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Rishin Haldar

Bio: Rishin Haldar is an academic researcher from VIT University. The author has contributed to research in topics: Identification (biology) & Support vector machine. The author has an hindex of 4, co-authored 9 publications receiving 123 citations.

Papers
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TL;DR: An improvement has been made to this method by grouping some similar looking alphabets and reducing the weighted difference among members of the same group, and the results showed marked improvement over the traditional Levenshtein distance technique.
Abstract: Dictionary lookup methods are popular in dealing with ambiguous letters which were not recognized by Optical Character Readers. However, a robust dictionary lookup method can be complex as apriori probability calculation or a large dictionary size increases the overhead and the cost of searching. In this context, Levenshtein distance is a simple metric which can be an effective string approximation tool. After observing the effectiveness of this method, an improvement has been made to this method by grouping some similar looking alphabets and reducing the weighted difference among members of the same group. The results showed marked improvement over the traditional Levenshtein distance technique.

66 citations

Journal ArticleDOI
TL;DR: The shortcomings of existing IoT systems are analyzed and ways to tackle them are put forward by incorporating chatbots, a general architecture is proposed for implementing such a system, as well as platforms and frameworks which allow for the implementation of such systems.
Abstract: Internet of Things (IoT) is emerging as a significant technology in shaping the future by connecting physical devices or things with the web. It also presents various opportunities for the intersection of other technological trends which can allow it to become even more intelligent and efficient. In this paper, we focus our attention on the integration of Intelligent Conversational Software Agents or Chatbots with IoT. Prior literature has covered various applications, features, underlying technologies and known challenges of IoT. On the other hand, Chatbots are a relatively new concept, being widely adopted due to significant progress in the development of platforms and frameworks. The novelty of this paper lies in the specific integration of Chatbots in the IoT scenario. We analyzed the shortcomings of existing IoT systems and put forward ways to tackle them by incorporating chatbots. A general architecture is proposed for implementing such a system, as well as platforms and frameworks – both commercial and open source – which allow for the implementation of such systems. Identification of the newer challenges and possible future research directions with this new integration have also been addressed.

61 citations

Book ChapterDOI
TL;DR: The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.
Abstract: Sjogren–Larsson syndrome (SLS) is an autoimmune disorder inherited in an autosomal recessive pattern. To date, 80 missense mutations have been identified in association with the Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2) gene causing SLS. Disruption of the function of ALDH3A2 leads to excessive accumulation of fat in the cells, which interferes with the normal function of protective membranes or materials that are necessary for the body to function normally. We retrieved 54 missense mutations in the ALDH3A2 from the OMIM, UniProt, dbSNP, and HGMD databases that are known to cause SLS. These mutations were examined with various in silico stability tools, which predicted that the mutations p.S308N and p.R423H that are located at the protein-protein interaction domains are the most destabilizing. Furthermore, to determine the atomistic-level differences within the protein-protein interactions owing to mutations, we performed macromolecular simulation (MMS) using GROMACS to validate the motion patterns and dynamic behavior of the biological system. We found that both mutations (p.S380N and p.R423H) had significant effects on the protein-protein interaction and disrupted the dimeric interactions. The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.

