Arrhythmia is a medical condition where a patient experiences an irregular heartbeat. the presence of arrhythmia indicates a much more serious heart condition, which, if not treated, may worsen and even lead to death....
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the proceedings contain 10 papers. the topics discussed include: comparative analysis on enhanced image compression algorithms;military detection during close combat situations and border monitoring using adaptive cus...
the proceedings contain 10 papers. the topics discussed include: comparative analysis on enhanced image compression algorithms;military detection during close combat situations and border monitoring using adaptive customized convolutional neural network;machinelearning-based rainfall prediction for various regions;methodology for the registration of images and their inclusion in medica image management systems, based on the DICOM protocol;mobile application success prediction using machinelearning;a secure mobile application for speech to text conversion using artificial intelligence techniques;a comparative study of heart disease prediction using machinelearning;method for teaching medical imaging diagnosis, applying virtual reality techniques;heart disease prediction using machinelearning method – a review article;and web system for the management of clinical laboratory test, applied to health facilities in rural areas.
Numerical analysis plays a significant role in the development of passenger comfort in modern vehicles. Customer perception of quality and comfort is largely influenced by its vibro-acoustic properties. To develop wel...
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ISBN:
(纸本)9783658465544
Numerical analysis plays a significant role in the development of passenger comfort in modern vehicles. Customer perception of quality and comfort is largely influenced by its vibro-acoustic properties. To develop well engineered and robust vehicles that meet such customer requirements, many vibro-acoustic aspects and loading conditions need to be accounted for. this is only feasible when numerical analysis techniques, like the Finite Element Method, are applied over all design stages including the early concept phase. As vibro-acoustic behaviour is typically of global nature, meaning vibrations of different components mutually interact throughout the whole system, complex full-vehicle analyses accounting for all components as well as the interior air cavity are required. Moreover, withthe rise of electric vehicle development, the rotational motor speeds and hence the excitation frequencies of interest increase dramatically compared to conventional combustion engine based vehicles. Such large-scale simulations challenge even today’s high-performance computing hardware and software to its limits. For this reason, efficient numerical reduction schemes of the underlying systems of equations are essential to avoid excessive evaluation times and costs, which becomes especially relevant when optimization approaches are considered. the presented paper illustrates the structure-borne acoustic analysis and optimization procedure of an electric full vehicle model subject to high frequency motor load cases. To reduce simulation times, the Frequency Response Function-Substructure reduction technique is applied to model components that are not modified in the successive optimization procedure enabling a very efficient reuse of precomputed data in each iteration cycle. As the focus in this work is on modifications in the powertrain, sub-systems like trimmed body and chassis are viable to be reduced. A new machinelearning based optimization approach is applied that combines the
the classification accuracy of a multi-layer Perceptron Neural Networks depends on the selection of its parameters such the connection weights and biases. Generating an optimal value of these parameters requires a sui...
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Long-tail problem is one of the major challenges in distantly supervised relation extraction. Some recent works on the long-tail problem attempt to transfer knowledge from data-rich and semantically similar head class...
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It has been demonstrated that deep learning can in certain instances outperform traditional statistical methods at forecasting. this outperformance, however, does not address the challenge of quantifying forecast unce...
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ISBN:
(纸本)9781728175591
It has been demonstrated that deep learning can in certain instances outperform traditional statistical methods at forecasting. this outperformance, however, does not address the challenge of quantifying forecast uncertainty (prediction intervals). Artificial neural networks often do not have probability distributions linked to their point forecasts, which complicates the construction of prediction intervals. In this paper, we explore computational methods of artificially deriving said probability distributions and constructing prediction intervals. the point forecasts, and the associated constructed prediction intervals are compared to those produced by means of the oft-preferred traditional statistical counterparts. Our finding is deep learning outperforms (or al the very least is competitive to) the former. We focus on three deep learning architectures, namely, cascaded neural networks, reservoir computing and long short-term memory recurrent neural networks.
the license plate angle is unfixed, the vehicle position is ununiform, and the picture is not sufficiently illuminated which leads to the decrease of license plate recognition accuracy. In order to improve the accurac...
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the detection of locomotion patterns and trajectory semantics has long relied on the positioning information. however, positioning information can be unavailable in specific scenarios. In addition, the position values...
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ISBN:
(纸本)9781728175591
the detection of locomotion patterns and trajectory semantics has long relied on the positioning information. however, positioning information can be unavailable in specific scenarios. In addition, the position values are sensitive to rotation and hence a bias can exist during the training process in deep learning models. this paper introduces an alternative model that classifies trajectories based on accelerometers, instead of positioning systems. We built up a convolutional neural network that inputs the degree of velocity and turning angles at several time scales and converts this information into a semantic class. We examined the model in a simulated environment and also a benchmark task. this model has exhibited a competitive performance even compared withthose models based on positioning information.
Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated the catastrophic forgetting in fully connected networks with some earlier work explo...
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ISBN:
(纸本)9781728175591
Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated the catastrophic forgetting in fully connected networks with some earlier work exploring activation functions and learning algorithms. Applications of neural networks have been extended to include similarity and metric learning. It is of significant interest to understand how metric learning loss functions would be affected by catastrophic forgetting. Our research investigates catastrophic forgetting for four well-known metric-based loss functions during incremental class learning. the loss functions are angular, contrastive, center, and triplet loss. Our results show that the rate of forgetting is different across loss functions on multiple datasets. Triplet loss was least affected followed by contrastive, center, and angular loss. Center and angular loss produce better embeddings on difficult tasks when trained on all available training data, however. they are the least robust to forgetting during incremental class learning. We argue that triplet loss provides the ideal middle ground for future improvements.
Aiming at the problems of low accuracy and slow recognition efficiency of the traditional traffic sign recognition algorithm in complex environment, a deep learning traffic sign recognition method based on YOLOv5 is p...
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