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Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set
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These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.

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Publication Date
Thu Mar 17 2016
Journal Name
International Journal Of Computer Applications
Analysis of Wind Speed Data and Annual Energy Potential at Three locations in Iraq
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Publication Date
Tue Jul 01 2025
Journal Name
Mastering The Minds Of Machines
The Intersection of AI and the Internet of Things (IoT): Transforming Data into Intelligence
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Publication Date
Thu Aug 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Some Estimation methods for the two models SPSEM and SPSAR for spatially dependent data
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ABSTRUCT

In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error          ( λ ) in the model (SPSEM), estimated  the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo

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Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
The Al-Abiadh Valley Drainage Basin Environmental Aspects Extraction Using Quantitatively Morphometric Analyses of Shuttle Radar Topographic Mission Data
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         The accurate extracting, studying, and analyzing of drainage basin morphometric aspects is important for the accurate determination of environmental factors that formed them, such as climate, tectonic activity, region lithology, and land covering vegetation.

This work was divided into three stages; the 1st stage was delineation of the Al-Abiadh basin borders using a new approach that depends on three-dimensional modeling of the studied region and a drainage network pattern extraction using (Shuttle Radar Topographic Mission) data, the 2nd was the classification of the Al-Abiadh basin streams according to their shape and widenings, and the 3rd was ex

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Publication Date
Wed Jan 29 2020
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Measuring the efficiency of quality health services in the province of Karbala: Models using the Data envelopment analysis (DEA)
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  The research aims to measure  the efficiency of health services Quality  in the province of Karbala, using the Data Envelopment analysis Models in ( 2006). According to these models the degree of efficiency ranging between zero and unity. We estimate Scale efficiency  for two types of orientation direction, which are input and output oriented direction.

  The results showed, according Input-oriented efficiency that the levels of Scale efficiency on average is ( 0.975), in the province of Karbala. While the index of Output-oriented efficiency on average is (o.946).

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
An An Accurate Estimation of Shear Wave Velocity Using Well Logging Data for Khasib Carbonate Reservoir - Amara Oil Field
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Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Applications
Big data analysis by using one covariate at a time multiple testing (Ocmt) method: Early school dropout in iraq
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Publication Date
Thu Jan 15 2026
Journal Name
Oncology In Clinical Practice
Unveiling the role of atezolizumab in non-small cell lung cancer and coexisting chronic obstructive pulmonary disease — a comprehensive review
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Introduction. The coexistence of non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is commonly observed, primarily due to overlapping risk factors like smoking and environmental exposures. This dual diagnosis introduces complex clinical scenarios, often associated with worsened outcomes and heightened vulnerability to treatment-related side effects. Immune checkpoint inhibitors, especially atezolizumab, have emerged as pivotal agents in enhancing clinical outcomes for individuals diagnosed with NSCLC. Recent evidence suggests that atezolizumab remains effective and well-tolerated in NSCLC patients with coexisting COPD. This review evaluates the efficacy and safety of atezolizumab in NSCLC patients with coex

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Publication Date
Wed Jun 04 2025
Journal Name
Engineering, Technology & Applied Science Research
Evaluation of the Accuracy of Machine Learning Classifiers and Spectral Indices in Land Cover Classification
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Population growth and economic and industrial development coupled have significantly accelerated the rate of Land Use and Land Cover (LULC) changes, particularly in developing countries, so finding optimum ways to observe these change has become a pressing issue. Quantification evaluation of these changes is crucial to comprehend and oversee land management conversion, therefore, it is necessary to evaluate the accuracy of various algorithms for LULC classification to determine the most effective classifier for Earth observation applications. The performance of Maximum Likelihood (ML), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) was examined in this study, based on Sentinel 2A satellite images. T

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Publication Date
Sat Aug 12 2017
Journal Name
Journal Of Engineering
Prepare rules spatial data for soils and the Calculation of an Area in Iraq for Industrial Purposes using Geographic Information Systems (GIS)
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      The process of soil classification in Iraq for industrial purposes is important topics that need to be extensive and specialized studies. In order for the advancement of reality service and industrial in our dear country, that a lot of scientific research touched upon the soil classification in the agricultural, commercial and other fields. No source and research can be found that touched upon the classification of land for industrial purposes directly. In this research specialized programs have been used such as geographic information system software The geographical information system permits the study of local distribution of phenomena, activities and the aims that can be determined in the loca

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