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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Chitra Sabapathy Ranganathan Arshadh Ariff Mohamed Abuthahir Riazulhameed G. Swathi Pavithra M.R S. Senthil C. Srinivasan Associate Vice President Mphasis Corporation Chennai Tamil Nadu India Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Assistant Professor Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Assistant Professor Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. Real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

关键词： data analysis Ecosystems Transforms Propulsion Nutrients Real-time systems Internet of Things

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A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords

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Heliyon 2024年第19期10卷 e38448页

作者： Turki, Houcemeddine Chebil, Khalil Dossou, Bonaventure F.P. Emezue, Chris Chinenye Owodunni, Abraham Toluwase Hadj Taieb, Mohamed Ali Ben Aouicha, Mohamed Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia SisonkeBiotik Research Community Johannesburg South Africa Department of Computer Science University of the People Pasadena CA United States Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al Kharj 11942 Saudi Arabia Mila Quebec AI Institute Montreal Canada McGill University Montreal Canada Lelapa AI Johannesburg South Africa Technical University of Munich Munich Germany Faculty of Life Sciences University of Ilorin Ilorin Nigeria

This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs. © 2024 The Authors

关键词： Biomedical relation identification Crowdsourcing MeSH keywords Open biological and biomedical ontologies PubMed Wikidata

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Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater Management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： P S Thanigaivelu A. Sairam P. Vijayan A. Suresh Kumar N. Mohankumar T. R. GaneshBabu Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Management Studies Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage.

关键词： Ethics Urban areas Water quality Prediction algorithms Real-time systems Stormwater Floods

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Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery

arXiv

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arXiv 2022年

作者： Tong, Xin-Yi Xia, Gui-Song Zhu, Xiao Xiang Remote Sensing Technology Institute German Aerospace Center Germany State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China National Engineering Research Center for Multi-media Software School of Computer Science Institute of Artificial Intelligence Wuhan University China Data Science in Earth Observation Technical University of Munich Germany

High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries caused by, e.g., geographical differences or acquisition conditions, few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 km2, the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data

arXiv

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arXiv 2024年

作者： Hartung, Michael Maier, Andreas Delgado-Chaves, Fernando Burankova, Yuliya Isaeva, Olga I. de Sá Patroni, Fábio Malta He, Daniel Shannon, Casey Kaufmann, Katharina Lohmann, Jens Savchik, Alexey Hartebrodt, Anne Chervontseva, Zoe Firoozbakht, Farzaneh Probul, Niklas Zotova, Evgenia Tsoy, Olga Blumenthal, David B. Ester, Martin Laske, Tanja Baumbach, Jan Zolotareva, Olga Institute for Computational Systems Biology University of Hamburg Albert-Einstein-Ring 10 Hamburg Germany Chair of Proteomics and Bioanalytics TUM School of Life Sciences Technical University of Munich Freising Germany Division of Tumor Biology & Immunology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam Netherlands University of Campinas Campinas Brazil University of British Columbia Vancouver Canada ACMetric Amsterdam Netherlands Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Altius Institute for Biomedical Sciences Seattle United States Simon Fraser University Burnaby Canada Vancouver Prostate Centre Vancouver Canada Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Data Science in Systems Biology TUM School of Life Sciences Technical University of Munich Freising Germany

Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches. However, existing computational patient stratification methods have been benchmarked almost exclusively on cancer omics data and only perform well when mutually exclusive subtypes can be characterized by many biomarkers. Here, we contribute with a massive evaluation attempt, quantitatively exploring the power of 22 unsupervised patient stratification methods using both, simulated and real transcriptome data. From this experience, we developed UnPaSt (https://***/unpast/) optimizing unsupervised patient stratification, working even with only a limited number of subtype-predictive biomarkers. We evaluated all 23 methods on real-world breast cancer and asthma transcriptomics data. Although many methods reliably detected major breast cancer subtypes, only few identified Th2-high asthma, and UnPaSt significantly outperformed its closest competitors in both test datasets. Essentially, we showed that UnPaSt can detect many biologically insightful and reproducible patterns in omic datasets. © 2024, CC BY.

