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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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SN computer science 2024年第1期5卷 99页

作者： Purushothaman, Sony Shanmugam, G. Siva Nagarajan, Sureshkumar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science Govt. Model Engineering College Kerala Kochi India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnakovil India

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of science articles discussing semantic interoperability solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Internet of medical things Privacy Semantic interoperability

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Video-based Hand Gesture Recognition using Random Forest for Sign Language Interpretation

Video-based Hand Gesture Recognition using Random Forest for...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Himasree, J. Jeevitha, P.L. Deekshitha, K. Kolisetty, Aashrita Naveen, Soumyalatha REVA University School of Computer Science & Information Technology Bangalore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798350361537

Effective communication is essential for all individuals, yet it can be particularly challenging for the deaf community, as sign language is their major means of communication. We are researching how to create a reliable and effective system that can understand sign language motions correctly without the help of a human, realizing the importance of this *** this research work we have chosen to construct our own dataset rather than depending on pre-existing ones that may be accessed online because we understand how important authenticity and relevance are. This method gives us more control over the quality of the data and guarantees that the dataset closely matches our study goals. In addition, our system can be readily expanded and recognizes a wide range of hand motions and signs, including the alphabet. We can support a broad variety of sign language expressions thanks to this versatility, which improves inclusivity and usability. Random Forest is used to encode temporal variations among motions in sign language. The system's overall accuracy and reliability are increased thanks to this methodology, which enables it to detect minute variations in hand gestures and movements. Using the Keras and OpenCV libraries in conjunction with the Python programming language, we build a strong foundation for sign language recognition. In addition to streamlining the development process, these technological tools open the system to a larger developer and research community. In alignment with the research, the proposed work achieved an accuracy of 97% with overall precision, recall and F1 score of 99%. © 2024 IEEE.

关键词： Gesture recognition

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Contamination 5

Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Co...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Sanath, G. Deekshitha, S. Shravya, H. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Computer Science and Engineering Bengaluru India Cambridge Institute of Technology Information Science and Engineering Bengaluru India Senior Data Science Trainer Dhaarini Academy Bengaluru India

ISBN: (纸本)9798331528539

Effective monitoring of water quality remains a problem, in real-time contamination detection and prediction. Despite advancements in the sector, many existing approaches continue to rely on labor-intensive laboratory testing, which limits their capacity to give rapid and accurate results. By leveraging advanced ML/DL models, it is possible to build a more robust and adaptable platform for real-time analysis, prediction, and decision support. Approaches based on models such as Random Forest, XGBoost, CNN, RNN, LSTM, and GRU have been good with the accuracy of water quality predictions. They help identify pollution sources and diagnose water quality issues, providing essential information to stakeholders in sustainable water resource management. The ML and DL approaches help improve water quality monitoring and boost future research in environmental sustainability and natural resource management. © 2024 IEEE.

关键词： Resource allocation

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DRCS: Document Recommendation Framework for Cultural Studies as a Prospective Domain 15th

DRCS: Document Recommendation Framework for Cultural Studies...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Deepak, Gerard Hariharan, Nitin Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Karaikudi India

ISBN: (纸本)9783031889912

The need for extensions of documents for highly special sensitive domains like cultural studies which involves countries, and which involves cultural relationships and political scenarios between cultures and across countries. As the worldwide web continues to evolve, it is transitioning into a densely populated semantic web, which is considered the standard for the next generation of the web, commonly known as web 3.0. This paper suggests a framework for Document Recommendation framework for Cultural Studies (DRCS) that utilizes methods like term frequency - inverse document frequency (TF-IDF) and LDA (Latent Dirichlet Allocation) to analyze the term data set. Metadata generation is used to collect and arrange the information, employing transformers for classification. The XGBoost classifier, which is feature-controlled, is used for data set classification. To measure semantic relevance, strong similarity measures like APMI (Adaptive Pointwise Mutual Information) and Pianka Index, Ren-Konen Index, Sim Rank are used. The model incorporates LDA for topic modelling and utilizes resources like the Google Knowledge Graph API to access community-verified knowledge. To optimize the solutions, the UnderCricket Algorithm optimization algorithm is employed. The proposed model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 94.75%, 96.74%, 95.745%, and 95.7346598% respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.06% is achieved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

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Design and beam dynamics study of a magnet system for an 11 MeV superconducting isochronous cyclotron

