Narrow Results

In view of requirements of low-resource consumption and high-efficiency in real-time Ambulatory Electrocardiograph Diagnosis (AED) applications, a novel Cardiac Arrhythmias Detection (CAD) algorithm is proposed. This algorithm consists of three core modules: an automatic-learning machine that models diagnostic criteria and grades the emergency events of cardiac arrhythmias by studying morphological characteristics of ECG signals and experiential knowledge of cardiologists; a rhythm classifier that recognizes and classifies heart rhythms basing on statistical features comparison and linear discriminant with confidence interval estimation; and an arrhythmias interpreter that assesses emergency events of cardia arrhythmias basing on a two rule-relative interpretation mechanisms. The experiential results on off-line MIT-BIH cardiac arrhythmia database as well as online clinical testing explore that this algorithm has 92.8% sensitivity and 97.5% specificity in average, so that it is suitable for real-time cardiac arrhythmias monitoring.

Agriculture is considered the leading field around the world, which is also the backbone of India. Agriculture is in a flawed state because the temperature changes, along with their uncertainty, cause huge damage to the crops during the manufacturing process. So, the appropriate prediction of crop expansion plays a vital role in the management of crop growth. This prediction can enhance the federated industries to make their sustainability toward the occupation. Recently, the farmers have not selected suitable crops for their cultivation based on soil factors. This makes a negative impact on crop yield, and thus, the Indian farmers can suffer from severe losses besides the monetary front. Hence, the optimal crop recommendation model has to consider different parameters of the soil for forecasting the best crop for cultivation, which increases crop growth and crop production. Thus, this research work explores a new crop recommendation model for precision agriculture intending to promote crop yield and alleviate the loss to farmers. Initially, this research work gathers the standard data regarding the agricultural parameters of some areas. Then, the deep features using an autoencoder, and statistical features are gathered along with the Principal Component Analysis (PCA)-based features. Next, all three sets of features are fused and fed to the developed Adaptive Henry Gas Solubility Optimization (AHGSO) for selecting the optimal features. Finally, the chosen optimal features are fed to the recommendation stage, where a Gated Recurrent Unit with Ridge Classifier (GRU-RC) is suggested for getting the precise outcome regarding the recommended crop suitable to that agricultural parameter. Here, the optimal solutions are attained by tuning the parameters of GRU and ridge classifier with the same I-HGSO. At last, the results obtained from the hybrid method can be considered more efficient.

Breast cancer is life threatening and dangerous diseases among the women across the world. In this paper, mammogram image classification performed using LS-SVM with various kernels functions namely, Gaussian Radial Basis Function (GRBF) kernel, Polynomial kernel, Quadratic kernel, Linear kernel and MLP kernel. Shearlet transform is a multidimensional version of the composite dilation wavelet transform, and is especially designed to address anisotropic and directional information at various scales and directions, which is used to decompose the regions of interest (ROI) image after preprocessing stage. Initially, mammogram images are transformed into different resolution levels from 2 levels to 4 levels with various directions varying from 2 to 64. The evaluation of the system is carried out on the Mammography Image Analysis Society (MIAS) database. From the experimental analysis, based on classification accuracy and Receiver Operating Characteristics (ROC), it is concluded that LS-SVM with Gaussian RBF kernel function outperforms than Quadratic, polynomial, linear and MLP kernel functions. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes.

Trip-related falls are a major problem in the elderly population and research in the area has received much attention recently. The focus has been on devising ways of identifying individuals at risk of sustaining such falls. The main aim of this work is to explore the effectiveness of models based on Support Vector Machines (SVMs) for the automated recognition of gait patterns that exhibit falling behavior. Minimum toe clearance (MTC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with balance problems and with a history of tripping falls. Statistical features obtained from MTC histograms were used as inputs to the SVM model to classify between the healthy and balance-impaired subjects. The leave-one-out technique was utilized for training the SVM model in order to find the optimal model parameters. Tests were conducted with various kernels (linear, Gaussian and polynomial) and with a change in the regularization parameter, C, in an effort to identify the optimum model for this gait data. The receiver operating characteristic (ROC) plots of sensitivity and specificity were further used to evaluate the diagnostic performance of the model. The maximum accuracy was found to be 90% using a Gaussian kernel with σ² = 10 and the maximum ROC area 0.98 (80% sensitivity and 100% specificity), when all statistical features were used by the SVM models to diagnose gait patterns of healthy and balance-impaired individuals. This accuracy was further improved by using a feature selection method in order to reduce the effect of redundant features. It was found that two features (standard deviation and maximum value) were adequate to give an improved accuracy of 95% (90% sensitivity and 100% specificity) using a polynomial kernel of degree 2. These preliminary results are encouraging and could be useful not only for diagnostic applications but also for evaluating improvements in gait function in the clinical/rehabilitation contexts.

