Radio Spectrum Sensing Framework for Large-Scale, High-Density IoT Sensor Networks by Partemie-Marian Mutescu, Alexandru Lavric, Adrian-Ioan Petrariu, Alin-Mihai Cailean, Valentin Popa

“…In live deployment tests, the framework processes spectrograms with an average latency of 30.12 ms. Through an integrated detection tracking mechanism, it efficiently generates advanced radio channel analytics, enabling better use of spectral resources and interference mitigation in large-scale IoT networks, making it a valuable tool for optimizing IoT deployments. …”
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METHOD FOR THE DIAGNOSTICS OF SYNCHRONIZATION DISTURBANCES IN THE TELECOMMUNICATIONS NETWORK OF A CRITICAL USED COMPUTER SYSTEM by Vladimir Rudnytsky, Mykhailo Mozhaiev, Nina Kuchuk

“…To improve the quality of service (QoS), the work of all network technologies, protocols, and individual traffic management mechanisms is constantly optimized. …”
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Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) by Nurilmiyanti Wardhani, Billy Eden William Asrul, Antonius Riman Tampang, Sitti Zuhriyah, Abdul Latief Arda

“…Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. …”
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A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses by Zixuan Zhou, Lihua Li, Hao Xue, Yuchen Jia, Yao Yu, Zongkui Xie, Yuhan Gu

“…After ventilation strategy optimization, panting prevalence decreased by 65 %, establishing a closed-loop ''monitoring-assessment-regulation'' dynamic feedback mechanism. …”
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Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Paula Sáez

“…Addressing these challenges requires intelligent scheduling strategies capable of dynamically managing patient waitlists based on clinical urgency while optimizing resource allocation. In this study, we propose a novel framework that integrates a digital twin (DT) of the MRI operational environment with a reinforcement learning (RL) agent trained via Deep Q-Networks (DQN). …”
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Optimizing Cloud Computing Performance With an Enhanced Dynamic Load Balancing Algorithm for Superior Task Allocation by Raiymbek Zhanuzak, Mohammed Alaa Ala'Anzy, Mohamed Othman, Abdulmohsen Algarni

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EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks by Muhammad Akram, Sibghat Ullah Bazai, Muhammad Imran Ghafoor, Saira Akram, Qazi Mudassar Ilyas, Abid Mehmood, Sajid Iqbal, Muhammad Asim Rafique

“…Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. …”
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A Latency Composition Analysis for Telerobotic Performance Insights Across Various Network Scenarios by Nick Bray, Matthew Boeding, Michael Hempel, Hamid Sharif, Tapio Heikkilä, Markku Suomalainen, Tuomas Seppälä

“…The results show stable average round-trip latency of 6.6 ms for local network connection, 58.4 ms when connecting over Wi-Fi, 115.4 ms when connecting through cellular, and 240.7 ms when connecting from Finland to the United States over a VPN access-controlled network. …”
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Security decision method for the edge of multi-layer satellite network based on reinforcement learning by Peiliang ZUO, Shaolong HOU, Chao GUO, Hua JIANG, Wenbo WANG

“…This paper uses deep reinforcement learning algorithms to implement edge security decisions for satellite networks. Specifically, the edge center node obtains the environmental state of the satellite network through the perception system, and on this basis, uses the ability of deep reinforcement learning algorithm to learn independently, and obtains the optimal data offloading strategy in the scene by fitting, and obtains the optimal link planning., so that the onboard resources can be fully utilized, so as to achieve the goal of minimizing the average return delay of many observation tasks.First,the edge center node observes the environment and obtains state elements such as the data volume, channel conditions, and edge node processing capability of the observation satellite mission in the environment. …”
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AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction by Fang He, Fang He, Fang He, Fang He, Lian Duan, Lian Duan, Lian Duan, Lian Duan, Guodong Xing, Guodong Xing, Guodong Xing, Guodong Xing, Xiaojing Chang, Xiaojing Chang, Xiaojing Chang, Xiaojing Chang, Huixia Zhou, Huixia Zhou, Huixia Zhou, Huixia Zhou, Mengnan Yu, Mengnan Yu, Mengnan Yu, Mengnan Yu

