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Pediatric oral health detection using Swin transformer 3

Pediatric oral health detection using Swin transformer

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Bhat, Suvarna Birajdar, Gajanan K. Patil, Mukesh D. Ramrao Adik Institute Of Technology D.Y. Patil Deemed To Be University Department Of Electonics Engineering Navi Mumbai India

ISBN: (纸本)9798350318609

Oral health exams in children are important for a variety of reasons, including their impact on a child's overall well-being and development. Traditional dental health detection methods still need to be improved, particularly in actual diag-nostic environments. Artificial intelligence (AI) has the potential to have a substantial impact on pediatric dentistry by providing innovative solutions to numerous difficulties in children's oral healthcare. By analyzing X-rays or intra-oral scans, AI can aid in the early detection of disorders like tooth decay, and developmental anomalies, when correction is most successful. AI can provide additional insight by analyzing X-rays and data, allowing dentists to make more accurate treatment plan decisions. Transformer is an instance of a deep neural network that was first employed for natural language processing. Its main foundation is the self-attention mechanism. In this study, we assess the swin transformer model's effectiveness in pediatric oral health detection. The data classes were enhanced as a pre-processing step using the fuzzy color approach, and the images that were structured with original X-rays were stacked. The proposed system achieved an accuracy of 85.34 percent. The result obtained implies that swin transformer has prospects in medical imaging and pre-training may be a useful strategy for enhancing its model's accuracy on oral health detection tasks. © 2024 IEEE.

关键词： Pediatrics

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The Improved Inception Networks based on Attention Mechanism 23

The Improved Inception Networks based on Attention Mechanism

引用

8th international conference on Intelligent Information processing, ICIIP 2023

作者： Yu, Yiwang Sun, Yongke Cao, Yong Big data and intelligence engineering school Southwest Forestry University China

ISBN: (纸本)9798400708091

Deep learning is a machine learning method and it have enabled major progresses in image, natural language process and other more various and wide fields. Inception is a classic deep learning module included in Google net and it has good performance in image recognition, classification and segmentation. Attention is essentially a distribution mechanism, with the core idea of highlighting certain important characteristics of the object. In this paper, we introduced attention mechanism into Inception net model. We applied our model on MNIST, CIFAR-10 and CIFAR-100 public dataset to train and test. The performance of the model based on Inception and Attention Mechanism is improved greatly compared to the other models. At the same time, we also apply SENet model with attention mechanism to the same datasets and the experiment results show our improved Inception model is superior to SENet. These demonstrated that our proposed attention mechanism has better effectiveness. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Image recognition

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Safety Stock Forecasting based on Materials Correlation 15

Safety Stock Forecasting based on Materials Correlation

引用

15th IEEE international conference on Wireless Communications and Signal processing, WCSP 2023

作者： Zhang, Yaqiang Tao, Xiaoming Cui, Zhoujuan Duan, Yiping Lu, Jianhua Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350324662

Safety stock is an inventory to cope with the randomness and risk in all aspects of supply, production, and sales. In order to solve the problems of low inventory level leading to production interruption and high inventory level leading to high inventory cost and overstocking, safety inventory forecasting is needed. With the consideration, we propose a safety stock forecasting method based on spatial-temporal sequence model. Firstly, we adopt a clustering method based on time series shape to classify the materials, and the results of the classification are used as classification prior knowledge to enhance the prediction performance. Then, the dosage forecasting model is established for different classes, respectively. By proposing the correlation matrix, the co-occurrence frequency of different materials in the product dosage pattern is used as the adjacency matrix, which is correlation prior knowledge. With the combined effect of classification and correlation prior knowledge, the accuracy of predicting the dosage of a specific material can be greatly improved in a period of time in the future. Through experiments, it is proved that our method greatly improves the prediction accuracy and successfully explores the correlation between materials, compared with the traditional method. © 2023 IEEE.

关键词： clustering graph convolutional networks machine learning Safety stock time series forecasting

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A knowledge-Driven Graph Convolutional Network for Spectrum Prediction 24

A Knowledge-Driven Graph Convolutional Network for Spectrum ...

