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Research on microservice gateway technology based on data processing characteristics of Service-Oriented Architecture design models 2

Research on microservice gateway technology based on data pr...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： He, Hongjie Hu, Yashan Wang, Fei Fan, Zikai Dai, Jiao Li, Wenpu Wang, Xingtao Xue, Meng State Grid Jiangsu Electric Power Co. Ltd. Economic and Technical Research Institute Nanjing210000 China State Grid Information and Communication Industry Group Co. Ltd. Beijing100052 China

ISBN: (纸本)9781510689053

With the rapid development of cloud computing, big data and IoT (Internet of Things) technologies, microservice architecture has gradually become the mainstream design model for enterprise IT (Information Technology) systems. Based on the SOA (Service-Oriented Architecture) design model, this study systematically analyzes the data processing capabilities of microservice gateways and proposes a comprehensive set of technical frameworks. The research centers around the key functions of microservice gateways, including message format conversion, data validation and filtering, as well as service routing and load balancing, to explore and innovate the technology. By implementing a unified message format standard and JSON Schema validation, this research ensures data accuracy for cross-service communication. In addition, by integrating JSON (JavaScript Object Notation) Schema and regular expressions, efficient data validation and security filtering are realized. For service management, this study employs Consul and Netflix Ribbon techniques to achieve dynamic discovery of services and load balancing. The experimental evaluation reveals the potential of the proposed technologies in enhancing data processing efficiency and system stability, providing professional guidance and practical basis for further research and application development of microservice gateways. © 2025 SPIE.

关键词： Service oriented architecture (SOA)

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval 19

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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19th international conference on Ubiquitous Information Management and communication, IMCOM 2025

作者： Kim, Ju-Chan Van Nguyen, Pham Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

This paper presents NDAS (Noise-Decomposed Abnormal Segmentation), an innovative framework for robust medical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS’s superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability. © 2025 IEEE.

关键词： Image segmentation

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Real-Time Energy Consumption Insights for Hospitals with IoT and Machine Learning 5

Real-Time Energy Consumption Insights for Hospitals with IoT...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Uthayakumar, C. Mohankumar, N. Elakkiya, K. Lakshmi, E. Rama Ganeshbabu, T.R. Srinivasan, C. Vivekanandha College of Engineering For Women Department of Electronics and Communication Engineering Tamil Nadu Tiruchengode India Symbiosis Institute of Technology Nagpur Campus Maharastra Pune India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Design Andhra Pradesh Bhimavaram India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331522100

Reducing operating expenses and minimizing environmental effects requires efficient hospital energy management. This research provides a comprehensive approach for tracking and analyzing energy consumption in real time. With the Internet of Things (IoT) and Machine Learning, especially Support Vector Regression (SVR). Energy consumption, environmental factors, and operating parameters are continually monitored by the proposed system's network of IoT sensors. A centralized cloud platform receives data streams from these sensors in real-time, with high resolution. A powerful machine learning method, SVR, processes and analyzes data. It is well known for its accuracy in predicting continuous variables. SVR models energy consumption trends, spot outliers, and predict future energy use. The predictive features of the system allow hospital managers to spot inefficiencies, forecast peak energy needs, and take proactive actions to optimize energy consumption. Energy data can be easily interpreted, and decisions can be made with the help of the system's visualization and analytics features. This system's combination of IoT and SVR offers a scalable and inexpensive method to improve healthcare facilities' energy efficiency. According to experimental data from pilot installations, there was a notable improvement in overall energy management techniques and a considerable decrease in energy waste. According to this research, modern IoT and machine learning technology may drive sustainable hospital operations and environmental stewardship. © 2025 IEEE.

关键词： Power management (telecommunication)

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A cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Breaking Language Barriers for Real-Time Translation Solutions in Tourism Using cloud computing and AI 3

Breaking Language Barriers for Real-Time Translation Solutio...

