Beta-thalassemia major (β-TM) is inheritable condition with many complications especially in children. The blood-borne viral infection was proposed as a risk factor due to recurrent blood transfusion regimen (hemotherapy).

Remote sensing provide the best means to monitoring change in vegetation over a wide range of temporal scales over large areas. In this study, the vegetation index which has been applied known as the Stress Related Vegetation Index (STVI) on in the area around the Euphrates River and part of Al-Habbaniyah lake which located at western side of the river in Ramadi city, Al-Anbar province at Iraq to study the vegetation cover changes and detect the areas of changes, using two satellite sensors multispectral images such as TM and ALI, after geometric correction procedure to rectifying these images. The STVI-4 index result was the best than other vegetation indices (STVI-1 and STVI-3) to discriminate the vegetable cover distribution. The diff

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

The extracting of personal sprite from the whole image faced many problems in separating the sprite edge from the unneeded parts, some image software try to automate this process, but usually they couldn't find the edge or have false result. In this paper, the authors have made an enhancement on the use of Canny edge detection to locate the sprite from the whole image by adding some enhancement steps by using MATLAB. Moreover, remove all the non-relevant information from the image by selecting only the sprite and place it in a transparent background. The results of comparing the Canny edge detection with the proposed method shows improvement in the edge detection.

Recently, detecting dynamic patterns for growing communities in social networks have attracted significant attention. The objective of dynamic community detection is to analyze and identify clusters in complex networks that change over time. Different optimization algorithms, including both single-objective and multi-objective approaches, have been employed to address the challenge of dynamic community detection. Although current evolutionary algorithms for identifying community structure can traverse extensive areas of partition space, they often become stuck in local minima. In addition, limited studies have addressed this issue by integrating local search strategies with evolutionary algorithms for community identification. This paper in

A reduced-order extended state observer (RESO) based a continuous sliding mode control (SMC) is proposed in this paper for the tracking problem of high order Brunovsky systems with the existence of external perturbations and system uncertainties. For this purpose, a composite control is constituted by two consecutive steps. First, the reduced-order ESO (RESO) technique is designed to estimate unknown system states and total disturbance without estimating an available state. Second, the continuous SMC law is designed based on the estimations supplied by the RESO estimator in order to govern the nominal system part. More importantly, the robustness performance is well achieved by compensating not only the lumped disturbance, but also its esti

An attempt was made to evaluate the PV performance of one-axis daily tracking and fixed system for Baghdad, Iraq. Two experimental simulations were conducted on a PV module for that purpose. Measurements included incident solar radiation, load voltage and load current. The first experiment was carried out for six months of winter half of year to simulate the one-axis daily tracking. The azimuth angle was due south while the tilt angle was being set to optimum according to each day of simulation. The second experiment was done at one day to simulate the PV module of fixed angles. It is found that there is a significant power gain of 29.6% for the tracking system in respect to the fixed one. The one-axis daily tracking was much more effect

The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then  proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is impleme