Utilizing Machine Learning Approach to Forecast Average Location Determination Errors in Wireless Sensor Networks by Zhihui Zhu, Meifang Zhu

“…Using a limited number of beacons and anchor nodes, the proposed approach leverages machine learning techniques, specifically Random Forest Regression (RFR) enhanced by Smell Agent Optimization (SAO) and Golden Jackal Optimization Algorithm (GJOA), to optimize network performance and minimize localization errors. …”
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Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network by Qingjun Song, Jiuxin Wang, Qinghui Song, Kai Li, Wenchao Hao, Haiyan Jiang

“…Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults. …”
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Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks by K. Lakshmi Prabha, Hanan Abdullah Mengash, Hamed Alqahtani, Randa Allafi

“…Experimental results demonstrate that the proposed model significantly outperforms existing methods such as Grid Search RF, Bayesian Optimized RF, Gradient Boosting, and Deep Neural Networks. …”
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Long Short-Term Memory Networks and Bayesian Optimization for Predicting the Time-Weighted Average Pressure of Shield Supporting Cycles by Wanzi Yan, Junhui Wang, Jingyi Cheng, Zhijun Wan, Keke Xing, Kuidong Gao

“…In this study, a hybrid machine learning model integrating the long short-term memory (LSTM) networks and the Bayesian optimization (BO) algorithm was developed to predict TWAP based on the setting pressure (SP), revised setting pressure (RSP), final pressure (FP), number of yielding (NY), TWAP in the last supporting cycle (TWAP (last)), and loading rate in each period. …”
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JASBO: Jaya Average Subtraction Based Optimization with Deep Learning Model for Multi-Classification of Infectious Disease from Unstructured Data by Vian Sabeeh, Ahmed Bahaaulddin A. Alwahhab, Ali Abdulmunim Ibrahim Al-kharaz

Subjects: “…Average and Subtraction-Based Optimizer (ASBO), Bidirectional-LSTM (Bi-LSTM), Convolutional Neural Network (CNN), Infectious Disease Network (ID-Net), Jaya algorithm.…”
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An optimized deep learning based hybrid model for prediction of daily average global solar irradiance using CNN SLSTM architecture by Yuvaraj Mariappan, Karthikeyan Ramasamy, Durgadevi Velusamy

“…This study aims to develop a hybrid deep learning model that integrates a Convolutional Neural Network and Stacked Long Short-Term Memory (CNN-SLSTM) to predict the daily average global solar irradiance using real time meteorological parameters and daily solar irradiance data recorded in the study site. …”
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Reinforcement learning meets technical analysis: combining moving average rules for optimal alpha by Javier H. Ospina-Holguín, Ana M. Padilla-Ospina

“…An evaluation network calculates and optimizes alpha by adjusting the policy network’s parameters. …”
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Survivable Network Design and Optimization with Network Families by Brody Todd, Abiose Ibigbami, John Doucette

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Construction and application of optimized model for mine water inflow prediction based on neural network and ARIMA model by Xiaoyu Gong, Bo Li, Yu Yang, MengHua Li, Tao Li, Beibei Zhang, Lulin Zheng, Hongfei Duan, Pu Liu, Xin Hu, Xin Xiang, Xinju Zhou

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Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks by Jin-Ho Chung, Yoora Kim

“…Through numerical studies, we demonstrate the effectiveness of the proposed optimization in reducing the average PAoI.…”
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Evaluation and Analysis of Factors Affecting Operation and Inspection Cost of Distribution Network Equipment Assets by Chaoyueling LUO, Zhiwei LI, Zhenyu XU, Xuejun TANG, Li LI, Zhao FANG

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Traffic weaver: Semi-synthetic time-varying traffic generator based on averaged time series by Piotr Lechowicz, Aleksandra Knapińska, Adam Włodarczyk, Krzysztof Walkowiak

“…The primary motivation behind Traffic Weaver is to furnish semi-synthetic time-varying traffic in telecommunication networks, facilitating the development and validation of traffic prediction models, as well as aiding in the deployment of network optimization algorithms tailored for time-varying traffic.…”
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Topology optimization of the plate using neural networks by Yu. S. Selivanov, K. A. Matveev