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3rd international conference on Intelligent Systems, Advanced computing, and communication, ISACC 2025

作者： Jame, S. Lourdu Nageswari, C. Shobana Jayaprakash, S. Punarselvam, E. Meenakshi, R. Muthulekshmi, M. SRM Institute of Science and Technology Kattankulathur Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Muthayammal Engineering College (Autonomous) Department of Information Technology Tamil Nadu Namakkal India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions. © 2025 IEEE.

关键词： AI-driven tourism communication enhancement Language barriers Language diversity Tourism technology Translation accuracy

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th international conference on Ubiquitous Information Management and communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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9th EAI international conference on IoT as a Service, IoTaaS 2023

9th EAI International Conference on IoT as a Service, IoTaaS...

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9th EAI international conference on IoT as a Service, IoTaaS 2023

ISBN: (纸本)9783031705069

The proceedings contain 33 papers. The special focus in this conference is on IoT as a Service. The topics include: Highway Obstacle Recognition Based on Improved YOLOv7 and Defogging Algorithm;cloud Gaming Resource Management Platform Based on Edge Intelligence;hardware-Efficient Polar Decoder for 5G Internet of Things communication;Design and Optimization of a Solar-Powered IRS and Relay Assisted MEC System;highway Image Enhancement Method Based on Jade;method and Practice of Borehole Group Detection in Borehole Drilling Based on Six-Axis Sensing;Deep Reinforcement Learning-Based Channel and Power Allocation in Multibeam LEO Satellite Systems;CSI-Based Signal Reconstruction for WiFi Localization;an Indoor Navigation Algorithm Using Multi-dimensional Euclidean Distance and the Adaptive Particle Filter;Research on the PID Controllers Algorithm of the Quad Rotor;Based on the Nonlinear State Equations of 6-DOF Robot with Application to Autonomous Flight;a Study on Collaborative Lane Change Decision Making of Multi-automated Vehicles Based on Deep Graph Reinforcement Learning;a Short Term Traffic Prediction Model Based on Deep Capture of Temporal Periodic Drift;multi-agent Reinforcement Learning for Cooperative On-Ramp Merging of Connected Automated Vehicles;SVM Multi-class Classification Method for Device Identification Using Eye Diagram Parameters;users Collaborative computing for Hierarchical Federated Learning Based on Incentive Mechanism;spatio-Temporal Traffic Prediction of Wireless communication Network Based on Multi-source Data;flying Ad-Hoc Network Routing Protocol Based on Backward Algorithm;A Novel HPSO-IGWO Algorithm for Rapidly Searching Optimal Fire Rescue Paths Based on IoT Architecture;A Hybrid WOA-MTCNN Algorithm for Accurate Face Detection Based on IoT Architecture.

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RhinoGuardNet: An Integrated Deep Learning System for Monitoring, Movement Prediction, and Threat Detection in Javan Rhino Conservation 2

RhinoGuardNet: An Integrated Deep Learning System for Monito...

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Darney, P. Ebby Fernando, A. Vegi Krishnan, R. Santhana Raj, J. Relin Francis Lavanya, V. Kumar, V. Vinoth Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India Bangalore India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331505745

The Javan rhinoceros (Rhinoceros sondaicus), one of the most critically endangered species, faces numerous threats such as poaching, habitat destruction, and limited monitoring capabilities. Traditional conservation methods are often ineffective due to the vast and difficult-to-access nature of rhino habitats. This paper proposes a Hybrid Deep Learning (HDL) framework, combining CNNs, RNNs, and GNNs, to address these challenges and enhance conservation efforts. The proposed system offers a comprehensive solution for real-time rhino monitoring, poacher detection, and habitat preservation. The framework integrates CNNs for image-based monitoring, enabling the detection of rhinos, poachers, and habitat changes using camera trap images, drone footage, and satellite images. RNNs, specifically LSTM networks, are employed to analyze bioacoustic recordings from forest microphones, capturing long-term dependencies in sound data to detect rhino vocalizations and poaching sounds like gunshots or chainsaws. In addition, GNNs are utilized to process GPS tracking data to model spatial-temporal relationships and predict rhino movement, optimizing anti-poaching patrol routes and identifying high-risk zones for poaching. The proposed framework has been assessed using various evaluation metrics such as mAP for object detection, F1-Score for audio classification, and RMSE for movement prediction. The system is designed to be deployed in the field on a cloud-based platform, enabling real-time monitoring and decision-making. Edge computing on drones and sensor devices ensures low-latency processing and immediate action in response to detected threats. This model is expected to significantly enhance Javan rhino conservation by providing accurate, real-time data and actionable insights. © 2025 IEEE.

