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This paper discusses the problems of applying deep learning methods for intrusion detection in network traffic. The results of analyzing the relevant studies and reviews of deep learning applications for intrusion detection are presented. The most ...

Internet shutdowns, often enforced by governments to control communication and access to information, have significant socio-political and economic implications. This study presents a machine learning approach to predict the likelihood of internet ...

With increasingly larger and more complex telecommunication networks, there is a need for improved monitoring and reliability. Requirements increase further when working with mission-critical systems requiring stable operations to meet precise ...

The evolution of recommendation systems has revolutionized user experiences by providing personalized recommendations. Although conventional systems such as collaborative and content-based filtering are reliable, they still suffer from inherent ...

N4-acetylcytidine(ac4C) is one of the highly conserved epigenetic modifications in RNA, possessing the ability to facilitate mRNA expression, enhance its stability, and influence mRNA decoding efficiency, thereby promoting substrate translation. The ...

Stock price prediction models have attracted much research interest in recent years. However, stock prices are high-dimensional financial time series. The application of artificial intelligent (AI) algorithms are widely used in stock price ...

To improve the accuracy of benign and malignant classification of lung nodules in CT images, a method based on feature fusion and improved random forest is proposed to classify benign and malignant lung nodules. First, the high-order features of lung ...

In this article, we focus on the problem of variable selection in a functional regression framework. This question is motivated by practical applications in the field of agronomy: In this field, identifying the temporal periods during which ...

Open Source Software (OSS) hosting platforms like GitHub also contain many non-software projects that should be excluded from the dataset for most software engineering research studies. However, due to the lack of obvious indicators, researchers have to ...

Height is an important parameter to evaluate the crops growing state, being strongly correlated to plant other biophysical features. An accurate of this variable can allow farmers to carry out interventions aimed at optimizing crop development, ...

Prior work has addressed the problem of confidential inference in decision trees. Both traditional order-preserving cryptography (OPE) and order-preserving NTRU cryptography have been used to ensure data and model privacy in decision trees. Furthermore, ...

With the increasing emphasis on health awareness, the healthcare industry has emerged as a key pillar of the national economy. Stock price forecasts have been receiving a lot of attention as an important basis for investment decisions. This paper ...

In order to build a risk prediction model to predict stress of college students and identify risk factors, we took 18,403 college students as participants and collected their multi-source mental health data. The feature variables were screened by ...

In order to improve the accuracy of landslide prediction, this paper proposes a landslide displacement prediction model based on the random forests and optimized support vector regression. Firstly, Random Forests (RF) is used for preliminary prediction ...

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, ...

Root-cause analysis for integrated systems has become increasingly challenging due to their growing complexity. To tackle these challenges, machine learning (ML) has been applied to enhance root-cause analysis. Nonetheless, ML-based root-cause analysis ...

Understanding genomics variations underlying observed phenotype differences between individuals of a species is of a fundamental biological interest. Predicting phenotype aims to know at individual level the risk factors for diseases or important ...

Levels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no ...

An on-time and accurate analysis of the problem is essential to prevent and treat any illness. The utilization of machine learning (ML) for diagnosing a wide range of diseases is increasingly prevalent in the field of medical science based on ...

In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three ...