SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields by Ke Tang, Yurong Qian, Hualong Dong, Yuning Huang, Yi Lu, Palidan Tuerxun, Qin Li

“…The model demonstrates remarkable robustness on other pest datasets while maintaining a manageable parameter size and computational complexity suitable for edge devices.…”
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Algorithm and Hardware Design of a Fast Intra Frame Mode Decision Module for H.264/AVC Encoders by Daniel Palomino, Guilherme Corrêa, Cláudio Diniz, Sergio Bampi, Luciano Agostini, Altamiro Susin

“…In the rate-distortion optimization (RDO), the process of choosing the best prediction mode is performed through exhaustive executions of the whole encoding process, increasing significantly the encoder computational complexity. Considering H.264/AVC intra frame prediction, there are several modes to encode a macroblock (MB). …”
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A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes by Praveen Sai Bere, Mohammed Zafar Ali Khan, Lajos Hanzo

“…By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. …”
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LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation by Guanghong Deng, Bing Yu, Wenlong Jing, Yong Li, Xiaodan Zhao

“…While improving model performance, it does not introduce higher computational complexity and does not have a major impact on the processing speed of a single image.…”
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A measure of the difference between test sets for generating controlled random tests by V. N. Yarmolik, V. V. Petrovskaya, I. Mrozek

“…The main attention is paid to binary test sets, when the task of calculating given difference metric is reduced to the classical assignment problem using the Hungarian algorithm. The computational complexity of the Hungarian algorithm is estimated by the relation O(n4). …”
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Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, Muhammad Sheraz, Teong Chee Chuah

“…Additionally, we conducted a comparative analysis with a hybrid model, demonstrating that the proposed TCN delivers superior performance with lower computational complexity, fewer parameters, a smaller model size, and shorter computation times. …”
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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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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 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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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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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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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models by Adel Zga, Farouq Zitouni, Saad Harous, Karam Sallam, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

“…This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. …”
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A Comparison of Classification Algorithms for Predicting Dis-tinctive Characteristics in Fine Aroma Cocoa Flowers Using WE-KA Modeler by Daniel Tineo, Yuriko S. Murillo, Mercedes Marín, Darwin Gomez, Victor H. Taboada, Malluri Goñas, Lenin Quiñones Huatangari

“…The algorithms Simple Logistic and LMT were the most accurate and specific, while Naive Bayes was the most efficient in terms of computational complexity for model building. This research provides a comprehensive overview of the use of machine learning to analyze functional traits of flowers that most influence cocoa genetic diversity. …”
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