In order to adapt to the rapid development of network technology and network security detection in different scenarios, the generalization ability of the classifier needs to be further improved and has the ability to detect unknown attacks. However,...
We present a novel approach that combines the concept of reconstructed phase spaces with neural network time-series predictions. The presented methodology aims to reduce the parametrization problem of neural networks and improve autoregressive neural...
Modern cybersecurity threats continue to evolve in both complexity and prevalence, demanding advanced solutions for intrusion detection. Traditional AI-based detection systems face significant challenges in model selection, as performance varies cons...
Path loss is one of the most important factors affecting base-station positioning in cellular networks. Traditionally, to determine the optimal installation position of a base station, path-loss measurements are conducted through numerous field tests...
As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and c...
Specific emitter identification (SEI) is a technology for extracting fingerprint features from a signal and identifying the emitter. In this paper, the author proposes an SEI method based on ensemble neural networks (ENN) and signal graphs, with the...
Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as...
The emergence of connected and autonomous vehicles has led to complex network architectures for electronic control unit (ECU) communication. The controller area network (CAN) enables the transmission of data inside vehicle networks. However, although...
In order to solve the problem of imbalanced and noisy data samples for the fault diagnosis of rolling bearings, a novel ensemble capsule network (Capsnet) with a convolutional block attention module (CBAM) that is based on a weighted majority voting...
Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble...
Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In t...
With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The...
Medical audio classification for lung abnormality diagnosis is a challenging problem owing to comparatively unstructured audio signals present in the respiratory sound clips. To tackle such challenges, we propose an ensemble model by incorporating di...
Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies...
Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrat...
To extract facial features with different receptive fields and improve the decision fusion performance of network ensemble, a symmetric multi-scale residual network (SMResNet) ensemble with a weighted evidence fusion (WEF) strategy for facial express...
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizin...
Skin diseases are a prevalent and diverse group of medical conditions that affect a significant portion of the global population. One critical drawback includes difficulty in accurately diagnosing certain skin conditions, as many diseases can share s...
Mobile Service selection is an important but challenging problem in service and mobile computing. Quality of service (QoS) predication is a critical step in service selection in 5G network environments. The traditional methods, such as collaborative...
Connectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality a...
The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmolog...
With the rapid development of power grid informatization, the power system has evolved into a multi-dimensional heterogeneous complex system with high cyber-physical integration, denoting the Cyber-Physical Power System (CPPS). Network attack, in add...
In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-stream with the three i...
Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memo...
The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning...
Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volu...
Water level simulation for complex water river networks is complex, and existing forecasting models are mainly used for single-channel rivers. In this paper, we present a new data assimilation model based on the ensemble Kalman filter (EnKF) for accu...
In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under differen...
Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and st...
A classification consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready...
With the rapid increase in the renewable energy penetration rate in distribution networks, the volatility and uncertainty on the power supply side have become prominent; thus, it is urgent to fully utilize the regulation potential of the flexible loa...
Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict...
At present, the amount of network equipment, servers, and network traffic is increasing exponentially, and the way in which operators allocate and efficiently utilize network resources has attracted considerable attention from traffic forecasting res...
The common analog approach and ensemble methods in photovoltaic (PV) power forecasting are based on the forecasts from several numerical weather prediction (NWP) models. These may be not applicable to the very-short-term PV power forecasting, since f...
Network Intrusion Detection System (NIDS) is one of the key technologies to prevent network attacks and data leakage. In combination with machine learning, intrusion detection has achieved great progress in recent years. However, due to the diversity...
With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis...
The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to esta...
The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing inter...
Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interr...
Software reliability is a crucial factor in determining software quality quantitatively. It is also used to estimate the software testing duration. In software reliability testing, traditional parametric software reliability growth models (SRGMs) are...
The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of...
The Dicentric Chromosome Assay (DCA) is widely used in biological dosimetry, where the number of dicentric chromosomes induced by ionizing radiation (IR) exposure is quantified to estimate the absorbed radiation dose an individual has received. Dicen...
In this paper, the problem of aero-engines ensemble modeling under sparse data is addressed. Firstly, the Makima method is used to interpolate and complement the sparse data by analyzing the experimental data of a specific real aero-engine. In this w...
Natural gas hydrates have great strategic potential as an energy source and have become a global energy research hotspot because of their large reserves and clean and pollution-free characteristics. Hydrate saturation affecting the electrical and aco...
Rolling bearings are the core components of rotating machinery. Their health directly affects the performance, stability and life of rotating machinery. To prevent possible damage, it is necessary to detect the condition of rolling bearings for fault...
Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a un...
This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The conventional EAKF adopts linear and Gaussian assumptions, making it difficult to handle cross-component updates in strongly couple...
Accurate path loss prediction within train carriages is crucial for deploying base stations along high-speed railway lines. The field strength at receiving points inside carriages is influenced by outdoor signal transmission, penetration through wind...
(1) Background: Link flooding attacks (LFA) are a spatiotemporal attack pattern of distributed denial-of-service (DDoS) that arranges bots to send low-speed traffic to backbone links and paralyze servers in the target area. (2) Problem: The tradition...
Soil is an important element in the agricultural domain because it serves as the media that bridges the water consumption and supply processes. In this study, a neural network ensemble (NNE) method was employed to predict the soil moisture to elimina...
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