The most fatal disease on the earth is thought to be illness of the heart. There are a lot of features that change the heart’s composition or functionality. In most cases, it is hard for doctors to make a diagnosis accurately and quickly. This study’s objective is to determine critical factors and methods of data mining which can improve the accuracy for prediction of heart disease. Further, it is essential to make use of automatic technologies in diagnosing heart diseases as early as possible. To develop a new prediction technique for heart disease that comprises four phases such as “(a) Pre-processing, (b) Feature extraction, (c) Feature selection and (d) Classification”. The initial stage of pre-processing is when the incoming data is treated to the elimination of redundant and missed numbers. Then, from the initial stage of data, the higher-order statistical and statistical characteristics, chi-squared features and symmetrical uncertainty attributes are derived. However, when working with a greater number of characteristics, the curse of dimensionality was a severe issue. Hence, the characteristics of optimal features are planned from the overall set of features. A novel Hybrid Bull and Elephant Algorithm (HB-EA) is introduced for the selection of optimal features. Consequently, the selected set of features is subjected to various classifiers as an ensemble model that contains “Naïve Bayes (NB), Decision Tree (DT), Neural Network (NN), Support Vector Machine (SVM), Optimized Recurrent Neural Network (RNN) and Linear Regression (LR)”. The final step is to log off efficiency for outputs obtained from the group of classifiers and determine the outcome. The RNN weights are ideally tuned by the suggested HB-EA technique to boost the system’s accuracy. The proposed model is finally evaluated against existing techniques to determine its superiority. The suggested technique for dataset 1 achieved maximum accuracy (0.916), and it is 15.24%, 8.76%, 7.56%, 4.09% and 1.89% better than convolution schemes like Random Forest (RF), Deep Belief Network (DBN), SVM, $K$-Nearest Neighbor (KNN) and Elephant Herding Optimization (EHO) models.

An essential component of the immune system that aids in the fight against pathogens is white blood cells. One of the most prevalent blood diseases, leukemia can be fatal if not properly diagnosed. Diagnosing this disease at an early stage may reduce the severity of the disease. This research intends to propose an ensemble model with improved U-net for leukemia detection (EMIULD) with the following four phases: preprocessing, segmentation, feature extraction and detection. The preprocessing step involves preprocessing the blood smear image, which includes filtering and scaling the image. The segmentation phase is applied to the preprocessed image, and U-Net-based segmentation is used to segment the image. As a result, features for the segmented images are extracted, including better Local Gabor XOR Pattern (LGXP), area, and grid-based form features. The extracted features are fed into the suggested ensemble model, which consists of Deep Convolutional Neural Network (DCNN), Support Vector Machine (SVM) and Random Forest (RF) classifiers, with the purpose of detecting leukemia. Finally, the proposed Bidirectional Long Short-Term Memory (Bi-LSTM) network to predict whether the given blood smear image is leukemia or not. The suggested model attained the best outcome when evaluated over the extant approaches.

This chapter describes image processing methods for document image analysis. The methods are grouped into four categories, namely, image acquisition, image transformation, image segmentation, and feature extraction. In image acquisition, we describe the process of converting a document into its numerical representation, including image coding as a means to reduce the storage requirement. Image transformation addresses image-to-image operations, which comprise a large spectrum of techniques ranging from geometrical correction, filtering and figure-background separation to boundary detection and thinning. In image segmentation, we describe four popular techniques, namely, connected component labeling, X-Y-tree decomposition, run-length smearing, and Hough transform. Finally, a number of feature extraction methods, which constitute the basis of image classification, are presented.