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

In cyber security, the most crucial subject in information security is user authentication. Robust text-based password methods may offer a certain level of protection. Strong passwords are hard to remember, though, so people who use them frequently write them on paper or store them in file for computer .Numerous of computer systems, networks, and Internet-based environments have experimented with using graphical authentication techniques for user authentication in recent years. The two main characteristics of all graphical passwords are their security and usability. Regretfully, none of these methods could adequately address both of these factors concurrently. The ISO usability standards and associated characteristics for graphical

Secure information transmission over the internet is becoming an important requirement in data communication. These days, authenticity, secrecy, and confidentiality are the most important concerns in securing data communication. For that reason, information hiding methods are used, such as Cryptography, Steganography and Watermarking methods, to secure data transmission, where cryptography method is used to encrypt the information in an unreadable form. At the same time, steganography covers the information within images, audio or video. Finally, watermarking is used to protect information from intruders. This paper proposed a new cryptography method by using thre

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

Abstract

The research aims to shed light on the extent to which the practices of performance management in achieving organizational excellence in one of the formations and the Ministry of Finance (GCT). The importance of the selection of these organizations is that they occupies a large and exceptional importance in the national economy through income redistribution add it to cover a large part of the state budget revenues, these organizations possess functionally diverse cadre of them pregnant initial certification and other senior and he fairly stable To meet this target, and on the basis of the data search exploratory researcher built model hypothesis for the search included variable impressionist and

The research aimed to study the job satisfaction of the staff of the Federal board of supreme Audit and its relation to the effectiveness of their performance, The questionnaire was adopted as a main tool in the collection of data and information from a random sample of (54) employees of the Federal board of supreme Audit. In light of this, the data were collected and analyzed and the hypotheses were tested using the statistical program (SPSS).

The researchers reached a number of conclusions, the most important of which were: (1) the respondents' response to the variables of job satisfaction and the effectiveness of the performance were medium; (2) there was a significant relationship between job satisfaction and performance effe

Objectives: To assess nurses-midwives knowledge and performance regarding immediate newborn care in delivery rooms, and to identify the relationship between nurses-midwives knowledge and performance and their socio-demographic and professional variables regarding immediate newborn care.

Methodology: A descriptive study was conducted from 1^st of October, 2019 to 20^TH of October, 2020 at three maternity teaching hospitals in Baghdad city/Al-Russafa Health Directorate: Al-Elwiya Maternity Teaching Hospital, Fatema Al- Zahra Maternity and Pediatric Hospital, Al- Za'faraniyah General Hospital. Non-probability (purposive sample) include