In this article, the high accuracy and effectiveness of forecasting global gold prices are verified using a hybrid machine learning algorithm incorporating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The hybrid approach had successes that enabled it to be a good strategy for practical use. The ARIMA-ANFIS hybrid methodology was used to forecast global gold prices. The ARIMA model is implemented on real data, and then its nonlinear residuals are predicted by ANFIS, ANFIS-PSO, and ANFIS-GWO. The results indicate that hybrid models improve the accuracy of single ARIMA and ANFIS models in forecasting. Finally, a comparison was made between the hybrid foreca

Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, t

يؤدي عرض معلومات مضللة او محرفة ضمن القوائم المالية والتي تعد أهم مصادر المعلومات الموثوقة التي يُعول عليها لاتخاذ القرارات السليمة الى عدم قدرتها على عكس نتيجة النشاط والمركز المالي لها او اعمال الوحدة الاقتصادية لتلك الفترات الزمنية بصورة صادقة وعادلة نتيجة لنوعية المعلومات المفصح عنها في القوائم المالية لذلك زاد الاهتمام بتطوير الممارسات المحاسبية لتتضمن افصاحات كافية بغرض اعطائهم صورة صادقة وعادلة

The goal of the research is to identify the effectiveness of using a proposed strategy according to the Fraunhofer model of knowledge management in mathematics achievement for second-grade female students in middle and high schools affiliated with the General Directorate of Education in Baghdad / Al-Karkh II. The objective was to prove the following null hypothesis: "The average scores of the experimental group who will study with the proposed strategy according to the Fraunhofer model and the scores of the control group students who will study in the usual way in the mathematics achievement test are not statistically significant different at the significance level (0.05)." The General Directorate of Education of Baghdad / Al-Karkh

 Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

Abstract:

The main objective of the research is to build an optimal investment portfolio of stocks’ listed at the Iraqi Stock Exchange after employing the multi-objective genetic algorithm within the period of time between 1/1/2006 and 1/6/2018 in the light of closing prices (43) companies after the completion of their data and met the conditions of the inspection, as the literature review has supported the diagnosis of the knowledge gap and the identification of deficiencies in the level of experimentation was the current direction of research was to reflect the aspects of the unseen and untreated by other researchers in particular, the missing data and non-reversed pieces the reality of trading at the level of compani