Multiple Linear Regression and Machine Learning for Predicting the Drinking Water Quality Index in Al-Seine Lake Raed Jafar, Adel Awad, Iyad Hatem, Kamel Jafar, Edmond Awad, Isam Shahrour Smart Cities, 2023 Ensuring safe and clean drinking water for communities is crucial, and necessitates effective tools to monitor and predict water quality due to challenges from population growth, industrial activities, and environmental pollution. This paper evaluates the performance of multiple linear regression (MLR) and nineteen machine learning (ML) models, including algorithms based on regression, decision tree, and boosting. Models include linear regression (LR), least angle regression (LAR), Bayesian ridge chain (BR), ridge regression (Ridge), k-nearest neighbor regression (K-NN), extra tree regression (ET), and extreme gradient boosting (XGBoost). The research’s objective is to estimate the surface water quality of Al-Seine Lake in Lattakia governorate using the MLR and ML models. We used water quality data from the drinking water lake of Lattakia City, Syria, during years 2021–2022 to determine the water quality index (WQI). The predictive performance of both the MLR and ML models was evaluated using statistical methods such as the coefficient of determination (R2) and the root mean square error (RMSE) to estimate their efficiency. The results indicated that the MLR model and three of the ML models, namely linear regression (LR), least angle regression (LAR), and Bayesian ridge chain (BR), performed well in predicting the WQI. The MLR model had an R2 of 0.999 and an RMSE of 0.149, while the three ML models had an R2 of 1.0 and an RMSE of approximately 0.0. These results support using both MLR and ML models for predicting the WQI with very high accuracy, which will contribute to improving water quality management.
Solving Some Partial Differential Equations by Using Double Laplace Transform in the Sense of Nonconformable Fractional Calculus Sami Injrou, Iyad Hatem Genetics Research, 2022 In this paper, we introduce the non-conformable double Laplace transform. Its properties are studied, and it is applied to solve some fractional PDEs involving the nonconformable fractional derivative. Graphical representations of the obtained solutions are shown in figures. The study shows that this transform is effective and easy to apply to create an exact solution for types of fractional PDEs.
COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet Adnan Saood, Iyad Hatem BMC Medical Imaging, 2021 BackgroundCurrently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, and , are investigated for semantically segmenting infected tissue regions in CT lung images.MethodsWe propose to use two known deep learning networks, and , for image tissue classification. is characterized as a scene segmentation network and as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly.ResultsThe results show the superior ability of in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the shows better results as a multi-class segmentor (with 0.91 mean accuracy).ConclusionSemantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.
Two Proposed Indoor Multi-Cameras Positioning Systems Compared to Classical Geometry System Adnan Saood, Nada Salman, Ali Alreyahi, Iyad Hatem 2018 International Conference on Computational Approach in Smart Systems Design and Applications Icassda 2018, 2018 Positioning systems in indoor environments are of a great concern in automation and robotics domains where performing critical tasks requires precision. However, to make these systems widely applicable they must be cost-effective. The objective of this paper is to develop two different 3D positioning systems based on neural networks and adaptive neuro-fuzzy techniques. Sample images of a recognizable object were taken using three low-cost cameras as training and testing data for these systems. Positioning results of the proposed systems are compared with results of the classical geometrical method. The results show positioning errors on the scale of millimeters and the neural network system produces the smallest error.
Image processing of hematoxylin and eosin-stained tissues for pathological evaluation Xioqiu Liu, Jinglu Tan, Iyad Hatem, Barry L. Smith Toxicology Mechanisms and Methods, 2004 Color and geometric characteristics of stained areas in histochemical slides are among the features pathologists assess to evaluate the severity of lesions. In this research, image processing techniques were used to perform objective quantification of these characteristics in images of H&E-stained spleen tissues. A segmentation algorithm was developed to isolate the areas of interest in microscopic tissue images. Image features important to pathological evaluation were then extracted. These features were used to build statistical and neural network models to predict pathologist scores. A linear regression model predicted the scores to an R2-value of 0.6, and a neural network model classified samples to an accuracy of 75%. The results show the usefulness of image processing as a tool for pathological evaluation.
Set point determination from sensory evaluations for food process control S. KUPONGSAK, J. TAN, I. HATEM, W. LU, B. GUTHRIE, M. TANOFF Journal of Food Process Engineering, 2004 Sensory evaluation is often the ultimate measure of food quality, but food process control relies on instrumental measurements. Effective techniques are needed to convert desired sensory quality targets into instrumental process set points. This paper describes techniques developed for determining instrumental process set points from sensory evaluations. Various cases and different approaches depending on the nature of the sensory‐instrumental relationships are outlined. the major issues addressed include additional constraints for underdetermined cases and reverse mapping with neural networks for nonlinear multivariate cases. These techniques were illustrated and tested with experimental data based on waffie samples. Seven sensory attributes were evaluated by trained panelists and instrumental measurements were obtained with a color computer vision system. For nonlinear multivariate cases, reverse mapping with neural networks successfully mapped sensory measurements to instrumental process set points with average errors less than 1.3%. the results demonstrate the effectiveness of the techniques developed.
Cartilage and Bone Segmentation in Vertebra Images Transactions of the American Society of Agricultural Engineers, 2003
Cartilage segmentation in vertebra images 2000 ASAE Annual International Meeting Technical Papers Engineering Solutions for A New Century, 2000
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