The advances in Deep Learning (DL) resulted in the development Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) based Online Signature Verification (OSV) frameworks. The main drawback with LSTM based networks is the limited parallelization of model training. The CNN based frameworks are efficient in learning local feature dependencies, but fail to apprehend long term feature dependencies. The current works confirmed the success of Transformer based models in long term time series classification (LTTSC) problems due to efficient capturing of context-dependent global feature interactions. Hence, to achieve higher classification accuracy, in this work, we propose a first of its kind of an attempt, in which, we combine CNN and Transformer for Online Signature Verification, named OSVConTramer. The proposed OSVConTramer efficiently learns optimal local and global dependencies of an input signature feature vector and outperforms previous CNN and LSTM based OSV frameworks achieving state-of-the-art classification accuracy. On the widely used MCYT-100, SVC, and SUSIG datasets, specific to one shot learning, our model achieves a SOTA EER of 10.85%, 5.45%, and 6.32%, respectively. The results of the experimental analysis confirms that the accuracy outcomes of OSV frameworks is improved significantly by the optimal learning of the relationships between local and global feature dependency.

Online Signature Verification (OSV) is a widely used biometric feature for recognized and authorized technique to authenticate a writers's distinctiveness and behavioral characteristic. Owing to huge intra-individual changeability, OSV is a challenging problem. Usage of online signatures in m-commerce and m-payment demands for light weight frameworks to classify a signature. The recent OSV models grounded on convolutional neural networks (CNN) and its variants are heavy weight and computationally intensive due to higher amount of parameters to learn. In this context, we put forward a CNN centric OSV framework which uses a stack of depthwise separable (DWS) convolution layers, which makes the framework light weight and enables the few shot learning for signature verification with quite higher accuracy compared to conventional deep learning models. To prove the robustness of our proposed framework, we performed exhaustive experimental evaluations with three standard datasets i.e. MCYT-100 (DB1), SUSIG-Visual corpus and SVC-2004-Task2. Experimental results confirm the efficiency of depthwise separable convolutions grounded OSV by realizing a lesser error rate as related to various current and state-of-the art OSV frameworks.

Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to large intra-individual variability, there is a critical requirement to accurately learn the intrapersonal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extract robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increase inter-individual variability (Genuine-Forgery) and guides the DCSN for effective discriminative representation learning for online signatures. Experiments conducted on three widely accepted datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 emphasize the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of the proposed DCSN in one shot learning by achieving a lower error rate as compared to many recent and state-of-the art OSV models.

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of 1, 512, 868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFAR-VGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset “CRCHistoPhenotypes”. The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized in terms of the training time and data over-fitting, respectively.

Kernel k-means is seen as a non-linear extension of the k-means clustering method, with good performance in identifying non-isotropic and linearly inseparable clusters. However space and time requirement of kernel k-means is expensive with O(n2) complexity. Present applications with large in-memory computations make this method insuitable for large data sets. Recently, a simple prototype based hybrid approach speedsup kernel k-means method for large data sets [1]. The time complexity of this method is O(n + p2), where p is the number of prototypes. Each prototype is a representative pattern of a group-let of size (threshold) τ . The time complexity of this method not only depends upon p but which in turn depends on clustering threshold. Increasing the threshold value can decrease the number of prototypes p, but, quality of the clustering result might suffer. Hence fixing the appropriate value of the threshold is the major challenge in this approach. This paper, presents a solution to this problem, by allowing τ to vary, depending on the location of the group-let in the space. Intuitively, If the grouplet is close to a cluster center (and away from others) then its size could be large, but if it is lying somewhere between two cluster centers, then its size should be small. It is experimentally shown that this reduces the clustering time and also increases the clustering accuracy. The presented method is a suitable one for large data sets like in data mining.

Surname (family name) analysis is used in geography to understand population origins, migration, identity, social norms and cultural customs. Some of these are supposedly evolved over generations. Surnames exhibit good statistical properties that can be used to extract information in names data set such as automatic detection of ethnic or community groups in names. An e-mail address, often contains surname as a substring. This containment may be full or partial. An e-mail address categorization based on semantics of surnames is the objective of this paper. This is achieved in two phases. First phase deals with surname representation and clustering. Here, a vector space model is proposed where latent semantic analysis is performed. Clustering is done using the method called average-linkage method. In the second phase, an email is categorized as belonging to one of the categories (discovered in first phase). For this, substring matching is required, which is done in an efficient way by using suffix tree data structure. We perform experimental evaluation for the 500 most frequently occurring surnames in India and United Kingdom. Also, we categorize the e-mail addresses that have these surnames as substrings.