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Considering the extended size of modern networks and the complexity of big network traffic data, the problem exceeds the substantial limits of human managing capabilities. In this regard, we propose a novel methodology which combines the benefits of self-taught learning and MAPE-K frameworks to deliver a scalable, self-adaptive, and autonomous misuse IDS.

This study aimed to examine the factors that potentially impact the self-directed use of mobile English learning resources (MELR). The participants were 206 Chinese undergraduate EFL learners at Yangzhou University in Mainland China.

Due to its rapid progression, early detection is essential; however, definitive diagnosis requires invasive kidney biopsy. This study aims to develop non-invasive predictive models for diagnosing MCD by machine learning.

However, most of them assume the training face dataset is sufficiently large, well cropped or aligned. This paper, proposes a novel example‐based face hallucination method, based on cluster consistent dictionary learning with the assumption that human faces have similar facial structures.

The objective of this work is to obtain the DT of a Photovoltaic Solar Farm (PVSF) with a deep-learning (DL) approach. To build such a DT, sensor-based time series are properly analyzed and processed.

This results in a relatively high generalization error. In this study, we propose to address this problem by employing the stacking ensemble learning method (SELM) that is well-known for its generalization ability.

Selection of important features is very vital in machine learning tasks involving high-dimensional dataset with large features. It helps in reducing the dimensionality of a dataset and improving model performance.

This study aims to develop a valid mathematics e-comic learning media for class VIII students on statistical material. The design of the e-comic mathematics learning media development using the PPE stage includes planning (planning), production (production), and evaluation (evaluation).

A comprehensive dataset was generated using the CARLA simulator, encompassing both nominal and adverse weather scenarios. The model is trained via imitation learning from an expert driver equipped with weather-adaptive MPC planner and PID controllers, enabling robust control under perception uncertainty.

Modern technology is expanding the possibilities of classes and determining the need for new forms of learning. The study of multimedia technology is currently developing in an academic way.

The aim of the current study was to investigate whether catch trials during force field learning could influence the consolidation of motor memory in more complex tasks. Thereby, the force field was more than double the force field of previous studies (35 Ns/m).

A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accur…