Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, inclu...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, including satellite-based monitoring and ground surveillance, suffer from limitations such as delayed detection, high false alarm rates, and dependency on manual intervention. Existing sensor-based detection systems often lack integration withintelligent decision-making frameworks, leading to inefficient fire prevention strategies. the absence of real-time processing and predictive capabilities further reduces the effectiveness of current methodologies in mitigating wildfire risks. this work presents an IoT-based deep learning approach utilizing Convolutional Neural Networks (CNNs) for real-time forest fire detection and prevention. data is collected from satellite images, drone feeds, and IoT sensors, including temperature, humidity, gas concentration, and infrared readings. Advanced data preprocessing techniques, including image augmentation, normalization, and feature extraction, enhance the robustness of the model. A CNN-based classification model is implemented to analyze fire patterns and assess risk levels. the system is trained using sensor data and image datasets, ensuring high detection accuracy with minimal false positives. the key features of this methodology include real-time wildfire detection, automated risk assessment, and deployment on IoT edge devices for immediate decision-making. the integration of sensor-driven insights with deep learning improves early detection capabilities while reducing reliance on manual surveillance. Efficient data handling and cloud-based alert mechanisms enable proactive measures against fire outbreaks. the proposed system enhances wildfire monitoring by ensuring fast response times, improved accuracy, and scalable deployment in fire-prone regions.
In this paper we present a machine learning system that can accurately predict the transitions between frames in a video sequence. We propose a set of novel features and describe how to use dominant features based on ...
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ISBN:
(纸本)3540228810
In this paper we present a machine learning system that can accurately predict the transitions between frames in a video sequence. We propose a set of novel features and describe how to use dominant features based on a coarse-to-fine strategy to accurately predict video transitions.
A common objective in image analysis is dimensionality reduction. the most often used data-exploratory technique withthis objective is principal component analysis. We propose a new method based on the projection of ...
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ISBN:
(纸本)3540228810
A common objective in image analysis is dimensionality reduction. the most often used data-exploratory technique withthis objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.
AURA (Advanced Uncertain Reasoning Architecture) is a parallel pattern matching technology intended for high-speed approximate search and match operations on large unstructured datasets. this paper represents how the ...
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ISBN:
(纸本)3540228810
AURA (Advanced Uncertain Reasoning Architecture) is a parallel pattern matching technology intended for high-speed approximate search and match operations on large unstructured datasets. this paper represents how the AURA technology is extended and used to search the engine data within a major UK eScience Grid project (DAME) for maintenance of Rolls-Royce aero-engines and how it may be applied in other areas. Examples of its use will be presented.
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. there exists a tradeoff a...
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ISBN:
(纸本)3540228810
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. there exists a tradeoff as to what should be the optimal measures of diversity and accuracy. the aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. the DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly.
In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, t...
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ISBN:
(纸本)3540228810
In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.
Finite mixture models are commonly used in pattern recognition. Parameters of these models are usually estimated via the Expectation Maximization algorithm. this algorithm is modified earlier to handle incomplete data...
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ISBN:
(纸本)3540228810
Finite mixture models are commonly used in pattern recognition. Parameters of these models are usually estimated via the Expectation Maximization algorithm. this algorithm is modified earlier to handle incomplete data. However, the modified algorithm is sensitive to the occurrence of outliers in the data and to the overlap among data classes in the data space. Meanwhile, it requires the number of missing values to be small in order to produce good estimations of the model parameters. therefore, a new algorithm is proposed in this paper to overcome these problems. A comparison study shows the preference of the proposed algorithm to other algorithms commonly used in the literature including the modified Expectation Maximization algorithm.
Multiple classifier systems based on neural networks can give proved generalisation performance as compared with single classifier systems. We examine collaboration in multi-net systems through in-situ learning explor...
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ISBN:
(纸本)3540228810
Multiple classifier systems based on neural networks can give proved generalisation performance as compared with single classifier systems. We examine collaboration in multi-net systems through in-situ learning exploring how generalisation can be improved through the simultaneous learning in networks and their combination. We present two in-situ trained systems;first, one based upon the simple ensemble, combining supervised networks in parallel, and second, a combination of unsupervised and supervised networks in, sequence. Results for these are compared with existing approaches demonstrating that in-situ trained systems perform better than similar pre-trained systems.
Model uncertainty refers to the risk associated with basing prediction on only one model. In semi-supervised learning, this uncertainty is greater than in supervised learning (for the same total number of instances) g...
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ISBN:
(纸本)3540228810
Model uncertainty refers to the risk associated with basing prediction on only one model. In semi-supervised learning, this uncertainty is greater than in supervised learning (for the same total number of instances) given that many data points are ufflabelled. An optimal Bayes classifier (OBC) reduces model uncertainty by averaging predictions across the entire model space weighted by the models' posterior probabilities. For a given model space and prior distribution OBC produces the lowest risk. We propose an information theoretic method to construct an OBC for probabilistic semi-supervised learning using Markov chain Monte Carlo sampling. this contrasts with typical semi-supervised learningthat attempts to find the single most probable model using EM. Empirical results verify that OBC yields more accurate predictions than the best single model.
Many time series exhibit dynamics over vastly different time scales. the standard way to capture this behavior is to assume that the slow dynamics are a "trend", to de-trend the data, and then to model the f...
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ISBN:
(纸本)3540228810
Many time series exhibit dynamics over vastly different time scales. the standard way to capture this behavior is to assume that the slow dynamics are a "trend", to de-trend the data, and then to model the fast dynamics. However, for nonlinear dynamical systems this is generally insufficient. In this paper we describe a new method, utilizing two distinct nonlinear modeling architectures to capture both fast and slow dynamics. Slow dynamics are modeled withthe method of analogues, and fast dynamics with a deterministic radial basis function network. When combined the resulting model out-performs either individual system.
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