Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

Background: War represents a major human crisis; it destroys communities and results in ingrained consequences for public health and well-being

Objective: We set this study to shed light on the public health status in Iraq after the successive wars, sanctions, sectarian conflicts, and terrorism, in light of certain health indicators.

Design: The primary source of data for this analysis comes from the Iraqi Ministry of Health, and The World Health Organization disease surveillance.

Results: Most of the morbidity indicators are high, even those that are relatively declining recently, are still higher than those repor

Toxoplasmosis is a widespread infection usually caused by Toxoplasma gondii (T. gondii) parasite. It occurs in humans and other warm blooded animals, causing severe problems. It was found that there is an alteration in the trace elements concentrations levels associated with some human diseases. This study aimed to investigate the changes in the concentrations of some trace elements (Mg, Fe , Zn, and Cu) in the sera of 60 immunocompetent patients with chronic toxoplasmosis and 82 healthy individuals as a control group. Measuring the serum level of seropositivity rate of anti-T. gondii antibodies was done by Enzyme Linked Immunosorbent Assay (ELISA) Kit, while the concentrations of trace elements were measured by absorption spectrophotometry

Abstract

     Drug information resources are the information that is used in medications discovery, utilization, and management. Little information about different types of resources used by Iraqi community pharmacists is known. Therefore, the objectives were to determine drug information resources' type do the pharmacists used and the common drug information questions they faced during their work in community pharmacy. A cross-sectional descriptive study was conducted in different Iraqi provinces and online self-reported survey was introduced through Google Form Software to an appropriate sample of graduated pharmacists who were working in a private community pharmacy and having at least one

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed.  A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec

    A mounted specimen of a mustelid animal deposited in the Kurdistan Museum of Natural History, Salahaddin University, Erbil proved to be Mustela erminea (Linnaeus, 1758) and represents a new record for the mammalian fauna of Iraq. Its measurements and some biological noted are provided. Also, two passerine birds; the Red-headed bunting, Emberiza bruniceps Brandt, 1841(Family, Emberizidae) and the Variable wheatear, Oenanthe picata (Blyth, 1847) (Family, Muscicapidae) were recorded for the first time in Iraq. Furthermore, the tree frog Hyla savignyi Audouin, 1829 was found in two locations north east of Iraq with spotted dorsum and having interesting behavior in having the capabil

The present study reports Morchella conica Pers.1818, which belongs to the family, Morchellaceae as a new record of Iraqi macromycota based on the morphological and molecular methods. During their short and often sporadic fruiting season, this fungal species was found in mixed forest unburned areas in Branan ranges (Suliamaniya Province, Northeast Iraq). Currently, M. conica is the second Morchella species reported from Iraq. The current study aimed to introduce this new record, which is poorly studied in the Middle East. M. conica is morphologically described and phylogenetically confirmed. The relationship between this species and other species within the genus was studied using the nrDNA ITS sequences from different species and divers