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Sign language recognition (SLR) has got wide applicability. SLR system is considered to be a challenging one. This paper presents empirical analysis of different mathematical models for Pakistan SLR (PSLR). The proposed method is using the parameterization of sign signature. Each sign is represented with a mathematical function and then coefficients of these functions are used as the feature vector. This approach is based on exhaustive experimentation and analysis for getting the best suitable mathematical representation for each sign. This extensive empirical analysis, results in a very small feature vector and hence to a very efficient system. The robust proposed method has got general applicability as it just need a new training set and it can work equally good for any other dataset. Sign set used is quite complex in the sense that intersign similarity distance is very small but even then proposed methodology has given quite promising results.

We describe a computationally efficient method to produce a specific Bayesian mixture of all the models in a finite set of feature-based models that assign a probability to the observed data set. Special attention is given to the bound on the regret of using the mixture instead of the best model in the set. It is proven theoretically and verified through synthetic data that this bound is relatively tight. Comparing the workload of the proposed method with the direct implementation of the Bayesian mixture shows an almost exponential improvement of computing time.

Locating the center of the pupils is the most important foundation and the core component of gaze tracking. The accuracy of gaze tracking largely depends on the quality of images, but additional constraints and large amount of calculation make gaze tracking impractical on high-resolution images. Although some eye-gaze trackers can get accurate result, improving the accuracy of pupil feature on low-resolution images and accurately recognizing closed eye images are still common tasks in the field of gaze estimation. Our aim is to get the accurate localization of pupil center on low-resolution image. To this aim, we proposed a simple but effective method which can accurately locate pupil center in real time. The method first gets initial eye center based on improved scale-invariant feature transform (SIFT) descriptor and support vector machine (SVM) classifier, and then gets final position of the pupil center through a size variable correction rectangular block. In this paper, comparing with the reported state-of-the-art methods,the experimental results demonstrate that our system can achieve a more accurate result on low-resolution images. On top of that, our approach shows robustness on closed eye images while some other methods would not recognize the closed eye images.

Writer recognition is to identify a person on the basis of handwriting, and great progress has been achieved in the past decades. In this paper, we concentrate ourselves on the issue of off-line text-independent writer recognition by summarizing the state of the art methods from the perspectives of feature extraction and classification. We also exhibit some public datasets and compare the performance of the existing prominent methods. The comparison demonstrates that the performance of the methods based on frequency domain features decreases seriously when the number of writers becomes larger, and that spatial distribution features are superior to both frequency domain features and shape features in capturing the individual traits.

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)²PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)²PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Mastitis is the major cause of loss in dairy farming. Somatic cells are one of most important standards to detect this infection. This paper proposes a novel image processing algorithm to recognize four types of somatic cells in bovine milk automatically. First, cloud model uses to segment cell images. Second, a variety of features are extracted from regions of interest. Finally, most differential features are selected using ReliefF algorithm and performances of two classifiers, Back propagation networks (BPN) and support vector machine (SVM), are compared. The experimental results are obtained using a large set of images from different sources. The results of our proposed method is not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

In this paper, a hybrid approach of fundus image classification for diabetic retinopathy (DR) lesions is proposed. Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique is applied to a high-dimensional scale invariant feature transform (SIFT) representation of fundus image for lesion classification. The applied NDR technique gives a low-dimensional intrinsic feature vector for lesion classification in fundus images. The publicly available databases are used for demonstrating the implemented strategy. The performance of applied technique can be evaluated based on sensitivity, specificity and accuracy using Support vector classifier. Compared to other feature vectors, the implemented LE-based feature vector yielded better classification performance. The accuracy obtained is 96.6% for SIFT-LE-SVM.

Recently, Human Activity Recognition (HAR) has become an important research area because of its wide range of applications in several domains such as health care, elder care, sports monitoring systems, etc. The use of wearable sensors — specifically the use of inertial sensors such as accelerometers and gyroscopes — has become the most common approach to recognize physical activities because of their unobtrusiveness and ubiquity. Overall, the process of building a HAR system starts with a feature extraction phase and then a classification model is trained. In the work of Siirtola et al. is proposed an intermediate clustering step to find the homogeneous groups of activities. For the recognition step, an instance is assigned to one of the groups and the final classification is performed inside that group. In this work we evaluate the clustering-based approach for activity classification proposed by Siirtola with two additional improvements: automatic selection of the number of groups and an instance reassignment procedure. In the original work, they evaluated their method using decision trees on a sports activities dataset. For our experiments, we evaluated seven different classification models on four public activity recognition datasets. Our results with 10-fold Cross Validation showed that the method proposed by Siirtola with our additional two improvements performed better in the majority of cases as compared to using the single classification model under consideration. When using Leave One User Out Cross Validation (user independent model) we found no differences between the proposed method and the single classification model.

