<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

     Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the prefe

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

     The great progress in information and communication technology has led to a huge increase in data available. Traditional systems can't keep up with this growth and can't handle this huge amount of data. Recommendation systems are one of the most important areas of research right now because they help people make decisions and find what they want among all this data. This study looked at the research trends published in Google Scholar within the period 2018-2022 related to recommending, reviewing, analysing, and comparing ebooks research papers. At first, the research papers were collected and classified based on the recommendation model used, the year of publication, and then they were compared in terms of techniques, datasets u

Coherent noise such as ground roll and guided wave is present in the seismic line DE21 (East Diwaniya south eastern Iraq) and it obscures seismic signal and degrades from the quality of data. To attenuate the coherent noise from the shot gather and the Stack of the seismic line, AGORA filter was applied in order to obtain the good signal as the hyperbola shape in shot gather and the reflectors will be clearly to interpret it later. It has given good results and the coherent noise was attenuated in high ratio on all the line. The spectrum analysis is confirmed the effectiveness of AGORA filter to attenuate the coherent noise. 

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

    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 aim of this study is to determine the level of pollution with heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) and their potential sources in dust samples collected from schools in Ramadi City, Iraq. The dust samples were collected from 40 primary schools and two kindergartens and analyzed by using atomic absorption spectrophotometer. The heavy metal concentrations were found to follow the order Cr > Cu > Pb > Ni > Zn > Cd.  The results indicated that the concentrations of Cd, Cu, and Pb exceeded the permitted background values. The pollution level was assessed using the geo-accumulation index (I_geo) and pollution load index (PLI). The classification of dust samples according to I_geo values showed that

Audio-visual detection and recognition system is thought to become the most promising methods for many applications includes surveillance, speech recognition, eavesdropping devices, intelligence operations, etc. In the recent field of human recognition, the majority of the research be- coming performed presently is focused on the reidentification of various body images taken by several cameras or its focuses on recognized audio-only. However, in some cases these traditional methods can- not be useful when used alone such as in indoor surveillance systems, that are installed close to the ceiling and capture images right from above in a downwards direction and in some cases people don't look straight the cameras or it cannot be added in some

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database