In this paper we investigate the use of two types of local search methods (LSM), the Simulated Annealing (SA) and Particle Swarm Optimization (PSO), to solve the problems ( ) and . The results of the two LSMs are compared with the Branch and Bound method and good heuristic methods. This work shows the good performance of SA and PSO compared with the exact and heuristic methods in terms of best solutions and CPU time.

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

     In recent years images have been used widely by online social networks providers or numerous organizations such as governments, police departments, colleges, universities, and private companies. It held in vast databases. Thus, efficient storage of such images is advantageous and its compression is an appealing application. Image compression generally represents the significant image information compactly with a smaller size of bytes while insignificant image information (redundancy) already been removed for this reason image compression has an important role in data transfer and storage especially due to the data explosion that is increasing significantly. It is a challenging task since there are highly complex unknown correlat

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

Background: Neuropathy stands out as the highest-prevalence diabetes-related complication, impacting no less than 50% of individuals with diabetes throughout their lifespan. As The most common reason for disability due to walking difficulties, foot ulcerations, and limb loss, DPN is worthy of study, and early diagnosis of DPN signs is required.

Objectives: This study aims to aid in the identification of diabetic peripheral neuropathy (DPN) by determining the muscle thickness of the lower extremities in patients with DPN.

Patients and Methods: The study included 24 subjects with diabetic peripheral neuropathy (DPN) and 25 individuals as a co

Knowledge management contribute to the overall private university libraries to develop libraries for the purpose of creating human and technological resources by investing research and development, as well as education and training for life.
This study followed the methodology of the descriptive and historical pillars of knowledge management for the years 1990 to the present day in 2017, depending on statistical figures obtained by the researchers from the General Secretariat of the Central Library at the University of Baghdad, and the Human Resources Division, which specialized human resources training after 2004 (and before those years from 1982 until 2002, where the continuing education was committee of the fixed committees are wor

   The study aims to test the relationship of work pressure to its dimensions (role conflict, ambiguity of role, workload and nature of work) as an independent variable and its effect on organizational alienation by its dimensions (disability, lack of power, indifference, animosity, social isolation and self-alienation) (Restraint and confidence in negation, initiative, adaptation and living conscience) as a mediator variable, in some faculties of Baghdad University of Science (Medicine and Engineering) and Humanity (Education and Literature). The data was collected on the practical side, which was applied randomly (306) of the teachers and teachers of the colleges (56) items, which included the main research variables

The present study was conducted to investigate the relationship between critical thinking, epistemological beliefs, and learning strategies with the academic performance of high school first-grade male and female students in Yazd. For this purpose, from among all first-grade students, as many as 250 students (130 females and 120 males) were selected by using multistage cluster sampling. The data needed were then collected through using California Critical Thinking Skills Test, Schommer's Epistemological Beliefs Questionnaire, Biggs’ Revised Two Factor Study Process Questionnaire. The findings indicated that there is a positive significant relationship between critical thinking and academic performance and achievement. Moreover, four fa

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

In present work an investigation for precise hole drilling via continuous wave (CW) CO2 laser at 150 W maximum output power and wavelength 10.6 μm was achieved with the assistance of computerized numerical controlled (CNC) machine and assist gases. The drilling process was done for thin sheets (0.1 – 0.3 mm) of two types of metals; stainless steel (sst) 321H, steel 33 (st). Changing light and process parameters such as laser power, exposure time and gas pressure was important for getting the optimum results. The obtained results were supported with computational results using the COMSOL 3.5a software code.