关键词： Deep learning

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On the Challenge of Generating Multivariate Time Series Data from Distributed Sensors in IoT-enabled Scenarios 24

On the Challenge of Generating Multivariate Time Series Data...

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14th international conference on the Internet of Things, IoT 2024

作者： Bañuelos, Julián Jerónimo Costa-Requena, Jose He, Jiayuan Salim, Flora D. Sigg, Stephan Department of Information and Communications Engineering School of Electrical Engineering Aalto University Espoo Uusimaa Finland School of Computing Technologies RMIT University MelbourneVIC Australia Cumucore Oy Espoo Uusima Finland School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798400712852

Smart sensing environments supported by an Internet of Things (IoT) infrastructure are enabling a wide array of possibilities for data-intensive remote monitoring applications. Although nowadays it is possible to access and deploy complex setups of sophisticated sensors, the telecommunications infrastructures are struggling to deal with the massive amounts of generated data for transmission, processing, and storage from the diverse range of IoT devices. As data corruption and the loss of data are realistic scenarios in current IoT deployments, it is relevant to find an alternative solution that circumvents the limitations of the physical infrastructure for dealing with potential missing data in remote sensing environments. In this paper, we analyze the feasibility of reproducing missing sensor data due to a communication failure from correlated sources in the same experimental context. We rely on generative adversarial models for the prediction of acceleration data and evaluate the usefulness of the synthetic data with a standard human activity recognition (HAR) classifier. © 2024 Copyright held by the owner/author(s).

关键词： Time series

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international conference on 6G communications Networking and Signal Processing, SGCNSP 2023

International Conference on 6G Communications Networking and...

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international conference on 6G communications Networking and Signal Processing, SGCNSP 2023

ISBN: (纸本)9789819778751

The proceedings contain 34 papers. The special focus in this conference is on 6G communications Networking and Signal Processing. The topics include: Secure Clustering and Routing Based on Modified Whale Optimization Algorithm in MANET;improved Whale Optimization Algorithm Based Energy Efficient Secure Routing in Mobile Wireless Sensor Networks;resource Allocation in cloud computing Using Quasi-Opposition Learning Based Aquila Optimizer;the Quasi-Oppositional-Based Learning Aquila Optimization Routing Protocol in Energy-Aware Wireless communication;exponential-Weighted Ant Lion Optimization Based Clustering Algorithm for Wireless Body Sensor Networks;Optimized Routing Strategy in Mobile Sink Based WSN Using Multi-objective Osprey Optimization Algorithm;the Coverage Optimization in Wireless Sensor Networks Using Improved Artificial Rabbit Optimization;improved Moth Flame Optimization Based Cluster Head Selection and Data Aggregation Using Machine Learning Approach;Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN;an Effective Energy Consumption and Weighted Clustering Using Improved K-Means Clustering;adaptive Weight Based Harris Hawks Optimization for Energy-Efficient Cluster Head Selection and Routing in Wireless Sensor Networks;An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor Networks;peak-to-Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Using Improved Butterfly Optimization Algorithm;energy Efficient-Based Cluster Head Selection and Routing Protocol for Wireless Sensor Networks Using Aquila Optimizer;ensemble Artificial Neural Network and Support Vector Machine Based Parameter Evaluation in Wireless Sensor Networks;distributed Denial of Service Attack Detection in IoT Utilizing Attention Mechanism Based Long Short-Term Memory;e-commerce Payment System Empowered by Blockchain for Enhanced Security.

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