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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Gopalakrishnan, Athira Soman, K.P. Amrita School of Engineering Dept. Computational Engineering and Networking Tamil Nadu Coimbatore India

ISBN: (纸本)9789380544519

This paper focuses on dataset class imbalances to address Named Entity Recognition (NER) difficulties in low-resource languages like Malayalam. The main goal is to draw attention to how important data sampling is for improving NER performance. Through an exploration of various algorithms, particularly advanced models, our research demonstrates that data sampling significantly improves model precision and recall by harmonizing class distribution. Skewed datasets in low-resource languages make entity recognition less accurate, mostly because of the overrepresentation of the 'O' class. The work highlights how data sampling helps to progress NLP research and applications in low-resource linguistic situations and promotes successful transfer learning. The results highlight how crucial it is to correct dataset imbalances to create NER systems that are more reliable and accurate. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Learning Machine Learning Named Entity Recognition Sampling Transformer Learning

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Offensive Language Identification in Dravidian Codemixed Dataset 4

Offensive Language Identification in Dravidian Codemixed Dat...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Vasudevan, Nandu Sachin Kumar, S. Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore641112 India

ISBN: (纸本)9798350349900

This research uses an English-Malayalam dataset taken from YouTube comments to investigate improper language detection in multilingual settings. To capture the subtleties of language usage - such as code-mixing - that are common in the under-resourced Dravidian language family, we use autoencoders and ensemble techniques using BERT models as features. We use LIME in addition to improve interpretability. A lazy classifier that uses a variety of classifiers also helps to determine which classifier is the most efficient. Supervised learning methods are enhanced by unsupervised k-means clustering. Our results surpass standards and demonstrate the effectiveness of our method in offensive language identification. The field's understanding is advanced by this confluence of methodologies. © 2024 IEEE.

关键词： K-means clustering

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A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers 14

A Few-Shot Approach to Dysarthric Speech Intelligibility Lev...

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14th International Conference on Computing Communication and networking Technologies, ICCCNT 2023

作者： Chowdary, Paleti Nikhil Akshay, Menta Sai Aravind, Vadlapudi Sai Aashish, Menta Sai Vardhan, Gorantla V N S L Vishnu Jyothish Lal, G. Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking Coimbatore India

ISBN: (纸本)9798350335095

Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Detection of dysarthria is important for several reasons as it can be used to develop a treatment plan and help improve a person's quality of life and ability to communicate effectively. Much of the literature focused on improving ASR systems for dysarthric speech. The objective of the current work is to develop models that can accurately classify the presence of dysarthria and also give information about the intelligibility level using limited data by employing a few-shot approach using a transformer model. This work also aims to tackle the data leakage that is present in previous studies. Our whisper-large-v2 transformer model trained on a subset of the UASpeech dataset containing medium intelligibility level patients achieved an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and specificity of 0.91. Experimental results also demonstrate that the model trained using the 'words' dataset performed better compared to the model trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model achieved an accuracy of 67%. © 2023 IEEE.

关键词： Speech intelligibility

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Random Fourier Features based approach for Covid-19 Twitter Sentiment Classification using Machine Learning and Deep Learning 3

Random Fourier Features based approach for Covid-19 Twitter ...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Vignesh, E. Sachin Kumar, S. Mohan, Neethu Soman, K.P. Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking Coimbatore India

