A dust storm in Iraq is a climatic phenomenon common in arid and semi-arid regions . The frequency of the occurrence has increased drastically in the last decade and it is increasing continuously .Baghdad city like the rest of Iraq is suffering from the significant increase in dust storms . In this research , the study of the phenomenon of dust storms for all types (Suspended dust , rising dust , dust storm) , and its relationship with some climate variables (Temperature , rainfall ,wind speed) .The statement of the impact of climate change on this phenomenon to Baghdad station  for the period (1981 – 2012) . Time series has been addressing the phenomenon of storms and cli

In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

Finding the shortest route in wireless mesh networks is an important aspect. Many techniques are used to solve this problem like dynamic programming, evolutionary algorithms, weighted-sum techniques, and others. In this paper, we use dynamic programming techniques to find the shortest path in wireless mesh networks due to their generality, reduction of complexity and facilitation of numerical computation, simplicity in incorporating constraints, and their onformity to the stochastic nature of some problems. The routing problem is a multi-objective optimization problem with some constraints such as path capacity and end-to-end delay. Single-constraint routing problems and solutions using Dijkstra, Bellman-Ford, and Floyd-Warshall algorith

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Magnetic plaster kiln dust (MPKD) was synthesized as a unique, low-cost composite reused of byproduct plaster kiln dust (PKD), which is considered a source of air pollution. The FESEM, EDS, XRD, FTIR, VSM, and BET tests were used to characterize the MPKD. The characterization revealed that the MPKD was nanotubes non-agglomerated and super-paramagnetic with a high specific surface area (102.7 m²/g). Compared with the specific area of other materials (composites), the MPKD could be considered a promising substance in the field of water/wastewater treatment.

Currently regions in the world are much affected with dust storms, rising and suspended dust, as Iraq is suffering of this state as well within an eye-catching manner, whereas the storms leave negative traces on environment and public health and many of the other vital utilities. This research aims to diagnosis of the reasons that resulted in increment of dust storms in Iraq basing on some recorded data and to be analyzed in conformity with the prevalent theories and concepts at the atmospheric science and environmental physics. Generally the study, has illustrated that the climate changes and increase of temperatures averages because of green house effect, that followed by clear changes at thermal wind and jet stream, that caused to ins

 To expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo