A Ship Underwater Radiated Noise Prediction Method Based on Semi-Supervised Ensemble Learning by Xin Huang, Rongwu Xu, Ruibiao Li

“…However, the labeled data available for the training of URN prediction model is limited. Semi-supervised learning (SSL) can improve the model performance by using unlabeled data in the case of a lack of labeled data. …”
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Privacy-preserving federated learning framework with dynamic weight aggregation by Zuobin YING, Yichen FANG, Yiwen ZHANG

“…There are two problems with the privacy-preserving federal learning framework under an unreliable central server.① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server.However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility.② Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server.To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight.The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment.In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants.Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models.…”
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A novel LSTPA methodology for managing energy in electrical/thermal microgrids through CHP, battery resources, thermal storage, and demand-side strategies by Elmira Akhavan Maroofi, Mahmoud Samiei Moghaddam, Azita Azarfar, Reza Davarzani, Mojtaba Vahedi

“…Abstract This paper presents a stochastic optimization model for integrated energy management in electrical and thermal microgrids, addressing uncertainties in renewable energy resources. …”
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Expected Length of the Shortest Path of the Traveling Salesman Problem in 3D Space by Hongtai Yang, Xiaoqian Lu, Xinan Zhou, Rong Zheng, Yugang Liu, Siyu Tao

“…Under each scenario, the specified number of demand points is randomly generated, and an improved genetic algorithm and Gurobi are used to find the shortest path. …”
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Green and Reliable Freight Routing Problem in the Road-Rail Intermodal Transportation Network with Uncertain Parameters: A Fuzzy Goal Programming Approach by Yan Sun

“…In this study, the author focuses on modeling and optimizing a freight routing problem in a road-rail intermodal transportation network that combines the hub-and-spoke and point-to-point structures. …”
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Deep Reinforcement Learning-Based Attention Decision Network for Agile Earth Observation Satellite Scheduling by Dongning Liu, Guanghui Zhou

“…Moreover, a start-time-shift-based local search is proposed to improve the observation plan generated by the ADN model. …”
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Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends by Chen Boshan

“…Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. …”
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Two-stage denoising method for complex underground tunnel scene three-dimensional point clouds by Zhuli REN, Ruifu YUAN, Liguan WANG, Haokun DENG, Wen WANG, Jinlong ZHANG

“…When the angle threshold is less than 1°, the optimal denoising effect can be achieved. Through the two-stage optimization algorithm, effective repair of surface holes on the tunnel is achieved. …”
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Boundary Guidance Strategy and Method for Urban Traffic Congestion Region Management in Internet of Vehicles Environment by Chuanxiang Ren, Zhen Wang, Changchang Yin, Hui Xu, Li Wang, Luyao Guo, Juntao Li

“…Meanwhile, a method for the boundary guidance strategy is presented in which the macroscopic fundamental diagram (MFD) is used to determine the optimal accumulation, a traffic flow equilibrium model is established to calculate the real-time accumulation, and a fuzzy adaptive PID control algorithm is designed to calculate the optimal traffic inflow of the traffic congestion region. …”
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Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties by Hong Zhang, Qianwei Xi, Lei Chen, Yong Min, Xiongxiong Fan, Wenjin Fang, Nan Tian, Fei Xu

“…Taking the lowest comprehensive operation cost as the economic objective, the smallest emissions of CO2 and atmospheric pollutants as environmental objective and the largest user’s comprehensive satisfaction with electricity as the social objective, based on the robust fuzzy theory, the multi-objective uncertainty optimal scheduling model is constructed, which is transformed into deterministic model and then solved by intelligent optimization algorithm. …”
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Finding Radial Network Configuration of Distribution System Based on Modified Symbiotic Organisms Search by Thuan Thanh Nguyen, Thanh-Quyen Ngo, Thanh Long Duong, Thang Trung Nguyen

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Editorial by Christian Gütl

“…Besma Hezili and Hichem Talbi from Algeria address the collaborative auto-diversified optimization scheme (CADOS) for solving continuous and combinatorial optimization problems by exploring the synergy of various optimization algorithms and enhance their effectiveness and efficiency, particularly for higher-dimensional problems. …”
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TDMA-based user scheduling policies for federated learning by Meixia TAO, Dong WANG, Rui SUN, Naifu ZHANG

“…To improve the communication efficiency in FL (federated learning), for the scenario with heterogeneous edge user's computing capacity and channel state, a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the system delay in each round of model training subject to a given sample size constraint required for computing in each round.In addition, the convergence rate of the proposed scheduling algorithms was analyzed from a theoretical perspective to study the tradeoff between the convergence performance and the total system delay.The selection of the optimal batch size was further analyzed.Simulation results show that the convergence rate of the proposed algorithm is at least 30% higher than all the considered benchmarks.…”
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Research on Fuzzy Decision-Making Method of Task Allocation for Ship Multiagent Collaborative Design by Jinghua Li, Yiying Wang, Boxin Yang, Qinghua Zhou, Feihui Yuan

“…Finally, through example verification and comparative analysis with the Round-Robin algorithm (RR) and the Weighted Round-Robin (WRR) algorithm, the validity, feasibility, and stability of the multidesign agent-task allocation decision-making method proposed in this paper are verified, and it is proved that the task allocation method takes the bilateral needs of the task and the design agent into account, solves the optimal allocation strategy of collaborative design tasks, and realizes the balanced allocation between the ship collaborative design task and the design agent.…”
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Research on detection and tracking methods of unmanned ship water targets based on light vision by LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao

“…In terms of target detection, the YOLOv7 algorithm is used, which effectively improves the accuracy and recall rate of target detection by optimizing the loss function. …”
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MULTIDIMENSIONAL ANALYSIS OF PHYSICOCHEMICAL TRANSFORMATIONS AND SENSORY ATTRIBUTES OF GREEN AND ROASTED COFFEE by OTTO KETNEY, IULIAN CHIRILOV, OLGA DRĂGHICI

“…Utilizing a multi-objective optimization algorithm, Ethiopian coffee emerged as possessing the most optimal physicochemical characteristics. …”
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Scheduling Approach for the Simulation of a Sustainable Resource Supply Chain by Henning Strubelt, Sebastian Trojahn, Sebastian Lang, Abdulrahman Nahhas

“…This paper deals with the optimization of logistics processes at an underground waste storage site by means of solving scheduling issues and reducing setup times, with the help of a simulation model. …”
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Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study by Seung-Yup Lee, Leslie W Hayes, Bunyamin Ozaydin, Steven Howard, Alison M Garretson, Heather M Bradley, Andrew M Land, Erin W DeLaney, Amy O Pritchett, Amanda L Furr, Ashleigh Allgood, Matthew C Wyatt, Allyson G Hall, Jane C Banaszak-Holl

“… BackgroundThe use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes. …”
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Automatic text generation system for endangered languages based on conditional generative adversarial networks by Zhong Luo

“…Future research directions include optimizing CGAN parameters using algorithms like particle swarm optimization, refining discriminator outputs in loss calculations, and incorporating cultural and linguistic features specific to endangered languages to improve the quality of the generated texts.…”
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AI-driven energy management system based on hesitant bipolar complex fuzzy Hamacher power aggregation operators and their applications in MADM by Walid Emam, Hafiz Muhammad Waqas, Tahir Mahmood, Ubaid ur Rehman, Dragan Pamucar

“…Abstract Artificial Intelligence (AI) based energy management systems utilize sophisticated AI algorithms to improve and control the consumption of energy in various sectors, such as power utilities, industrial systems, and smart buildings. …”
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