“…Furthermore, a Kolmogorov-Arnold network is employed to perform weighted fusion of various final features, optimizing prediction performance.ResultsAMFGNN demonstrates a significant advantage in predictive performance, achieving an average AUC value of 0.9453, which reflects the model‘s high accuracy in prediction.DiscussionCross-validation results across multiple datasets indicate that AMFGNN outperforms seven advanced drug-disease association prediction methods. …”
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A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems by Md Shahab Uddin, Ahsan Ahmed, Md Aktarujjaman, Mohammad Moniruzzaman, Mumtahina Ahmed, M. F. Mridha, Md. Jakir Hossen

“…This paper proposes a novel hybrid framework that integrates reinforcement learning (RL) with knowledge graph-augmented neural networks to optimize billing decisions while preserving diagnostic accuracy. …”
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Optimization control strategy for active distribution areas with E-SOP considering energy storage characteristics by Weixu YU, Yewen WEI, Peng YUAN, Sijia ZHANG, Zhipeng ZHOU, Jie ZHANG

“…To address issues such as reverse power flow, voltage fluctuations, and insufficient power supply capacity resulting from the increased penetration of distributed generation in distribution networks, an optimized control strategy for active distribution substations incorporating a three-terminal intelligent soft open point (E-SOP) for energy storage is proposed. …”
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Integration of electric vehicle charging stations with distributed generation using multi-objective metaheuristic optimization by Aya Desoky Gaber, E.M. Abdallah, M.I. Elsayed, Ahmed Abdelbaset

“…This paper uses a multi-objective optimization approach metaheuristic algorithm, specifically the Whale Optimization Algorithm (WOA), Zebra Optimization Algorithm (ZOA), and Puma Optimization Algorithm (POA), to determine the optimal size and placement of DG units in the presence of EVCS. …”
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Reducing transmission expansion by co-optimizing sizing of wind, solar, storage and grid connection capacity by Aneesha Manocha, Gabriel Mantegna, Neha Patankar, Jesse D Jenkins

“…Given the coarse representation of transmission networks in our modeling, this outcome likely overstates the real-world importance of storage co-location with VREs. …”
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Paths Optimization by Jointing Link Management and Channel Estimation Using Variational Autoencoder With Attention for IRS-MIMO Systems by Meng-Hsun Wu, Hong-Yunn Chen, Ta-Wei Yang, Chih-Chuan Hsu, Chih-Wei Huang, Cheng-Fu Chou

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FORECASTING VALUES OF CHROMATICITY OF DRINKING AND SOURCE WATERS USING ARIMA MODEL AND NEURAL NETWORK by D. V. Makarov, E. A. Kantor, N. A. Krasulina, A. V. Greb, Z. Z. Berezhnova

“…The parameters of the models were estimated by 85% of the time series values, and the remaining 15% of the values (the test period) compared the forecast values with the actual ones. Optimal configurations of ARIMA-models were determined from the results of comparing the averaged values of the root mean squared errors (RMSE); optimal configurations of ANN were determined from the results of comparing the averaged values of RMSE and correlation coefficients (CC) on the test periods.Results. …”
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Self-supervised denoising method for single neutron image based on the S2S-NR network by Wangtao Yu, Peng Xu, Xinghui Cai, Man Zhou, Jie Bao

“…The results are estimated by averaging the predictions from various instances of the network with dropout. …”
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River Surface Space–Time Image Velocimetry Based on Dual-Channel Residual Network by Ling Gao, Zhen Zhang, Lin Chen, Huabao Li

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Direct observation of the topological pruning in silica glass network; the key for realizing extreme transparency by Madoka Ono, Yasuhito Tanabe, Masaya Fujioka, Hiroki Yamada, Koji Ohara, Shinji Kohara, Masanori Fujinami, Junji Nishii

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Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks by MU Siqi, WEN Shuo, LU Yang, AI Bo

“…The simulation results show that the proposed optimization scheme outperforms existing baseline methods, effectively meeting the dynamic priority requirements of tasks in BAN and reducing the energy consumption as well as the average delay required for task completion.…”
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