In this paper, we propose an efficient regression algorithm based on primal formulation of twin support vector machine. This is an efficient approach to solve the optimization problem leading to reduced computation time. The proposed method is termed as twin parametric insensitive support vector regression (UPTPISVR). The optimization problems of the proposed (UPTPISVR) are a pair of unconstrained convex minimization problems. Moreover, the objective functions of UPTPISVR are strongly convex, differentiable and piecewise quadratic. Therefore, an approximate solution is obtained in primal variables instead of solving the dual formulation. Further, an absolute value equation problem is solved by using a functional iterative algorithm for UPTPISVR, termed as FUPTPISVR. The objective function of the proposed formulation involves the plus function which is non-smooth and therefore, smooth approximation functions are used to replace the plus function, termed as SUPTPISVR. The Newton-Armijo algorithm is then used to iteratively obtain the solutions, thus eliminates the requirement of any optimization toolbox. Various numerical experiments on synthetic and benchmark real-world datasets are presented for justifying the applicability and effectiveness of the proposed UPTPISVR. The results clearly indicate that the proposed algorithms outperform the existing algorithms in terms of root mean square error (RMSE) on most datasets.

To handle the fuzziness and spatial uncertainties among pixels entailed in color images, this paper proposes a novel fuzzy entropy function for multi-threshold image segmentation based on the energy curve concept and minimum fuzzy entropy criterion. The proposed energy curve based new fuzzy entropy function (ECFE) considers intensity distribution and spatial contextual information among the pixels. To improve efficiency and threshold selection process of the method, cuckoo search algorithm is employed. For comparison, backtracking search algorithm, and Lévy flight based firefly algorithm included. Comparison with recent color image multilevel segmentation techniques presented to test the effectiveness of the proposed algorithm. The performance of the proposed technique is evaluated using different satellite and natural color images. Quantitative and qualitative results demonstrate that the proposed algorithm is highly accurate, robust, and efficient for color image multilevel segmentation.

To improve the generalization performance, we develop a new technique for handling the impacts of outliers using Lagrangian twin bounded SVM (TBSVM) with kernel fuzzy membership values, which is termed kernel-target alignment-based fuzzy Lagrangian twin bounded support vector machine (KTA-FLTBSVM). Here, the objective functions are having L2-norm vectors of the slack variable that leads to the optimization problem more convex and yields a unique global solution. Also, the fuzzy membership values are employing the importance of data samples assigned to each sample to minimize the impacts of outlier and noise. Further, we have suggested a linearly convergent iterative approach to obtain the solution of the problem unlike in place to solve the quadratic programming problem in Twin SVM (TSVM) and TBSVM. To investigate the effectiveness of the proposed KTA-FLTBSVM, the comprehensive experiments demonstrate with other reported models on artificial datasets along with benchmark real-life publicly available datasets. Our KTA-FLTBSVM outperforms to other models in terms of better classification accuracy.

Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.

Electroencephalogram signal is the signal used for the detection of a neurological disorder as epilepsy disorder, sleep disorder and many more. The types of EEG signal gives the hidden information regarding the distribution of the data that may consist of a large volume of the poor and noisy signal. In order to reduce the outlier effects and noise, incorporation of prior knowledge in the model, universum may help and enhance the better generalization ability of the model. This paper proposes a regularized universum twin support vector machine (RUTWSVM) for classification of the healthy and seizure EEG signals. Here, the selection of the universum data points is obtained in two ways (i). Universum data has been generated from the healthy and seizure EEG signals itself and (ii). Interictal EEG signal has been used as universum data which may help to handle the outlier effects. Further, various feature selection techniques are applied to extract the important noise free features from the EEG signals. We have performed a comparative analysis of proposed RUTWSVM with USVM and UTWSVM to classify the EEG signals as well as benchmark real-world datasets in an optimum way. The experiment results clearly exhibit the applicability and usability of the proposed RUTWSVM with interictal EEG signals as universum data points as well as benchmark real-world datasets.