Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

Subjects:

Matlab code of a version using numerical optimization for Q. by Hiroko Kato Solvang (5718250)

R code for a version using numerical optimization for Q. by Hiroko Kato Solvang (5718250)

Algorithm optimization process. by Zhihao Li (359073)

“…Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (<i>SOC</i>) as the optimization objectives. …”

Optimization results of different algorithms. by Haijian Wang (746739)

“…Finally, the effectiveness of the optimization results was verified using simulation experiments. …”

Illustration of the continuation algorithm used for optimization in the inversion method. by Karoline Horgmo Jæger (6785450)

Optimization flow chart of the AO algorithm. by Haijian Wang (746739)

“…Finally, the effectiveness of the optimization results was verified using simulation experiments. …”

Iteration curves of different algorithms. by Tengfei Ma (597633)

Flowchart of OP-ZOA algorithm. by Tengfei Ma (597633)

Event-driven data flow processing. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Flowchart of the operation of TLK-DBO algorithm. by Zhihao Li (359073)

“…Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (<i>SOC</i>) as the optimization objectives. …”

Algorithms runtime comparison. by Meilin Zhu (688698)

“…Firstly, from the perspective of data-driven, it crawls the historical data of driving speed through Baidu map big data platform, and uses a BP neural network optimized by genetic algorithm to predict the driving speed of vehicles in different periods. …”

Convergence factor <i>α</i> change graph. by ZeSheng Lin (20501356)

Solution results of different algorithms. by Meilin Zhu (688698)

“…Firstly, from the perspective of data-driven, it crawls the historical data of driving speed through Baidu map big data platform, and uses a BP neural network optimized by genetic algorithm to predict the driving speed of vehicles in different periods. …”

Thermoenergetic optimization of a school building: discussion of the performance of four multiobjective evolutionary algorithms by Thalita dos Santos Maciel (12656181)

“…This study discusses the performance of four multi-objective optimization algorithms applied to a standard project on early childhood education, developed by the Brazilian government program Proinfância, implemented in bioclimatic zone 2. …”

Table2_A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering.DOCX by Xing Huang (129439)

Table1_A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering.DOCX by Xing Huang (129439)

N2O mass fraction in Fig 15-turbulence changed. by Feng Gao (3548)

S1 Data - by Ningyan Chen (14675833)

“…Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. …”

Real estate index and stock data preprocessing. by Ningyan Chen (14675833)

“…Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. …”