An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings by Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

“…For improved performance, these models were then combined using a voting classifier technique. …”
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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

“…The optimized model thus resulted in the prediction of disease conversion probability from Mild Cognitive Impairment to AD because of significant structural features that are key-requisite for affected geriatric cohorts.…”
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Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation by Guanglin Yin, Chaoning Lin, Taozhen Sheng, Wenbo Xue, Tongchun Li, Siyu Chen

“…To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). …”
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Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control by Rongjun Cheng, Haoli Lou, Qi Wei

“…The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. …”
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Research on intelligent computing offloading model based on reputation value in mobile edge computing by Jin QI, Hairong SUN, Kun GONG, Bin XU, Shunyi ZHANG, Yanfei SUN

“…Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.…”
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Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty by Chao Chen, Weifeng Peng, Cheng Xie, Shufeng Dong, Yibo Hua

“…The model considers system constraints and employs the sparrow search algorithm (SSA) to optimize the maximum PV hosting capacity. …”
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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

“…Future work will focus on algorithm optimization, expanded datasets, and real-time monitoring integration to enable more precise, dynamic risk evaluation for improved DF prevention and early intervention.…”
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Digital security risk identification and model construction of smart city based on deep learning by Zhilei Zhao

“…Abstract In view of the network security risks caused by the integration of the Industrial Internet of Things (IIoT) in the construction of smart cities, this research proposes a digital security identification model (DL-DSIM) based on deep learning, which aims to improve the data transmission efficiency and system security in the smart city environment. …”
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Study of Synergetic Optimization Operation for Distribution Network Considering Multiple Reactive Power Output Modes of Photovoltaics and Different Port Numbers of Flexible Interco... by Yijin Li, Jibo Wang, Zihao Zhang, Wenhao Xu, Ming Wu, Geng Niu

“…Three algorithms were used for solving the multi-objective optimization model. …”
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Parametric Sensitivity Analysis of Safe Train Interval Model Based on Relative Braking Distance by QIAN Hua, LYU Haojiong

“…The qualitative analysis based on a safe time interval model and the quantitative analysis based on simulations using the model plus an algorithm, were conducted, shedding light on the extent of influence from key parameters in the safe interval model on stopping time differences. …”
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Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval by Syed Furqan Rafique, Zhang Jianhua, Rizwan Rafique, Jing Guo, Irfan Jamil

“…In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. …”
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Variable Speed Limit Strategies Based on the Macro Hierarchical Control Traffic Flow Model by Shubin Li, Tao Wang, Hualing Ren, Baiying Shi, Xiangke Kong, Jianyong Chai, Xuejuan Wang

“…Then, the prediction information of traffic variables and the VSL strategy are introduced to macro hierarchical control traffic flow model. A solution algorithm is further developed to find the optimal parameters of VSL by minimizing the total travel time and delay. …”
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Expanding Irrigation Intensifies the Humid Heat Stress in China: Evidence From Observations and Modeling by Dan Yan, Mengmeng Li, Xinyi Jiang, Yuting Lu, Yiting Zhou, Tijian Wang

“…Application of province‐specific soil moisture thresholds generates improved simulation of irrigation water amount and a lower model error by 30%. …”
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Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications by Asghar Khan, Saeed Islam, Muhammad Ismail, Abdulaziz Alotaibi

“…A case study on wearable sensor selection demonstrates the proposed model’s efficiency. We present an algorithm and a flowchart to guide the decision-making process, alongside a computational example that verifies the method’s reliability. …”
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Mobile Location With Multi-Spot Measurements Model Considering Anchor Node Position Errors by Chenyu Yang, Rui Ren, Dongkun Jin, Jiyan Huang

“…By leveraging multiple measurements collected over short intervals, this models can significantly improve localization performance compared to traditional single-measurement approaches. …”
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EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model by Weikang He, Xizhe Li, Yujin Wan, Honming Zhan, Nan Wan, Sijie He, Yaoqiang Lin, Longyi Wang, Wenxuan Yu, Liqing Chen

“…The hyperparameters of the model were optimized using the Rabbit Optimization Algorithm (ROA), and 10-fold cross-validation was employed to improve the stability and reliability of model evaluation, mitigating overfitting and bias. …”
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Prototype Mobile Vision System for Automatic Length Estimation of Olive Flounder (<i>Paralichthys olivaceus</i>) in Indoor Aquaculture by Inyeong Kwon, Hang Thi Phuong Nguyen, Paththige Waruni Prasadini Fernando, Hieyong Jeong, Sungju Jung, Taeho Kim

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Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs by Hatem Gasmi, Azher M. Abed, Ashit Kumar Dutta, Fahad M. Alhomayani, Ibrahim Mahariq, Fahad Alturise, Salem Alkhalaf, Tamim Alkhalifah, Yasser Elmasry, Baseem Khan

“…This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. …”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…These can be mitigated by employing surrogate modeling methods, especially to expedite design workflows like local/global optimization or uncertainty quantification. …”
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