Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

Emotional exhaustion considered one of the critical factors in the formation and composition of organizational behavior of individuals within organizations, as well as social behavior and psychological, and emotional exhaustion is one of the three components of burnout, as well as depersonalization (cynicism) and low achievement, the emergence of research relevant to this concept began at the beginning of the seventies of the twentieth century, then started to become clear features in the eighties it. This research aims to build intellectual framework for draining emotional exhaustion through highlight on most important philosophical contents, as well as review and analysis of some models associated with this concept, and then a

Numerical simulations have been carried out on the solar chimney power plant systems. This paper gives the flow field analysis for a solar chimney power generation project located in Baghdad. The continuity, Naver-stockes, energy and radiation transfer equations have been solved and carried out by Fluent software. The governing equations are solved for incompressible, 3-D, steady state, turbulent is approximated by a standard k -  model with Boussiuesq approximation to study and evaluate the performance of solar chimney power plant in Baghdad city of Iraq. The different geometric parameters of project are assumed such as collector diameter and chimney height at different working conditions of solar radiation intensity (300,450,600,750

In Incremental sheet metal forming process, one important step is to produce tool path, an
accurate tool path is one of the main challenge of incremental sheet metal forming
process. Various factors should be considered prior to generation of the tool path i.e.
mechanical properties of sheet metal, the holding mechanism, tool speed, feed rate and
tool size. In this work investigation studies have been carried out to find the different tool
path strategies to control the twist effect in the final product manufactured by single point
incremental sheet metal forming (SPIF), an adaptive tool path strategy was proposed and
examined for several Aluminum conical models. The comparison of the proposed tool path with t

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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

Digital change detection is the process that helps in determining the changes associated with land use and land cover properties with reference to geo-registered multi temporal remote sensing data. In this research change detection techniques have been employed to detect the changes in marshes in south of Iraq for two period the first one from 1973 to 1984 and the other from 1973 to 2014 three satellite images had been captured by land sat in different period. Preprocessing such as geo-registered, rectification and mosaic process have been done to prepare the satellite images for monitoring process. supervised classification techniques such maximum likelihood classification has been used to classify the studied area, change detection aft