The Fourth Industrial Revolution represents an advanced stage of technological development, characterized by the integration of digital, physical, and biological technologies, with a strong focus on smart connectivity and advanced data analysis. At the core of this revolution stands Artificial Intelligence (AI), which enables the processing of vast amounts of data, decision-making with speed and accuracy, automation of processes, and enhancement of productivity and quality. This research examines the transformative role of AI in the humanities, particularly in archaeological, historical, and geographical studies, where traditional methods face limitations in handling complex and extensive datasets.The study aims to highlight these l

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

The purpose of this research is to a treatment the impact of Views outliers to the estimators of a distributed arrival and service to the theory of queues and estimate the distribution parameters depending on the robust estimators, and when he was outliers greatest impact in the process of estimating the both distributions mentioned parameters, it was necessary to use way to test that does these data contain abnormal values ​​or not? it was used the method ( Tukey ) for this purpose and is of the most popular ways to discover the outliers , it shows that there are views abnormal (outliers ) in the estimators of each of the distributional arrival and service, which have a significant impact on the calculation of these estimato

تعد صناعة السمنت في العراق من اقدم الصناعات الحديثة واكثرها تطورا وتقدما ومن اقواها تاثيرا في الاقتصاد القومي. واذ توفر في صناعة السمنت العراقي كافة المستلزمات الناجحة من حيث توفر المواد الاولية والخبرات الفنية والتقنية واسواق ثابتة وراسخة محليا وعالميا فقد كان من المفروض ان يتم التوسع في هذه الصناعة، وان التخطيط لهذه الصناعة امرا ضروريا خاصة وان مادة السمنت هي احدى اهم المواد الرئيسة التي يؤثر توفره

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia

This research discusses the rehabilitation of heritage markets in the historical centers of Islamic Arab cities and their use in the field of cultural tourism as one of the most important tourist attractions and the subsequent revival of the national economy in addition to preserving the urban heritage as these markets part of the historical centers of cities. The research also discusses the preservation of the continuity of heritage markets as a product of cultural heritage value within an integrated framework to preserve the urban heritage in the historic centers of cities. The study then reviews a number of experiences of Arab and Islamic countries in the field of preserving and rehabilitating heritage markets, which qualify them to b