The road networks is considered to be one of the determinants that controls to specify the areas of human activities, which it depend on to specify the arrival cost , in addition it is useful to achieve the connectivity for interaction and human activities , and shorten the distance and time between the population and places of service. The density of the road network in any space directly affected by the density of population and the type of economic activities and administrative functions performed by the space. On this basis, the subject of this study is reflected in the quantitative analysis of the roads network in the Governorate of Karbala. The study consists the quantitative analysis for the roads network and the Urban Nodes in th

Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer

Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K

<span lang="EN-US">The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of e

􀀨􀀅􀀆􀀔􀀜􀀄􀀂􀀆􀀈􀀓􀀌􀀩􀀏􀀆􀀉􀀅􀀆􀀉􀀅􀀉􀀚􀀝􀀔􀀄􀀗􀀉􀀚􀀆􀀂􀀓􀀚􀀁􀀔􀀄􀀓􀀅􀀆􀀄􀀂􀀆􀀔􀀓􀀆􀀃􀀂􀀔􀀄􀀟􀀉􀀔􀀃􀀆􀀔􀀜􀀃􀀆􀀛􀀃􀀜􀀄􀀗􀀚􀀃􀀆􀀗􀀌􀀄􀀔􀀄􀀗􀀉􀀚􀀆􀀂􀀘􀀃􀀃􀀊􀀂􀀆􀀓􀀖􀀆􀀓􀀛􀀃􀀌􀀔􀀁􀀌􀀅􀀄􀀅􀀕􀀢􀀆􀀪􀀜􀀃􀀆􀀄􀀅􀀛􀀃􀀂􀀔􀀄􀀕􀀉􀀔􀀄􀀓􀀅􀀂􀀆 􀀉􀀌􀀃􀀆􀀙􀀉􀀂􀀃􀀊􀀆􀀓􀀅􀀆􀀉􀀁􀀔􀀓􀀟􀀓􀀙􀀄􀀚􀀃􀀆􀀚􀀉􀀔􀀃􀀌􀀉􀀚􀀆􀀂􀀘􀀉􀀗􀀃􀀆􀀟􀀄􀀊􀀂􀀔􀀆􀀔􀀜􀀃􀀆􀀗􀀃􀀅􀀔􀀃􀀌􀀚􀀄􀀅􀀃􀀂􀀆􀀓􀀖􀀆􀀔􀀜􀀃􀀆􀀙􀀉􀀂􀀃􀀆