Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl^-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their perfor

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

The aim of the research is to determine the extent to which young people watch television sports programs and the impact of viewing on the level of sports culture among young people and the impact of demographic variables on youth over the viewing of sports programs, The study was conducted on a sample of (200) male and female students from the faculties of the University of Baghdad, where a questionnaire form composed of several axes and questions was prepared to measure the effectiveness of sports programs in youth culture, The statistical program used spss to empty data and extract results by frequency, percentages, correlations, The research reached several results, the most important of which is that there is a strong influence of s

An integrated GIS-VBA (Geographical Information System – Visual Basic for Application), model is developed for selecting an optimum water harvesting dam location among an available locations in a watershed. The proposed model allows quick and precise estimation of an adopted weighted objective function for each selected location. In addition to that for each location, a different dam height is used as a nominee for optimum selection. The VBA model includes an optimization model with a weighted objective function that includes beneficiary items (positive) , such as the available storage , the dam height allowed by the site as an indicator for the potential of hydroelectric power generation , the rainfall rate as a source of water . In a

The objective of the present study is to determine the nature and direction of the correlation between mathematical excellence and learning styles as defined by the Entwistle, model in fifth-grade scientific female students. The descriptive correlational approach was implemented by the two researchers to accomplish the research objectives. A scale was developed to assess the learning styles of female students in the sample in accordance with the Entwistle, model. : (Knowledge, understanding, application, analysis, synthesis, evaluation, systematic thinking, creativity), and the research community was determined by the female students of the scientific fifth grade in the morning preparatory and secondary schools of the General Direct

In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.

Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

: Summary The establishment of the modern Iraqi state necessitated the establishment of diplomatic relations with neighboring countries. However, during the British Mandate period, these relations remained under the management of the British Embassy, which assumed responsibility for Iraq's foreign affairs. As the political situation evolved, the Iraqi Ministry of Foreign Affairs began making strenuous efforts to build a competent and effective diplomatic apparatus. Iraqi diplomatic representatives played an active role in strengthening and developing Iraq's foreign relations by implementing the Ministry of Foreign Affairs' directives, which focused on monitoring political, economic, and social developments in neighboring countries. This app

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D