evaluating vegetation damage and promoting restoration.
India's agricultural sector has been grappling with the adverse effects of climate change over the last two decades, resulting in the diminished performance of various crops. Predicting crop yields well in advance...
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This empirical study examines the manner in which artificial intelligence (AI) is transforming several sectors and the way it might help realize the full potential of people. The study employs an extensive technique t...
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Software development practices to enhance software quality and help teams better develop collaboratively have received attention by the academic community. Among these techniques is Behavior-Driven Development (BDD), ...
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ISBN:
(纸本)9789897585623
Software development practices to enhance software quality and help teams better develop collaboratively have received attention by the academic community. Among these techniques is Behavior-Driven Development (BDD), a development approach which proposes software to be developed focusing primarily on its expected behavior. teaching-wise, introducing BDD on software engineering classes and/or training courses for software developers has become important. In this context, this study presents a body of knowledge on the impacts of teaching BDD in active learning environments (ALE). To achieve this, we have triangulated data from four data sources: (i) a systematic literature review;(ii) an expert panel with active-learning experts, (iii) a survey with participants in a software development course which teaches through active learning, and (iv) a case study on the effects of teaching and using BDD in an ALE. This study results are (i) the-state-of-the-art literature on this topic, (ii) an assessment of benefits and challenges of BDD in ALEs, and (iii) a set of best practices when teaching BDD in ALEs. We concluded that BDD has more positive than negative outcomes and we present a body of knowledge regarding BDD in ALEs.
Considering cardiovascular difficulties as the primary cause of worldwide mortality, authors examined learning models for predicting acute myocardial infarction (AMI) with the highest possible rate of accuracy, precis...
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ISBN:
(纸本)9783031490613;9783031490620
Considering cardiovascular difficulties as the primary cause of worldwide mortality, authors examined learning models for predicting acute myocardial infarction (AMI) with the highest possible rate of accuracy, precision and other performance metrics. Prediction of the acute myocardial infarction is developed using Machine learning (ML) algorithms such as: Multilayer perceptron (MLP), Support Vector Machine (SVM), XGBoost, and Naive Bayes (NB). Accuracy higher than 80% is expected for correct predictions of infarction. The models of examined studies are mostly tested in the Python programming language. The importance of this research lies in its commitment to achieving scientific and experimental validation by considering all the essential factors necessary for a rigorous and credible scientific paper. By employing exploratory data analysis (EDA) on input parameters, various models are constructed, and the best feasible prediction models for AMI are presented, which may be further enhanced by integrating more specific risk variables.
The recognition of emotions has significant importance in the domains of human-computer interaction and affective computing. The integration of facial expressions, voice analysis, and EEG data in multimodal techniques...
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