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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

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A Robust Design of Fault Nodes Identification and Recovery Model Over Wireless Sensor Network 9

A Robust Design of Fault Nodes Identification and Recovery M...

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9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Vijaya Vardan Reddy, S.P. Kishore, V. Vijaya Diwakaran, S. Sujatha, V. Jayakumar, T. R.M.K Engineering College Department of Ece Chennai Kavarapettai India Department of Ece Andhra Pradesh Kavarapettai India Kalasalingam Academy of Research And Education Department of Ece Srivilliputhur India S.A.Engineering College Department of Master of Computer Applications Chennai India Kcg College of Technology Department of Mechatronics Engineering Chennai India

ISBN: (纸本)9798350365092

From environmental monitoring to industrial automation, wireless sensor networks (WSNs) are essential in many different applications. However, the reliability and robustness of WSNs are often compromised by faults such as node failures, communication disruptions, and data packet losses. this paper presents a comprehensive approach to fault management in WSNs, encompassing fault detection, localization, and recovery strategies. In the fault detection phase, machine learning techniques, including the utilization of the MobileNet model, are employed to classify normal and faulty behavior based on sensor data. Statistical analysis methods, such as hypothesis testing, complement machine learning approaches to detect anomalies in sensor readings. A hybrid approach combining machine learning with rule-based systems enhances fault detection accuracy. Subsequently, fault localization techniques leverage network topology analysis and localization algorithms to identify the location of faulty nodes within the network. By analyzing the connectivity between nodes and potential communication paths, vulnerable areas are pinpointed, facilitating efficient fault localization efforts. In the fault recovery phase, dynamic reconfiguration algorithms dynamically adjust network routing and data aggregation strategies in response to detected faults. Redundancy mechanisms, dynamic reconfiguration, and load balancing techniques are deployed to mitigate the impact of node failures and ensure uninterrupted data flow. © 2024 IEEE.

关键词： Fault detection

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AI - Enhanced Hospital Language Interpretation Services using Cloud Computing for Effective Communication 9

AI - Enhanced Hospital Language Interpretation Services usin...

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9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Sree Southry, S. Mohankumar, N. thanalakshmi, S. Devi, R. Aiyshwariva Srinivasan, C. Ganeshbabu, T.R. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Nagpur Campus Pune India Gojan School of Business and Technology Department of Management Studies Redhills Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Puduvoyal Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350377972

Impaired communication between patients and healthcare professionals due to language problems may hurt the quality of treatment and patient outcomes. the objective is to develop an AI -driven., cloud-based NLP system for real-time., precise language interpretation., enabling seamless interaction in multilingual health care environments. the solution to this problem is a cloud-based language interpretation service for hospitals powered by artificial intelligence (AI). As a real-time translation service., it employs advanced natural language processing (NLP) models trained in situations and vocabulary particular to healthcare. this technology provides quick implementation., smooth interaction with current healthcare systems., and ongoing development via machine learning by using the scalability and flexibility of cloud computing. Both patients and medical staff may benefit from the intuitive interface's support for text-based and speech-based communication. Security measures are put in place to comply with regulations and safeguard patient confidentiality. Automated documenting of translated interactions is made possible by integration with electronic health records (EHR)., which helps with the continuity of treatment. Continuous improvement is guided by monitoring tools that measure system performance and user satisfaction. Enhance patient care and results by bridging language barriers in healthcare by promoting clear and accurate communication. © 2024 IEEE.

关键词： Health care accessibility Language barriers Natural language processing Patient care Real-time translation Resource optimization

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Banking credit qualification based on blockchain and zero-knowledge proofs 9

Banking credit qualification based on blockchain and zero-kn...

引用

9th International conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Xue, QingShui He, YiXun Shanghai Institute of Technology School of computer science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

In the context of the new era, the financing needs of enterprises grow significantly with the continuous expansion of their scale. However, in the financing process, small and micro enterprises often encounter challenges such as insufficient credit ratings, lack of working capital, difficulty in accessing information, and lack of guarantee mechanisms, while medium and large enterprises are more concerned about the security of confidential data. To address these challenges, this paper innovatively proposes a credit model based on zero-knowledge proof, ZKPrivateLoan, which combines blockchain technology with distributed zk-SNARK technology to not only efficiently generate proof of credentials of confidential data to ensure the security of data privacy, but also enhance the trust of commercial banks in the credit credentials of micro- and small enterprises. In addition, this paper also realizes the batch verification mechanism based on the ZKPrivateLoan model, which effectively reduces the verification time and blockchain uploading cost and provides a more efficient and secure solution for enterprise financing. © 2024 IEEE.

关键词： Blockchain Distributed Zero knowledge Proof Financing Certification

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IEEE 9th International conference on Smart Structures and Systems, ICSSS 2023

IEEE 9th International Conference on Smart Structures and Sy...

