A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

Machine Intelligence Research - This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things (IoT) analytics problems targeted on...     

EMERS: a tree matching–based performance evaluation of mathematical expression recognition systems

Performance evaluation of mathematical expression recognition systems is attempted. The proposed method assumes expressions (input as well as recognition output) are coded following MathML or T_EX/LaT_EX (which also gets converted into MathML) format. Since any MathML representation follows a tree structure, evaluation of performance has been modeled as a tree-matching problem. The tree corresponding to the expression generated by the recognizer is compared with the groundtruthed one by comparing the corresponding Euler strings. The changes required to convert the tree corresponding to the expression generated by the recognizer into the groundtruthed one are noted. The number of changes required to make such a conversion is basically the distance between the trees. This distance gives the performance measure for the system under testing. The proposed algorithm also pinpoints the positions of the changes in the output MathML file. Testing of the proposed evaluation method considers a set of example groundtruthed expressions and their corresponding recognized results produced by an expression recognition system.     

Simplification of English and Bengali Sentences for Improving Quality of Machine Translation

Neural Processing Letters - Training translation systems with complex and compound sentences are generally considered computationally tough and such systems fail to process, the large syntactical...     

All‐Organic,Temporally Pure White Afterglow in Amorphous Films Using Complementary Blue and Greenish‐Yellow Ultralong Room Temperature Phosphors

Organic molecules exhibiting afterglow emission (lifetime longer than 0.1 s) under ambient conditions have sparked tremendous attention in recent years as a sustainable energy source with potential applications in displays, lighting, and bioimaging. However, white afterglow organic materials with color purity during the entire period of delayed emission, after the cessation of excitation source, are yet to be achieved due to the different excited state lifetimes of its primary or complementary components. Herein, a remarkable, ambient “temporally pure white afterglow,” which lasts for over 7 s, by coorganizing complementary blue and greenish‐yellow organic room temperature phosphors with similar ultralong lifetimes and efficiency, in an amorphous polymer film is demonstrated. One of the most efficient blue afterglow room temperature phosphors is also reported, with an ultralong lifetime up to 2.26 s and maximum quantum efficiency of 36.8%, from purely organic triazatruxenes en route to the realization of this white afterglow. Further, broad and complementary absorption features of the coorganized phosphors in the visible region facilitates an excitation‐dependent dynamic color‐tuning of the afterglow from sky‐blue to greenish‐yellow.     

Prototype reduction using an artificial immune model

Artificial immune system (AIS)-based pattern classification approach is relatively new in the field of pattern recognition. The study explores the potentiality of this paradigm in the context of prototype selection task that is primarily effective in improving the classification performance of nearest-neighbor (NN) classifier and also partially in reducing its storage and computing time requirement. The clonal selection model of immunology has been incorporated to condense the original prototype set, and performance is verified by employing the proposed technique in a practical optical character recognition (OCR) system as well as for training and testing of a set of benchmark databases available in the public domain. The effect of control parameters is analyzed and the efficiency of the method is compared with another existing techniques often used for prototype selection. In the case of the OCR system, empirical study shows that the proposed approach exhibits very good generalization ability in generating a smaller prototype library from a larger one and at the same time giving a substantial improvement in the classification accuracy of the underlying NN classifier. The improvement in performance has been statistically verified. Consideration of both OCR data and public domain datasets demonstrate that the proposed method gives results better than or at least comparable to that of some existing techniques.     

Recognition of online handwritten mathematical expressions

This paper aims at automatic understanding of online handwritten mathematical expressions (MEs) written on an electronic tablet. The proposed technique involves two major stages: symbol recognition and structural analysis. Combination of two different classifiers have been used to achieve high accuracy for the recognition of symbols. Several online and offline features are used in the structural analysis phase to identify the spatial relationships among symbols. A context-free grammar has been designed to convert the input expressions into their corresponding T(E)X strings which are subsequently converted into MathML format. Contextual information has been used to correct several structure interpretation errors. A new method for evaluating performance of the proposed system has been formulated. Experiments on a dataset of considerable size strongly support the feasibility of the proposed system.     

A corpus for OCR research on mathematical expressions

This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is discussed. A statistical investigation of the corpus is presented, and usefulness of this analysis is demonstrated in the related research problems, namely, (i) identification and segmentation of expression zones from the rest of the document, (ii) recognition of expression symbols, (iii) interpretation of expression structures, and (iv) performance evaluation of a mathematical expression recognition system. Moreover, a groundtruthing format has been proposed to facilitate automatic evaluation of expression recognition techniques. Received: 10 July 2003, Accepted: 22 November 2004, Published online: 18 March 2005 Correspondence to: Utpal Garain     

Detection of COVID-19 from CT scan images: A spiking neural network-based approach

Neural Computing and Applications - The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community...     

Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition

Multimedia Tools and Applications - Face recognition is itself a very challenging task and it becomes more challenging when the input images have intra class variations and inter class similarities...