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Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a CT lung cancer dataset consisting of 1000 images and four different classes. The data augmentation process is applied to prevent overfitting, increase the size of the data, and enhance the training process. Score-level fusion and ensemble learning are also used to get the best performance and solve the low accuracy problem. All models were evaluated using accuracy, precision, recall, and the F1-score. Results: Experiments show the high performance of the ensemble model with 99.44% accuracy, which is better than all of the current state-of-the art methodologies. Conclusion: The current study's findings demonstrate the high accuracy and robustness of the proposed ensemble transfer deep learning using various transfer learning models

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Publication Date
Mon May 21 2007
Journal Name
Journal Of Planner And Development
Determination of levels of interaction between urban land use and the adoption of spatial analysis techniques (study area, Najaf City)
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the student of the structure of the city and its constituent elements will clearly sense the invisible relationships that underlie the different forms of urban activity, which in turn are defined by the generality of the urban patterns in that city, which will vary clearly according to the location in the city. These relations will be embodied in their true form in the interactions between the different uses of the earth, and the change that will result from their regularity in the form of entities in independent groups, which may share with each other a component of it.
Therefore, the process of controlling the functional interactions between the uses of the urban land and the awareness of t

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Publication Date
Fri Jun 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
"RUF procedures forgetting the best subset linear regression model"
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The purpose behind building the linear regression model is to describe the real linear relation between any explanatory variable in the model and the dependent one, on the basis of the fact that the dependent variable is a linear function of the explanatory variables and one can use it for prediction and control. This purpose does not cometrue without getting significant, stable and reasonable estimatros for the parameters of the model, specifically regression-coefficients. The researcher found that "RUF" the criterian that he had suggested accurate and sufficient to accomplish that purpose when multicollinearity exists provided that the adequate model that satisfies the standard assumpitions of the error-term can be assigned. It

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Publication Date
Fri Jun 27 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Salivary tumor marker CA15-3 and selected elements in relation to oral health status among a group of breast cancer women
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Background: Breast cancer is the commonest type of malignancy worldwide and in Iraq. It is a serious disease that affects the general health and cause systemic changes that affect the physical and chemical properties of saliva leading to adverse effects on oral health. This study was conducted toassess the tumor marker CA15-3 and selected elements in saliva and their relation to oral health status among breast cancer patients compared to control group. Materials and Methods: The total sample consisted of 60 women aged 35-45 years. 30 women were newly diagnosed with breast cancer before taking any treatment and surgery (study group) and 30 women without clinical signs and symptoms of breast cancer as a control group. Dental caries was record

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Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Between the Methods Estimate Nonparametric and Semiparametric Transfer Function Model in Time Series Using Simulation
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Publication Date
Sun Mar 17 2019
Journal Name
Baghdad Science Journal
A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures
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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

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Publication Date
Wed Mar 02 2022
Journal Name
Journal Of Educational And Psychological Researches
The Role of School Administration in Achieving Educational and Learning Goals as Seen by Educational Supervisors in the Stage of Basic Education
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This study aimed to identify the role of school administration in achieving educational and learning goals from the point of view of educational supervisors in the stage of basic education. The descriptive method was adopted. As for the sample size, it has reached (59) educational supervisors. A questionnaire consisting of 29 paragraphs divided into four areas was used. The data were statistically analyzed on the Chi-square test, the percentage, and the mono-variance analysis. The result showed that the school administration contributes to achieving educational goals. It also works to solve problems in democratic ways, and in modern methods, there are differences in the criteria for choosing the headmaster. The study recommended that sch

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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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Publication Date
Fri Jun 27 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Risk Factors of Oral Cancer and Potentially malignant disorders (PMDs) – Developing a High / Low Risk Profiling System
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Background: Oral squamous cell carcinoma (OSCC) remains a lethal and deforming disease, with a significant mortality and a rising incidence in younger and female patients. It is thus imperative to identify potential risk factors for OSCC and oral PMDs and to design an accurate data collection tool to try to identify patients at high risk of OSCC development. 14 factors consistently found to be associated with the pathogenesis of OSCC and oral PMDs. Eight of themwere identified as high risk (including tobacco, alcohol, betel quid, marijuana, genetic factors, age, diet and immunodeficiency) and 6 low risk (such as oral health, socioeconomic status, HPV, candida infection, alcoholic mouth wash and diabetes) were stratified according to severit

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Publication Date
Sun Sep 03 2023
Journal Name
Wireless Personal Communications
Application of Healthcare Management Technologies for COVID-19 Pandemic Using Internet of Things and Machine Learning Algorithms
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Simplified Novel Approach for Accurate Employee Churn Categorization using MCDM, De-Pareto Principle Approach, and Machine Learning
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Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

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