This study investigates the challenges encountered by first-grade intermediate students in learning the Arabic language. It aims to identify specific obstacles that hinder language acquisition and proficiency among this demographic. Through qualitative and quantitative methods, including surveys and interviews with students, teachers, and parents, the research highlights key issues such as limited vocabulary, difficulties in grammar, lack of engagement with the material, and inadequate teaching resources. The findings reveal a complex interplay between cognitive, social, and educational factors that contribute to these challenges. The study underscores the need for targeted interventions, such as enhanced pedagogical strategies and improved

The study aims at finding out the effect of the lead time strategy on the first intermediate class pupils' achievement in geography The partial experimental design of two groups, experimental and control, with pre-post tests is used. The sample is represented in (73) female pupils. The sample is divided into two groups (37) experimental group and (36) control one. The sam ple is selected from first intermediate class pupils ( Al Batol intermediate school for girls) Baghdad Al-karkh-3, for academic year 2015-2016 The researcher has equalized the two groups in several variables: the previous achievement tests, intelligence, age in months, the scores of geography test of first course

Minister Yacoub Ben Keles distinguished himself with leadership and administrative talents, as well as his abilities in the field of jurisprudence, which made him the top political, administrative and cultural scene of the Fatimid state and left its mark on it by influencing its fateful decisions.
He was the son of Kels of the Jews of Baghdad, where he learned writing and arithmetic, and moved with his father to Syria and then carried him to Egypt.
Egypt embraced the son of Kels, living in a transitional period from the Achaishid era to the Fatimid period. Both these two covenants reconciled this man to his career until he became minister in the Fatimids in 368 A.H. / 978 A.D.
His character was overshadowed by most of the state'

 Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B