Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing artificial TABU algorithm to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as sport, chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement.

In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

This study was aimed to measure marketing efficiency and study important factors affecting , using TOBIT qualitative response model for wheat crop in Salahalddin province. Results revealed that independent factors such as (marketing type, crops duration in the field, average marketing cost, distance between farm and marketing center, and average productivity) had an impact on wheat marketing efficiency. This impact varied in size and direction due to value of parameters. Values of marketing efficiency fluctuated within cities and towns in the province. The average value on the province level was 76.75%. This study was recommended developing marketing infrastructures which is essential to efficiency increases. In addition, it is impo

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

In this paper a comparison of the experimental of evacuated tube solar water heater systems with and without mirror flat reflector. The aim of using the reflector to improve thermal efficiency, and the data gathered which are (temperature, solar irradiation and time) for three days were compared. the results from compared data the temperature lower increase in evacuated tube solar water heater system without reflector than the temperature increase in evacuated tube solar water heater system with reflector .The results show (53, 39, 35) % for three days respectively that the evacuated tube solar water heater system with reflector has higher thermal efficiencies than the results (47, 28, 30) % for three days respectively thermal efficiencies

Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,

Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squar

An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. Th