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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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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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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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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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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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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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MARS: A Hybrid Deep CNN-based Multi-Accent Recognition System for English Language 1

MARS: A Hybrid Deep CNN-based Multi-Accent Recognition Syste...

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1st International Conference on Artificial Intelligence Trends and Pattern Recognition, ICAITPR 2022

作者： Darshana, S. Theivaprakasham, H. Jyothish Lal, G. Premjith, B. Sowmya, V. Soman, K. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Classifying the speech of non-native English speakers is challenging due to various features that distinguish accents. Accents vary by sex, age, formality, social status, geographical area, mother tongue, quality of the voice, phoneme, and prosody. This paper proposes a novel, well-structured database of non-native Indian English speaker accents, referred to as IndicAccentDB. IndicAccentDB contains speech samples from 6 different states to address the unbalanced dataset (gender-bias) and speaker mismatch problems observed in the past. The proposed work also discusses the requirements for creating the IndicAccentDB database and pre-processing tasks performed on the dataset. Furthermore, we experimented with accent classification models, namely 1D-CNN, Support Vector Machines, Random forest, Decision tree, ResNet18, ResNet50, and xResNet18, using MFCC and Mel-Spectrogram features to build the robust Multi-Accent Recognition System (MARS). At last, we evaluated the performance of proposed models on the novel database and compared the results using evaluation metrics like precision, accuracy, F1-score, and recall. Based on our findings, xResNet18 was able to identify the accent classes with significant accuracy. © 2022 IEEE.

关键词： Decision trees

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Flood Susceptibility Map of Periyar River Basin Using Geo-spatial Technology and Machine Learning Approach

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 1-21页

作者： Sreekala, S. Geetha, P. Madhu, Dhanya Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri Coimbatore India Center for Computational Engineering & Networking Amrita Vishwa Vidyapeetham Coimbatore India Department of Physics Amrita Vishwa Vidyapeetham Amritapuri Coimbatore India

Floods are among nature’s most destructive disasters because they create extremely extensive damage to structures, the environment, and people. Therefore, it is important to determine the causes of floods as well as areas that are vulnerable to flooding, which can be done by performing a flood susceptibility model. This research identified flood-prone locations in the Periyar River Basin using historical flood records from 2000 to 2020 and some of the conditioning features. The ten variables considered in the present study include elevation, slope, aspect, flow direction, drainage density, rainfall, Normalized Difference Water Index (NDWI), Stream Power Index (SPI), Sediment Transport Index (STI), and Topographic position Index (TPI). In order to create a flood susceptibility map and examine the correlation between flood incidence, the logistic regression (LR), support vector machine, naive Bayes, random forest, AdaBoost, gradient boost, and extreme gradient boost models were developed and validated. The model accuracies were measured using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). In addition to this, some other indices like precision, recall, sensitivity, specificity, F1 score, and overall accuracy matrices are used for model evaluation. The results demonstrated that every model can identify flood-prone locations with reasonable accuracy. However, compared to other models, the random forest model showed a better performance and prediction rate (AUC = 94). Furthermore, all models indicated that low-lying places near water bodies and in the western region of the study area had the largest probability of flooding. According to the study, machine learning techniques are a useful tool for mapping and predicting flood-prone areas and for creating flood mitigation strategies and plans. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Feature importance Flood inventory map Flood susceptibility map (FSM) GIS

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Deep Learning with Attention Mechanism for Cryptocurrency Price Forecasting 7th

Deep Learning with Attention Mechanism for Cryptocurrency Pr...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Yazhini, V. Nimal Madhu, M. Premjith, B. Gopalakrishnan, E.A. Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Computing Amrita Vishwa Vidhyapeetham Bangalore India

ISBN: (纸本)9789819951659

Cryptocurrencies are a hot topic in recent years. This study aims to predict the future closing price of Bitcoin and Ethereum using different combinations of Long Short-Term Memory (LSTM), bidirectional-LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms like Bahdanau and Luong. To achieve this, data from different time scales are taken. The tuning of model’s hyperparameters is done to improve the performance, and it is evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The best results were observed for Ethereum in the very short term when using GRU with Bahdanau's attention. Similarly, the best results for Bitcoin were found in the very short term, when using Bi-LSTM with Bahdanau's attention. Overall results of the experiments reveal that the tuning of hyperparameters improves the performance of the model, and the use of attention mechanism on Bi-LSTM and GRU gives a better prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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