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Article
Detection and quantification of anomalies in communication networks based on LSTM-ARIMA combined model
The anomaly detection for communication networks is significant for improve the quality of communication services and network reliability. However, traditional communication monitoring methods lack proactive m...
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Article
A Rough-Fermatean DEMATEL Approach for Sustainable Development Evaluation for the Manufacturing Industry
With the increasing awareness of the importance of sustainable development, the evaluation of sustainability performance for the manufacturing has become an emerging issue of importance. Although many scholars...
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Article
FewJoint: few-shot learning for joint dialogue understanding
Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely relate...
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Article
RETRACTED ARTICLE: Optimization effect of ecological restoration based on high-resolution remote sensing images in the ecological construction of soil and water conservation
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Article
Open AccessModeling stochastic service time for complex on-demand food delivery
Uncertainty is everywhere in the food delivery process, which significantly influences decision-making for complex on-demand food delivery problems, affecting delivery efficiency and customer satisfaction. Esp...
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Article
Open AccessImperceptible black-box waveform-level adversarial attack towards automatic speaker recognition
Automatic speaker recognition is an important biometric authentication approach with emerging applications. However, recent research has shown its vulnerability on adversarial attacks. In this paper, we propos...
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Article
Open AccessStudy on non-iterative algorithms for center-of-sets type-reduction of Takagi–Sugeno–Kang type general type-2 fuzzy logic systems
The paper performs the center-of-sets (COS) type-reduction (TR) and de-fuzzification for Takagi–Sugeno–Kang (TSK) type general type-2 fuzzy logic systems (GT2 FLSs) on the basis of the
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Open AccessInvestigation of the Causal Relationship Between Alcohol Consumption and COVID-19: A Two-Sample Mendelian Randomization Study
Association between alcohol intake and Coronavirus disease 2019 (COVID-19) risk has been explored in several observational studies, but the results are still controversial. These associations may be biased by ...
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Article
Open AccessReinforcement-learning-based parameter adaptation method for particle swarm optimization
Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence. In this...
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Article
Open AccessA real-time semantic visual SLAM for dynamic environment based on deep learning and dynamic probabilistic propagation
Most existing visual simultaneous localization and map** (SLAM) algorithms rely heavily on the static world assumption. Combined with deep learning, semantic SLAM has become a popular solution for dynamic sc...
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Article
Open AccessProgress in physical modeling of compressible wall-bounded turbulent flows
Understanding, modeling and control of the high-speed wall-bounded transition and turbulence not only receive wide academic interests but also are vitally important for high-speed vehicle design and energy sav...
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Article
Open AccessRFDANet: an FMCW and TOF radar fusion approach for driver activity recognition using multi-level attention based CNN and LSTM network
Dangerous driving behavior is a major contributing factor to road traffic accidents. Identifying and intervening in drivers’ unsafe driving behaviors is thus crucial for preventing accidents and ensuring road ...
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Article
Open AccessImperceptible graph injection attack on graph neural networks
In recent years, Graph Neural Networks (GNNs) have achieved excellent applications in classification or prediction tasks. Recent studies have demonstrated that GNNs are vulnerable to adversarial attacks. Graph...
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Article
Open AccessDynamic scheduling method for data relay satellite networks considering hybrid system disturbances
System disturbances, such as the change of required service durations, the failure of resources, and temporary tasks during the scheduling process of data relay satellite network (DRSN), are difficult to be pr...
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Article
Open AccessTransient Data Caching Based on Maximum Entropy Actor–Critic in Internet-of-Things Networks
With the rapid development of the Internet-of-Things (IoT), a massive amount of transient data is transmitted in edge networks. Transient data are highly time-sensitive, such as monitoring data generated by in...
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Article
Open AccessSpeech Keyword Spotting Method Based on Swin-Transformer Model
With the rapid advancements in deep learning technology, the Transformer-based attention neural network has shown promising performance in keyword spotting (KWS). However, this method suffers from high computa...
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Article
Open AccessMulti-constraint non-negative matrix factorization for community detection: orthogonal regular sparse constraint non-negative matrix factorization
Community detection is an important method to analyze the characteristics and structure of community networks, which can excavate the potential links between nodes and further discover subgroups from complex n...
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Article
Open AccessA Region-Selective Anti-compression Image Encryption Algorithm Based on Deep Networks
In recent years, related research has focused on how to safely transfer and protect the privacy of images in social network services while providing easy access by authorized users. To safeguard privacy, we su...
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Article
Open AccessBi-DNE: bilayer evolutionary pattern preserved embedding for dynamic networks
Network embedding is a technique used to generate low-dimensional vectors representing each node in a network while maintaining the original topology and properties of the network. This technology enables a wi...
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Article
Open AccessDynamic multi-label feature selection algorithm based on label importance and label correlation
Multi-label distribution is a popular direction in current machine learning research and is relevant to many practical problems. In multi-label learning, samples are usually described by high-dimensional featu...