ISBN: (纸本)9798350338607

The outbreak of Corona has significantly impacted the daily lives of thousands of people. Many individuals turned to social media for guidance and information during this pandemic. However, while social media can be a valuable source of information, it also presents certain drawbacks such as the spread of misinformation. Despite this, social media played an essential role in sharing accurate health information and providing support for individuals struggling with mental health during the pandemic. Social networking sites like Twitter provided a means for individuals to connect and share their experiences during this difficult time. To evaluate the impact of COVID-19, we propose a strategy that utilizes sentiment analysis of tweets from Twitter users. This analysis can help identify the emotions that people are feeling towards COVID-19, such as hope, pessimism, fear, annoyance, sadness, or nervousness. We utilized feature engineering techniques for sentiment analysis to categorize tweets into positive, negative, or neutral categories. We also utilized machine learning models to evaluate the effectiveness of various feature extraction and engineering techniques. In addition, we utilized class imbalance strategies to address the imbalance of emotion classes. Our study compares different feature extraction methods for text data, including statistical methods, word embedding-based methods, kernel feature maps, and hybrid methods. We achieved superior accuracy, precision, f1-score, and recall compared to previous studies when applied to the Covid Senti - A, Covid Senti -B, and Covid Senti -C datasets. Our findings suggest the assessment of public sentiment towards COVID-19 through the analysis of social media data can be a useful resource. © 2023 IEEE.

关键词： Sentiment analysis

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Brain Signal Classification Using Decomposition Techniques and Deep Learning

Brain Signal Classification Using Decomposition Techniques a...

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International Conference on Advances in Data Science and Computing Technologies, ASDC 2022

作者： Karthik, T.R. Ramakrishnan, Rohith Vadakedath, Anirudh Sowmya, V. Gopalakrishnan, E.A. Jyothish Lal, G. Centre for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819936557

Brain Computer Interface (BCI) enables the direct communication between computers and humans. Event related potentials (ERPs) are created using speller paradigms, such as the P300 wave on user-selected targets. This technique is used in a P300 speller, which allows the user to type characters by detecting P300 waves. The P300 speller is a binary classification problem. The classification is to classify a P300 and Non P300 wave in the EEG signal. Deep learning-based classification of EEG signals using decomposition techniques like Variational Mode Decomposition and Wavelet is used in this work. The proposed approach uses raw signal and decomposed signal for classification of EEG signals recorded from two subjects. The parameters used for evaluation are accuracy, precision, recall, and F1-score. The accuracy of 82% was obtained for the raw signals and the accuracy of 83% was obtained for the decomposed signals using wavelet and variational mode decomposition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wavelet decomposition

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Unsupervised intrusion detection system for in-vehicle communication networks

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Journal of Safety Science and Resilience 2024年第2期5卷 119-129页

作者： Kabilan N Vinayakumar Ravi V Sowmya Center for Computational Engineering and Networking(CEN) Amrita School of Artificial InteligenceAmrita Vishwa VidyapeethamCoimbatoreIndia Center for Artificial Intlligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia

In-vehicle communication has been optimized day to day to keep updated of the *** area network(CAN)is used as a standard communication method because of its efficient and reliable ***,CAN is prone to several network level attacks because of its lack in security *** methods have been introduced to incorporate this in *** proposed an unsupervised method of intrusion detection for in-vehicle communication networks by combining the optimal feature extracting ability of autoencoders and more precise clustering using fuzzy C-means(FCM).The proposed method is light weight and requires less computation *** performed an extensive experiment and achieved an accuracy of 75.51%with the ML35o in-vehicle intrusion *** experimental result,the proposed method also works better for other intrusion detection problems like wireless intrusion detection datasets such as WNS-DS with accuracy of 84.05%and network intrusion detection datasets such as KDDCup with accuracy 60.63%,UNSW_NB15 with accuracy 73.62%and Information Security Center of Excellence(Iscx)with accuracy 74.83%.Overall,the proposed method outperforms the existing methods and avoids labeled datasets when training an in-vehicle intrusion detection *** results of the experiment of our proposed method performed on various intru-sion detection datasets indicate that the proposed approach is generalized and robust in detecting intrusions and can be effectively deployed in real time to monitor CAN traffic in vehicles and proactively alert during attacks.

