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Improving Healthcare Question Answering System by Identifying Suitable Answers 2

Improving Healthcare Question Answering System by Identifyin...

引用

2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Das, Baivab Nirmala, S. Jaya National Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruchirappalli India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Healthcare question answering is an important task in the field of Natural Language Processing. As the era of the internet has already begun, textual data related to the healthcare field has also increased rapidly. There is a huge opportunity to utilize the full potential of deep learning-based methods to achieve better accuracy in healthcare question answering. Unfortunately, the open-domain pre-trained models do not perform well in healthcare question answering. As the terminologies used in the healthcare field are unique, the concept of understanding them is hard. To resolve this issue, in this project, the BioBERT model is used to answer questions related to the healthcare field. BioBERT is based on BERT (Bidirectional Encoder Representations from Transformers) and it is pre-trained on large biomedical corpora which gives it the ability to understand healthcare-related terminologies. As the BERT model has already proved its efficiency as a bidirectional language model, the BioBERT model here is trained with the SQUAD question-answering dataset. The model is able to answer questions related to the healthcare field with an F1 score of 88. © 2022 IEEE.

关键词： Terminology

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Identification of Texts Using ML Algorithm for The Accurate Recognition

Identification of Texts Using ML Algorithm for The Accurate ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pasha, M Jahir Kajendran, K. Deshmukh, Shrikala Kumar, G. Sreekanth, N. Pund, Sachin G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Panimalar Engineering College Dept. of Computer Science and Engineering Chennai India Amity University Mumbai India Bharati Vidyapeeth College of Engineering Pune India Srm Institute of Science and Technology Faculty of Management Kattankulathur India Mallareddy Engineering College for Women Hyderabad India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

A Comparative evaluation of device mastering Algorithms for textual content classification textual content category is an vital assignment in herbal language processing, where the aim is to routinely assign a label or category to a given text file. With the increasing quantity of text facts available in diverse domain names, the need for efficient and accurate text class algorithms has turn out to be extra vital. In this paper, we gift a comparative analysis of machine mastering algorithms for text category. We experiment with one of a kind algorithms, such as aid Vector Machines, Naive Bayes, Random Forests, and Multilayer Perceptron, on numerous benchmark datasets. Our results reveal that the choice of set of rules considerably affects the overall performance of textual content classification, and there may be no unmarried high-quality algorithm that works well for all datasets. We also provide insights into the strengths and weaknesses of each set of rules and talk their applicability in specific eventualities. Our findings can guide researchers and practitioners in selecting the maximum suitable system gaining knowledge of algorithm for their text category duties. Textual content classification, device learning, assist vector machines, naive bays, random forests, multilayer perceptron, and comparative analysis. © 2023 IEEE.

关键词： Multilayer neural networks

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Recurrent Convolution based Graph Neural Network for Node Classification in Graph structure data 12

Recurrent Convolution based Graph Neural Network for Node Cl...

引用

12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Waikhom, Lilapati Patgiri, Ripon National Institute Of Technology Silchar Dept Of Computer Science Engineering Assam Cachar788010 India

ISBN: (纸本)9781665437011

Many datasets in various machine learning applications are structural and naturally represented as graphs. They comprise data from the analyses of social and communication networks, predictions of traffic, and fraud detection. Graphbased Deep Learning (DL) aims to construct and train graph datasets attuned models for various graph-structured based tasks. In this work, we presented a model of Graph Neural Network (GNN) for the node classification task. We have compared our proposed model with a baseline model on three citation network datasets: CORA, PUBMED, and CITESEER. We examined the baseline and proposed models predictions on new data instances by randomly generating binary work vectors concerning the work presence probabilities for all three datasets. The proposed model is significantly better than the baseline model on the CORA and CITESEER datasets. © 2022 IEEE.

关键词： Graph neural networks

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A Toxic Content Detection Technique in Sentimental Analysis with Convolution Neural Networks 11

A Toxic Content Detection Technique in Sentimental Analysis ...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Mishra, Varun Tripathi, Monika Shri Krishna University Dept. of Computer Science and Engineering Madhya Pradesh Chhatarpur India

ISBN: (纸本)9781665480383

In the recent time, the usage of various social media platforms has drastically increased which involves the positive or negative impact on human lives. One of the aspects is directly associated with comment/opinion which individual user/person passes through different social networking sites. This paper provides the discussion on different approaches to analyze classification and modelling techniques for detecting toxic comments using convolution neural networks (CNN). It also specifies algorithm based on outlier detection on given data stream that can evaluate the posted material on social platforms and check its positive and negative impact on the society. © 2022 IEEE.

