The problem of unemployment is one of the most important problems faced by most countries of the world, and it is one of the intractable problems in developing countries, and in Iraq unemployment occupies great importance due to its high rates. This problem in itself is a serious condition, because it results from mismanagement and the structure of the economy, and despite its great importance, it has not been carefully monitored. There are studies and strategies that deal with the analysis and study of those causes that lead to this problem, such as traditional statistical methods, various mathematical and statistical methods, in this research proposed a method uses machine learning methods to find the factors that affect the causes of this problem, as well as the multiple linear regression method.

The Sidr tree is an important fruit tree in many parts of the world, but it is often affected by various diseases and pests that can impact the quality and quantity of its fruit production. This paper proposes the use of ontology and semantic web technologies with Python programing language to program a diagnosis service for the Sidr tree diseases. Programming a Semantic Web application system and services requires knowledge of many technologies such as Resource Description Framework (RDF), Web Ontology Language (OWL), and SPARQL Query. The underline Web service (Sidr tree disease Web service), has to be programmed in connection to a pre-existing ontology and knowledge base. The service program is made of three main components. These components are the user interface, diagnosis service-related functions, Knowledge base, and query engine. The Python 3.10 code listing in this paper represents only the code related to disease diagnosing service and the Sidr tree ontology (SidrTreeonto). In addition to the listed statements and functions, the complete diagnosing service code contains some other unlisted HTML static and dynamic Web pages. The code is run and tested and found to be simple and easy to use even by unexpert users. Overall, this application will provide a useful tool for Sidr tree farmers and researchers to diagnose and manage diseases affecting their trees.

Face recognition systems are one of the most important applications in the field of computer vision. Where these systems enter into the development of many applications, especially in the field of medicine, security and Human Computer Interaction (HCI), In addition to the development of robotics. The system proposed in this paper includes the design of a software model capable of distinguishing two of the most important expressions of the human face: happiness and sadness, plus to the natural expression of the human face. The proposed system includes the approach to the design of any recognition system, starting from image acquisition and pre-processing, through the extraction of attributes. Finally to classify and give the output of the recognition. In the first stage a hybrid algorithm was adopted to extract the oval face. Two dimensions principal component analysis (2DPCA) for stage of features extraction. In the classification stage was used Euclidean distance. The results obtained showed high accuracy to distinguish the proposed expressions, so that the results of the recognition reached (95 %) percent when testing 120 samples from a Multimedia Understanding Group (MUG) Database [1].