Design of PID Controller for Magnetic Levitation System using Harris Hawks Optimization

In most real-time industrial systems, optimal controller implementation is very essential to maintain the output based on the reference input. The controller design problem becomes a complex task when the real-time system model becomes greatly non-linear and unstable. The proposed research aims to design the finest PID controller for the unstable Magnetic Levitation System (MLS) using the Harris Hawks Optimization (HHO) algorithm. The MLS is a highly unstable electro-mechanical system and hence the design of the controller is a complex task. The proposed work implements one Degree of Freedom (1DOF) and 2DOF PID for the system. In this work, the essential controller is designed with a two-step process; (i) Initial optimization search to find the P-controller (Kp) gain to stabilize the system and (ii) Tuning the integral (Ki) and derivative (Kd) gains to reduce the deviation between the reference input and MLS output. The performance of the proposed controller is validated with the servo and regulatory operations and the result of this study confirms that the proposed method helps to get better error value and time domain specifications compared to other available methods

Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

In the current era, image evaluations play a foremost role in a variety of domains, where the processing of digital images is essential to identify vital information. The image multi-thresholding is a vital image pre-processing field in which the available digital image is enhanced by grouping similar pixel values. Normally, the digital test images are available in RGB/greyscale format and the appropriate processing methodology is essential to treat the images with a chosen methodology. In the proposed approach, Tsallis Entropy (TE) supported multi-level thresholding is planned for the benchmark greyscale imagery of dimension 512x512x1 pixels using a chosen threshold values (T=2,3,4,5). This work suggests the possible Cost Value (CV) that can be considered during the optimization search and the proposed work is executed by considering the maximization of the TE as the CV. The entire thresholding task is executed using Moth-Flame Algorithm (MFA) and the accomplished results are validated based on the image quality measures of various thresholds. The attained result with MFO is better compared to the result of CS, BFO, PSO, and GA

ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features

The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pretrained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers

Image multi-level-thresholding with Mayfly optimization

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization(BFO), firefly-algorithm(FA), bat algorithm (BA), cuckoo search(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this wor

Modulation of glucose transporter proteins by polyphenolic extract of Ichnocarpus frutescens (L.) W. T. Aiton in experimental type 2 diabetic rats

172-180Traditionally, in India, the decoction of Black creeper, Ichnocarpus frutescens (L.) W. T. Aiton leaves is used to treatment diabetes mellitus. However, its molecular mechanisms of antihyperglycemic effects have not been completely studied. Due to the potential antidiabetic effect of I. frutescens, we hypothesized that the polyphonic extract might add to glucose uptake through improvement in the expression of genes of the glucose transporter (GLUT) family messenger RNA (mRNA) in the liver and adipose tissues. Experimentally, diabetes mellitus was induced in Wistar rats through i.p. injection of freshly prepared solution of streptozotocin (45 mg/kg). This was done 15 minutes after the administration of nicotinamide (120 mg/kg, ip). Serum level of insulin and C-peptide were analyzed using standard methods. Glucose metabolism by the hepatocytes and adipocytes were also analyzed by quantitative RT-PCR mRNA expression levels of phosphoenolpyruvate carboxykinase 1 (PCK1), GLUT2 in the hepatocytes, and GLUT4 in the adipocytes. The hemidiaphragm were also isolated and processed to study in-vitro peripheral glucose utilization. Results of the present investigation suggest that STZ-NA induced diabetes is associated with hyperglycemia, altered levels of PCK1 and glucose transporters gene expression as well as decreased levels of insulin and C-peptide. On the other hand, the outcome of the daily oral administration of PPE to STZ-NA induced diabetic rats at different doses (150 and 300 mg/kg bodywt.) for 30 days supports our hypothesis by showing significant improvement of insulin levels, C-peptide level, downregulation of PCK1 and upregulation of GLUT (2, 4) mRNA expression levels when compared to those of diabetic rats. The administration of PPE had also increased the uptake of glucose by rat hemidiaphragm significantly. Findings from this study demonstrate that PPE enhances peripheral glucose uptake through glycogenesis pathway, mediated by upregulation of GLUT2 and GLUT4, and downregulation of PCK1. Our study suggests that the leaf of I. frutescens is a rich source of polyphenolic compounds, including those with an insulin-sensitizing function that may have the potential for treating or managing diabetes or insulin resistance

Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores

Deep and handcrafted feature supported diabetic retinopathy detection: A study

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database

A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection

Brain tumor (BT) is one of the brain abnormalities which arises due to various reasons. The unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical level assessment of BT is normally performed using the bio-imaging technique, and MRI-assisted brain screening is one of the universal techniques. The proposed work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices. This work proposes the following DLAs to detect the BT: (i) implementing the pre-trained DLAs, such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 with the deep-features-based SoftMax classifier; (ii) pre-trained DLAs with deep-features-based classification using decision tree (DT), k nearest neighbor (KNN), SVM-linear and SVM-RBF; and (iii) a customized VGG19 network with serially-fused deep-features and handcrafted-features to improve the BT detection accuracy. The experimental investigation was separately executed using Flair, T2 and T1C modality MRI slices, and a ten-fold cross validation was implemented to substantiate the performance of proposed DLA. The results of this work confirm that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair (>99%), T2 (>98%), T1C (>97%) and clinical images (>98%)

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard's coefficient, Dice's coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur's thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy