Nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will lose plenty of competitive advantages as well. The core point of information security (InfoSecu) is risk management. There are a great deal of research works and standards in security risk management (ISRM) including NIST 800-30 and ISO/IEC 27005. However, only few works of research focus on InfoSecu risk reduction, while the standards explain general principles and guidelines. They do not provide any implementation details regarding ISRM; as such reducing the InfoSecu risks in uncertain environments is painstaking. Thus, this paper applied a genetic algorithm (GA) for InfoSecu risk reduction in uncertainty. Finally, the ef

     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

      This work addressed the assignment problem (AP) based on fuzzy costs, where the objective, in this study, is to minimize the cost. A triangular, or trapezoidal, fuzzy numbers were assigned for each fuzzy cost. In addition, the assignment models were applied on linguistic variables which were initially converted to quantitative fuzzy data by using the Yager’sorankingi method. The paper results have showed that the quantitative date have a considerable effect when considered in fuzzy-mathematic models.

   The main focus of this research is to examine the Travelling Salesman Problem (TSP) and the methods used to solve this problem where this problem is considered as one of the combinatorial optimization problems which met wide publicity and attention from the researches for to it's simple formulation and important applications and engagement to the rest of combinatorial problems , which is based on finding the optimal path through known number of cities where the salesman visits each city only once before returning to the city of departure n this research , the benefits  of( FMOLP)   algorithm is employed as one of the best methods to solve the (TSP) problem and the application of the algorithm in conjun

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

The gas-lift method is crucial for maintaining oil production, particularly from an established field when the natural energy of the reservoirs is depleted. To maximize oil production, a major field's gas injection rate must be distributed as efficiently as possible across its gas-lift network system. Common gas-lift optimization techniques may lose their effectiveness and become unable to replicate the gas-lift optimum in a large network system due to problems with multi-objective, multi-constrained & restricted gas injection rate distribution. The main objective of the research is to determine the possibility of using the genetic algorithm (GA) technique to achieve the optimum distribution for the continuous gas-lift injectio

The aim of the research is to apply fibrewise multi-emisssions of the paramount separation axioms of normally topology namely fibrewise multi-T0. spaces, fibrewise multi-T1 spaces, fibrewise multi-R0 spaces, fibrewise multi-Hausdorff spaces, fibrewise multi-functionally Hausdorff spaces, fibrewise multi-regular spaces, fibrewise multi-completely regular spaces, fibrewise multi-normal spaces and fibrewise multi-functionally normal spaces. Also we give many score regarding it.

Credential compromise is one of the most widespread security threats, allowing adversaries to bypass traditional authentication measures and impersonate legitimate users. Traditional intrusion detection systems are often based on network-level or macro-behavioral indicators, which can be easily spoofed by an attacker, thus compromising the effectiveness of those mechanisms. This study presents an improved adaptive intrusion detection system to authenticate user behavior based on micro-digital behavioral profiling. It involves the use of timing of keystrokes, micro-mouse, navigation in the application, and interaction rhythm signatures. The proposed system uses a hybrid model consisting of Long Short-Term Memory (LSTM) sequence predi

he assignment model represents a mathematical model that aims at expressing an important problem facing enterprises and companies in the public and private sectors, which are characterized by ensuring their activities, in order to take the appropriate decision to get the best allocation of tasks for machines or jobs or workers on the machines that he owns in order to increase profits or reduce costs and time As this model is called multi-objective assignment because it takes into account the factors of time and cost together and hence we have two goals for the assignment problem, so it is not possible to solve by the usual methods and has been resorted to the use of multiple programming The objectives were to solve the problem of

A large number of researchers had attempted to identify the pattern of the functional relationship between fertility from a side and economic and social characteristics of the population from another, with the strength of effect of each. So, this research aims to monitor and analyze changes in the level of fertility temporally and spatially in recent decades, in addition to estimating fertility levels in Iraq for the period (1977-2011) and then make forecasting to the level of fertility in Iraq at the national level (except for the Kurdistan region), and for the period of (2012-2031). To achieve this goal has been the use of the Lee-Carter model to estimate fertility rates and predictable as well. As this is the form often has been familiar