IRS-Assisted Wireless Sensor Networks With Hybrid TDMA-NOMA by Haitham Al-Obiedollah, Haythem Bany Salameh, Kanapathippillai Cumanan

“…To overcome these challenges, an iterative algorithm is proposed to solve the formulated optimization problem. …”
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Multi-Modal Federated Learning Over Cell-Free Massive MIMO Systems for Activity Recognition by Seyed Mohammad Sheikholeslami, Pai Chet Ng, Jamshid Abouei, Konstantinos N. Plataniotis

“…Additionally, we employ a modified Particle Swarm Optimization (PSO) algorithm for efficient resource allocation. …”
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SafeTrack: Secure Tracking Protocol for Mobile Sensor Nodes in Unstable Wireless Sensor Networks by Ali Adnan Al-Khazraji, Fatimah Abdulridha Rashid

“…It also incorporates an Adaptive Routing Algorithm (ARA) and an Energy Optimization Module (EOM) to enable both efficient resource utilization and resilience to changes in dynamic network conditions. …”
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Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing by Salman Bari, Xiagong Wang, Ahmad Schoha Haidari, Dirk Wollherr

“…By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. …”
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Retracted: Computer Medical Image Segmentation Based on Neural Network by Xiaopeng Wang, Lei Gu, Zhongyi Wang

“…This article proposes an image segmentation method based on an optimized cellular neural network. 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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Deep Reinforcement Learning for Dynamic Pricing and Ordering Policies in Perishable Inventory Management by Yusuke Nomura, Ziang Liu, Tatsushi Nishi

“…To tackle this challenge, we propose eliminating irrational actions in dynamic programming without sacrificing optimality. To solve this problem more efficiently, we also implement a deep reinforcement learning algorithm, proximal policy optimization, to solve this problem. …”
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A Deformation Reconstruction Strategy for Integrated Truss Structures Subjected to Thermal–Mechanical Load by Zexing Yu, Xiaofei Ma, Jialong Zhu, Dayu Zhang, Yonggang Xue, Pengfei Huang, Yichen Li, Hao Li

“…The proposed approach utilizes Ko displacement theory as the core algorithm, while the least squares optimization method is employed to determine the integration with unknown initial values during the reconstruction process. …”
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Research on Adaptive Identification Technology for Rolling Bearing Performance Degradation Based on Vibration–Temperature Fusion by Zhenghui Li, Lixia Ying, Liwei Zhan, Shi Zhuo, Hui Li, Xiaofeng Bai

“…Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. …”
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MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction by Haoran Wang, Haijun Liu, Jing Yuan, Huijun Le, Weifeng Shan, Liangchao Li

“…It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. …”
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A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar by Bo Wang, Tao Lai, Qingsong Wang, Haifeng Huang

“…Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. …”
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A Framework for Synergy Measurement Between Transportation and Production–Living–Ecological Space Using Volume-to-Capacity Ratio, Accessibility, and Coordination by Xiaoyi Ma, Mingmin Liu, Jingru Huang, Ruihua Hu, Hongjie He

“…By introducing an improved accessibility evaluation model and developing a coordination measurement algorithm, a three-dimensional evaluation mechanism covering development potential assessment, service efficiency diagnosis, and resource allocation optimization is established. …”
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Charging Station Management Strategy for Returns Maximization via Improved TD3 Deep Reinforcement Learning by Hengjie Li, Jianghao Zhu, Yun Zhou, Qi Feng, Donghan Feng

“…Also, we use marginalised importance sampling to calculate the Q-return function for TD3, which ensures that the constructed strategies are more likely to learn high-value experiences while having higher robustness. It is shown in numerical experiments that the charging station management strategy (CSMS) based on the modified TD3 obtains the fastest convergence speed and the highest robustness and achieves the largest operational returns compared to the CSMS constructed using deep deterministic policy gradient (DDPG), actor-critic using Kronecker-factored trust region (ACKTR), trust region policy optimization (TRPO), proximal policy optimization (PPO), soft actor-critic (SAC), and the original TD3.…”
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Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing by Ahmet Hakan İnce, Serkan Özbay

“…Seven models were evaluated, including standard 1PL-IRT and six response-time-adjusted variants: TP-IRT, STP-IRT, TWD-IRT, NRT-IRT, DTA-IRT, and ART-IRT. Three optimization techniques—Maximum Likelihood Estimation (MLE), full parameter optimization, and K-fold Cross-Validation (CV)—were employed to assess model performance. …”
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Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images by Zhendong Ji, Lingzhi Yin, Jinhong Wang

“…Principal Component Analysis (PCA) was applied to reduce feature dimensionality, and the Random Forest classification algorithm was optimized with Bayesian Optimization and Tree-structured Parzen Estimators (TPE) for improved performance. …”
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Scalable FPGA Implementation of a Reliability-Based Direct Turbo Decoder for Short Block Codes by Senthil Murugan, Ramesh Bhakthavatchalu, Karthi Balasubramanian, B. Yamuna, Deepak Mishra, Sree Ranjani Rajendran

“…This paper presents the first hardware implementation of the RCODD algorithm on an FPGA, featuring a serial architecture optimized for minimal memory usage and logic complexity. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…Compared with the traditional method, the innovation of this paper is that a non-destructive prediction method is proposed, which enables high-precision predictions of the crushing force by integrating multi-dimensional phenotypic features and an intelligent optimization algorithm. Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. …”
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DRACO: Decentralized Asynchronous Federated Learning Over Row-Stochastic Wireless Networks by Eunjeong Jeong, Marios Kountouris

“…Through a thorough convergence analysis, we show that DRACO achieves high performance in decentralized optimization while maintaining low variance across users even without predefined scheduling policies. …”
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Balancing resource utilization and slice dissatisfaction through dynamic soft slicing for 6G wireless networks by Zeinab Sasan, Siavash Khorsandi

“…The problem is formulated as a Mixed Integer Linear Programming (MILP) model, aiming to maximize network utilization while minimizing slice dissatisfaction. Given the NP-hard nature of the problem, we propose the Heuristic Resource Allocation for Soft Slicing (HRASS) algorithm, which achieves near-optimal performance with significantly reduced computational complexity. …”
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Post-Quantum Cryptographic Frameworks for Internet of Things (IoT) and Internet of Medical Things (IoMT) Authentication Systems by Thomas L Barna, Samson Isaac, Christopher Habu, Saratu Habu, Abimbola A Joseph

“…The framework incorporates three innovations: (1) an NTRU-based key establishment protocol, leveraging the Shortest Vector Problem in polynomial lattices, (2) a hash-based authentication mechanism with zero-knowledge proof verification, and (3) a genetic algorithm that dynamically optimizes cryptographic parameters in real-time, based on device capabilities and network conditions. …”
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Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments by Yang Lei, Jian Hou, Peixin Ma, Mingze Ma

“…In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system. …”
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