The study aimed to reveal the possibility of predicting academic procrastination through both Cognitive distortions and time management among students of Al-Aqsa Community College, as well as to reveal the level of both cognitive distortions, time management, and academic procrastination. Additionally, it aimed to identify the size of the correlation between cognitive distortions, time management, and academic procrastination. The study sample consisted of (250) students from Al-Aqsa community college students. The results of the study concluded that the mean for each level of cognitive distortions and academic procrastination is average. The mean level of time management is high. There is a statistically significant positive relationshi

The present work represents description of three new species of genus Anthrenus
Geoffory from Iraq, these are : A. aradensis sp. nov., A. fabrici sp. nov. and A.
unicolor sp. nov. Locality, host plants and date of collection were given.

This investigation showed (31) species belonging to (15) genera under (five) families and two orders. The leafminers Dipter families (Agromozidae, Anthomyiidae, Drosophilidae), Agromyzid flies is the highest level of investigated many host plants, but other families have lowest host plants. The synonyms of species were provided from GBIF scarlet's. The date and localities of sampling collection were recorded.

The present study introduces description of new species of leafhopper Cicadella latreilla nov. External morphological characters particularly female genitalia were discussed and illustrated, shape of male abdominal apodeme, chaetotaxy of genital style and genital plate of male genitalia, shape of Aedeagus and connective . The external morphological characters were: spotting patterns of vertex, face, pronotum of prothorax, and metothorax, and of lower and upper valves of ovipositors,shape of female seventh abdominasternum.

Abstract                                                                             

The government spending in Iraq and witnessed the changes and developments, especially after 2003, which outweighed consumer spending at the expense of capital expenditure and increased support and diversity of trends towards improving pension conditions for member

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti