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 amount of energy, especially during the training phase. The transmission of big data between service providers, users and data centres emits carbon dioxide as a result of high power consumption. This chapter proposes a theoretical framework for big data analytics using computational intelligent algorithms that has the potential to reduce energy consumption and enhance performance. We suggest that researchers should focus more attention on the issue of energy within big data analytics in relation to computational intelligent algorithms, before this becomes a widespread and urgent problem.
The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti
... Show MoreThis paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors. This system is built using a personal computer and two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor. The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique. Results showed that the motor current signature analysis (MCSA) technique, and measurement of vibration technique have high possibility in the detection and diagnosis of most mechanical faults with high accuracy. Subsequently, diagnosi
... Show MoreThe effect of refrigerant injection techniques on the performance of heat pump system based on exergy analysis was studied theoretically. Three refrigerant injection techniques were used; the first was achieved by injected vapour in volume ratios from 1 to 7% in the accumulator. The second was injection liquid refrigerant in the discharge line with the aid of Liquid Pressure Amplification (LPA) pump, with volume ratios from 1 to 10%. The third was a hybrid injection with volume ratios of injected vapour and liquid varied from 1 to 3% and 1 to 10%; respectively. The following improvements in cycle performance were observed. For vapour injection technique, the best ratio of injection was 5%, the exergy destruction reduced
... Show MoreProjects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo
Internal conversion coefficients (ICC) and electron–positron pair conversion coefficients (PCC) for multipole transition of the core nucleus 88Sr have been calculated theoretically. The calculation is based on the relativistic Dirac–Fock (DF) solutions using the so called ‘‘Frozen Orbital’’ approximation, takes into account the effect of atomic vacancies created in the conversion process, covering a transition energies of 1–5000 keV. A large number of points were used to minimize any errors due to mesh-size effects. The internal conversion coefficients display a smooth monotonic dependence on transition energy, multipolarity and atomic shell. Comparing the values of PCC to ICC, it is interesting to note, that the energy dep
... Show MoreThe physical behavior for the energy distribution function (EDF) of the reactant particles depending upon the gases (fuel) temperature are completely described by a physical model covering the global formulas controlling the EDF profile. Results about the energy distribution for the reactant system indicate a standard EDF, in which it’s arrive a steady state form shape and intern lead to fix the optimum selected temperature.
Construction and operation of (2 m) parabolic solar dish for hot water application were illustrated. The heater was designed to supply hot water up to 100 oC using the clean solar thermal energy. The system includes the design and construction of solar tracking unit in order to increase system performance. Experimental test results, which obtained from clear and sunny day, refer to highly energy-conversion efficiency and promising a well-performed water heating system.