Optimal allocation of STATCOM for multi-objective ORPD problem on thermal wind solar hydro scheduling using driving training based optimization by Tushnik Sarkar, Sabyasachi Gupta, Chandan Paul, Susanta Dutta, Provas Kumar Roy, Anagha Bhattacharya, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

“…Abstract On IEEE 30, 57, 118 & 300-bus experimental networks, this work aims to solve the optimal reactive power dispatch (ORPD) problem. …”
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Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images by Rashid Abbasi, Farhan Amin, Amerah Alabrah, Gyu Sang Choi, Salabat Khan, Md. Belal Bin Heyat, Muhammad Shahid Iqbal, Huiling Chen

“…Abstract Diabetic retinopathy (DR) is an age-related macular degeneration eye disease problem that causes pathological changes in the retinal neural and vascular system. …”
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The Combined Decision Problem: “Pull” vs. “Push” and the Degree of Centralization of Warehousing in the Field of Physical Distribution with a Special Focus on the Polish Market... by Dariusz Milewski

“…The article hypothesized that the choice of how to replenish stocks in these warehouses—“Pull” or “Push”—and the choice of the degree of centralization of the distribution network (number of warehouses) were two decision problems that should be considered together. …”
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Correspondence distribution over a network in designing public urban passenger transportation tasks by A. I. Fadeev, A. M. Iliankov, V. V. Ukaderov

“…The calculation of the parameters of the transport offer in the process of solving the problems of transportation design is carried out based on the results of the passenger correspondence distribution on the route network (PAP), which is a complex problem unsolved today. …”
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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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Long-Term Secrecy Throughput and Fairness Tradeoff for Wireless Powered Secure IoT Networks by Lei Zheng, Xiaolong Lan, Juanjuan Ren, Yong Liu, Qingchun Chen

“…In order to ensure network fairness, we construct a virtual fairness queue for each device to characterize the gap between its averaged secrecy rate and the expected secrecy min-rate, and incorporate its status into the transmission design. …”
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Prospects of application of artificial neural networks for forecasting of cargo transportation volume in transport systems by D. T. Yakupov, O. N. Rozhko

“…When predicting the volumes of freight transportation by rail, satisfactory indicators of the verification of forecasting by both the method of exponential smoothing and ANN had been obtained, although the neural network showed the best result (the average relative forecast error was 8.97% for ANN and 11.21% for the method of exponential smoothing, respectively). …”
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Educational Behaviour Analysis Using Convolutional Neural Network and Particle Swarm Optimization Algorithm by Zhenjiang Dong

“…In this paper, we proposed a network model based on the ResNet50 network fused with a bilinear hybrid attention mechanism and proposed an adaptive pooling weight algorithm based on the average pooling algorithm for the problems of image feature extraction caused by traditional pooling algorithm such as mutilation and blurring. …”
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Verification of a static (off-line) signature using a convolutional neural network by U. Yu. Akhundjanov, V. V. Starovoitov

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Heuristic Algorithms for One-Slot Link Scheduling in Wireless Sensor Networks under SINR by Hui Deng, Jiguo Yu, Dongxiao Yu, Guangshun Li, Baogui Huang

“…One-slot link scheduling is important for enhancing the throughput capacity of wireless sensor networks. It includes two aspects: maximum links scheduling (MLS) and maximum weighted links scheduling (MWLS). …”
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Investment in an accessible multimodal active network: How project life cycle and investment horizon can impact the design by Mehrdad Memarpour, Ali Najmi, S. Travis Waller, Taha Hossein Rashidi

“…However, in this paradigm shift, they must deal with the challenge of allocating limited resources to non-car users of transport networks in the most efficient manner. In this study, we address the resource allocation problem to maximise the accessibility of active modes on a multimodal network, accounting for the comfort of car and bus users, from a cost-benefit analysis (CBA) perspective. …”
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An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems by V. Ranganayaki, S. N. Deepa

“…The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. …”
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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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FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation by Abdul Wahab Mamond, Majid Kundroo, Seong-eun Yoo, Seonghoon Kim, Taehong Kim

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Towards arbitrary QUBO optimization: analysis of classical and quantum-activated feedforward neural networks by Chia-Tso Lai, Carsten Blank, Peter Schmelcher, Rick Mukherjee

“…To address this challenge, we developed a powerful feedforward neural network (FNN) optimizer for arbitrary QUBO problems. …”
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Ecological network construction and land degradation risk identification in the Yellow River source area by Haoxiang Zhang, Qian Song, Shiru Wang, Jianyun Zhao, Wenming Gao

“…Based on ordered weighted average operator, morphological spatial pattern analysis, ecological sensitivity, circuit theory, CA-Markov model, kernel density analysis, and other methods, this study aims to consider the positive and negative connections of the network thus constructing the ecological and risk network and predicting the potential risk areas in the future. …”
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BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network by Wanwei Huang, Huicong Yu, Yingying Li, Xi He, Rui Chen

“…To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. …”
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Forecasting time series of the market indicators based on a nonlinear autoregressive neural network by Denis V. Domashchenko, Edvard E. Nikulin

“…Thus, the model showed accurate results when predicting dynamic series and can be used to solve other forecasting problems. In particular, it is expedient to use the model as one of the factors when making investment decisions. …”
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Innovative Dombi Aggregation Operators in Linguistic Intuitionistic Fuzzy Environments for Optimizing Telecommunication Networks by Dilshad Alghazzawi, Misbah Hayat, Ghaliah Alhamzi, Abdul Wakil Baidar

“…These strategies are successfully applied to present a solution to an MADM problem concerning the selection of an optimal strategy to enhance the efficiency of telecommunication network systems to demonstrate their effectiveness and superiority. …”
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Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network by Qiyin Lin, Feiyu Gu, Chen Wang, Hao Guan, Tao Wang, Kaiyi Zhou, Lian Liu, Desheng Yao

“…In this study, we propose an innovative intelligent design framework integrating Conditional Deep Convolutional Generative Adversarial Networks (CDCGAN) with SIMP, capable of creating topology structures that meet prescribed thermal conduction performance. …”
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