Enhancing Road Scene Segmentation With an Optimized DeepLabV3+ by Zhe Ren, Libao Wang, Tianming Song, Yihang Li, Jian Zhang, Fengfeng Zhao

“…Second, the Atrous Spatial Pyramid Pooling (ASPP) module is optimized by introducing depthwise separable convolutions and a hierarchical feature fusion strategy, reducing computational complexity and mitigating the grid effect, a limitation in many current models. …”
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YOLOv8n-CA: Improved YOLOv8n Model for Tomato Fruit Recognition at Different Stages of Ripeness by Xin Gao, Jieyuan Ding, Ruihong Zhang, Xiaobo Xi

“…Experimental results showed that the YOLOv8n-CA model had a parameter count of only 2.45 × 10⁶, computational complexity of 6.9 GFLOPs, and a weight file size of just 4.90 MB. …”
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Knowledge Graph Completion With Pattern-Based Methods by Maryam Sabet, Mohammadreza Pajoohan, Mohammad Reza Moosavi

“…Some methods take advantage of external information such as entity description but at the cost of more computational complexity. Besides, most of the current techniques focus solely on local information in the KG. …”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…This study addresses these challenges by proposing a lightweight YOLOv8-based model, incorporating DualConv and the novel C2f-GND module to enhance feature extraction and reduce computational complexity. Evaluated on the newly developed Shine-Muscat-Complex dataset of 4715 images, the proposed model achieved a 2.6% improvement in mean Average Precision (mAP) over YOLOv8n while reducing parameters by 36.8%, FLOPs by 34.1%, and inference time by 15%. …”
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Coupled Dynamics of Vehicle-Bridge Interaction System Using High Efficiency Method by Lu Sun, Xingzhuang Zhao

“…This paper studies the accuracy and efficiency of discretizing the beam in space as lumped masses using the flexibility method and as finite elements using the stiffness method. Computational complexity analysis is carried out along with a numerical case study to compare the accuracy and efficiency of both methods against the analytical solutions. …”
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Real-Time Interference Mitigation for Reliable Target Detection with FMCW Radar in Interference Environments by Youlong Weng, Ziang Zhang, Guangzhi Chen, Yaru Zhang, Jiabao Chen, Hongzhan Song

“…The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. …”
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Constellation Optimization for MIMO VLC System With Signal-Dependent Noise: Receiver-Side Design and Lookup Table Establishment by Jiaqi Wei, Yuan Wang, Nuo Huang, Yan-Yu Zhang, Yi-Jun Zhu, Wenliang Hao, Chen Gong

“…For the scenario where receiver randomly moves, the constellation lookup table is proposed to transform real-time optimization into table lookup operation, which effectively reduces the real-time computational complexity. Simulation results show that the optimized constellation leads to lower symbol error rate (SER) than the method of maximizing minimum Euclidean distance. …”
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Fast Video Encoding Algorithm for the Internet of Things Environment Based on High Efficiency Video Coding by Jong-Hyeok Lee, Kyung-Soon Jang, Byung-Gyu Kim, Seyoon Jeong, Jin Soo Choi

“…In this paper, a fast intraprediction unit decision method is proposed to reduce the computational complexity of the HEVC RExt encoder. To design intramode decision algorithm, Local Binary Pattern (LBP) of the current prediction unit is used as texture feature. …”
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Mixed Multinomial Probit Model Accommodating Flexible Covariance Structure and Random Taste Variation: An Application to Commute Mode Choice Behavior by Ke Wang, Xin Ye, Hongcheng Gan

“…Compared with the MMNL model, the MMNP model can accommodate more random coefficients without increasing computational complexity.…”
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AFF-LightNet: A Lightweight Ship Detection Architecture Based on Attentional Feature Fusion by Yingxiu Yuan, Xiaoyan Yu, Xianwei Rong, Xiaozhou Wang

“…The experimental results show that compared with the standard YOLOv8n, the improved network has an average accuracy of 98.8%, an increase of 0.4%, a reduction of 1.9 G in computational complexity, and a reduction of 0.19 M in parameter count.…”
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Channel Mixer Layer: Multimodal Fusion Toward Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content by Peng Liu, Tatsuhiro Yokoyama, Takuya Sori, Mamoru Yamamoto

“…Experiment results suggest that the multimodal fusion prediction of existing model backbones by proposed method improves the prediction accuracy up to 15% with almost the same computational complexity compared to that of graphic prediction without auxiliary factors input, having the real‐time inference speed of 34 frames/second and minimum mean absolute error of 0.94/2.63 TEC unit during low/high solar activity period respectively. …”
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C-parameter version of robust bounded one-class support vector classification by Junyou Ye, Zhixia Yang, Yongxing Hu, Zheng Zhang

“…The theoretical properties of the proposed method are successively derived, including the relationship between the solutions to the primal and dual problems, the connections between our C-BOCSVC and $$\nu$$ -OCSVC and the computational complexity. Experimental results over massive datasets demonstrate the feasibility and reliability of our C-BOCSVC, and highlight the superior performance of C-RBOCSVC compared to other state-of-the-art one-class classifiers when data is contaminated. …”
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Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs by Hassen Louati, Ali Louati, Khalid Mansour, Elham Kariri

“…However, building accurate and efficient deep learning models for X-ray image classification remains challenging, requiring both optimized architectures and low computational complexity. In this paper, we present a three-stage framework to enhance X-ray image classification using Neural Architecture Search (NAS), Transfer Learning, and Model Compression via filter pruning, specifically targeting the ChestX-Ray14 dataset. …”
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Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order by J. G. Avalos, A. Rodriguez, H. M. Martinez, J. C. Sanchez, H. M. Perez

“…However, their high computational complexity can restrict their use in certain practical applications. …”
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Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis by Jia Li, Pei Wu, Feilong Kang, Lina Zhang, Chuanzhong Xuan

“…By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. …”
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Experimental Results of Novel DoA Estimation Algorithms for Compact Reconfigurable Antennas by Henna Paaso, Aki Hakkarainen, Nikhil Gulati, Damiano Patron, Kapil R. Dandekar, Mikko Valkama, Aarne Mämmelä

“…We study how the computational complexity and the performance of the algorithms depend on number of selected radiation patterns. …”
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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment by Praveen Vijai, Bagavathi Sivakumar P

“…The proposed model addresses key challenges, including data imbalance, interpretability issues, and computational complexity. By leveraging the bidirectional capability of BiLSTM in the encoder and decoder, the model captures comprehensive temporal dependencies, enabling more effective anomaly detection. …”
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Approximate CNN Hardware Accelerators for Resource Constrained Devices by P Thejaswini, Gautham Suresh, V. Chiraag, Sukumar Nandi

“…Implementation of Convolutional Neural Networks (CNNs) on edge devices require reduction in computational complexity. Leveraging optimization techniques or approximate computing techniques can reduce the overhead associated with hardware implementation. …”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…Hence, in this study, we adopt the PPO technique which utilizes first-order optimization to minimize the computational complexity and devise a DRL scheme for a partially observable flexible link robot manipulator system. …”
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Feature selection based on Mahalanobis distance for early Parkinson disease classification by Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ahmed M. Mahdi, Ayman Ibaida

“…Standard classifiers struggle with high-dimensional datasets due to increased computational complexity, difficulty in visualization and interpretation, and challenges in handling redundant or irrelevant features. …”
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