“…The algorithm is based on the reparametrization of virtual densities by neural network parameters, which are optimized. The method of adaptive moment estimation is used as an optimization algorithm directly by the parameter of the neural network. …”
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Research on edge offloading delay optimization of cellular networks based on optimal transport theory by Xiangyu LYU, Yong XIAO, Yi ZHONG, Qiang LI, Xiaohu GE

“…With the development of the internet of things, a large number of user device (UD) were connected to cellular network.Since the changes in the spatial distribution of UD and application requirements, it is necessary to dynamically adjust the UD’ offloading decision.Comprehensively considering various parameter information in the networks such as the spatial distribution of UD, application requirements, and the processing capability of the edge servers on the base station (BS) side, the offloading decision of UD were optimized from the perspective of distribution.Based on the optimal transport theory, a delay optimization algorithm was proposed to reduce the average delay of the UD’ computing tasks offloading process by reasonably planning the offloading BS of the UD in the networks.The simulation results show that the average delay can be reduced by 81.06% using the proposed offloading mechanism based on delay optimization, and the traffic handled by each BS is balanced.…”
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On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning by Nansha Gao, Mou Wang, Xiao Liang, Guang Pan

“…The overall loss function is derived by combining the mean square error between the expected average sound absorption coefficient and its predicted value and the network-optimized loss function to ensure that the 20 sensitive parameters that meet the acoustic performance can be predicted. …”
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Optimal Routing Control in Disconnected Machine-to-Machine Networks by Yahui Wu, Su Deng, Hongbin Huang

“…First, we mathematically characterize the average delivery ratio under different policies. Then we get the optimal policy through Pontryagin's maximum principle, and prove that the optimal policy conforms to the threshold form when the fees that other nodes require satisfy certain conditions. …”
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Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network by Haomiao Zhang, Qing Liu

“…Increase inertia improves the traditional BP neural network algorithm, using the average path length, clustering coefficient, and connectivity distribution index analysis of the complex network; the improved Hopfield neural network is utilized to confirm each user volume size; it is concluded that their users are able to get the number of subchannels, through the instantaneous channel coarse pair gain dynamic channel allocation, calculating bit load matrix at the same time, minimize transmission power, and achieve bit loading and power allocation and communication resource allocation optimization. …”
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Optimizing wireless sensor network topology with node load consideration by Ruizhi Chen

“…Background: With the development of the Internet, the topology optimization of wireless sensor networks has received increasing attention. …”
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Fair Probabilistic Multi-Armed Bandit With Applications to Network Optimization by Zhiwu Guo, Chicheng Zhang, Ming Li, Marwan Krunz

“…Online learning, particularly Multi-Armed Bandit (MAB) algorithms, has been extensively adopted in various real-world networking applications. In certain applications, such as fair heterogeneous networks coexistence, multiple links (individual arms) are selected in each round, and the throughputs (rewards) of these arms depend on the chosen set of links. …”
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Network slicing resource allocation strategy based on joint optimization by Zaijian WANG, Huimin GU

“…To improve network resource utilization that was decreased by different applications with different requirements in 5G networks, a network slicing resource allocation strategy based on joint optimization was proposed, which was utilized to maximize both network resource utilization and network revenue by comprehensively considering in tra-slice and inter-slice resource schedule.Firstly, the user’s average satisfaction function was defined in the inter-slicing resource allocation problem.Furthermore, in terms of the number of users, slicing schedule delay and priority, a proportional fair resource allocation algorithm based on quality of service (QoS) was proposed, which was employed to achieve the best tradeoff between fairness and the users’ requirements among slices.Secondly, after two functions (service degradation and resource migration) were introduced in the inter-slice resource schedule problem, two price models were established for internal access users and external access users respectively, where congestion and non-congestion conditions were analyzed.According to the proposed price models, a Stackelberg game between the base station and users was constructed, and a global search algorithm with low complexity was leveraged to obtain the best response of the game, where the best tradeoff between the base station revenue and user utility was obtained.Simulation results show that the proposed strategy can effectively improve resource utilization and network revenue while reducing network congestion.Therefore, it can better realize fairness in resource allocation.…”
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