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9th IEEE International conference on Smart Structures and Systems, ICSSS 2023

ISBN: (纸本)9798350384208

the proceedings contain 121 papers. the topics discussed include: animal activity recognition using convolutional neural network;enhancing early detection of lung cancer through advanced medical imaging and automation: a focus on CT scans;accurate detection of forgery in signature using deep learning algorithm in comparison with random forest model;vehicle speed determination using haarcascade algorithm;harnessing machine learning for IoT-driven atmospheric parameter monitoring and predictive analytics;unleashing the potential of artificial intelligence and deep learning in pneumonia detection systems;performance analysis of fir filters using high speed vlsi adders for FECG monitoring;forest wild fire detection using deep learning approach;and coco-based crop protection system with integrated buzzer.

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Design of an Intelligent Voice-Controlled Home System based on Raspberry Pi 25

Design of an Intelligent Voice-Controlled Home System Based ...

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25th IEEE International conferences on High Performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Kaiyi Li, Jiaying Yu, Jiabin Hua, Yuhao Xiao, Binggang Wang, Wu College of Information Engineering China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province Hangzhou China School of Computer Science and Technology Xidian University Xi'an China School of Psychological and Cognitive Sciences Peking University Multisensory Integration Laboratory Beijing China

ISBN: (纸本)9798350330014

A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. based on the intelligent voice interaction model of Raspberry Pi 3b+ and Alibaba Cloud platform, this paper realized a high recognition accurate, stable, and efficient intelligent voice-controlled home system. We used the Raspberry Pi to control the hardware interface, collected voice data through an external microphone, and performed command control with external sensors and controllable devices in the hardware part;the software was based on Alibaba Cloud intelligent voice interaction platform to realize voice data recognition. the system constructed can achieve the expected function after repeatedly testing the controlled device. the test results show that the recognition accuracy and recognition efficiency of the system's voice control instructions are both high in a relatively quiet environment. © 2023 IEEE.

关键词： Speech recognition

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A Medical Image Segmentation Method based on Multi-scale Features and Contour Loss Constrain 36

A Medical Image Segmentation Method based on Multi-scale Fea...

引用

36th International conference on software engineering and knowledge engineering, SEKE 2024

作者： Niu, Jian Fang, Zhijun Qian, Chen Qiu, Xihe School of Computer Science and Technology Donghua University Shanghai China School of Electrical and Electronic Engineering Shanghai University Of Engineering Science Shanghai China

ISBN: (纸本)1891706594

In recent years, deep learning has made breakthroughs in medical image segmentation, especially the U-Net architecture, which is becoming a benchmark for various medical image segmentation tasks due to the accuracy of its segmentation results. Although U-Net has achieved great success in many medical image segmentation tasks, it is still unsatisfactory in segmenting object boundaries and small objects. this is due to the fact that segmentation networks gradually lose information, especially edge information and small object information, during the process of convolution and downsampling of features. In order to solve the above problems, we design a novel method that uses a multi-scale module as a feature extractor in the contraction path of the U-shaped structure, which better captures the scale changes of the target object by acquiring image features at different scales;in the prediction stage, a contour prediction branch is constructed to constrain the loss of the target's contour, so that the segmentation network pays more attention to the boundaries of the target. We have validated the performance of our method on the Automated Cardiac Diagnosis Challenge (ACDC) and the spleen segmentation tasks of the Medical Segmentation Decathlon (MSD). the results show that our method obtained the best 95% Hausdorff Distance (HD) metrics on both the ACDC dataset and the Spleen dataset, as well as being quite competitive with other state-of-the-art methods in terms of Dice scores. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Image segmentation

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Multi-Objective Optimization of Small Centrifugal Pumps for Liquid Hydrogen Transport Applications Using CFD Tools 9

Multi-Objective Optimization of Small Centrifugal Pumps for ...

引用

9th International engineering, Sciences and Technology conference, IESTEC 2024

作者： De La Hoz, Jose M. Sanchez Marquez, Juan S. Mosquera Toscano, Andres D. Rodriguez Mendoza, Wilson A. Beleno Jimenez, Jorge L. Miranda De Luque Rojas, Osvaldo E. Universidad de la Costa Department of Energy Barranquilla Colombia Universidad Del Norte Department of Engineering Barranquilla Colombia

ISBN: (纸本)9798350377156

Due to the climate change effects and high fossil fuel consumption to supply the global energy demand, scientific studies have been conducted to reduce the pollutant emissions and improve the energy generation processes. In this sense, cryogenic flow transport processes for hydrogen and electrical energy generation have promoted the application of alternative fuels under complex working conditions to reduce greenhouse gas emissions. With the aim to analyze and optimize the application of mechanical devices for liquid hydrogen transport processes, this research proposes a predictive analysis of a cryogenic pumps based on numerical simulations with CFD tools in a virtual environment. To achieve accurate results, turbulence models are applied to study and predict the liquid hydrogen behavior and its interaction with impeller blades of centrifugal pumps. In consideration with the complex analysis of turbulent and cryogenic flows, a CFD tool is computed to solve and describe the workflow behavior around the blades under different working conditions. Finally, a multi-objective optimization method based on CFD simulations is proposed to improve the impeller design of the small centrifugal pump and reduce costs with computational tools. © 2024 IEEE.

