Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
The objective of this quantitative study was to evaluate the effectiveness of neural network–based anomaly detection models—particularly temporal architectures—in accurately identifying cyberattacks in real-time within large-scale computing networks. A comprehensive network traffic dataset containing both normal and malicious flows was preprocessed and used to train multiple deep learning architectures, including feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) models. Feature normalization, sequence tran
Multiple Linear Regression Analysis (MLRA) is an analytical approach employed to foretell the value of a dependent variable versus multiple independent variables. It models the linear regression between these variables, tolerating forecasters to understand the impact of each independent variable on the single one while regulating others. This paper leverages predictive analytics to identify at-risk students as early as possible, using a model to predict the dependent variable for new points of analysis. The trend and strength of the linear relationship between the dependent variable and each i
The research objective is to determine which format, circular or square cross-section, is better for the convergent and convergent-divergent nozzles used in a double-row single-stage steam turbine. In this paper, it will carry out the simulation process using ANSYS and analyze several types of nozzles with different dimensions that have either circular or square cross-sections. The study was conducted for steam turbines of 800 kW and 1200 kW. Models for both types of nozzles were built based on dimensions and engineering data obtained from the palm oil industry, catalogues, and field surveys.
The early diagnosis of neurological disorders is often hindered by subtle and overlapping electrophysiological abnormalities present in electrocardiogram (ECG) and electroencephalogram (EEG) signals. The application of machine learning (ML) and deep learning (DL) algorithms offers a promising avenue for automated, accurate, and scalable interpretation of such biosignals. This study aimed to quantitatively evaluate the performance of automated ECG and EEG interpretation using ML and DL models, emphasizing accuracy, sensitivity, and specificity for early neurological disorder detection. A retros
The paper considers an approach to automating the evaluation of tender applications using an intelligent decision support algorithm based on a scoring model. The algorithm is implemented using logistic regression and ROC analysis, which allows for a quantitative assessment of the relevance of tenders. The model has been tested on historical data from a software company that participated in government and commercial procurement. The paper confirms the feasibility of using a scoring approach to improve the efficiency of selecting tenders. The proposed method is expected to be expanded to other p