In order to secure the networks and detect the attacks at various sub-levels, there is a keen interest in implementing an efficient machine learning methodology to seek the malignant from benign. Anomaly detection, supervised or unsupervised deals to handle the perturbations from the normal network,...
Machine learning algorithms only depend on the training data to predict the outputs; hence, we can detect the symbol even without the use of cyclic prefix or channel estimation which can save a lot of time and data if the input data is large. A comparative study on the performance of ...
Machine learning (ML) algorithms have evolved as efficient tools for data-driven simulation in present times. In this study, we predict the GPP of one of the major forested regions in India, namely the Western Ghats, and seek to evaluate the efficacy of several ML models in this process ...
There are three key issues about online classification: observation window size, feature selection, and classification algorithms.In this paper, by collecting five types of typical network flow data as the experiment sample data, the authorsfound observation window size 7 is the best for the sample...
we treat the problem as a text classification challenge. We leverage machine learning algorithms and natural language processing techniques to analyze and classify the text data of the issues. By applying these techniques, we are able to extract relevant information from the issue descriptions, such ...
In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inela...
With the aim of enhancing predictive performance, we employ ensemble methods that leverage a diverse range of machine-learning algorithms. To evaluate the effectiveness of our system, we employ widely recognized metrics such as accuracy, precision, recall, and F1-score which serve as indicators of ...
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decodin
Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue. This study was carried out using GIS and R open source software at Abha Basin, Asir Region, Saudi Arabia. First, a total of 243 landslide locations were identified at ...
The algorithms proposed in that study appeared generally robust in the identification and counting of FP. However, their algorithm seemed to present poor prediction in the lowest range of papillae counts, probably due to the very few number of pictures (n = 9) used to validated the model...