An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification by Cuiping Shi, Mengxiang Ding, Liguo Wang

“…The experimental results indicate that the proposed IRCHKD outperforms than some state-of-the-art RSSC approaches with a large margin in lower computational complexity.…”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

“…Key research gaps are identified, including the need for large training samples, challenges in Maximum Intensity Projection (MIP) imaging, limitations of 2D architectures, and issues related to overfitting and computational complexity. The review also observes that shallow networks and pretrained models are effective in addressing these challenges. …”
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ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection by Dewei Kong, Guoshun Yuan, Hongjiang Yu, Shuai Wang, Bo Zhang

“…However, existing methods, while pursuing high detection accuracy, are often associated with high computational complexity, making them unsuitable for resource-constrained environments. …”
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The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models by Marcin Wrona, Andrej Prša

“…Optimization of the ANN architecture yielded a model with six hidden layers, each containing 512 nodes, providing an optimized balance between accuracy and computational complexity. Extensive testing enabled us to establish ANN's applicability limits and to quantify the systematic and statistical errors associated with using such networks for EB analysis. …”
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Supralinear dendritic integration in murine dendrite-targeting interneurons by Simonas Griesius, Amy Richardson, Dimitri Michael Kullmann

“…Dendritic boosting of spatially clustered synaptic signals argues for previously unappreciated computational complexity in dendrite-projecting inhibitory cells of the hippocampus.…”
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An Innovative Real-Time Recursive Framework for Techno-Economical Self-Healing in Large Power Microgrids Against Cyber–Physical Attacks Using Large Change Sensitivity Analysis by Mehdi Zareian Jahromi, Elnaz Yaghoubi, Elaheh Yaghoubi, Mohammad Reza Maghami, Harold R. Chamorro

“…This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. …”
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Design and Implementation of Hybrid GA-PSO-Based Harmonic Mitigation Technique for Modified Packed U-Cell Inverters by Hasan Iqbal, Arif Sarwat

“…Other appealing features are reduced computational complexity and improved waveform quality; hence, the method is highly suitable for both grid-tied and standalone renewable energy applications. …”
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Energy-efficient and fast memristor-based serial multipliers applicable in image processing by Seyed Erfan Fatemieh, Bahareh Bagheralmoosavi, Mohammad Reza Reshadinezhad

“…Serial Material Implication (IMPLY) logic design implements arithmetic circuits by applying emerging memristive technology that enables CIM Array (CIM-A). The computational complexity of IMPLY-based multipliers for use in the CIM-A architecture is a significant design challenge. …”
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Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm by Jinduo Liu, Jihao Zhai, Junzhong Ji

“…However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. …”
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Development of algorithm for receiving and decoding input signal using MTD decoder for subscriber terminals operating with low-orbit spacecraft by D. P. Sedunov, A. S. Zhunusova

“…The comparison allows us to conclude that the application of the developed algorithm is justified in systems where more stringent requirements are imposed on the probability of an error in the communication channel, while the «Messi» decoder is more appropriate to use in the presence of restrictions on computational complexity.…”
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Multi-query based key node mining algorithm for social networks by Guodong XIN, Tengwei ZHU, Junheng HUANG, Jiayang Wei, Runxuan Liu, Wei WANG

“…Mining key nodes in complex networks has been a hotly debated topic as it played an important role in solving real-world problems.However, the existing key node mining algorithms focused on finding key nodes from a global perspective.This approach became problematic for large-scale social networks due to the unacceptable storage and computing resource overhead and the inability to utilize known query node information.A key node mining algorithm based on multiple query nodes was proposed to address the issue of key suspect mining.In this method, the known suspects were treated as query nodes, and the local topology was extracted.By calculating the critical degree of non-query nodes in the local topology, nodes with higher critical degrees were selected for recommendation.Aiming to overcome the high computational complexity of key node mining and the difficulty of effectively utilizing known query node information in existing methods, a two-stage key node mining algorithm based on multi-query was proposed to integrate the local topology information and the global node aggregation feature information of multiple query nodes.It reduced the calculation range from global to local and quantified the criticality of related nodes.Specifically, the local topology of multiple query nodes was obtained using the random walk algorithm with restart strategy.An unsupervised graph neural network model was constructed based on the graphsage model to obtain the embedding vector of nodes.The model combined the unique characteristics of nodes with the aggregation characteristics of neighbors to generate the embedding vector, providing input for similarity calculations in the algorithm framework.Finally, the criticality of nodes in the local topology was measured based on their similarity to the features of the query nodes.Experimental results demonstrated that the proposed algorithm outperformed traditional key node mining algorithms in terms of time efficiency and result effectiveness.…”
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KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters by Siyuan Shen, Jichen Chen, Guanfeng Yu, Zhengjun Zhai, Pujie Han

“…Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation. …”
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An Efficient Retinal Fluid Segmentation Network Based on Large Receptive Field Context Capture for Optical Coherence Tomography Images by Hang Qi, Weijiang Wang, Hua Dang, Yueyang Chen, Minli Jia, Xiaohua Wang

“…With only 1.02 million parameters and a computational complexity of 3.82 G FLOPs, LKMU-Lite achieves state-of-the-art performance across multiple metrics on the ICF and RETOUCH datasets, demonstrating both its efficiency and generalizability compared to existing methods.…”
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Optimisation of sparse deep autoencoders for dynamic network embedding by Huimei Tang, Yutao Zhang, Lijia Ma, Qiuzhen Lin, Liping Huang, Jianqiang Li, Maoguo Gong

“…A sparse deep autoencoder (called SPDNE) for dynamic NE is proposed, aiming to learn the network structures while preserving the node evolution with a low computational complexity. SPDNE tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic NE. …”
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ReluformerN: Lightweight High-Low Frequency Enhanced for Hyperspectral Agricultural Lancover Classification by LIU Yi, ZHANG Yanjun

“…This indicated that Reluformer not only effectively extracted global features but also reduced computational complexity. Finally, the effectiveness of the high-frequency and low-frequency branch networks was verified. …”
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MetaQ: fast, scalable and accurate metacell inference via single-cell quantization by Yunfan Li, Hancong Li, Yijie Lin, Dan Zhang, Dezhong Peng, Xiting Liu, Jie Xie, Peng Hu, Lu Chen, Han Luo, Xi Peng

“…This approach reduces computational complexity from exponential to linear while maintaining or surpassing the performance of existing metacell algorithms. …”
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Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture by Anju Das, N. Neelima, K. Deepa, Tolga Ozer

“…The abundance of features introduces high dimensionality, contributing to high computational complexity and resource demands. Furthermore, the presence of redundant or highly correlated selected features may lead to multicollinearity issues. …”
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Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher by Kaifeng Yang, Li Mu, Dongdong Yang, Feng Zou, Lei Wang, Qiaoyong Jiang

“…Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. …”
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A Lightweight Human Activity Recognition Method for Ultra-wideband Radar Based on Spatiotemporal Features of Point Clouds by Yongkun SONG, Tianxing YAN, Ke ZHANG, Xian LIU, Yongpeng DAI, Tian JIN

“…To address the issues of high computational complexity and extensive network parameters in existing action recognition algorithms, this study proposes an efficient and lightweight human activity recognition method using UWB radar based on spatiotemporal point clouds. …”
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Gridless DOA Estimation with Extended Array Aperture in Automotive Radar Applications by Pengyu Jiang, Silin Gao, Jie Zhao, Zhe Zhang, Bingchen Zhang

“…Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination.…”
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