Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-ba

new six mixed ligand complexes of some transition metal ions Manganese (II), Cobalt(II), Iron (II), Nickel (II) , and non transition metal ion zinc (II) And Cadmium(II) with L-valine (Val H ) as a primary ligand and Saccharin (HSac) as a secondary ligands have been prepared. All the prepared complexes have been characterized by molar conductance, magnetic susceptibility infrared, electronic spectral, Elemental microanalysis (C.H.N) and AA . The complexes with the formulas [M(Val)2(HSac)2] M= Mn (II) , Fe (II) , Co(II) ,Ni(II), Cu (II),Zn(II) and Cd(II) L- Val H= (C5H11NO2) , C7H5NO3S The study shows that these complexes have octahedral geometry; The metal complexes have been screened for their in microbiological activities against bacteria.

Phosphorus‐based Schiff base were synthesized by treating bis{3‐[2‐(4‐amino‐1.5‐dimethyl‐2‐phenyl‐pyrazol‐3‐ylideneamino)ethyl]‐indol‐1‐ylmethyl}‐phosphinic acid with paraformaldehyde and characterized as a novel antioxidant. Its corresponding complexes [(VO)₂L(SO₄)₂], [Ni₂LCl₄], [Co₂LCl₄], [Cu₂LCl₄], [Zn₂LCl₄], [Cd₂LCl₄], [Hg₂LCl₄], [Pd₂LCl₄], and [PtL

One of the most significant environmental issues facing the planet today is air pollution. Due to development in industry and population density, air pollution has lately gotten worse. Like many developing nations, Iraq suffers from air pollution, particularly in its urban areas with heavy industry. Our research was carried out in Baghdad's Al-Nahrawan neighbourhood. Recently, ground surveys and remote sensing were used to study the monitoring of air pollution. In order to extract different gaseous and particle data, Earth Data source, Google Earth Engine (GEE), and Geographic Information Systems (GIS) software were all employed. The findings demonstrated that there is a significant positive connection between data collected by ground-ba