引用

24th IEEE international conference on Communication Technology, ICCT 2024

作者： Guo, Yibo Huang, Yang Zhu, Qiuming Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing China

ISBN: (纸本)9798350363760

In the 6G mobile communication systems, the integration of sensing, communication, and array signal processing technologies heralds a new era of advanced situational awareness. However, this advancement raises critical concerns about privacy and security, especially with the widespread deployment of devices equipped with radar-like sensing capability, including malicious ones. Moreover, suffering from rapidly changing spectrum environments and the restricted capability of spectrum prediction, jamming effectiveness evaluation values may not be fully obtained. Therefore, in order to effectively jam the malicious sensing device and improve the spectrum utilization, spectrum prediction is expected to be a key enabler, which features predicting the jamming effectiveness evaluation values and recommending frequency-domain subbands for utilization. In order to characterize the relation between jamming devices and malicious sensing devices when predicting spectrum, we construct a spectrum knowledge graph (SKG). In order to encode the KGE vectors from the triplets composed of entities and relations, we develop the CNN-based KGE model, where the CNN can be exploited to derive embedding encoded with knowledge of relations. In order to address the problem of predicting spectrum, we convert this issue into a node regression problem based graph model and graph convolution network (GCN). In order to address the problem of sparse feature vectors related to entities and relationships within the SKG, we exploit KGE vectors generated by a CNN-based KGE algorithm to replace the one-hot encoding method, which can be used to formulate the feature matrix in the graph model. Simulation results indicate that the spectrum prediction algorithm based on a knowledge-driven graph convolutional network (KGCN) can significantly improve the accuracy of spectrum prediction. © 2024 IEEE.

关键词： knowledge graph

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An Intelligent Search Engine Based on knowledge Graph for Power Equipment Management 5

An Intelligent Search Engine Based on Knowledge Graph for Po...

引用

5th international conference on Energy, Electrical and Power engineering, CEEPE 2022

作者： Wu, Xingping Tang, Yachen Zhou, Chunlei Zhu, Guangxin Song, Jinwei Liu, Guangyi State Grid Corporation of China Big Data Center Beijing China Envision Digital Redwood CityCA United States

ISBN: (纸本)9781665479059

The intelligent search engine (ISE) can accurately identify users' search intention and interests automatically by parsing the natural language questions, can provide fast retrieval and relevance ranking of direct and associate search results, and can filter and push information to the user intelligently. Since ISE can save inestimable time and workforce in data search and correlation analysis, it has been widely used in the Internet field. This article proposed an ISE based on a constructed electrical power knowledge graph to extract the target and conditions of natural interrogation based on semantic analysis, to generate index network via vertex-to-edge mapping and ontology network path analysis, and to perform the direct and associate knowledge searches to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop data mining and correlation analysis in a domain knowledge graph. The proposed work can also provide a basis for automatic identification of user interests, intelligent information filtering and push. © 2022 IEEE.

关键词： knowledge engineering Power engineering Correlation Semantic search natural languages Process control Search engines

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Comment Compass: An Approach to Analyze YouTube Comments through Web Scraping, Sentiment Analysis, and Generative AI for Actionable Insights 1

Comment Compass: An Approach to Analyze YouTube Comments thr...

引用

1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Rathod, Manav Yadav, Ankita Phutane, Soham Bhise, Suvarna Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798350365016

In this paper, we introduce 'Comment Compass,' a novel system designed to revolutionize the analysis of YouTube video comments by content creators. The hypothesis driving this research is that integrating web scraping, sentiment analysis, and generative AI can provide content creators with deeper insights into their audience's sentiments and perspectives, thereby enhancing content strategy and engagement. Methodologically, 'Comment Compass' employs Python-based web scraping to extract comments from YouTube videos. Sentiment analysis is conducted using the SentimentIntensityAnalyzer from the natural language Toolkit (NLTK) library, which produces a pie chart depicting the distribution of positive, negative, and neutral comments. Additionally, the system leverages Generative models to generate further insights from the comments, such as future content ideas, criticisms, and inquiries. Our results indicate that 'Comment Compass' offers content creators actionable insights that can significantly optimize content strategies for better audience engagement and impact. This work demonstrates the substantial potential of combining web scraping, sentiment analysis, and generative AI, contributing valuable knowledge to these fields. The findings are contextualized within existing literature, underscoring the innovative contributions of this research. © 2024 IEEE.

关键词： Video analysis

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Unlocking Transfer Learning's Potential in natural language processing: An Extensive Investigation and Evaluation

Unlocking Transfer Learning's Potential in Natural Language ...

引用

Advances in Computing Research on Science engineering and Technology (ACROSET), international conference on

作者： Pradipta Banerjee Swati Kashyap Department of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

