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

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

For businesses that provide delivery services, the efficiency of the delivery process in terms of punctuality is very important. In addition to increasing customer trust, efficient route management, and selection are required to reduce vehicle fuel costs and expedite delivery. Some small and medium businesses still use conventional methods to manage delivery routes. Decisions to manage delivery schedules and routes do not use any specific methods to expedite the delivery settlement process. This process is inefficient, takes a long time, increases costs and is prone to errors. Therefore, the Dijkstra algorithm has been used to improve the delivery management process. A delivery management system was developed to help managers and drivers

The present studies are focused on the modification of the properties of epoxy resin with different additives namely aluminum, copper by preparing of composites systems with percentage (20%, 40% and 50%) of the above additives. The experimental results show that the D.C of conductivity on wt% filler content at ( 293-413 ) K electrical conductivity of all above composites increased with temperature for composites with filler contact and find the excellent electrical conductivity of copper and lie between (2.6*10-10 - 2.1*10-10)?.cm . The activation energy of the electrical conductivity is determined and found to decrease with increasing the filler concentration.

This study has been conducted to investigate the influence of preservatives(sodium propionate and potassium sorbate) at different levels to extend the storage life of laboratory processed biscuit.The results have shown that When%0.10 sodium propionate was added bacteria and fungi was not found for three months while found in the fourth month of storage.However bacteria and mold didnot grow until the sixth month of storage as %0.20 and %0.30 concentration was used. On the other side as %0.03 potassium sorbate was used,the growth of bacteria was cheched for 3 months and 4 months for molds..When %0.06 potassium sorbate was used no growth of bacterial or mold was found until the sixth month of storage . Three types of bacteria have been appeare