Although existing sparse restricted Boltzmann machine (SRBM) can make some hidden units activated, the major disadvantage is that the sparseness of data distribution is usually overlooked and the reconstruction error becomes very large after the hidden unit variables become sparse. Different from the SRBMs which only incorporate a sparse constraint term in the energy function formula from the original restricted Boltzmann machine (RBM), an energy function constraint SRBM (ESRBM) is proposed in this paper. The proposed ESRBM takes into account the sparseness of the data distribution so that the learned features can better reflect the intrinsic features of data. Simulations show that compared with SRBM, ESRBM has smaller reconstruction error and lower computational complexity, and that for supervised learning classification, ESRBM obtains higher accuracy rates than SRBM, classification RBM, and Softmax classifier.

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten $p$-norm and ${\ell }_p$-norm, instead of the nuclear norm and ${\ell }_1$-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

In this paper, an effective method based on the color quaternion wavelet transform (CQWT) for image forensics is proposed. Compared to discrete wavelet transform (DWT), the CQWT provides more information, such as the quaternion’s magnitude and phase measures, to discriminate between computer generated (CG) and photographic (PG) images. Meanwhile, we extend the classic Markov features into the quaternion domain to develop the quaternion Markov statistical features for color images. Experimental results show that the proposed scheme can achieve the classification rate of 92.70%, which is 6.89% higher than the classic Markov features.

This paper proposes boundary parallel-like index (BPI) to describe shape features for high-resolution remote sensing image classification. Parallel-like boundary is found to be a discriminating clue which can reveal the shape regularity of segmented objects. Therefore, multi-orientation distance projections were constructed to measure and quantify parallel-like information. The discriminating ability was tested using original and segmented ground objects, respectively. The proposed BPI showed better discrimination for both original and segmented data than for other shape features, especially for buildings. This was also confirmed by the considerably higher accuracy of BPI in building classification experiments of high-resolution remote sensing imagery. It suggests the proposed BPI is useful for building related applications.

Many classification algorithms aim to minimize just their training error count; however, it is often desirable to minimize a more general cost metric, where distinct instances have different costs. In this paper, an instance-based cost-sensitive Bayesian consistent version of exponential loss function is proposed. Using the modified loss function, the derivation of instance-based cost-sensitive extensions of AdaBoost, RealBoost and GentleBoost are developed which are termed as ICSAdaBoost, ICSRealBoost and ICSGentleBoost, respectively. In this research, a new instance-based cost generation method is proposed instead of doing this expensive process by experts. Thus, each sample takes two cost values; a class cost and a sample cost. The first cost is equally assigned to all samples of each class while the second cost is generated according to the probability of each sample within its class probability density function. Experimental results of the proposed schemes imply 12% enhancement in terms of $F$-measure and 13% on cost-per-sample over a variety of UCI datasets, compared to the state-of-the-art methods. The significant priority of the proposed method is supported by applying the pair of $T$-tests to the results.

The most significant part of any autonomous intelligent robot is the localization module that gives the robot knowledge about its position and orientation. This knowledge assists the robot to move to the location of its desired goal and complete its task. Visual Odometry (VO) measures the displacement of the robots’ camera in consecutive frames which results in the estimation of the robot position and orientation. Deep Learning, nowadays, helps to learn rich and informative features for the problem of VO to estimate frame-by-frame camera movement. Recent Deep Learning-based VO methods train an end-by-end network to solve VO as a regression problem directly without visualizing and sensing the label of training data in the training procedure. In this paper, a new approach to train Convolutional Neural Networks (CNNs) for the regression problems, such as VO, is proposed. The proposed method first changes the problem to a classification problem to learn different subspaces with similar observations. After solving the classification problem, the problem converts to the original regression problem to solve using the knowledge achieved by solving the classification problem. This approach helps CNN to solve regression problem globally in a local domain learned in the classification step, and improves the performance of the regression module for approximately 10%.

There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 $+$ve) and uninfected (COVID-19 $-$ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.

