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.
This study aimed to evaluate the statistical reliability of machine learning–based intrusion detection models in real-time high-traffic network environments. Traditional offline evaluations often overestimate model performance due to random data partitioning, while temporal and real-time assessments provide more operationally relevant insights. A quantitative experimental design was employed, using three benchmark intrusion detection datasets: CICIDS2017, UNSW-NB15, and CSE-CIC-IDS2018. Models evaluated included Random Forest, Gradient Boosting (XGBoost), Support Vector Machine, Deep Neural
Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR) are key indicators of health and socioeconomic conditions in developing countries, with Bangladesh as a prominent case. This study aims to identify risk factors associated with maternal and infant mortality in Bangladesh and to construct prediction models for MMR and IMR using machine learning techniques. It also investigates common features and similarities between MMR and IMR, which are closely related and often move together over time. We analyze annual data for Bangladesh from 1991 to 2017, sourced from the World Bank’s 2021 W
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.