关键词： Impellers

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Transformer Digital Twin Development Framework and Functionality Demonstration

Transformer Digital Twin Development Framework and Functiona...

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IEEE International conference on High Voltage engineering and Applications (ICHVE)

作者： Wang, Tingxuan Liu, Ziyuan Wang, Zhaozheng Liu, Jing Matharage, Shanika Yasantha Wang, Zhongdong Univ Manchester Manchester M13 9PL Lancs England

ISBN: (纸本)9798350374995;9798350374988

Industries are embracing the implementation of digital twin to improve reliability and efficiency of their systems. Even though the concept of digital twin is simple, its implementation is challenging due to various issues including the detailed framework which can vary depending on the type of industry. Power transformers are one of the greatest engineering inventions in the 19(th) century that helped achieve today's highly efficient electricity transport process. they are indispensable for the reliable operation of the power network, and managing these assets to their optimal lifetime without unexpected in-service failures is vital. While various modelling approaches have been utilized in design, operation and condition monitoring of power transformers, it is expected that implementation of digital twin will incorporate various physics/knowledge based, data driven models into a single platform where data are of high fidelity, live and interchanged among models and support the best utilization of transformers with high reliability. this paper provides a demonstration of implementing a digital twin platform for power transformers through MATLAB software.

关键词： digital twin power transformer physics-based model data-driven model experience-based model

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A software Defect Prediction Method based on Multi-type Features and Feature Selection 35

A Software Defect Prediction Method based on Multi-type Feat...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Liu, Jiaxin Xu, Hao Lu, Lu Zou, Quanyi Yang, Zhanyu School of Computer Science and Engineering South China University of Technology Guangzhou China School of Journalism and Communication South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China

Numerous software defect prediction methods utilize semantic information and software metrics as code features, neglecting the structural knowledge inherent in the source code. Other studies improve feature completeness by simply combining different types of defect indicators, which causes information redundancy. To address these challenges, this paper proposes a novel software defect prediction method that incorporates multi-type features and performs feature selection. Firstly, semantic and structural features are extracted by Text Convolutional Neural Network (TextCNN) and Graph Isomorphism Network (GIN) from Abstract Syntax Tree (AST) and Program Dependency Graph (PDG), respectively, which are combined with software metrics to build a multi-type feature set. then, Recursive Feature Elimination with Cross-Validation (RFECV) integrating a novel feature importance measure is utilized to remove redundant features and generate a feature subset. Finally, a prediction model for classification is established based on the feature subset. the experiments validated the effectiveness of multi-type features and the improved RFECV. Overall our proposed method outperforms state-of-the-art techniques on nine Java open-source projects. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Abstracting

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A Robust Development to Predict Black Fungus Disease with Enhanced Learning Principles 9

A Robust Development to Predict Black Fungus Disease with En...

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9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Valarmathi, P. Vasudevan, P. Suganya, Y. Jahirhussain, T. MookambigaiCollege of Engineering Department of Computer Science Pudukottai622502 India

ISBN: (纸本)9798350365092

Presently, a very visible virus known as COVID-19 is afflicting nations all over the globe. this illness has caused several deaths and is still causing problems in most nations. the initial wave of the COVID-19 pandemic was so deadly that it devastated whole countries and devastated economies throughout the globe. In 2021, during the second wave of the COVID-19 pandemic (April-July), a potentially fatal illness known as Mucormycosis (also dubbed black fungus) was found all across the globe. By analyzing the mood of social media data, we can learn how the public views these pandemics. Fungus causes a wide range of infections that can be fatal to people, and it is very unpleasant and harmful to human health. there are many of types of fungus, and spores are always floating around. Its symptoms and signs are not specific. A fungal infection manifests itself with a variety of symptoms, including but not limited to: nasal and eye discoloration, double or blurred vision, chest discomfort, difficulty breathing, fever, and cough. Using a fungus detection system and algorithms that automatically identify fungus using machine and deep learning approaches, this study primarily aims to analyze and estimate the infection likelihood based on black fungus photos. Presenting Hybrid Learning based Fungus Identification (HLFI), a new algorithm for black fungus prediction that relies on deep learning;this paper also introduces the more traditional Convolutional-Neural-Network (CNN) as a learning model to assess the efficacy of the proposed scheme. © 2024 IEEE.

关键词： Forecasting

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