Practically, the measured correlation coefficients between different channels during parallel sampling is below 10-2 as shown Fig. 2e. For a single symbol the mean of the distribution is directly connected to th
aiming to unlock key hallmarks of biological intelligence by porting primitives and computational strategies employed in the brain into engineered computing devices and algorithms4,5,6. Neuromorphic systems hold a critical position in the investigation of novel architectures, as the brain exemplifies an e...
In recent years, convolutional neural network (CNN) models have been extensively used to analyze medical images [11]. The integration of deep-learning techniques in the medical field assists pathologists in developing treatment plans for patients [12]. While CNNs excel at extracting local features ...
The outcomes, evaluated using statistical metrics such as mean square error (MSE), squared correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) underscore the exceptional accuracy and performance of GWO and IWO algorithms in predicting and estimating rock ...
GEO-DLS exceled in the multi-objective optimization by concurrently optimizing the multiple service quality parameters such as bandwidth, delay, and reliability. Utilizing a Pareto optimal solution approach, GEO-DLS identified the balanced solutions across multiple objectives and delivered the comprehensive...
Additionally, they prioritize the placement of migratable VMs on hosts exhibiting a minimal correlation coefficient, thereby optimizing VM placement. In [44], a technique is put forward to consolidate VMs in cloud computing with a focus on both security and energy efficiency. The method aims to ...
reducing the likelihood of regular patterns and correlations in the output sequence. Each tempered valueyis then incremented to the next index position for the next generation cycle. This tempering process ensures that the outputs from the generator pass various statistical tests for randomness, making...
Recurrent Neural Networks (RNNs) excel in sequential data processing, especially in natural language applications. Unlike MLPs and CNNs, RNNs can use their outputs as inputs for subsequent layers, enabling information persistence [42]. However, they face challenges with long sequences due to the...
The correlation coefficient between number of publications and citations is r = 0.134, and that of CT/PT and H is r = −0.001, indicating a low degree of coherence. Thus, while the ranking by number of publications is led by Tavernelli, Ivano; Bravyi, Sergey; Gambetta, Jay M.; and...
In the model evaluation stage, we used key metrics such as sensitivity (SEN), specificity (SPE), area under the curve (AUC) and Matthews correlation coefficient (MCC), and confirmed the robustness of model through 10-fold cross-validation. Furthermore, the performance of GACNN model is bench...