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Nuclear science and Techniques 2025年第6期36卷 56-65页

作者： Pan-Pan Zheng Xiang-Hui Wang Zi-Feng He Yue Wu Zhi-Min Dai Wei-Shi Wan School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China School of Computer Engineering Wefang UniversityWeifang 261061China

In recent years,due to the scarcity of domestic radioisotopes,the Chinese government has strongly supported the development of dedicated radioisotope production *** paper presents conceptual design simulations of an 11 MeV,50μA,H^(-) compact superconducting cyclotron for radioisotope *** paper focuses primarily on four aspects:magnet system design,central region configuration,beam dynamics analysis,and extraction system *** paper outlines the cyclotron's primary parameters and key steps in the development process.

关键词： Superconducting cyclotron magnet Beam dynamics Central region Stripping extraction

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Farmer's Crop Welfare System 15

Farmer's Crop Welfare System

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Dhamodhar, G. Krishna, B. Vamsi Reddy, D. Sampath Reddy, A. Sameer Amutha, S. Ganesan, Kothai Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Virudhnagar India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Dist. Tamil Nadu India

ISBN: (纸本)9798331300579

The "Farmer's Crop Welfare System" is a technology that uses advanced data analytics and artificial intelligence to improve crop management and yield. It combines crop prediction, disease detection, and fertilizer recommendation. Crop prediction uses historical and real- time data, while disease detection uses image recognition and deep learning to identify early signs of diseases, promoting sustainable farming practices. © Grenze Scientific Society, 2024.

关键词： Livestock

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From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

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IEEE Transactions on Affective Computing 2024年第2期16卷 624-638页

作者： Chen, Yin Li, Jia Shan, Shiguang Wang, Meng Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations, e.g., insufficient quantity and diversity of pose, occlusion and illumination, as well as the inherent ambiguity of facial expressions. In contrast, static facial expression recognition (SFER) currently shows much higher performance and can benefit from more abundant high-quality training data. Moreover, the appearance features and dynamic dependencies of DFER remain largely unexplored. Recognizing the potential in leveraging SFER knowledge for DFER, we introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features, thereby significantly improving DFER performance. Firstly, we build and train an image model for SFER, which incorporates a standard Vision Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling Adapters (TMAs) into the image model. MCPs enhance facial expression features with landmark-aware features inferred by an off-the-shelf facial landmark detector. And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters (less than +10%) to the original image model. Moreover, we present a novel Emotion-Anchors (i.e., reference samples for each emotion category) based Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion labels, further enhancing our S2D. Experiments conducted on popular SFER and DFER datasets show that we have achieved a new state of the art. © 2010-2012 IEEE.

关键词： Face recognition

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Remote Sensing Classification of River Using Machine Learning Methods 12

Remote Sensing Classification of River Using Machine Learnin...

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12th Academic Conference of Geology Resource Management and Sustainable Development, GRMSD 2024

作者： Cui, Hanwen Li, Cheng Tian, Xiaolin School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Academy of Interdisciplinary Studies Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9798331314545

In the realm of global sustainable development, effective management of water resources and the protection of ecological environments have increasingly emerged as focal points for both governmental and international organizations. Rivers, as critical conduits of water resources, fulfill various essential functions, including transportation, irrigation, aquaculture, tourism, and climate regulation, in addition to their provision of water for human consumption and agricultural purposes. This study predominantly explores the application of remote sensing technology and machine learning methodologies to classify rivers. The research is situated in Zhuhai City, characterized by a vertically distributed river network. It employs eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), both based on gradient boosting decision trees (GBDT). The data source for this investigation consists of high spatial resolution imagery from Gaofen-2 (GF-2) remote sensing satellites. The results indicate that both models achieve an overall accuracy (OA) exceeding 96.50%, with Kappa coefficients surpassing 0.92. Notably, the LightGBM model demonstrates superior performance over XGBoost in terms of both classification accuracy and operational efficiency. The utilization of remote sensing images in conjunction with machine learning techniques for the classification and monitoring of rivers offers a robust scientific framework for enhancing water resource management. Furthermore, these methodologies can assist in the formulation of effective strategies for the sustainable utilization and protection of water resources. © 2024 Proceedings of the 12th Academic Conference of Geology Resource Management and Sustainable Development, GRMSD 2024. All rights reserved.

关键词： Rivers

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