The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98).

Alzheimer's-disease (AD) is one of the prevalent diseases that afflict the elderly. The medical field defines Alzheimer is the destruction of brain cells so that the person loses knowledge and perception, afflict both sexes and is called dementia. The medical field often suffers from accurate diagnosis and detection of the disease in the early stages. This paper presents a diagnostic approach of Alzheimer based on K-mean clustering algorithm with Markov random field segmentation on Magnetic Reasoning Images (MRI) to build software able to help the medical staff identifying and diagnosis the disease. The experimental result shows that 91% accuracy is achieved, which demonstrate the system's reliability in the medical diagnostic environment.

With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

One of the widely present elements in the groundwater and surface water is phosphate due to two reasons; firstly, it is available at high concentrations in the soil, and secondly, it is widely available in wastewaters (industrial, agricultural and domestic wastewaters). Although phosphate causes many problems to the aquatic environment, eutrophication is the most severe problem due to its effects on water quality, economy, and health. Therefore, a number of studies have been made to evaluate the ability of different remedies to eliminate phosphates from wastewaters. Recently, phosphorus extraction may be achieved by filtering the contaminated solution. However, the cost of filtration materials is still high. Due to this reason, research to date has focused on employing inexpensive materials to reduce the cost of the filtering process. In this research, a by-product of steel manufacturing, kiln bottom ashes, was used to extract phosphates from polluted wastewater, considering the impacts of a number of operating parameters, such as to achieve the best possible extraction efficiency for the lowest possible cost. The findings of this study proved the excellent ability of the bottom ash in the extraction of phosphate from wastewater, where it removed more than 90% of 5 mg/L of phosphate after 40 minutes of treatment using 530 mg/L of bottom ash.

Biometric system is considered of an important type of security systems nowadays, because it relays on the individual traits (physical or behavioral) non-participation between any two people that can't be lost on lifetime and can't be stolen. This paper, will present the individual classification system based on hand geometry and palm texture feature where it is one of the parts of the human body, which has an impressive set of information capable to distinguish and identify individuals. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system consists of three phases: image preprocessing, hand feature extraction and pattern classification. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system utilized complete hand image inside database consists of 600 pictures, include 100 people, each one has six images. Experimental results show that 98.3% is achieved and that illustrate the applicability of the system in the security's average of different environments.

One of the most important healthcare institutions in Iraq is Outpatient clinic that requires a lot of thinking to improve the way to provide services and the nature of care. Outpatient clinics are increasingly keen to meet the needs of care, and this has been recognized as a fundamental issue related to service quality. Therefore, many researchers in various fields have taken this matter as a basis for their research, as it is considered a rich material for research due to the problems these institutions contain. The most important problems faced by outpatient clinics are the waiting time and the insufficient number of clinic staff to perform the various tasks. The aim of this paper is to reduce the waiting time by building a model for the clinic environment, especially dental clinics, and trying to benefit from all the existing medical staff and exploit their experiences. Since the patient spends a long time between registration, returning to the doctor and finally the result or process that the doctor performs, building such a model might help in identifying and improving the problem. The simulation model built in this research for the clinic is based on modelling the discrete events inside the dental clinic using the Arena software. This form is used to assess the quality of services provided by dental outpatient clinics in Iraq.