Spiking Neural Networks (SNNs), are inspired by the biological brain's complicated signaling mechanisms and possess unique characteristics that set them apart from traditional artificial neural networks. This rese...
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ISBN:
(数字)9798350386059
ISBN:
(纸本)9798350386066
Spiking Neural Networks (SNNs), are inspired by the biological brain's complicated signaling mechanisms and possess unique characteristics that set them apart from traditional artificial neural networks. This research study explores the challenging domain of image classification, specifically utilizing the well-known MNIST dataset through the development and thorough evaluation of different neural models for edge computing. However, the primary contribution is the autonomous selection of the best-performing SNN model through various early stopping approaches and validation functions, allowing the models to autonomously adapt during training. In addition, this article presents the standalone AutoML-SNN model, which is the introduction of dynamic elements into selected SNN domains, enhancing their adaptability to complex patterns within the dataset. Furthermore, the early stopping methodologies are used to reduce overfitting hazards, and using the 3000-neuron set, the LIF appeared as the most proficient neural model.
Software testing is a critical process for achieving product quality. Its importance is more and more recognized, and there is a growing concern in improving the accomplishment of this process. In this context, Knowle...
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Software testing is a critical process for achieving product quality. Its importance is more and more recognized, and there is a growing concern in improving the accomplishment of this process. In this context, Knowledge Management emerges as an important supporting tool. However, managing relevant knowledge to reuse is difficult and it requires some means to represent and to associate semantics to a large volume of test information. In order to address this problem, we have developed a Reference Ontology on Software Testing (ROost). ROost is built reusing ontology patterns from the Software Process Ontology Pattern Language (SP-OPL). In this paper, we discuss how ROost was developed, and present a fragment of Roost that concerns with software testing process, its activities, artifacts, and procedures.
The advent of Artificial Intelligence (AI) has opened up new possibilities for improving productivity in various industry sectors. In this paper, we propose a novel framework aimed at optimizing systematic literature ...
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Multi-level qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decohere...
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