Artificial Intelligence and MCDA in Circular Economy: Governance Strategies and Optimization for Reverse Supply Chains of Solid Waste by Joel Joaquim de Santana Filho, Arminda do Paço, Pedro Dinis Gaspar

“…The results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, and genetic algorithms, enhance decision-making automation, reduce costs, and improve waste traceability. …”
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AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm by Lin Yang

“…AI-based classification models for low-energy buildings are essential in promoting the economic development of smart cities by optimizing energy use, reducing operational costs, and enhancing overall efficiency. …”
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Quantum-inspired robust optimization for coordinated scheduling of PV-hydrogen microgrids under multi-dimensional uncertainties by Yunxiao Bai, Yu Sui, Xiaoyu Deng, Xiangbing Wang

“…Stress-testing under scenarios such as 48-hour grid failure, signal-based cyberattacks, and 40% PV output curtailment reveals substantial gains: operational costs are reduced by 9.3%, resilience scores improve by over 20% in adverse conditions, and convergence speed increases by 42% relative to classical optimization. …”
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Design and energy management research of integrated microgrid with light storage and charging with double-layer optimization scheduling model by Pan Zhai

“…On the basis of considering user needs, a time of use electricity pricing scheduling control strategy is proposed to achieve control over energy flow, and an improved particle swarm algorithm is used to handle the objective function. …”
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Algorithm for assigning different 6-month short-term chemotherapy regimens in patients with drug-resistant tuberculosis: how to make the right choice for the clinician by Yu.I. Feshchenko, N.A. Lytvynenko, L.M. Protsyk, M.V. Pohrebna, Yu.O. Senko

“…For patients with pre-XDR-TB, the BPaL regimen offers the best balance of efficacy, tolerability, and cost-effectiveness. The proposed prescription algorithms enable a personalized treatment approach and improve therapeutic outcomes.…”
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Optimizing drone-based pollination method by using efficient target detection and path planning for complex durian orchards by Ruipeng Tang, Jianxun Tang, Mohamad Sofian Abu Talip, Narendra Kumar Aridas, Binghong Guan

“…For path planning, we develop an Enhanced TSP (EN-TSP) algorithm based on a branch and bound strategy with least-cost optimization. …”
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Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning by Suqing Yan, Baihui Luo, Xiyan Sun, Jianming Xiao, Yuanfa Ji, Kamarul Hawari bin Ghazali

“…The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. …”
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Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT by Sinan Andan Diwan

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Path planning of unmanned ships based on A* and dynamic window approach by FU Long, SHEN Zhen, TAO Hao, HAN Yunjun, DONG Xisong, XIONG Gang

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Transactional configuration of innovative development of business ecosystem by E. V. Popov, A. D. Tikhonova, A. S. Zyrianov

“…As a result, the authors have developed an algorithm that aims to create a pool of stakeholders in the business ecosystem in order to achieve sustainable development within the innovative economy through optimized transaction costs. …”
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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification by Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J

“…Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. Traditional methods, such as Genetic Algorithms (GA) and Maximum Relevance Minimum Redundancy (MRMR), have been widely used, but bio-inspired algorithms like Ant Colony Optimization (ACO) and Ant Lion Optimizer (ALO) have shown promising results. …”
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A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…Not only does the framework optimize the use of resources, but also it minimizes carbon footprint as well as costs of operation for a more efficient and sustainable smart city infrastructure.…”
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Prediction of UHPC mechanical properties using optimized hybrid machine learning model with robust sensitivity and uncertainty analysis by ZhiGuang Zhou, Jagaran Chakma, Md Ahatasamul Hoque, Vaskar Chakma, Asif Ahmed

“…Each dataset was standardized and split into training (80%) and testing (20%) subsets. Hyperparameter optimization was conducted using a random search algorithm to improve prediction accuracy. …”
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Comparative techno-economic analysis of grid-connected solar PV-battery and PV-fuel cell systems for educational institutions sustainable academic laboratories by Takele Ferede Agajie, Fuseini Seidu Ibrahim, Isaac Amoussou, Elsabet Ferede Agajie, Eriisa Yiga Paddy, Yayehyirad Ayalew Awoke, Wirnkar Basil Nsanyuy, Mohit Bajaj

“…Techno-economic decision criteria were implemented using MATLAB. Three algorithms were used: Teaching–Learning-Based Optimization (TLBO), Water Cycle Algorithm (WCA), and Improved Grey Wolf Optimization (I-GWO). …”
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The effect of using the algorithm of restrictive intraoperative fluid therapy, early immune enteral nutrition, and early patient mobilization on orthotopic liver transplantation ou... by A. M. Dzyadz'ko, M. L. Katin, O. A. Chugunova, A. F. Minov, E. G. Oganova, E. O. Santotskiy, A. E. Shcherba, O. O. Rummo, I. I. Kanus

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Research on energy storage planning methods for distributed renewable energy integrated rural power distribution networks by Haoshuai Jia, Renqing Feng, Hai Jiang, Chao Gao, Yue Zhao, Rui Zhang, Ziyi Xuan

“…The optimal locations and capacities of energy storage systems are determined using YALMIP toolbox and the beetle swarm optimization (BSO) algorithm, and the proposed method is validated on a modified IEEE 33-bus system. …”
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Collaborative Optimization of Passenger Control Strategy and Train Operation Plan with Variable Formations for a Rail Transit Network by Yonghao Yin, Jun Chen, Wenrui Zhao

“…We proposed a passenger control strategy and train operation plan collaborative optimization (PCS&TOP) model to schedule the train operation that improves the efficiency of passengers’ travel and reduces the cost of train operation for a rail transit network. …”
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Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model by Kyu-Jeong Lee, So-Won Choi, Eul-Bum Lee

“…Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. …”
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Research on low carbon welding scheduling based on production process by Rong Hua Meng, Zan Yang Wang, Wen Hui Zeng, Feng Guan, Ding Kun Lei, Zheng Jia Wu, Shao Hua Deng

“…The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. …”
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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. …”
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