20 citations

Journal ArticleDOI
TL;DR: The proposed LDDEP descriptor is compared with the existing methods on two databases, namely National Institute of Standards Technology Special Database 4 (NIST SD 4) and Fingerprint Verification Competition (FVC), and gave higher accuracies compared to theexisting methods.
Abstract: Proper classification of fingerprints still poses difficult issues in large-scale databases due to ambiguity in intraclass and interclass structures, discontinuity in low-quality images, and ridges. To address these challenges, we propose a feature named local diagonal and directional extrema pattern (LDDEP) as a descriptor for classification of fingerprints. The proposed method utilizes first-order derivatives to find values and indices of local diagonal and directional extremas. The local extrema values are then compared with the central pixel intensity value to find the correlation with the neighbors. Eventually, the descriptor is generated with the help of the indices and local extrema values. Furthermore, the proposed descriptor is fed into K-nearest neighbor and support vector machine (SVM) for classifying the fingerprint images into four and five groups, respectively. The LDDEP descriptor is compared with the existing methods on two databases, namely National Institute of Standards Technology Special Database 4 (NIST SD 4) and Fingerprint Verification Competition (FVC). Our experiments have shown that, on the 4000 image NIST SD 4 test dataset, the proposed descriptor achieved a classification accuracy of 95.15% for five classes and 96.85% for four classes for half of the dataset, and an accuracy of 95.5% for five classes and 96.63% for four classes for the entire test dataset using SVM classifier. Similarly, FVC databases for the LDDEP descriptor gave classification accuracy of 98.2% using SVM classifier. The proposed method gave higher accuracies compared to the existing methods.

14 citations

Journal ArticleDOI
TL;DR: In this article, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally, which is a crucial step in understanding the disease's functional characteristics and finding an effective drug.

5 citations


Cited by
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Journal ArticleDOI
TL;DR: A unified framework for text detection and recognition in natural images using exactly the same features and classification scheme and a new dictionary search method is proposed, to correct the recognition errors usually caused by confusions among similar yet different characters.
Abstract: High level semantics embodied in scene texts are both rich and clear and thus can serve as important cues for a wide range of vision applications, for instance, image understanding, image indexing, video search, geolocation, and automatic navigation. In this paper, we present a unified framework for text detection and recognition in natural images. The contributions of this paper are threefold: 1) text detection and recognition are accomplished concurrently using exactly the same features and classification scheme; 2) in contrast to methods in the literature, which mainly focus on horizontal or near-horizontal texts, the proposed system is capable of localizing and reading texts of varying orientations; and 3) a new dictionary search method is proposed, to correct the recognition errors usually caused by confusions among similar yet different characters. As an additional contribution, a novel image database with texts of different scales, colors, fonts, and orientations in diverse real-world scenarios, is generated and released. Extensive experiments on standard benchmarks as well as the proposed database demonstrate that the proposed system achieves highly competitive performance, especially on multioriented texts.

268 citations

Journal ArticleDOI
15 Dec 2020
TL;DR: This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots, and compose a general architectural design that gathers critical details, and highlights crucial issues to take into account before system design.
Abstract: This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.

248 citations

Proceedings ArticleDOI
01 Aug 2017
TL;DR: This paper presents a method to evaluate the classification performance of NLU services, and presents two new corpora, one consisting of annotated questions with the corresponding answers, to enable both, researchers and companies to make more educated decisions about which service they should use.
Abstract: Conversational interfaces recently gained a lot of attention One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and so-called Natural Language Understanding (NLU) services While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically In this paper, we present a method to evaluate the classification performance of NLU services Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the corresponding answers Based on these corpora, we conduct an evaluation of some of the most popular NLU services Thereby we want to enable both, researchers and companies to make more educated decisions about which service they should use

174 citations

Journal ArticleDOI
TL;DR: A modified long short-term memory (LSTM) model for continuous sequences of gestures or continuous SLR that recognizes a sequence of connected gestures based on splitting of continuous signs into sub-units and modeling them with neural networks is proposed.
Abstract: Sign language facilitates communication between hearing impaired peoples and the rest of the society. A number of sign language recognition (SLR) systems have been developed by researchers, but they are limited to isolated sign gestures only. In this paper, we propose a modified long short-term memory (LSTM) model for continuous sequences of gestures or continuous SLR that recognizes a sequence of connected gestures. It is based on splitting of continuous signs into sub-units and modeling them with neural networks. Thus, the consideration of a different combination of sub-units is not required during training. The proposed system has been tested with 942 signed sentences of Indian Sign Language (ISL). These sign sentences are recognized using 35 different sign words. The average accuracy of 72.3% and 89.5% has been recorded on signed sentences and isolated sign words, respectively.

114 citations