Experimental setup
Datasets
To evaluate the performance of our proposed MOC-MAB algorithm in the domain of smart tourism, we conducted experiments using two datasets: a designed dataset that simulates real-world scenarios and the TripAdvisor…
Category: 7. Maths
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Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder
Study area
Data for our study was gathered from 64 LMSs in the districts of Mandi, Kinnaur, Kangra, Sirmour, and Solan in Himachal Pradesh. Because of their steep slopes, weak geology, and heavy rainfall, these districts are among the most prone…
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Design and experimental research of on device style transfer models for mobile environments
Environment
We implemented the style transfer pipeline using Python 3.9, PyTorch 2.21, CUDA 12.1, and cuDNN 9.5, and the training was conducted on a single GTX 4060 Ti 16GB GPU.
To efficiently perform inference on deep learning models in mobile…
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect
Comparative analysis of prediction errors among models
In this subsection, we evaluated and analyzed the performance of each model using mean absolute percentage error (MAPE), \(MAPE = \frac{100}{n} \sum _{i=1}^n \left| \frac{Y_i – \hat{Y}_i}{Y_i}…
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Research on target detection for autonomous driving based on ECS-spiking neural networks
The dataset and experimental environment of the experiment
The data sets of this experiment used 2d data sets of BDD100K and KITTI data sets.
The BDD100K dataset, published by the University of Berkeley AI Lab (BAIR), is one of the largest and most…
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Explaining basketball game performance with SHAP: insights from Chinese Basketball Association
Evaluation of prediction models for CBA game outcomes
Tables 4, 5 and 6 present the performance evaluation of three sets of indicators (basic data indicators, four-factor indicators, and composite indicators) across seven machine learning…
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Improving deep learning-based neural distinguisher with multiple ciphertext pairs for speck and Simon
To validate the efficiency of the different datasets, we train the neural distinguisher \(\:{ND}_{1}\) using the proposed datasets and compare the accuracy of the different neural distinguishers. Additionally, we test key recovery using the…
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Continuous metaplastic training on brain signals
Data preparation
The Temple University Hospital (TUH) EEG Seizure Corpus v1.5.4, is used for training and testing our model. The TUH dataset consists of recordings of 642 patients at 250 Hz. The training set encompasses 592 patients, and the…
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A study on neutrosophic $$\mathscr {T}_{\textrm{1k}}$$ -semantic segmentation for iris image recognition with Gaussian and Poisson noises
Fig. 3 Sample image visualization of \(\varGamma ( \mathscr {T}_{1k})\) semantic segmentation for \(k = 3 ,4, 5, 6\).
In this section, experiments are performed to evaluate the proposed technique. The details of the experimental data will be…
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Deep learning-based recognition model of football player’s technical action behavior using PCA–LBP algorithm
There is still a lack of scientific training for football, and football coaches can only train players based on their stadium experience. At the same time, football players do not fully understand their sports movements. The exercises are usually…
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