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

The current research discusses the topic of the formal data within the methodological framework through defining the research problem, limits and objectives and defining the most important terms mentioned in this research. The theoretical framework in the first section addressed (the concept of the Bauhaus school, the philosophy of the Bauhaus school and the logical bases of this school). The second section dealt with (the most important elements and structural bases of the Bauhaus school) which are considered the most important formal data of this school and their implications on the fabrics and costumes design. The research came up with the most important indicators resulting from the theoretical framework.
Chapter three defined the

Introduction: Nitrofurantoin (NFT) is abroad spectrum bactericidal antibiotic. The bioavailability of NFT is affected by many factors. Samafurantin® tablets containing 50 mg NFT were manufactured by Samarra drug industry. Urinary excretion studies were employed since; the urinary tract is the main site of NFT action and excretion. Objective: The objective of the study was to investigate the effect of Uricol® and food on secondary pharmacokinetic parameters of Samafurantin® tablets with different doses by applying urinary data. Methods: Twelve healthy male volunteers participated in this study. Urine samples were collected from each volunteer after overnight fasting at a specified time intervals which considered as a blank sample for meas

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

Abstract:                                        

   We can notice cluster data in social, health and behavioral sciences, so this type of data have a link between its observations and we can express these clusters through the relationship between measurements on units within the same group.

    In this research, I estimate the reliability function of cluster function by using the seemingly unrelate

Reverse Osmosis (RO) has already proved its worth as an efficient treatment method in chemical and environmental engineering applications. Various successful RO attempts for the rejection of organic and highly toxic pollutants from wastewater can be found in the literature over the last decade. Dimethylphenol is classified as a high-toxic organic compound found ubiquitously in wastewater. It poses a real threat to humans and the environment even at low concentration. In this paper, a model based framework was developed for the simulation and optimisation of RO process for the removal of dimethylphenol from wastewater. We incorporated our earlier developed and validated process model into the Species Conserving Genetic Algorithm (SCG

The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms