Faculty Publications
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Publications by NITK Faculty
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Item Long Short Term Memory Networks for Lexical Normalization of Tweets(Institute of Electrical and Electronics Engineers Inc., 2021) Nayak, P.; Praueeth, G.; Kulkarni, R.; Anand Kumar, M.Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbrevlatlons, skipping of words, spelling errors, etc. Hence such data needs to be appropriately processed before it can be used. The processing can be done by lexical normalization, which reduces randomness and converts the sentence structure to a predefined standard. Hence. lexical normalization can help in improving the performance of systems that use user-generated text as inputs. There are several ways to perform lexical normalization, such as dictionary lookups, most frequent replacements, etc. However, VVe aim to explore the domain of deep learning to find approaches that can be used to normalize texts lexically. © 2021 IEEE.Item Impact of Image Augmentation in COVID-19 Detection Using Chest X-Ray Images(Institute of Electrical and Electronics Engineers Inc., 2022) Azade, A.; Anand Kumar, M.COVID-19 continues to have a devastating impact on people's lives worldwide. In order to combat this condition, it is critical to test affected people in a timely and cost-effective manner. Radiological examination is one of the most efficient ways to attain this goal, with the most widely available and least expensive alternative being a CXR. The artificial intelligence and data science communities have aided in the global response to COVID-19, a novel coronavirus. Detection and diagnosis techniques have focused on developing rapid diagnostic approaches based on chest X-rays and deep learning. In this paper, we have analyzed the impact of augmentation in COVID-19 CXR images with normal lung opacity and viral pneumonia images and presented a model for the detection of COVID-19. © 2022 IEEE.Item Tamil NLP Technologies: Challenges, State of the Art, Trends and Future Scope(Springer Science and Business Media Deutschland GmbH, 2023) Rajendran, S.; Anand Kumar, M.; Rajalakshmi, R.; Dhanalakshmi, V.; Balasubramanian, P.; Padannayil, K.P.This paper aims to summarize the NLP-based technological development of the Tamil language. Tamil is one of the Dravidian languages that are serious about technological development. This phenomenon is reflected in its activities in developing language technology tools and the resources made for technological development. Tamil has successfully developed tools or systems for speech synthesis and recognition, grammatical analysis of grammar, semantics and social media text, along with machine translation. There are many types of research undertaken to orient towards this achievement. Similarly, many activities are developing resources to facilitate technological development. The activities include preparing text corpora for text including monolingual, parallel and lexical along with speech with lexical resources and grammar. What is needed now is to stock-take the achievement made so far and found out where Tamil is in the arena of technological development and looks forward further to its fast technological development. Computational linguistics in Tamil NLP is gaining more attraction, and various data sets available for research is highlighted in this work for further exploration. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Hate Speech Detection Using Audio in Portuguese Language(Springer Science and Business Media Deutschland GmbH, 2024) Tembe, L.A.; Anand Kumar, M.This study focuses on hate speech in Portuguese language using audio and introduces a novel methodology that integrates audio-to-text and self-image technologies to effectively tackle this problem. We utilize Machine Learning and Deep Learning models to differentiate between hate speech and normal speech. The research utilized a total of 200 datasets, which were categorized into hate speech and normal speech. These datasets were collected by me personally for this project. Four distinct models are presented in the analysis: LSTM, SVM, CNN, and Random Forest. The findings highlight the superior performance of the CNN model when applied to spectrogram data, achieving an accuracy rate of 90%. Conversely, the Random Forest model outperforms others when dealing with text data, achieving an impressive accuracy rate of 73.1%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Cognitive Chromatic Image Synthesis Using UNET and GAN(Institute of Electrical and Electronics Engineers Inc., 2024) Choubey, D.; Patil, V.; Anand Kumar, M.In the last decade, there has been a lot of interest for image colorization over a wide range of applications, especially in the restoration of old or damaged images. Because there are a lot of options when it comes to assigning color information, this problem is ill-posed by nature and is quite difficult to solve. Researchers have handled this issue in a variety of imaginative ways. More recent developments in automated colorization are focused on images that are repetitive in nature or images that require extensive editing. For instance, in such settings, semantic maps can be used as additional input to offer better control over the generalization of the colorization task with the help of conditional Deep Convolutional Generative Adversarial Networks (DCGANs).Our solution combines the techniques to allow computers to produce vivid visuals in this way. Monochrome or black and white images most of the times differ from the colored images in terms of visual detail and image content and colorizing them by hand is a tedious and often an artistic task. © 2024 IEEE.