AA Abbass, HL Hussein, WA Shukur, J Kaabi, R Tornai, Webology, 2022 Individual’s eye recognition is an important issue in applications such as security systems, credit card control and guilty identification. Using video images cause to destroy the limitation of fixed images and to be able to receive users’ image under any condition as well as doing the eye recognition. There are some challenges in these systems; changes of individual gestures, changes of light, face coverage, low quality of video images and changes of personal characteristics in each frame. There is a need for two phases in order to do the eye recognition using images; revelation and eye recognition which will use in the security systems to identify the persons. The mai

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Despite the significant increase in public spending in Iraq, it was not directed toward the important sectors that have an important role in correcting the productive structure. Rather, most of the public expenditure was going to cover the required imports, or to face shocks, whether security or health, and this caused a continuous decrease in the volume of Iraq's exports of goods and services, as the aim of the study was to review the importance of public debt and its impact on the trade balance, as well as to know the economic policies that can contribute to strengthening the trade balance, as the study proved through the (ARDL) model that there is a direct relationship between the domestic debt and the net trade balance, and an invers

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Abstract  

All central air conditioning systems contain piping system with various components, sizes, material, and layouts. If such systems in operating mode, the flow in piping system and its component such as valves can produce severe vibration due to some flow phenomenon’s. In this research, experimental measurements and numerical simulation are used to study the flow-induced vibration in valves. Computational fluid dynamics (CFD) concepts are included with one-way and two-way fluid-structure interaction concepts by using finite element software Package (ANSYS 14.57). Detection analysis is performed on flow characteristics under operation conditions and relations with structural vibration. Most of

Global warming has had considerable effects on vital ecosystems, which has also been caused by increased temperatures and CO₂ that follow changes in different abiotic factors, which poses threats to mangrove forests environment. This research was conducted to examine the physiological and morphological characteristics of the Rhizophora apiculata mangrove regarding higher air temperature for the variety of tree species that respond to climate change. Seedlings were cultivated for three months in regulated growth chambers with three varying temperatures of 38°C, 21°C under CO₂ at 450 ppm, and ambient CO₂ concentration i.e., 450 ± 20 ppm under average temperature at 28°C as the control condition

This study describes how fuzzy logic control FLC can be applied to sonars of mobile robot. The fuzzy logic approach has effects on the navigation of mobile robots in a partially known environment that are used in different industrial and society applications. The fuzzy logic provides a mechanism for combining sensor data from all sonar sensors which present different information. The FLC approach is achieved by means of Fuzzy Decision Making method type of fuzzy logic controller. The proposed controller is responsible for the obstacle avoidance of the mobile robot while traveling through a map from a home point to a goal point. The FLC is built as a subprogram based on the intelligent architecture (IA). The software program  uses th