A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity by Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao

“…Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. …”
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Predicting Subsurface Layer Thickness and Seismic Wave Velocity Using Deep Learning: Knowledge Distillation Approach by Amir Moslemi, Anna Briskina, Jason Li, Zubeka Dang, Peyman Moghaddam

“…Deep learning offers a promising solution to analyze complex geographical structures, but its computational complexity can be a barrier for deployment. This study introduces a deep learning-based approach enhanced by knowledge distillation (KD) to predict subsurface layer thickness and seismic wave velocity. …”
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Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation by Osman Mamun, Markus Bause, Bhuiyan Shameem Mahmood Ebna Hai

“…However, the application of these protocols in materials science, particularly in the design of novel alloys with multiple targeted properties, remains constrained by computational complexity and the absence of reliable and robust acquisition functions for multiobjective optimisation. …”
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Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification by Jinping Liu, Jiezhou He, Zhaohui Tang, Pengfei Xu, Wuxia Zhang, Weihua Gui

“…Multidirectional and multiscale TP features are then characterized by the SWDM parameters based on the oriented differential operators; in other words, texture images are convolved with multiscale and multidirectional Gaussian derivative filters (GDFs), including the steerable isotropic GDFs (SIGDFs) and the oriented anisotropic GDFs (OAGDFs), for the omnidirectional and multiscale SS detail exhibition with low computational complexity. Finally, SWDM-based TP feature parameters, demonstrated to be directly related to the human vision perception system with significant physical perception meaning, are extracted and used to TP classification with a partial least squares-discriminant analysis- (PLS-DA-) based classifier. …”
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Empirical analysis of control models for different converter topologies from a statistical perspective by Hole Shreyas Rajendra

“…This text also compares the evaluated models in terms of their conversion efficiency, cost of deployment, delay needed for control, scalability and computational complexity under different scenarios. Based on this comparison, researchers will be able to identify optimized models for their performance-specific deployments. …”
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Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems by Chenfei Xie, Yue Xiu, Songjie Yang, Qilong Miao, Lu Chen, Yong Gao, Zhongpei Zhang

“…The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. …”
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CNN-Based Time Series Decomposition Model for Video Prediction by Jinyoung Lee, Gyeyoung Kim

“…CNN-based models offer advantages over RNN and transformer-based models due to their ease of parallel processing and lower computational complexity, highlighting their significance in practical applications. …”
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metaRange: A framework to build mechanistic range models by Stefan Fallert, Lea Li, Juliano Sarmento Cabral

“…However, the computational complexity of mechanistic models limits their development and applicability to large spatiotemporal extents. …”
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CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People by Fahad Ashiq, Muhammad Asif, Maaz Bin Ahmad, Sadia Zafar, Khalid Masood, Toqeer Mahmood, Muhammad Tariq Mahmood, Ik Hyun Lee

“…The application uses MobileNet architecture due to its low computational complexity to run on low-power end devices. To assess the efficacy of the proposed system, six pilot studies have been performed that reflected satisfactory results. …”
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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

“…The ASHSC algorithm offers intuitive 3D guidance for uncertainty regions, while maintaining manageable computational complexity. The SQ-level-based on-demand correction strategy adaptively minimizes uncertainties inherent in deep-learning-based organ masks and advances automated auditing and correction methodologies.…”
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An MPPT method using phasor particle swarm optimization for PV‐based generation system under varying irradiance conditions by Injila Sajid, Adil Sarwar, Mohd Tariq, Shafiq Ahmad, Farhad Ilahi Bakhsh, Adamali Shah Noor Mohamed, Md. Rasidul Islam

“…The proposed algorithm is parameter‐less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP). …”
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Fast dynamical simulation combining harmonic-modal hybrid formulations with space separated representations by Claudia Germoso, Sebastian Rodriguez, Angelo Pasquale, Francisco Chinesta

“…The integration of space-separated representations allows for extremely fine 3D resolutions while maintaining computational complexity at a significantly reduced order of magnitude. …”
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WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion by Yaqi Zhang, Zhiqiang Lan, Zheng Shi, Yuhang Xue, Jie Wang, Kun Zhu, Jian He, Xiujian Chou

“…Due to the precise time-frequency analysis capabilities, flexibility, and low computational complexity, Wavelet Packet Decomposition (WPD) has become one of the most widely used noise reduction approaches in microseismic data enhancement. …”
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SS-YOLO: A Lightweight Deep Learning Model Focused on Side-Scan Sonar Target Detection by Na Yang, Guoyu Li, Shengli Wang, Zhengrong Wei, Hu Ren, Xiaobo Zhang, Yanliang Pei

“…The lightweight design is essential for reducing computational complexity and resource consumption, allowing the model to be more efficient on edge devices with limited processing power and storage. …”
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Bringing Intelligence to the Edge for Structural Health Monitoring: The Case Study of the Z24 Bridge by Ali Dabbous, Riccardo Berta, Matteo Fresta, Hadi Ballout, Luca Lazzaroni, Francesco Bellotti

“…Results show that a model based on WaveNet reaches state-of-the-art performance, also reducing model size and computational complexity. WaveNet proves perfectly suited to interpret the bridge vibration waveforms directly in the time domain, without any specific preprocessing. …”
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Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning by Yuanran Zhu, Jia Yin, Cian C Reeves, Chao Yang, Vojtěch Vlček

“…Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N _t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. …”
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MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Chunfang Tian, Xinliang Lü

“…While Transformers are proficient in capturing global features, they suffer from high computational complexity and require large amounts of data for training. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…This efficient model, termed ‘SSR-UNet,’ leverages bidirectional scanning to capture both global and local features in image data, achieving strong performance with low computational complexity. Traditional CNNs struggle with long-range dependencies, while Transformers, though excellent at global feature extraction, are computationally intensive and require large amounts of data. …”
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Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems by Maham Nayab, Asim Waris, Muhammad Jawad Khan, Dokhyl AlQahtani, Ahmed Imran, Syed Omer Gilani, Umer Hameed Shah

“…However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Methods:The network scalable degree was designed as a measure of scalability,and then a user association strategy to improve network scalable degree was studied by using optimization theory. 1) For modelling the optimization problem, firstly, the network coupling degree, representing the degree of association among nodes, was constructed to establish the mathematical relationship between the network scalable degree and AP group (APG).Thus,the problem of improving the network scalable degree was modeled as the problem of minimizing the network coupling degree.Then,a multi-objective optimization problem of minimum network coupling degree and maximum user rate was established to find the balance between network scalable degree and network service quality. 2) For solving the optimization problem,to avoid the high computational complexity,a heuristic user association strategy based on APG mergence and topological potential optimization was proposed.With the proposed algorithm,the number of APG could be reduced by APG mergence,and the number of APG that AP belongs to could be reduced by AP exiting APG. …”
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