Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Molecular-InChI: Automated Recognition of Optical Chemical Structure(Institute of Electrical and Electronics Engineers Inc., 2022) Kumar, N.; Rashmi, M.; Ramu, S.; Reddy Guddeti, R.M.With the advent of a new era dominated by digital media and publications in recent years, the importance of striking a balance between traditional and new modes of operation has become increasingly apparent. It has been standard practice in the field of chemistry for decades to express chemical compounds using their structural forms, referred to as the Skeletal formula. In this research, we tried to interpret these old chemical structure images, extracted from old literature, to transform pictures back to the underlying chemical structure labeled as InChI text using a huge set of synthetic image data produced by Bristol-Myers Squibb. In this paper, we propose an improved synthetic data and an Encoder-Decoder-based deep learning-based model to automatically represent these molecular images into their underlying InChI representation. © 2022 IEEE.Item Fake News Detection in Hindi Using Embedding Techniques(Institute of Electrical and Electronics Engineers Inc., 2022) Shailendra, P.; Rashmi, M.; Ramu, S.; Guddeti, R.M.R.Internet users have been rapidly increasing in recent years, especially in India. That is why nearly everything operates in an online mode. Sharing information has also become simple and easy due to the internet and social media. Almost everyone now shares news in the community without even considering the source of information. As a result, there is the issue of disseminating false, misleading, or fabricated data. Detecting fake news is a challenging task because it is presented in such a form that it looks like authentic information. This problem becomes more challenging when it comes to local languages. This paper discusses several deep learning models that utilize LSTM, BiLSTM, CNN+LSTM, and CNN+BiLSTM. On the Hostility detection dataset in Hindi, these models use Word2Vec, IndicNLP fastText, and Facebook's fastText embeddings for fake news detection. The proposed CNN+BiLSTM model with Facebook's fastText embedding achieved an F1-score of 75%, outperforming the baseline model. Additionally, the BiLSTM using Facebook's fastText outperforms CNN+BiLSTM using Facebook's fastText on the F1-score. © 2022 IEEE.Item Human Activity Recognition for Online Examination Environment Using CNN(Springer Science and Business Media Deutschland GmbH, 2023) Ramu, S.; Guddeti, R.M.R.; Mohan, B.R.Human Activity Recognition (HAR) is an intelligent system that recognizes activities based on a sequence of observations about human behavior. Human activity recognition is essential in human-to-human interactions to identify interesting patterns. It is not easy to extract patterns since it contains information about a person’s identity, personality, and state of mind. Many studies have been conducted on recognizing human behavior using machine learning techniques. However, HAR in an online examination environment has not yet been explored. As a result, the primary focus of this work is on the recognition of human activity in the context of an online examination. This work aims to classify normal and abnormal behavior during an online examination employing the Convolutional Neural Network (CNN) technique. In this work, we considered two, three and four layered CNN architectures and we fine-tuned the hyper-parameters of CNN architectures for obtaining better results. The three layered CNN architecture performed better than other CNN architectures in terms of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A Novel Fake Job Posting Detection: An Empirical Study and Performance Evaluation Using ML and Ensemble Techniques(Springer Science and Business Media Deutschland GmbH, 2023) Srikanth, C.; Rashmi, M.; Ramu, S.; Guddeti, R.M.R.Recently, everything can be accomplished online, including education, shopping, banking, etc. This technological advancement makes it easy for fraudsters to scam people online and acquire easy money. Numerous cyber crimes worldwide exist, including identity theft and fake job postings. Nowadays, many companies post job openings online, making recruitment simple. Consequently, fraudsters also post job openings online to obtain money and personal information from job seekers. In the proposed work, we aimed to decrease the frequency of such scams by using ensemble techniques such as AdaBoost, Gradient Boost, Stacking classifier, XgBoost, Bagging, and Random Forest to identify fake job postings from genuine ones. This paper proposes various featurization techniques such as Response coding with Laplace smoothing, Average Word2vec, and term frequency-inverse document frequency weighted Word2vec. We compared the performance of ensemble techniques with machine learning (ML) algorithms on publicly available EMSCAD dataset using accuracy and F1-score. Bagging classifier outperformed all the models with an accuracy of 98.85% and an F1-score of 0.88 on imbalanced dataset. On balanced dataset, XgBoost achieved 97.89% accuracy and 0.98 F1-score. From the experimental results, it is observed that a combination of ensemble and featurization techniques using Laplace smoothed Response coding and BoW stood superior to most of the state-of-the-art works on fake job posting detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item An Ensemble Deep Learning Approach for Emotion Monitoring System in Online Examinations(Institute of Electrical and Electronics Engineers Inc., 2024) Bhardwaj, S.; Ramu, S.; Guddeti, R.M.R.Around the world, a large number of students experience difficult life situations that have an effect on their emotional and mental health and, ultimately, their academic performance in examinations (exams in short). Emotions have an effect on a student's motivation, focus, and memory, and finally how they perform in exams. An Emotion Monitoring System could be really helpful for understanding how students are feeling during exams and how it affects their overall performance. The contribution of this paper involves in designing a novel facial emotion tracking system which can be used for analyzing facial expressions in real-time thus providing timely emotional support during exams. In this work, we utilized five pretrained deep learning models, namely: DenseNet-121, MobileNetV2, EfficientNet-B0, Inception-V3 and Xception - to classify emotions on processed Emoset dataset. Further, we developed an ensemble model by fusing aforementioned two top-performing deep learning models, thus harnessing the strengths of both models. From the results it can be inferred that ensemble model outperforms the individual pretrained models giving an accuracy of 98.67%. The superior performance of the ensemble models makes it an ideal choice for implementing emotion recognition in real-time applications like Emotion Monitoring in exams. © 2024 IEEE.