Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization by Anurag Singh, Shekhar Yadav, Nitesh Tiwari, Dinesh Kumar Nishad, Saifullah Khalid

“…Abstract Load frequency control (LFC) systems in power grids face challenges in maintaining stability while managing computational complexity. This research presents an optimized approach combining model order reduction techniques with Teaching Learning-Based Optimization (TLBO) for PID controller tuning in single-area LFC systems. …”
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Joint Active/Passive Beamforming Optimization via RWMMSE and Gradient Projection for Downlink IRS-Aided MU-MISO Systems by Seraphin F. Kimaryo, Eduard E. Bahingayi, Kyungchun Lee

“…The proper reconfiguration of its elements, which is the primary task towards achieving these prospects, has been previously explored by many researchers, where most proposals are characterized by high computational complexity. This study builds upon previous research on downlink IRS-assisted multi-user multiple-input single-output (MISO) systems by introducing innovative methods to accelerate their operations. …”
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Retracted: Computer Medical Image Segmentation Based on Neural Network by Xiaopeng Wang, Lei Gu, Zhongyi Wang

“…This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. …”
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TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements by Wenhui Fang, Weizhen Chen

“…To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. …”
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A Modified WASPAS Method for the Evaluation of E-Commerce Websites Based on Pythagorean Fuzzy Information by Xiufang Ou, Bingbin Chen

“…The proposed approach significantly enhanced performance and reduced computational complexity, making it a promising tool for real-time detection and prediction of the efficiency of employees in various organizations and a tool for growth.…”
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Autonomous security analysis and penetration testing model based on attack graph and deep Q-learning network by Cheng FAN, Guoqing HU, Taojie DING, Zhanhua ZHANG

“…With the continuous development and widespread application of network technology, network security issues have become increasingly prominent.Penetration testing has emerged as an important method for assessing and enhancing network security.However, traditional manual penetration testing methods suffer from inefficiency,human error, and tester skills, leading to high uncertainty and poor evaluation results.To address these challenges, an autonomous security analysis and penetration testing framework called ASAPT was proposed, based on attack graphs and deep Q-learning networks (DQN).The ASAPT framework was consisted of two main components:training data construction and model training.In the training data construction phase, attack graphs were utilized to model the threats in the target network by representing vulnerabilities and possible attacker attack paths as nodes and edges.By integrating the common vulnerability scoring system (CVSS) vulnerability database, a “state-action”transition matrix was constructed, which depicted the attacker’s behavior and transition probabilities in different states.This matrix comprehensively captured the attacker’s capabilities and network security status.To reduce computational complexity, a depth-first search (DFS) algorithm was innovatively applied to simplify the transition matrix, identifying and preserving all attack paths that lead to the final goal for subsequent model training.In the model training phase, a deep reinforcement learning algorithm based on DQN was employed to determine the optimal attack path during penetration testing.The algorithm interacted continuously with the environment, updating the Q-value function to progressively optimize the selection of attack paths.Simulation results demonstrate that ASAPT achieves an accuracy of 84% in identifying the optimal path and exhibits fast convergence speed.Compared to traditional Q-learning, ASAPT demonstrates superior adaptability in dealing with large-scale network environments, which could provide guidance for practical penetration testing.…”
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Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection by Ângela Semitela, Miguel Pereira, António Completo, Nuno Lau, José P. Santos

“…The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level. …”
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Improved printed circuit board defect detection scheme by Lufeng Bai, Wen Hao Xu

“…Furthermore, in order to further enhance the interaction and fusion of multi-scale features, we also add a SlimNeck module to the neck structure, which realizes efficient information transfer through streamlined design and reduces computational complexity at the same time. In addition, we draw on the advanced BiFPN structure, which enables the bidirectional flow of feature information between multiple layers and greatly improves the capture and integration of small target features. …”
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A multimodal multistream multilevel fusion network for finger joint angle estimation with hybrid sEMG and FMG sensing by Zhouping Chen, Mohamed Amin Gouda, Longcheng Ji, Hong Wang

“…The experimental results suggest that the MMMFNet has higher estimation accuracy (RMSE: 6.225±0.276 vs. 6.953±0.282, R2: 0.879±0.019 vs 0.846±0.021), shorter training time (4.146±0.074 min vs. 16.536±0.430 min), and lower computational complexity (5.4×106 FLOPs vs 8.8×106 FLOPs) than state-of-the-art fusion methods. …”
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Hybrid optimization of EDLP and high-low pricing strategies by Hamed Karimi

“…However, as retail applications scale up, the computational complexity becomes more challenging, necessitating the use of the Grey Wolf Optimization (GWO) algorithm. …”
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Key frame extraction algorithm for surveillance videos using an evolutionary approach by Manjusha Rajan, Latha Parameswaran

“…Furthermore, the computational complexity of the GA is intricately compared to that of DE and DL-based approaches, highlighting the distinct efficiencies and performance features.…”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…Hybrid approaches that combine multiple techniques have shown promising results, balancing accuracy and computational complexity. Challenges remain, including handwriting variability, noise, and the need for real-time performance. …”
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A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm by Sanyi Li, Wenjie Hou, Peng Liu, Weichao Yue, Qian Wang

“…SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. …”
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Fast Ways to Detect Outliers by Emad Obaid Merza, Nashaat Jasim Mohammed

“…In this research, we aim to develop easy methods for detecting outliers in big data, as the problem that this research addresses is that many of the newly developed methods for detecting outliers suffer from computational complexity or are efficient when the sample size is small. …”
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Time-Efficient Contact Consistent Whole-Body Control Framework via Reduced-Dimension Dynamics Construction by Junewhee Ahn, Jaesug Jung, Hokyun Lee, Jaeheung Park

“…Whole-body control frameworks for highly redundant, floating-base robots like humanoids are challenging due to the increased computational complexity arising from their high degrees of freedom. …”
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Inference Attacks on Genomic Data Based on Probabilistic Graphical Models by Zaobo He, Junxiu Zhou

“…Existing genome inferences have relatively high computational complexity with the input of tens of millions of SNPs and human traits. …”
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An Improved Hierarchical Optimization Framework for Walking Control of Underactuated Humanoid Robots Using Model Predictive Control and Whole Body Planner and Controller by Yuanji Liu, Haiming Mou, Hao Jiang, Qingdu Li, Jianwei Zhang

“…At the high level, we employ a matrix exponential (ME)-based discretization of the MPC, ensuring numerical stability across a wide range of step sizes (5 to 100 ms), thereby reducing computational complexity without sacrificing control quality. …”
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Artificial molecular communication network based on DNA nanostructures recognition by Junke Wang, Mo Xie, Lilin Ouyang, Jinggang Li, Lianhui Wang, Chunhai Fan, Jie Chao

“…By the establishment of a node partition algorithm for path traversal based on DR-AMCN, the computational complexity of the seven-node Hamiltonian path problem is reduced with the final solution directly obtained through the rate-zonal centrifugation method, and scalability of this approach is also demonstrated. …”
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A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems by Yuqi Fan, Junpeng Shao, Guitao Sun, Xuan Shao

“…The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. …”
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A novel transmission-augmented deep unfolding network with consideration of residual recovery by Zhijie Zhang, Huang Bai, Ljubiša Stanković, Junmei Sun, Xiumei Li

“…Nevertheless, these iterative algorithms suffer from the high computational complexity. The fashionable deep network-based methods can achieve high-precision CS reconstruction with satisfactory speed but are short of theoretical analysis and interpretability. …”
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