An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs by Arif Wibisono, Md. Jalil Piran, Hyoung-Kyu Song, Byung Moo Lee

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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

“…Prediction of the person’s position (based on 10 previous frames) is performed using the DT model, which is optimal in terms of accuracy and computation time relative to other options. …”
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Monitoring of the Deformation of Deep Foundation Pit Using 3D Laser Scanning by Sheng Bao, Xuanlue Fang, Hangdong Bu, Xiaofei Yu, Zhengzhou Cai

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Optimizing TCP Performance in Multi-AP Residential Broadband Connections via Minislot Access by Domenico Giustiniano, Eduard Goma, Alberto Lopez Toledo, George Athanasiou

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Improved Blockchain Technology for Performance Optimization Model Design of Sports Clubs by Shuang Wang

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Machine Learning-Based Network Detection Research for SDNs by Lai Jiayue

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Optimization of dynamic data traceability mechanism in Internet of Things based on consortium blockchain by Rui Qiao, Sifeng Zhu, Qingxian Wang, Jie Qin

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Image retrieval method based on data mining and deep residual network by Hongzhi Yuan, Wei Hu

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A Flexible Framework for Decentralized Composite Optimization with Compressed Communication by Zhongyi Chang, Zhen Zhang, Shaofu Yang, Jinde Cao

“…This paper addresses the decentralized composite optimization problem, where a network of agents cooperatively minimize the sum of their local objective functions with non-differentiable terms. …”
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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…Three different artificial neural network (ANN) configurations are trained and evaluated regarding the amount of training data required for accurate predictions and the computational costs. …”
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Real-Time Power Management of Plug-In Electric Vehicles and Renewable Energy Sources in Virtual Prosumer Networks with Integrated Physical and Network Security Using Blockchain by Nikolaos Sifakis, Konstantinos Armyras, Fotis Kanellos

“…The system further employs advanced Particle Swarm Optimization (PSO) to dynamically compute optimal power set-points, enabling adaptive and efficient energy distribution. …”
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Exploring strategy differences between humans and monkeys with recurrent neural networks. by Ben Tsuda, Barry J Richmond, Terrence J Sejnowski

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Learning on Bandwidth Constrained Multi-Source Data With MIMO-Inspired DPP MAP Inference by Xiwen Chen, Huayu Li, Rahul Amin, Abolfazl Razi

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Learning the Hit-or-Miss Transform-Based Morphological Neural Networks by Muhammad Aminul Islam, Sireesha Chimbili

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SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation by Aravind R. Voggu, Kanish R, Nishith Akula, Lohitaksh Maruvada, Takanori Shimizu, Madhav Rao

“…We comparatively evaluate the members of the family of networks to find a Pareto-optimal Neural Network Demodulator with a balance of demodulation accuracy and computational cost, achieving an average accuracy of 99.658% across 4 dB to 24 dB SNR while requiring less than 16,000 Floating-Point Operations (FLOPs) for every demodulated QAM-16 symbol. …”
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Finding large independent sets in networks using competitive dynamics by M. N. Mooij, I. Kryven

“…However, it remains unclear whether collective behaviour among biological species can also lead to solutions for computational tasks. By studying the coexistence of species that interact through simple rules on a network, we demonstrate that the underlying dynamical system can recover near-optimal solutions to the maximum independent set problem – a fundamental, computationally hard problem in graph theory. …”
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Markov prediction based handover in dynamic ultra dense network by Qingmin MENG, Yuanyuan ZHAO, Wenjing YUE, Yulong ZOU, Xiaoming WANG

“…In order to solve the problem of the communication and computational problems of large-scale machine communication in an ultra-dense cellular network,a Markov prediction based handover scheme (MPHS) was proposed.Firstly,a kind of heterogeneous network design with semi-structure and central control was considered which contains the densely deployed virtual nodes and thus realized a low cost and efficient coverage.The network can dynamically adjust the access point according to the user's mobility and network traffic.Secondly,a Markov model was constructed,and the idea of load-aware was introduced.By weighing the signal quality and the cell load,the user's next optimal access point was effectively predicted.The simulation results show the feasibility and effectiveness of the proposed scheme for cell handover predicting.…”
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A Novel Coverage Enhancement Algorithm for Image Sensor Networks by Haiping Huang, Lijuan Sun, Ruchuan Wang, Jing Li

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Enhancing the Efficiency of Unsupervised Network Alignment Using Quotient Graph by Lei Zhang, Feng Qian

“…To address these limitations, this paper proposes ENAMOR (Efficient Network AlignMent via Quotient gRaph), an unsupervised framework incorporating three key components: 1) multi-scale representation learning that hierarchically aggregates local and global structural patterns through GNN layers; 2) embedding-driven graph coarsening via hashing-based quotient graph construction, reducing computational complexity by 60–80% while preserving topological and attribute information; and 3) Matched Neighborhood Consistency (MNC) optimization, which iteratively refines alignment matrices by enforcing structural congruence constraints. …”
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Development of Model for Traffic Flows on Urban Street and Road Network by D. V. Kapskiy, D. V. Navoy, P. A. Pegin

“…The paper proposes and investigates a model that works on the basis of operationally obtained parameters of traffic flow intensity at characteristic points (sections) of street and road network. Efficiency of the first-level model has been equal to 8 % due to optimization of a traffic light cycle (reduction of transport delays during passage of stop lines). …”
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