关键词： Sentiment analysis

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Steganalysis on Covert Channel in IPv6 Flow Label

Steganalysis on Covert Channel in IPv6 Flow Label

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Lin, Da-Chih Wu, Quincy National Chi Nan University Dept. Computer Science and Informance Engineering Nantou Taiwan

ISBN: (纸本)9798350320282

Modern communication networks provide rich channels for steganographic tools to hide secret data. In addition to payload, an emerging trend is to utilize protocol headers to hide data as well. This paper studies the IPv6 packets with data hidden in their Flow Label field, and proposes an algorithm which utilizes chi-square test to detect the existence of hidden data by the conformance of uniform distribution. © 2022 IEEE.

关键词： Statistical tests

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Integrated Multiclass Sentiment Analysis of Movie Reviews: Combining Fine-Tuned DistilBERT, LightGBM and XAI for Interpretation

Integrated Multiclass Sentiment Analysis of Movie Reviews: C...

引用

Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Md. Polash Islam Shirin Sultana Md. Saifur Rahman Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Dept of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Sentiment analysis of movie reviews plays a critical role in understanding audience perspectives and predicting trends in the entertainment industry. This work presents an integrated approach that encourages a fine-tuned DistilBERT model for feature extraction, followed by LightGBM for classification, and SHAP (Shapley Additive Explanations) for model interpretability. By combining these advanced techniques, our approach achieves a high accuracy of 97%, significantly outperforming traditional methods. The use of DistilBERT enables precise contextual understanding of textual data while offering a more efficient and lightweight alternative to the full BERT model. LightGBM provides efficient and scalable classification, and SHAP ensures transparent and interpretable model decisions, allowing us to understand key factors driving sentiment predictions. This integrated framework enhances accuracy and also provides valuable insights into the model’s behavior, making it a robust tool for sentiment analysis in movie reviews.

关键词： Sentiment analysis Adaptation models Accuracy Reviews Computational modeling Scalability Motion pictures Feature extraction Data models Context modeling

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Image Translator: An Unsupervised Image-to-Image Translation Approach using GAN

Image Translator: An Unsupervised Image-to-Image Translation...

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2022 International Conference on Innovations in science, engineering and Technology, ICISET 2022

作者： Plabon, Silvia Satoar Khan, Mohammad Shabaj Khaliluzzaman, Md. Islam, Md. Rashedul International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9781665483971

Unpaired image-to-image translation uses unpaired training data to predict domain-to-domain mapping. Unpaired Image-to-Image is a versatile Generative Adversarial Network (GAN) model for image-to-image translation. Using the GAN architecture, you may train a generator model, which is often used to create graphics. To train the discriminator model, the generator is trained to fool the discriminator model into believing that the images are real. Generative adversarial networks (GAN) are a general-purpose solution for image-to-image translation challenges. These networks are concurrently learning a loss function that can be used to train the mapping they have learnt. This invention allows the use of the same fundamental strategy to solve problems that would otherwise need completely separate loss equations. We show that this approach may be used to synthesize photos from label maps, rebuild objects from edge maps, and colorize images. © 2022 IEEE.

关键词： Generative adversarial networks

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Exploring Educational Applicability of Virtual Touch System on Maker Space 5

Exploring Educational Applicability of Virtual Touch System ...

引用

5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Yu, Pao-Ta Lo, Hung-Ting Fang, Yu-Cheng Li, An-Fang Liao, Ying-Han Tsai, Cheng-Yu National Chung Cheng University Dept. of Computer Science & Information Engineering Chiayi Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

Interaction with gestures is more intuitive than traditional input with a keyboard and a mouse. It has gradually become the major technology for extended reality. However, for most users, gesture control is not familiar as other interactive devices, and most user interfaces of daily-use applications are currently not designed for gesture control. Thus, we propose a mid-air hand gesture control system, named vTouch to realize a touch device trained by deep learning with a convolutional neural network for image feature extraction, attention mechanisms for extracting time-series data features, and fully connected layer for classification. Besides, there is a customized driver in kernel mode which treats the vTouch system as a virtual multi-touch device. An experimental design is also proposed to evaluate the effectiveness of the training and the usability of the system. © 2022 IEEE.

关键词： User interfaces

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Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP 4

Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to D...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumar, Vinod Chauhan, Priyanka S. Lella, Jai Advitheeya Momidi, Midhilesh Gupta, Pranjal Upreti, Kamal SEAS GWU WashingtonDC United States Global Payments Atlanta United States Electrical & Comm. Engg. National Institute of Technology Calicut India Dell Technologies Bangalore India Computer Science Engineering ADGIPS Delhi India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. © 2024 IEEE.

关键词： Dimensionality reduction

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Hemkiran S R Kartik S Rathan Aswath K Kabilan Sudha Sadasivam G Dept. of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Dept. of Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly.

关键词： COVID-19 Proteins Machine learning algorithms Biological system modeling Computational modeling Predictive models Prediction algorithms Drug discovery Compounds Random forests

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