In subject area: Computer Science Runtime complexity refers to the computational time required by an algorithm to process each new observed timestep, with a complexity similar to the forward probability extension in the CHMM model, denoted as O(D|S|2). Here, D represents the depth of the de...
Formally, this property corresponds to obtaining lower time complexity for models without numerical instabilities and errors as illustrated in Table 1 (left). For example, Table 1 (left) shows that the complexity of a pth-order numerical ODE solver is \({{{\mathcal{O}}}(Kp)\), where K ...
With the continual development of the global economy, the air transportation demand has significantly increased across various industries, leading to a surge in flight traffic and airspace complexity. To optimize flight scheduling and improve operational efficiency, the traffic prediction is extensively stu...
Despite the outlined advantages of fog monitoring and MPSoCs, existing research still lacks a model-based development process to design, deploy and evaluate the predictability of fog monitoring of real-time control over MPSoCs. Considering that model-based development allows dealing with complexity, ve...
of high inference time complexity. By using gradient-boosted regression trees as a predictor of the labels obtained from nearest neighbor analysis, we demonstrate a significant increase in inference speed, improving by several orders of magnitude. We validate the effectiveness of our approach on a ...
Some important ML algorithms are decision tree (DT), ANN, fuzzy-rule classifier, and support vector machine (SVM). In ML's viewpoint, the OOS prediction is a pattern recognition task [20] and the aim is to find a relationship between input data and the output [2, 28]. As mentioned ...
A trust management mechanism for mobile ad hoc networks (MANETs) is proposed to cope with security issues that MANETs face due to time constraints as well as resource constraints in bandwidth, computational power, battery life, and unique wireless charac
We now consider the time complexity of our algorithm. We suppose the number of tasks in the workflow G is n, and the maximum number of types of VMs is m. The most time-consuming part of the Global Resource Provisioning for Real-time Workflow (GRP4RW) Algorithm is the WorkflowLayer and...
25. This consists of a single convolutional layer with max pooling followed by two LSTM layers with dropout followed by a dense layer that maps to a vector of probabilities over the six possible classes. For training, we use Adam optimisation with a learning rate of 0.0005, a batch size of...
dynamic problem with the dynamic information disturbance by an external environment. Thus, the traditional solution method is not applicable. Besides, the traditional mathematical modeling method often fails to find feasible solutions because of the complexity of the model and the limitation of computing...