关键词： Controlarea network Intrusiondetection Unsupervised learning Autoencoders Fuzzy C-means

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Efficient Text Analysis: A BERT-Based Approach to Named Entity Recognition (NER) and Classification for Malayalam Language

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Gopalakrishnan, Athira Soman, K.P. Rajendran, S. Raj, Kamal K. Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India SAAMA AI Research Lab Chennai India

The onset of the present millennium has witnessed a remarkable surge in the use of Indian language in various forms of multilingual interpersonal communication, including news forums, webpages, email, and social media conversations. The extensive dissemination of mobile phones has ignited an apparently unceasing utilization of well-known micro-blogging platforms—Facebook, Twitter, and Watsapp—to express thoughts, remarks, and impressions in the field of natural language processing (NLP). The primary objective of this research is to investigate Efficient Text Analysis using Bidirectional Encoder Representations from Transformers (BERT) Based for Named Entity Recognition and Classification in the Malayalam language. To conduct Named Entity Recognition (NER) in the Malayalam dialect, which is widely spoken in Kerala, India, this study employs both deep learning and the BERT approach. It involves customization of a pre-trained multilingual BERT (m-BERT) model to accurately capture the unique phonetic features of Malayalam. Lastly, the performance of the proposed approach is validated and compared with existing recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f1-score (%). The results show that the proposed approach attains the highest accuracy (97.10%), thereby outperforming the existing recent studies. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： BERT Deep Learning Information Extraction NER NLP

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Child Speech Recognition on End-to-End Neural ASR Models 2

Child Speech Recognition on End-to-End Neural ASR Models

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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Shraddha, S Jyothish Lal, G. Sachin Kumar, S. Computational Engineering and Networking Amrita Vishwa Vidhyapeetham Coimbatore India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Automatic speech recognition (ASR) is an area which is having maximum improvement and is developing to a very high extent as far as adult speech is concerned. Nevertheless, child speech recognition is an area which is least explored. This is because of the scarcity of a neatly labelled corpora. Precisely, to train a speech recognition model from scratch and to make it work child speech is a challenging task as the availability of child speech data is limited. In this context, exploring child speech recognition can also help us improve the current speech recognition systems since on validating it in such a manner that it will be useful for child speech may help the systems work in a good way such that we will be able to validate it for everyone irrespective of the age variation in which a speech utterance is produced. State-of-art models in this domain perform various complex speech recognition tasks such as machine translation and ASR on multilingual datasets as well as it is useful in performing self supervised speech recognition tasks. This process has proved to work well for speech data which consists of recordings with adult data and it is not yet validated on child speech. So the present work tries to validate the performance six state-of-the-art ASR models on children speech and report the corresponding word error rate. © 2022 IEEE.

关键词： Speech recognition

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Energy-Free Sensing and Context Recognition Using Photovoltaic Cells

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IEEE Sensors Journal 2025年第9期25卷 15598-15611页

作者： Shintani, Kaede Rizk, Hamada Yamaguchi, Hirozumi Osaka University Graduate School of Information Science and Technology Department of Information Networking Suita565-0871 Japan Tanta University Tanta31733 Egypt RIKEN Center for Computational Science Kobe650-0047 Japan RIKEN Center for Computational Science Department of Computer and Control Engineering Kobe650-0047 Japan

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this article, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user’s location. We propose a computationally efficient dynamic time warping (DTW) to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43 cm, respectively. This is achieved while the proposed system is nonintrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition. © 2001-2012 IEEE.

关键词： Photovoltaics

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Deep Learning-Based Topic Categorization of Tamil Social Media Text Content 3rd

Deep Learning-Based Topic Categorization of Tamil Social Med...

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3rd International Conference on Machine Intelligence and Signal Processing, MISP 2021

作者： Geerthana Anusha, M. Sachin Kumar, S. Soman, K.P. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789811908392

Nowadays, interpreting the topic of social media text content is an important task. It will take time when tried to do manually. AI with machine learning and deep learning approaches is used immensely for detecting the topic category. In the current work, Tamil text data from social media sources like YouTube, Facebook, Twitter are collected. The text content is categorized as entertainment, education, sports, politics, health, and others. The current work provides a comparative analysis of various machine learning and deep learning approaches for topic categorization on the collected data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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