To achieve excellence in the quality of performance in school sports administration, which has suffered a lot of problems and constraints on the administrative system, supervision and education level as well as the regulatory environment and available resources available and contribute to the provision of some processors and overcome difficulties to participate in the formation of the individual good of itself and society through sports activities. Hence the importance came this study to create a reference to the quality of the performance criteria school sports from the perspective of supervisors (specialists and technicians) in the districts of breeding Baghdad, to be of help to all those involved in school sports and maintaining an excep

In this study, we used Bayesian method to estimate scale parameter for the normal distribution. By considering three different prior distributions such as the square root inverted gamma (SRIG) distribution and the non-informative prior distribution and the natural conjugate family of priors. The Bayesian estimation based on squared error loss function, and compared it with the classical estimation methods to estimate the scale parameter for the normal distribution, such as the maximum likelihood estimation and th

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

Communication between unmanned aerial vehicle (UAV) swarms and flying ad-hoc networks is critical and challenging since it is over open radio networks and energy, memory and processing budgets are severely constrained. Lightweight cryptography is the solution and ASCON — the NIST standard for lightweight authenticated encryption — has become a popular option for securing UAV links at a fraction of the energy cost of AES. The security of ASCON relies on one nonlinear device — 5-bit substitution box (S-box). However, this S-box is fixed and publicly known, and allows an adversary to precompute the cryptographic properties one time and then reuse them against all the protected traffic, and moreover, it does not meet the strict av

Mishrif Formation was deposited during The Cenomanian-Early Turonian, which has been studied in selected Tuba and Zubair OilFields, these wells (TU-5, TU-24, TU-40, ZB-41, ZB-42, and ZB-46) are located within Mesopotamian basin at southern Iraq and considered as a major carbonate reservoir in Iraq and the Arabian Gulf. The palaeontological investigations mainly depending on benthonic foraminifera of the studied wells of Tuba and Zubair Oilfields in Mishrif Formation, twenty-four species belonging to fourteen genera are recognized of benthonic foraminifera, which has been recognized through this study, especially benthonic foraminiferal, indicating four zones as follows: