Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

    An investigation was provided in this work for the host range of brown soft scale Coccus hesperidum Linnaeus in Baghdad Province.  Five plant species were found infected by this insect, three of these species, Citrusaurantium L. (Rutaceae); Nerium oleander L. (Apocynaceae); Ficuscarica L. (Moraceae) reported earlier, and the remaining two, Dahlia pinnata Cav. (Asteraceae) and Myrtuscommunis L. (Myrtaceae) are recordedhere for the first time as host plants for this pest.

This research attempts to shed light on a topic that is considered one of the most important topics of HRMs management, which is the Employee centric approach by examining its philosophy and understanding . To achieve the goal, the research relied on the philosophical analytical method, which is one of the approaches used in theoretical studies. The research reached a set of conclusions, the most important of which are the theoretical studies that addressed this entry in the English language and the lack of it in the Arabic language, according to the researcher's knowledge. The research reached a set of recommendations, the most important of which was that this approach needs more research, analysis and study at the practical and th

This article deals with the approximate algorithm for two dimensional multi-space fractional bioheat equations (M-SFBHE). The application of the collection method will be expanding for presenting a numerical technique for solving M-SFBHE based on “shifted Jacobi-Gauss-Labatto polynomials” (SJ-GL-Ps) in the matrix form. The Caputo formula has been utilized to approximate the fractional derivative and to demonstrate its usefulness and accuracy, the proposed methodology was applied in two examples. The numerical results revealed that the used approach is very effective and gives high accuracy and good convergence.

The relationships between the related parties constitute a normal feature of trading and business processes. Entities may perform parts of their activities through subsidiary entities, joint ventures and associate entities. In these cases, the entity has the ability to influence the financial and operating policies of the investee through control, joint control or significant influence,  So could affect established knowledge of transactions and balances outstanding, including commitments, and relationships with related to the evaluation of its operations by users of financial statements, including the risks and opportunities facing the entity assess the parties. So research has gained importance of the importance of the availability

The research aimed to know the effect of the Parashot strategy in developing the reading comprehension skills of first-grade intermediate students in reading. The researchers put the following two null hypotheses: There is no statistically significant difference at the level (0.05) between the average scores of the experimental group students who study the subject Reading with the Parashot strategy in the pre and post-tests in developing reading comprehension skills as a whole. There is no statistically significant difference at the level (0.05) between the average scores of the experimental group students who study the reading material using the Parashot strategy and the average scores of the control group students who study the same subje

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a