Compressive Sensing Network Deeply Induced by Visual Mechanism by Mingkun Feng, Xiaole Han, Kai Zheng

“…To address these challenges, we propose a Full Visual Mechanism-based Compressive Sensing Network (FVM-CSNet) inspired by the human visual system’s process of perceiving and understanding images. …”
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An Improved Backbone Fusion Neural Network for Orchard Extraction by Baiyu Dong, Ziqi Wang, Chongzhi Chen, Ke Wang, Jing Zhang

“…Compared to single backbone networks, the F1 score and OA obtained based on the FSR strategy have increased by an average of 8.5% and 4.2%, respectively. …”
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Cognitive waveform design for radar-communication transceiver networks by Yu YAO, Yanjie LI, Lenan WU, Pu MIAO, Xiaoyu TANG

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Enhanced zebra optimization algorithm and applications of power allocation in SDWN by LI Jiajia, DONG Ligang, JIANG Xian

“…To address the issue of interference caused by shared resources in multi-user communications in software defined wireless network (SDWN), an enhanced zebra optimization algorithm (EZOA) was proposed to achieve power allocation that maximizes the secrecy rate. …”
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Interference Management in UAV-Assisted Multi-Cell Networks by Muchen Jiang, Honglin Ren, Yongxing Qi, Ting Wu

“…This article considers a multi-cell wireless network comprising of conventional user equipment (UE), sensor devices and unmanned aerial vehicles (UAVs) or drones. …”
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Modelling and optimization of TPMLMs with slotted stators based on Bayesian DNN by Tao Wu, Peipei Dai, Kai Zhu, Yachao Zhu

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Geometric constraints on tributary fluvial network junction angles by J. D. Pelletier, R. G. Hayes, O. Hoch, B. Fenerty, L. A. McGuire

“…This work demonstrates that the topography of a landscape prior to fluvial incision may exert a key constraint on tributary fluvial network junction angles. This work adds to the list of possible controls on fluvial network junction angles, including climate- and optimality-based models for junction angles that have been the primary focus of research during the past decade.…”
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ML-Augmented Optimization of LoRa Antennas for Drone Telemetry by Pothala Chaya Devi, Ramarakula Madhu

“…However, in this work a surrogate Artificial Neural Network (ANN) model trained on 1080 antenna designs replaces the heavy CST simulations. …”
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Deep Reinforcement Learning for Optimal Replenishment in Stochastic Assembly Systems by Lativa Sid Ahmed Abdellahi, Zeinebou Zoubeir, Yahya Mohamed, Ahmedou Haouba, Sidi Hmetty

“…The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. …”
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Optimal Res-UNET architecture with deep supervision for tumor segmentation by Rahman Maqsood, Fazeel Abid, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed, Onur Osman, Shtwai Alsubai

“…The model employed a Dice loss combined with focal loss to handle data imbalance effectively. The proposed network was evaluated using extensive ablation studies, examining the effects of encoder complexity, convolutional filter count, and strategic post-processing.ResultsThe proposed Res-UNET with deep supervision outperformed other variants, achieving an average Dice score of 0.9498 through five-fold cross-validation. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. …”
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CMACGSA: Improved Gravitational Search Algorithm Based on Cerebellar Model Articulation Controller for Optimization by Nazmiye Ebru Bulut, Emre Dandil, Ugur Yuzgec, Alpaslan Duysak

“…Metaheuristic algorithms have gained significant attention in recent years for addressing complex and challenging optimization problems, especially in engineering. These algorithms often take inspiration from natural phenomena, systems or biological behaviour to find optimal solutions. …”
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Computation offloading scheme for RIS-empowered UAV edge network by Bin LI, Wenshuai LIU, Wancheng XIE, Zesong FEI

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Custom Network Quantization Method for Lightweight CNN Acceleration on FPGAs by Lingjie Yi, Xianzhong Xie, Yi Wan, Bo Jiang, Junfan Chen

“…In comparison to the original model, the proposed optimization methods result in an average decrease of 1.2% in accuracy. …”
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A Methodology for Situation Assessing of Space-Based Information Networks by Sai Xu, Jun Liu, Jiawei Tang, Xiangjun Liu, Zhi Li

“…The on-orbit assessment of individual satellites is then performed using a particle swarm optimization-support vector machine algorithm. Finally, a fusion assessment of the space-based information networks is conducted at the ground cloud center, incorporating situation weighting factors. …”
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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition by Xinmin Ding, Zenghui Zhang, Kun Wang, Xiaolin Xiao, Minpeng Xu

“…The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. …”
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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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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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A novel software-defined networking controlled vehicular named-data networking for trustworthy emergency data dissemination and content retrieval assisted by evolved interest packe... by Mazen Alowish, Yoshiaki Shiraishi, Yasuhiro Takano, Masami Mohri, Masakatu Morii

“…Extensive simulation in NS-3 assures better interest satisfaction rate, interest satisfaction delay, forwarder interest packets, average hop count, and gain of scalability in software-defined networking controlled vehicular named-data networking than traditional vehicle ad hoc network.…”
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Correlations between an Urban Three-Dimensional Pedestrian Network and Service Industry Layouts Based on Graph Convolutional Neural Networks: A Case Study of Xinjiekou, Nanjing by Xinyu Hu, Ruxia Bai, Chen Li, Beixiang Shi, Hui Wang

“…Our findings provide valuable insights for catering format location decisions and the optimization of three-dimensional pedestrian networks, contributing to sustainable urban development.…”
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