In the last few years, ensemble learning has received more interest primarily for the task of classification. It is based on the postulation that combining the output of multiple experts is better than the output of any individual expert. Ensemble feature selection may improve the performance of the learning algorithms and has the ability to obtain more stable and robust results. However, during the process of feature aggregation and selection, selected feature subset may contain high levels of inter-feature redundancy. To address this issue, a novel algorithm based on feature rank aggregation and graph theoretic technique for ensemble feature selection (R-GEFS) with the fusion of Pearson and Spearman correlation metrics is proposed. The method works by aggregation of the profile of preferences of five feature rankers as the base feature selectors. Then similar features are grouped into clusters using graph theoretic approach. The most representative feature strongly co-related to target decision classes is drawn from each cluster. The efficiency and effectiveness of the R-GEFS algorithm are evaluated through an empirical study. Extensive experiments on 15 diverse benchmark datasets are carried out to compare R-GEFS with seven state-of-the-art feature selection models with respect to four popular classifiers, namely decision tree, k nearest neighbor, random forest, and support vector machine. The proposed method turns out to be effective by selecting smaller feature subsets with lesser computational complexities and it assists in increasing the classification accuracy.

Glaucoma is an eye disease that causes loss of vision and blindness by damaging a nerve in the back of the eye called optic nerve. The optic nerve collects the visual information from the eyes and transmits to the brain. Glaucoma is mainly caused by an abnormal high pressure in the eyes. Over time, the increased pressure can erode the tissues of optic nerve, leading to vision loss or blindness. If it is diagnosed in advance, then only it can prevent the vision loss. To diagnose the glaucoma, it must accurately differentiate between the optic disc (OD), optic cup (OC), and the retinal nerve fiber layer (RNFL). The segmentation of the OD, OC, and RNFL remains a challenging issue under a minimum contrast image of boundaries. Therefore, in this study, an innovative method of Hybrid Symbiotic Differential Evolution Moth-Flame Optimization (SDMFO)-Multi-Boost Ensemble and Support Vector Machine (MBSVM)-based segmentation and classification framework is proposed for accurately detecting the glaucoma disease. By using Group Search Optimizer (GSO), the affected parts of the OD, OC and RNFL are segmented. The proposed SDMFO-MBSVM method is executed in MATLAB site, its performance is analyzed with three existing methods. From the comparison, the accuracy of the proposed method in OD segmentation gives better results of 3.37%, 4.54% and 2.22%, OC segmentation gives better results of 2.22%, 3.37% and 4.54%, and RNFL segmentation gives the better results of 3.37%, 97.21% and 5.74%.

Facial emotion recognition (FER) is an interesting area of research. It has a wide range of applications, but there is still a deficiency of an accurate approach to provide better results. A novel FER system to maximize classification accuracy has been introduced in this paper. The proposed approach constitutes the following phases: pre-processing, feature extraction, feature selection, and classification. Initially, the images are pre-processed using the extended cascaded filter (ECF) and then the geometric and appearance-based features are extracted. An enhanced battle royale optimization (EBRO) for feature selection has been proposed to select the relevant features and to reduce the dimensionality problem. Then, the classification is carried out using a novel bidirectional Elman neural network (Bi-ENN) that offers high classification results. The proposed Bi-ENN-based emotion classification can accurately discriminate the input features. It enabled the model to predict the labels for classification accurately. The proposed model on evaluations attained an accuracy rate of 98.57% on JAFFE and 98.75% on CK+ datasets.

Type II Diabetes Mellitus (Type II DM) is a chronic condition that has detrimental effect on vital organs if left untreated, necessitating early diagnosis and treatment. Iridology, a subset of Complementary and Alternative Medicine (CAM), has the potential to serve as a tool for noninvasive early diagnosis of Type II DM. Iridology involves analyzing the characteristics of iris such as color and pattern for detection of organ and system defects. Deep learning algorithm is one of the promising methods in diagnosing various health-related issues. In this study, we have demonstrated the efficiency of iridology in diagnosing Type II DM using deep learning algorithms. Near Infra-Red images of iris were captured using iris scanner from 178 voluntary subjects belonging to two categories namely, Type II DM (95 subjects) and nondiabetic or healthy category (83 subjects). We have developed an algorithm using Fully Convolutional Neural network for effective iris segmentation. Normalized iris images were used to crop out our region of interest, pancreas, based on the iridology chart. Classification networks such as AlexNet, VGG-16, and ResNet-50 were used to classify Type II DM versus healthy category. Our proposed model for iris segmentation achieved an accuracy and sensitivity of 0.99, specificity and F-Score of 0.98, and a precision of 0.97. Results obtained using AlexNet classifier exhibits better classification accuracy of 95.85% for Zero-padding based resized image. The classifier yielded a sensitivity, specificity, and precision of 95.80%, 95.85%, and 96.11%, respectively. Our study results establish the efficacy and emphasize the importance of the proposed algorithm for diagnosing Type II DM.