关键词： Lung cancer

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Vision-Language Models in Remote Sensing: Current Progress and Future Trends

arXiv

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arXiv 2023年

作者： Li, Xiang Wen, Congcong Hu, Yuan Yuan, Zhenghang Zhu, Xiao Xiang The King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates The Institute of Remote Sensing and Geographic Information Systems Peking University Beijing100871 China Data Science in Earth Observation Technical University of Munich Munich80333 Germany The Munich Center for Machine Learning Munich80333 Germany

The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide intelligent solutions close to human thinking, enabling us to use general artificial intelligence to solve problems in various applications. However, in remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-language models excel, as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. Vision-language models can go beyond visual recognition of RS images, model semantic relationships, and generate natural language descriptions of the image. This makes them better suited for tasks requiring visual and textual understanding, such as image captioning, and visual question answering. This paper provides a comprehensive review of the research on vision-language models in remote sensing, summarizing the latest progress, highlighting challenges, and identifying potential research opportunities. Specifically, we review the application of vision-language models in mainstream remote sensing tasks, including image captioning, text-based image generation, text-based image retrieval, visual question answering, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. We also summarize the commonly used remote sensing vision-language datasets, codebases, and online accessible resources. Finally, we delineate the limitations of current studies and suggest potential avenues for future advancements. This review aims to provide a comprehensive review of the current research pro

关键词： Semantics

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Semantic Feature Division Multiple Access for Digital Semantic Multiple Access Channels

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Shen, Bin Ma, Shuai Chen, Rui Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China Peng cheng Laboratory Shenzhen518066 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

To solve the multi-user semantic communication network capacity bottlenecks, this paper proposes a semantic feature domain multiple access (SFDMA) scheme. By extracting and encoding semantic features and jointly processing multi-user data, the scheme can realize the encoding and transmission of multi-user semantic information in the separated feature subspace. Based on this scheme, a semantic digital multiple access network model is proposed to perform multi-user inference tasks, and an efficient semantic encoding and decoding scheme is developed by using variational information bottleneck (VIB) theory to ensure both the orthogonality in the feature domain and the classification accuracy. Furthermore, a SFDMA model for image reconstruction is designed based on the Swin Transformer. The modified model also considers the reconstruction quality and orthogonality of semantic feature domain. In addition, we design an optimization target for multiple access to implement an adaptive power allocation method for multi-user semantic communications. Simulation results show that our proposed SFDMA scheme exhibits semantic feature domain orthogonality in different applications, and the adaptive power allocation method shows excellent performance in the classification task, and effectively guarantee the quality of service (QoS) of the semantic communications. © 2015 IEEE.

关键词： Image reconstruction

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT dataset

A Horizontal Federated Learning Approach to IoT Malware Traf...

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International Conference on Advanced Communication Technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Multiscale fast correlation filtering tracking algorithm bas...

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作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

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Securing Pedestrian Crosswalks in Smart Cities: An Embedded Vision System for Pedestrian Detection and Safety Enhancement

Securing Pedestrian Crosswalks in Smart Cities: An Embedded ...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： S. Mohan Kumar S. Prabagar Elangovan Guruva Reddy N. Latha S. Chandramohan Professor and Director Research and Innovations CMR University Bangalore Karnataka India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University (Deemed University) Kanchipuram Tamil Nadu India

One of the most severe issues in the context of rapidly growing intelligent cities is the safety of pedestrians at urban crosswalks. To address this issue, the system developed an embedded vision system for real-time pedestrian detection and to enhance safety at crosswalks. A camera module captures real-time video, an image processor analyzes the captured data using a pedestrian detection algorithm, and a microcontroller manages the system's other functions. Our system can accurately identify pedestrians inside the crossing area using deep learning-based object detection models. If pedestrians are detected, an LED warning system alerts drivers to either slow down or stop, drastically decreasing the possibility of an accident. A real-time pedestrian count and any applicable safety warnings are also shown on an LCD screen. By connecting with existing systems, this smart technology may significantly improve the security of smart cities for pedestrians. Preliminary testing has demonstrated the technology has great potential for deployment in real-world urban environments.

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