The construction project is a very complicated work by its nature and requires specialized knowledge to lead it to success. The construction project is complicated socially, technically and economically in its planning, management and implementation aspects due to the fact that it has many variables and multiple stakeholders in addition to being affected by the surrounding environment. Successful projects depend on three fundamental points which are cost-time, performance and specifications. The project stakeholder's objective to achieve best specifications and the cost-time frame stipulated in the contract.

The question is, was the optimum implementation accomplished? The provision for the success of the project

A design for a photovoltaic-thermal (PVT) assembly with a water-cooled heat sink was planned, constructed, and experimentally evaluated in the climatic conditions of the southern region of Iraq during the summertime. The water-cooled heat sink was applied to thermally manage the PV cells, in order to boost the electrical output of the PVT system. A set of temperature sensors was installed to monitor the water intake, exit, and cell temperatures. The climatic parameters including the wind velocity, atmospheric pressure, and solar irradiation were also monitored on a daily basis. The effects of solar irradiation on the average PV temperature, electrical power, and overall electrical-thermal efficiency were investigated. The findings i

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

     Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing

The use of remote sensing technologies was gained more attention due to an increasing need to collect data for the environmental changes. Satellite image classification is a relatively recent type of remote sensing uses satellite imagery to indicate many key environment characteristics. This study aims at classifying and extracting vacant lands from high resolution satellite images of Baghdad city by supervised Classification tool in ENVI 5.3 program. The classification accuracy was 15%, which can be regarded as fairly acceptable given the difficulty of differentiating vacant land surfaces from other surfaces such as roof tops of buildings.

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Abstract:

                The aim of this research is to highlight the importance of achieving customer satisfaction by using information technology and Internet networks in the process of purchasing flight tickets, and switching from the traditional method of purchasing and payment operations to the electronic method, to reduce the financial and non-financial risks associated with the traditional purchasing process, as well as saving time, effort and costs for the customer. The researcher used the deductive approach in linking the variables (achieving customer satisfaction and Internet of Things technology for booking electronic tickets)

    A total of 680 fish specimens belonging to 31 species from the Yemeni coastal waters of the Red Sea were inspected for the isopod infestations. Four isopod species belonging to three families of the suborder Cymothoida were detected. These are: Aega psora (Linnaeus, 1758) from Lethrinus lentjan, Natatolana insignis Hobbins and Jones, 1993 from Abalistes stellatus, Excorallana tricornis (Hansen, 1890) from Epinephelus fuscoguttatus, E. guttatus and E. tauvina and Alcirona krebsi Hansen, 1890 from Epinephelus microdon. All these isopod species are reported here for the first time from the Yemeni coastal waters of the Red Se

The Internet of Things (IoT) has become a hot area of research in recent years due to the significant advancements in the semiconductor industry, wireless communication technologies, and the realization of its ability in numerous applications such as smart homes, health care, control systems, and military. Furthermore, IoT devices inefficient security has led to an increase cybersecurity risks such as IoT botnets, which have become a serious threat. To counter this threat there is a need to develop a model for detecting IoT botnets.

This paper's contribution is to formulate the IoT botnet detection problem and introduce multiple linear regression (MLR) for modelling IoT botnet features with discriminating capability and alleviatin