With the advent of transfer learning approaches, natural language processing (NLP) problems have experienced tremendous progress, as demonstrated by models such as Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). The usefulness of such transfer learning strategies across a range of NLP tasks and domains is investigated in this work. The study uses a methodical methodology to assess BERT and GPT's performance on a wide range of tasks. In addition, the study evaluates the generalizability and flexibility of these models across a broad variety of disciplines, including social media, finance, legal, and biological literature. The study's methodology entails rigorous assessment utilizing task-specific standard metrics after pre-trained BERT and GPT models have been fine-tuned using task-specific datasets. To determine the relative benefits and drawbacks of transfer learning strategies in various contexts, comparative studies are carried out against baseline models and other cutting-edge methodologies. Additionally, the study looks at how the performance of BERT and GPT is affected by variables including task difficulty, dataset size, and domain specificity. The results provide a comprehensive understanding of the benefits and drawbacks of transfer learning strategies in a variety of NLP tasks and domains. While BERT performs admirably on tests requiring semantic comprehension and contextual knowledge, GPT is superior at producing text that is both cohesive and appropriate to the situation. Both models, however, show sensitivity to dataset features and idiosyncrasies unique to the domain, indicating the necessity for customized fine-tuning techniques for best results. All things considered, this study advances our knowledge of the usefulness and efficiency of transfer learning strategies and provides insightful information for academics and practitioners who want to use BERT, GPT, and related models in a variety

关键词： Adaptation models Visualization Biological system modeling Transfer learning Semantics Bidirectional control Transformers natural language processing Encoding Context modeling

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Survey on Neural Networks in natural language processing

Survey on Neural Networks in Natural Language Processing

引用

Control, Electronics and Computer Technology (ICCECT), IEEE international conference on

作者： Fei Hu School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China

Neural Networks can learn natural language representations within levels of abstraction, and have recently shown much promise for natural language processing (NLP) applications. Kinds of Neural Network language Models (NNLMs) have gained a lot of success by predicting the next word given the other words in a context. These models have improved the state-of-the-art in word sense disambiguation, question answering, web search and many other domains in semantic parsing. Compared with "bag of word", Word Embedding has brought about breakthroughs in representations of words, sentences, or even documents. Recurrent Neural Network language Models (RNNLMs) have shone light. They can learn and store long-memory knowledge that would help extracting better representations for long documents.

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MentalHealthAI: Utilizing Deep Learning for Accurate Stress Level Prediction 3

MentalHealthAI: Utilizing Deep Learning for Accurate Stress ...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bharathi, S.T. Raj, J. Relin Francis Krishnan, R. Santhana Rajalakshmi, V. Kumar, V. Vinoth Anchitaalagammai, J.V. Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

MentalHealthAI is an advanced deep learning system designed to accurately predict stress levels by integrating various data sources, including user surveys, heart rate metrics, sentiment scores from social media interactions, and demographic information. This innovative approach employs a Fully Connected Neural Network (FCNN) architecture, enabling the model to uncover complex relationships among multiple stress-related factors, such as PHQ-9 and GAD-7 questionnaire scores, heart rate variability, sleep patterns, and calculated sentiment scores. By utilizing natural language processing (NLP) techniques, specifically the VADER model, the system analyzes social media text data to derive sentiment scores, providing insights into users' emotional states. This holistic assessment empowers individuals to understand the interplay between their mental health and daily activities, fostering proactive stress management. The advantages of MentalHealthAI include its capacity to deliver timely and personalized stress assessments, allowing users to make informed decisions about their mental well-being. By combining objective data from wearables with subjective sentiment analysis, the system offers a comprehensive perspective on stress dynamics. Ultimately, MentalHealthAI aims to enhance mental health support, providing valuable insights that encourage healthier lifestyles and improved emotional resilience. © 2024 IEEE.

关键词： Electronic health record

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NLP Tools for Epileptic Seizure Prediction Using EEG Data: A Comparative Study of Three ML Models 6th

NLP Tools for Epileptic Seizure Prediction Using EEG Data: A...

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6th international conference on Nanotechnologies and Biomedical engineering, ICNBME 2023

作者： Iapascurta, Victor Fiodorov, Ion Department of Software Engineering and Automation Technical University of Moldova Chisinău Moldova Department of Anesthesia and Intensive Care N. Testemitanu State University of Medicine and Pharmacy Chisinău Moldova

ISBN: (纸本)9783031427817

natural language processing (NLP) is an ever-evolving field of computer science that involves the development of algorithms that can process, analyze and understand human language. One of the most exciting areas of NLP is the creation of NLP language models with applications across almost every industry. However, most people only associate NLP with its traditional use in language translation, sentiment analysis, and chatbots. In reality, there are many less-common uses for NLP models that have the potential to transform businesses, improve customer experiences, and even save lives. In the healthcare industry, NLP models can be used to analyze unstructured medical data and help diagnose and treat patients more efficiently. For example, NLP can be used to analyze clinical notes, lab results, and other data combing through vast amounts of data to identify patterns and create targeted treatment plans. NLP-based medical diagnosis is still in its infancy, but it has the potential to revolutionize the healthcare industry in the coming years. This article explores a less common use of machine-learning language models built on transformed EEG data for epilepsy prediction using the Kolmogorov-Chaitin algorithmic complexity as the first step in generating text-like data, which are finally used for building machine learning models. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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