With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

Well integrity is a vital feature that should be upheld into the lifespan of the well, and one constituent of which casing, necessity to be capable to endure all the interior and outside loads. The casing, through its two basic essentials: casing design and casing depth adjustment, are fundamental to a unique wellbore that plays an important role in well integrity. Casing set depths are determined based on fracturing pressure and pore pressure in the well and can usually be obtained from well-specific information. Based on the analyzes using the improved techniques in this study, the following special proposition can be projected: The selection of the first class and materials must be done correctly and accurately in accordance with

Well integrity is a vital feature that should be upheld into the lifespan of the well, and one constituent of which casing, necessity to be capable to endure all the interior and outside loads. The casing, through its two basic essentials: casing design and casing depth adjustment, are fundamental to a unique wellbore that plays an important role in well integrity. Casing set depths are determined based on fracturing pressure and pore pressure in the well and can usually be obtained from well-specific information. Based on the analyzes using the improved techniques in this study, the following special proposition can be projected: The selection of the first class and materials must be done correctly and accurately in accordance with the

Abstract

         Uncertainty, the deeply-rooted fact that surrounding the investment environment, especially the stock market which just prices have taken a specific trend until they moved to another one for its up or down. This means that the volatility characteristic of financial market requires the rational investor an argument led towards the adoption of planned acts to gain greater benefit in the goal of wealth maximizing. There is no possibility to achieve this goal without the burden of uncertainty and the risk of systematic fluctuations of investment returns in the financial market after the facts of  efficient diversification have pro

The aim of this paper is to find out the effects of the strategy of productive thinking upon the student’s achievement for the subject of research methodology in the College of Islamic Sciences. Achieving this objective, the researchers set the null hypotheses: (1) No difference is noticed to be statistically significant at the level of significance (0.05) among the student’s mean scores in the experimental group who were taught by the strategy of productive thinking, and the student’s mean scores in the control group who studied by the traditional method in the achievement test. (2) At level of sig. (0.05), there is no statistically significant difference in the mean of scores of the pre-tests and post ones in the achievement test of

The aim of this paper is to find out the effects of the strategy of productive thinking upon the student’s achievement for the subject of research methodology in the College of Islamic Sciences. Achieving this objective, the researchers set the null hypotheses: (1) No difference is noticed to be statistically significant at the level of significance (0.05) among the student’s mean scores in the experimental group who were taught by the strategy of productive thinking, and the student’s mean scores in the control group who studied by the traditional method in the achievement test. (2) At level of sig. (0.05), there is no statistically significant difference in the mean of scores of the pre-tests and post ones in the achievement test of

Credit risk assessment has become an important topic in financial risk administration. Fuzzy clustering analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. Utilising similar degree between points to get similarity density, and then by means of maximum density points selecting; the modified Kohonen Network method generate clustering initial centres to get more reasonable clustering res