Merdin Shamal Salih’s research while affiliated with Duhok Polytechnic University and other places

Diabetes mellitus, a chronic condition, causes disruptions in the metabolic processes of carbohydrates, lipids, and proteins. Hyperglycemia, characterised by elevated blood sugar levels, is the primary distinguishing characteristic of all forms of diabetes. Diabetes is a disease that has significantly increased in prevalence due to the contemporary lifestyle. Consequently, it is essential to get an early-stage diagnosis of the illness. When constructing classification models, data pre-processing is a crucial step. The Pima Indian Diabetes dataset, available in the University of California Irvine (UCI) repository, is a challenging dataset with a higher proportion of missing values (48%) compared to comparable datasets. To improve the accuracy of the classification model, many rounds of data pre-processing are conducted on the Pima Diabetes dataset. The proposed approach consists of two stages: outlier removal and imputation in the first stage, and normalisation in the second stage. Regarding the feature aspect, we used a method called principal component analysis (PCA). Ultimately, to classify the PIMA dataset, we used many classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT). The testing revealed that the maximum achievable accuracy was 89.86% when 80% of the data was used for training. This was accomplished by integrating the feature selection technique with the classifier.

This research paper presents a comprehensive study on the development and evaluation of a brain tumor classification model using advanced image processing and deep learning techniques. The primary objective of this study was to create an accurate and robust system for distinguishing between brain tumors and normal brain images, utilizing both an original dataset and an augmented dataset. With a focus on improving medical diagnosis, the research aimed to enhance the performance of brain tumor detection by leveraging state-of-the-art machine learning methods. The model pipeline comprised various image preprocessing steps, including cropping, resizing, denoising, and normalization, followed by feature extraction using the DenseNet121 architecture and classification using sigmoid activation. The dataset was meticulously divided into training, validation, and testing sets, with an emphasis on achieving high recall, precision, F1-score, and accuracy as key research objectives. The results demonstrate that the model achieved impressive performance, with a training recall of 92.87%, precision of 93.82%, F1-score of 93.15%, and an accuracy of 94.83%. These findings underscore the potential of deep learning and data augmentation in enhancing brain tumor detection systems, supporting the research's core objective of advancing medical image analysis for clinical applications.

Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.

Recent progress in deep learning methods has shown that key steps in object detection and recognition, including feature extraction, region proposals, and classification, can be done using ImageAi libraries. Object detection is a computer vision technique that works to identify and locate objects within an image or video. Specifically, object detection draws bounding boxes around these detected objects, which allow us to locate where said objects are in a given scene. Object detection is commonly confused with image recognition, so before we proceed, it’s important that we clarify the distinctions between them. In that it aids in our comprehension and analysis of scenes in images or videos, object detection is intrinsically tied to other related computer vision techniques like image recognition and image segmentation. Significant variations. Image segmentation develops a pixel-level comprehension of a scene's elements while image recognition just produces a class label for an identified object. Object detection differs from these other jobs in that it has the capacity to specifically find objects inside an image or video. This enables us to count such things and later track them.