This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

The present study aimed to evaluate the effect of lead exposure on hemopoetic system (through the index delta-Aminolevulinic acid dehydratase ?-ALAD activity & hemoglobin concentration (Hb) ) and on iron status (levels of iron Fe, Ferritin Fr, Total iron binding capacity TIBC, percentage of transferine saturation TF%) in 44 Iraqi worker at lead batteries factory. Workers divided into two groups: smokers(n=21) mean aged (37.33±4.82 year)and non smokers(n=23) mean aged(40.78±7.89 year) and 45 healthy subjects mean aged (33.97±5.08)as control group . Activity of ?-ALAD ratio shows significant decrease (p ? 0.05) ,while Hb and hematocrit Hct were non significant (p ? 0.05) in smoker workers more than non smoker as compared to control . The r

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

Sustainable development has recently gained significant attention in the field of water Quality (WQ), which is critical for a healthy lifestyle. Our work suggests an intelligent hybrid model for assessing water quality using a water portability dataset, which was used to evaluate the water quality. The dataset contains physical and chemical features such as pH, Organic Carbon, sulfate, etc. Based on the combination of a Decision Tree Algorithm (DTA) and fuzzy logic Approaches, the findings revealed that sulfate was the most important factor in the model, with an accuracy of 1.00, followed by pH with an accuracy of 0.9667, and solids and chloramines with an accuracy of 0.95. The other parameters achieved a similar accuracy of 0.9167, showing