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

This research was designed to study effect of performance appraisal dimensions on organizational confidence.

Asset completion questionnaire was used to collect data of this research from a random simple represent forty employees who works in Iraqi Oil ministry. The main result of this research was positive relations and the effect between performance appraisal and organizational confidence. The research contains some conclusions, the main of it is unimplemented the performance appraisal results in the policies of employees in the ministry and it work in classic methods incentives and training.

The important recommendations must doing to achieve integrating between the result of pe

The study aims to identify the students’ attitudes toward recent techniques’ use in teaching; using power point software and data show to facilitate teaching approaches as well as following the approach of lecture in giving subjects, do these techniques facilitate, increase, and tackle the difficulties of subjects, do these attitudes positive or negative?, and to what extent these techniques raise up students’ motivation.   

The activation energy and optical band gap of different regions (p-type) polysilicon have been measured. Both microscopic studies and current-voltage characteristics of diodes prepared on different surface regions were carried out. Comparison of diodes parameters and microscopic studies indicate that the type of angles between boundaries has a significant effect on diodes parameters while the boundary lengths per unit area has less effect. The mechanism of Al-interaction with grain boundaries and their intersecting points at different temperature were also studies. The X-ray fluorescence spectrometry has been used for detection of diffused A1%.

Chemical spray pyrolysis technique was used at substrate temperature 250 ˚C with annealing temperature at 400 ˚C (for 1hour) to deposition tungsten oxide thin film with different doping concentration of Au nanoparticle (0, 10, 20, 30 and 40)% wt. on glass substrate with thickness about 100 nm. The structural, optical properties were investigated. The X-ray diffraction shows that the films at substrate temperature (250 ˚C) was amorphous while at annealing temperature have a polycrystalline structure with the preferred orientation of (200), all the samples have a hexagonal structure for WO3 and Au gold nanoparticles have a cubic structure. Atomic force microscopy (AFM) was used to characterize the morphology of the films. The optical pr