Journal of Theoretical and Applied Information Technology is a peer-reviewed electronic research papers & review papers journal with aim of promoting and publishing original high quality research dealing with theoretical and scientific aspects in all disciplines of IT (Informaiton Technology

Tillage appearance device is mechanical, electric-electronic design, getting Patent from the Central Organization for Standardization and Quality Control – Industrial Property Department - Ministry of Planning – The Republic of IRAQ under number Patent 3876 in 20 / 4 / 2014, calculates the number of clods per area by Tillage appearance device, This is done through the generation electrical impulses are sent to the controlled accurate calculates number clods required space and shows the result on the screen in order to see the tillage view per area. Three factor used in these experiment, first factor represents forward speed of tractor three levels (3.5, 4.5, and 5.5 km/h), second factor represent soil moisture content at two levels (14

If the sovereignty of the state is reflected in the taxation of its citizens, this sovereignty can not be completed and completed only if it works on its part to collect its debts, whether voluntary or compulsory, and the debt of the debt arises from the will of the individual and the will of the state alone, The existing management of seizure and collection is based on an unequal relationship between the State and the debtor from which the obligation arises. Naturally, this relationship has obligations and rights on both parties. The researcher used a set of studies and previous research, books and other sources related to the subject of research. This was done through the theoretical and practical aspects, which focused on direct and i

    Hartha Formation is an overburdened horizon in the X-oilfield which generates a lot of Non-Productive Time (NPT) associated with drilling mud losses. This study has been conducted to investigate the loss events in this formation as well as to provide geological interpretations based on datasets from nine wells in this field of interest. The interpretation was based on different analyses including wireline logs, cuttings descriptions, image logs, and analog data. Seismic and coherency data were also used to formulate the geological interpretations and calibrate that with the loss events of the Hartha Fm.

   The results revealed that the upper part of the Hartha Fm. was identified as an interval capable of creating potentia

Deep Learning Techniques For Skull Stripping of Brain MR Images

A new two-way nesting technique is presented for a multiple nested-grid ocean modelling system. The new technique uses explicit center finite difference and leapfrog schemes to exchange information between the different subcomponents of the nested-grid system. The performance of the different nesting techniques is compared, using two independent nested-grid modelling systems. In this paper, a new nesting algorithm is described and some preliminary results are demonstrated. The validity of the nesting method is shown in some problems for the depth averaged of 2D linear shallow water equation.

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p