Resource Adaptive Automated Task Scheduling Using Deep Deterministic Policy Gradient in Fog Computing by Prashanth Choppara, S. Sudheer Mangalampalli

“…This paper presents a novel adaptive scheduling framework based on the DDPG algorithm for task scheduling optimization in fog computing environments. …”
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Combating electricity fraud: Employing hybrid learning and computer vision for sustainable energy management by Jui-Sheng Chou, Nader Anwar Charaf, Dani Nugraha Limantono, Hoang-Minh Nguyen

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Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

“…To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of which performed global updated asynchronously with the global model, while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method (MMM) was proposed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM, an effective grouping algorithm was designed for minimizing the overall training completion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4% compared to the existing state-of-the-art methods.…”
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Collaborative optimal operation control of HVAC systems based on multi-agent by Chen Fu, Kaipeng Chen, Yan Xu, Dongyue Ming, Ruiwen Ye, Yingjun Wu, Lixia Sun

“…As a consequence, the computing efficiency of optimization algorithms is relatively low, posing challenges for real-time optimal operation control. …”
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Algorithm of reconstruction combined midface defects after resection malignant tumors by M. V. Bolotin, A. M. Mudunov, V. Yu. Sobolevsky, V. I. Sokorutov

“…In type IV resections satisfactory aesthetic results were obtained in all patients.Conclusion. Preoperative computer 3D modeling is necessary in planning of reconstruction. …”
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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models by Adel Zga, Farouq Zitouni, Saad Harous, Karam Sallam, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

“…The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. …”
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Soil Condition Classification Based on Natural Water Content Using Computer Vision Technique by Mark Miller, Yong Fang, Yubo Wang, Sergey Kharitonov, Vladimir Akulich

“…As a result, seven algorithms were obtained that allow classifying the soil images, which can later be used to optimize the tunnel construction process. …”
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Sorcha: Optimized Solar System Ephemeris Generation by Matthew J. Holman, Pedro H. Bernardinelli, Megan E. Schwamb, Mario Jurić, Drew Oldag, Maxine West, Kevin J. Napier, Stephanie R. Merritt, Grigori Fedorets, Samuel Cornwall, Jacob A. Kurlander, Siegfried Eggl, Jeremy Kubica, Kathleen Kiker, Joseph Murtagh, Shantanu P. Naidu, Colin Orion Chandler

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Using Genetic Algorithms for Hardware Core Placement and Mapping in NoC-Based Reconfigurable Systems by Jonas Gomes Filho, Marius Strum, Wang Jiang Chau

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Dynamic Programming Algorithms for Solving Single Machine Scheduling Problems with a Fixed Complexity Time by Kawa Al-Qazilchy

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C-mixture and multi-constraints based genetic algorithm for collaborative data publishing by Yogesh R. Kulkarni, T. Senthil Murugan

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Cutting-Edge Stochastic Approach: Efficient Monte Carlo Algorithms with Applications to Sensitivity Analysis by Ivan Dimov, Rayna Georgieva

“…In recent years, researchers have developed advanced algorithms based on the analysis of variance (ANOVA) technique for computing numerical sensitivity indicators. …”
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Forgetful Swarm Optimization for Astronomical Observation Scheduling by Nariman Nakhjiri, Maria Salamo, Miquel Sanchez-Marre, Christian Blum, Juan Carlos Morales

“…Additionally, it has significantly less computational cost than ILS and the base models of a global optimization algorithm such as EA.…”
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Research on consensus mechanism optimization for IoT blockchain by Qijie SONG, Tieming CHEN, Yuan CHEN, Dongjie MA, Zhengqiu WENG

“…The essence of the blockchain is a decentralized distributed ledger system.The Internet of things is interconnected by massive heterogeneous terminals and has natural distributed characteristics.Therefore,the combinedIoT blockchain is widely optimistic.At the same time,due to the heterogeneity of the IoT-aware terminal,the limited computing capacity and the ability of data transmission,the IoT blockchain faces great challenges.The password consensus technology has become a key issue.On the basis of summarizing the current blockchain consensus algorithm,its applicability to the IoT blockchain was analyzed,the current application status of several IoT blockchain platforms and consensus mechanisms was introduced,and the optimization research progress of the consensus mechanismin IoT blockchain was described.Finally,the optimization technology of the IoT blockchain and the potential research directions were summarized.…”
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Permission clustering-based attribute value optimization by Wenchao WU, Zhiyu REN, Xuehui DU

“…In new large-scale computing environment, the attributes of entities were massive and they had complex sources and uneven quality, which were great obstacles to the application of ABAC (attribute-based access control).The attributes were also hard to be corrected manually, making it difficult to be applied in access control system straightly.To solve the optimization problem of nominal attributes, a novel algorithm of attribute value optimization based on permission clustering was designed, in which entities were presented by the privilege set related to them.So that the entities were tagged by density-based clustering method with distances of their privilege set presentations.Then the attribute values were reduced and corrected based on rough set theory.Finally, the algorithm was verified on UCI data sets, which proved that after applying it, ABAC policy mining was improved in the evaluation criteria, such as the true positive rate and F1-score.…”
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Unmanned Aerial Vehicles (UAV) Networking Algorithms: Communication, Control, and AI-Based Approaches by Mien L. Trinh, Dung T. Nguyen, Long Q. Dinh, Mui D. Nguyen, De Rosal Ignatius Moses Setiadi, Minh T. Nguyen

“…Given the limitations of UAVs in both computations and communications, UAVs usually need algorithms for either low latency or energy efficiency. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

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System analysis and information processing to solve the problem of detecting breakdowns of computer information storage by N. M. Kodatsky, E. A. Revyakina, A. R. Gazizov

“…A tool for assessing the state of computer equipment based on the Random Forest algorithm has been developed using historical data from SMART tests. …”
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Efficient optimization of the Held–Karp lower bound by Righini, Giovanni

“…Although motivated by the optimization of the Held–Karp lower bound for the TSP, the algorithm solves a more general problem, allowing for the efficient pre-computation of alternative minimum spanning trees in weighted graphs where any vertex can be deleted.…”
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Supervised optimal control in complex continuous systems with trajectory imitation and reinforcement learning by Yingjun Liu, Fuchun Liu, Renwei Huang

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