Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.
In this work, two cone-inverted cylindrical and cross-hybrid dielectric resonator antennas are stacked and excited by the coaxial probe method with an operating standard resonant frequency of 5.438 GHz. A drawback of these standard Dielectric Resonator Antennas (DRAs) is their narrow bandwidth. For good antenna performance, a stacked DR geometry and a thick dielectric substrate having a low dielectric constant are desired since this provides large bandwidth, better radiation power, reduces conductor loss and nonappearance of surface waves. Many approaches, such as changing the shape of the dielectric resonator, have been used to enhance bandwidth. Using DRA, having the lowest dielectric constant, increases the bandwidth and the electroma
... Show MoreAcne vulgaris is a very common, chronic disorder, involving inflammation of the pilosebaceous units that can be varied in presentation and difficult to treat. Inflammatory acne may yield both scarring and pigmentary changes so early and adequate therapy will, in all cases, decrease its severity and may entirely suppress this disease. Serratiopeptidase has anti-inflammatory, anti-edemic and fibrinolytic activity and acts rapidly on localized inflammation. Serratiopeptidase was added in aim to hasten acne resolution. During March to July 2010, A comparative study for a 50 healthy patient suffering from acne was divided into 2 groups: 1st group treated by common acne modalities and the 2nd one with same modalities
... Show MoreBackground/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN
... Show MoreIn this paper a dynamic behavior and control of a jacketed continuous stirred tank reactor (CSTR) is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time.
The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller.
The results s
... Show MoreAttention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w
... Show MoreIntroduction: This study aimed to assess the color change of human teeth with artificial enamel white spot lesions (WSLs) after sandblasting with bioactive glass, resin infiltration, and microabrasion and to test color stability after pH cycling. Methods: Fifty extracted human mandibular first molars were randomly assigned into five groups: Sound, WSLs (untreated), and WSLs sandblasted with bioactive glass (Sylc), WSLs treated by resin infiltration (ICON), and WSLs treated by microabrasion (Opalustre), respectively. All specimens underwent a pH cycling procedure. The color parameters for each specimen were assessed using an Easyshade dental spectrophotometer at different time stages then the color changes (ΔE) were calculated. Results: The
... Show MoreEnergy efficiency is a significant aspect in designing robust routing protocols for wireless sensor networks (WSNs). A reliable routing protocol has to be energy efficient and adaptive to the network size. To achieve high energy conservation and data aggregation, there are two major techniques, clusters and chains. In clustering technique, sensor networks are often divided into non-overlapping subsets called clusters. In chain technique, sensor nodes will be connected with the closest two neighbors, starting with the farthest node from the base station till the closest node to the base station. Each technique has its own advantages and disadvantages which motivate some researchers to come up with a hybrid routing algorit
... Show MoreThe purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an
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