The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as

Background: Hemoglobin A1c (HbA1c) is a widely used test for glycemic control. It is done for chronic kidney disease (CKD) patients. Renal disease is accompanied by thyroid abnormalities, which affect HbA1c, especially in those taking erythropoiesis-stimulating agents (ESAs). We aimed to find the effect of thyroid dysfunction on HbA1c in hemodialysis patients taking ESAs and those who do not. Materials and Method: Fifty six patients were included in this study, which was done between September 2017 and June 2018, in Baghdad Teaching Hospital. Thyroid stimulating hormone, free T3, free T4 and HbA1c measurements were done. The patients were divided into 2 groups; those who took ESAs and those who did not, then they were subdivided into those

Abstract Background Hemoglobin A1c (HbA1c) is a widely used test for glycemic control. It is done for chronic kidney disease (CKD) patients. Renal disease is accompanied by thyroid abnormalities, which affect HbA1c, especially in those taking erythropoiesis-stimulating agents (ESAs). We aimed to find the effect of thyroid dysfunction on HbA1c in hemodialysis patients taking ESAs and those who do not. Materials and Method Fifty six patients were included in this study, which was done between September 2017 and June 2018, in Baghdad Teaching Hospital. Thyroid stimulating hormone, free T3, free T4 and HbA1c measurements were done. The patients were divided into 2 groups; those who took ESAs and those who did not, then they were subdivided into

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Research on the automated extraction of essential data from an electrocardiography (ECG) recording has been a significant topic for a long time. The main focus of digital processing processes is to measure fiducial points that determine the beginning and end of the P, QRS, and T waves based on their waveform properties. The presence of unavoidable noise during ECG data collection and inherent physiological differences among individuals make it challenging to accurately identify these reference points, resulting in suboptimal performance. This is done through several primary stages that rely on the idea of preliminary processing of the ECG electrical signal through a set of steps (preparing raw data and converting them into files tha

Adolescent pregnancy is common health problem still found in both developed and developing countries; as adolescent may have early sexual practice or early marriage. Adolescent mothers face substantially higher maternal and perinatal morbidity and mortality than adult women. This is a randomized prospective clinical study conducted at Al-Elwiya Maternity Teaching Hospital, Baghdad, Iraq. The objective of this work is to assess the adverse maternal, fetal and neonatal outcomes in early and late teenage pregnant mothers. Study sample consisted of 220 primigravid women with a singleton, cephalic, viable fetus and no congenital abnormality that gave birth at Al-Elwiya Maternity Teaching Hospital, Baghdad, Iraq. The 1stgroup: early teenage (46 w

This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s