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Articles by Cyber Engineering
Editorial: Multi-modal learning with large-scale models
The integration of multi-modal learning with large-scale models has become a transformative force in the fields of artificial intelligence and neurorobotics. Human perception naturally relies on the seamless fusion of various sensory inputs—visual, auditory, tactile, and beyond—to navigate and understand complex environments. Replicating this holistic capability in intelligent systems has historically been constrained by computational limitations and the difficulty of aligning heterogeneous data.
Sliding mode control of multi-agent systems with switching topologies and input constraints via Interval Type-2 fuzzy neural network
The coordinated control of nonlinear MASs has become an essential topic in advanced control theory and intelligent automation due to its wide-ranging applications in cooperative robotics, uncrewed aerial vehicles (UAVs), autonomous driving, satellite formation, and intelligent manufacturing (Li et al., 2023; Ju et al., 2023; Xu & Li, 2025, 2024; Xiong & Chen, 2025).
Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics
Discussion and conclusion Advances in next-generation sequencing ensure accurate transcription of genes at the cell level by preserving spatial locations, which provide a great opportunity for revealing mechanisms of biological systems.
ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation
Exploring complex pathological structures is crucial for understanding biology and pathological mechanisms, which is also an important basis for revealing potential causes and pathophysiological changes [1, 2]. In multicellular organisms, cells are usually organized into different groups (known as cell types) [3], which can be effectively tracked with the single-cell RNA-sequencing (scRNA-seq) techniques by dissociating cells from tissues to measure the transcriptional levels of genes [4].
Denoising smooth‐detail representation of MRI‐SPECT fusion
CONFLICT OF INTEREST STATEMENT No, there is no conflict of interest. REFERENCES 1, , . Joint patch clustering-based dictionary learning for multimodal image fusion. Inform Fusion. 2016; 27: 198-214. 2, , , et al. A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Trans Instrum Meas. 2021; 70: 1-23. 3, , . Anatomical-functional image fusion by information of interest in local Laplacian filtering domain. IEEE Trans Image Process. 2017; 12: 5855-5866. 4, .
Correction to: Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non‑stationary data patterns - Journal of Cloud Computing
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns - Journal of Cloud Computing
Research on time series data prediction can generally be divided into three directions, namely, research on prediction methods based on statistics, research on prediction methods based on machine learning, and research on prediction methods based on hybrid models. Among them, machine learning includes traditional machine learning and deep learning, and hybrid prediction models mainly consist of two parts: signal decomposition of time series data and time series prediction.
Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust
Shiyong Wang1†, Asad Khan2*, Ying Lin1, Zhuo Jiang3, Hao Tang4*, Suliman Yousef Alomar5, Muhammad Sanaullah6 and Uzair Aslam Bhatti4*† 1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China 2Metaverse Research Institute, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China 3College of Food Science, South China Agricultural University, Guangzhou, China 4School of Information and Communication Engineering,...
Why Security Leaders Can't Afford to Be Just 'Left-Brained'
The left side of the brain is logical and linear; the right side, creative. You have to use both sides of the brain to connect to your audience in your business. Although my lifelong passion for technology has served me well, one particular lesson truly transformed my career — the acknowledgment that there are two sides of the brain: the right side, which is the source of creativity, and the left side, where technical thinking takes place.
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