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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8120 条记录，以下是131-140 订阅

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Enhancing Question Answer Generation from PDFs: A Fusion of BERT, RAKE, T5 and DistilBERT with RQUGE Evaluation 5th

Enhancing Question Answer Generation from PDFs: A Fusion of ...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Mary, A. C. Akshitha Acharya, Prachi Rakshinee, R. Jeyaseelan, Stefani Nandakumar, thanvitha Sakthivel, V. Prakash, P. School of Computer Science and Engineering Vellore Institute of Technology Vandalur-Kelambakkam Road Chennai India

ISBN: (纸本)9789819788606

In educational institutes worldwide, the process of formulating questions for assessments and educational materials poses a recurring challenge. Traditional methods often involve manual question creation or keyword-based searches, leading to time-consuming and often inefficient processes, especially when dealing with extensive PDF documents. Addressing this prevalent issue, the research introduces a comprehensive approach to question–answer generation (QAG) from PDF documents. the system uses the most recent Natural Language processing (NLP) models to extract text from PDFs using pdfplumber, create document summaries using BERT, extract keywords with RAKE, and create questions using a retrained T5 model. DistilBERT is then used to generate the answers. the RQUGE metric is used to assess the system’s performance. the outcomes indicate how well the suggested method generates pertinent queries and precise responses, highlighting its potential for use in information retrieval and document comprehension. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BERT DistilBERT Information Retrieval Natural Language processing Question Answer Generation RAKE RQUGE Metric T5

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Developing and Deploying Open-Access Web-Based Educational Tools for Teaching Data Preprocessing and Classification in Bioinformatics 5th

Developing and Deploying Open-Access Web-Based Educational T...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Ottappurakkal, Bhavya Kumar, Dhanush Vijayan, Asha Radhamani, Rakhi Achuthan, Krishnashree Diwakar, Shyam Amrita Mind Brain Center Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Department of Electronics and Communication Engineering School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India

ISBN: (纸本)9789819747108

Machine learning and data mining techniques have become indispensable in the field of bioinformatics with the advancements in big data analysis such as omics, biomedical imaging, and signal processing. Toward developing a pedagogical solution for data preprocessing, this paper explores design architecture of easily accessible, free-cost novel open-source web-based virtual laboratory to simplify the data preprocessing and visualization steps for big data analysis. A user-friendly interface combines computational capabilities with HTML and JavaScript as the underlying technology. this virtual laboratory architecture was designed to be compatible for access using mobile phones, tablets, and desktop computers having an active internet connection, making the education resource usage independent of end user dependencies such as specific operating systems, installed apps or other not so easily available applications. the software implementation was created to allow student learners to preprocess and classify data, incorporating techniques like variable type conversion, normalization, standardization. With this web-based virtual laboratory, large datasets can also be preprocessed and classified that may help as education resources for students, data analysts and researchers from different domains. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Personal computers

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AI-Powered Soil Stability Assessment and Landslide Forecasting with Multi-sensor networks, Edge Computing, and Jetson Nano Integration

AI-Powered Soil Stability Assessment and Landslide Forecasti...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： N. Muthukumaran A. Vignesh S. Aruna M. Jenish T. Vinoth Kumar Shanthi Madasamy Department of ECE Sri Eshwar College of Engineering Coimbatore India Department of EEE Dr. N.G.P Institute of Technology Coimbatore India Department of EEE RVS College of Engineering and Technology Coimbatore India Department of EEE JCT College Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Landslides pose a severe threat to mountainous regions, often resulting in catastrophic damage and loss of life. Existing prediction methods rely on limited data sources and infrequent monitoring, leading to delays in early warnings. Previous technologies have explored individual sensor applications, such as Ground-Penetrating Radar (GPR) and seismic sensors, but lack a comprehensive multi-sensor approach with real-time processing. Notable cases, like the Wayanad landslides in Kerala, India, highlight the urgent need for advanced predictive systems. this study proposes an AI-driven landslide prediction system using a network of 40 sensor-equipped slave kits. Each kit integrates GPR, seismic, Electromagnetic Induction (EMI), ultrasonic, LiDAR, capacitance probes, gravimeters, Electrical Resistivity Tomography (ERT), and magnetometers onto a Teensy 4.1 microcontroller with an ESP8266 for wireless data transmission. Data is processed at a central master unit featuring a Jetson Nano module, employing the ALEXNET+BILSTM model for real-time analysis of soil stability and landslide risk. By leveraging edge computing, this system enables rapid decision-making and early warnings, addressing the limitations of existing methods. Its scalable, cost-effective architecture enhances environmental monitoring, offering a robust solution for landslide-prone communities and significantly improving disaster preparedness.

关键词： Wireless communication Landslides Wireless sensor networks Soft sensors Soil Tomography Stability analysis Real-time systems Terrain factors Edge computing

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A Review on Image processing Tecniques for In-Pipe Robots 5th

A Review on Image Processing Tecniques for In-Pipe Robots

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Suryawanshi, Shubhangi Chavan, Amol Bhosale, Digvijay G. Mahakal, Shivam Jakate, Govind Kanojiya, Abhay Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Maharashtra Pune 411 018 India Department of Mechatronics Engineering Rajarambapu Institute of Technology Shivaji University RajaramnagarKolhapur Maharashtra 415 414 India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pune 411 018 India Department of Automation and Robotics Dr. D. Y Patil Institute of Technology Maharashtra Pune 411 018 India

ISBN: (纸本)9789819788644

Pipelines are vital infrastructure for the transportation of water, sewage, oil, and gas because they offer a better safety option and fit into the minimum environmental hazardous than other modes. But pipelines are aging and getting damaged all the time, which can lead to significant loss of resources and contamination of the environment. Regular maintenance and inspections are necessary for pipelines to operate efficiently and prevent overspending. this paper reviews the core techniques used in each of the four missions—localization, mapping, navigation, and inspection—that these robots have completed. Additionally, because image processing has become a widely used and crucial strategy for completing all of the aforementioned missions, we made the decision to review the thorough classification of techniques for image processing in a separate section. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image processing In-pipe robot Mapping Navigation Pipe inspection

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Research on Conductive Rubber Array Flexible Tactile sensor Based on CNN-LSTM-Attention Hybrid Model

Research on Conductive Rubber Array Flexible Tactile Sensor ...

引用

sensors and Information Technology (ICSI), international conference on

作者： Dianchun Bai Qunyue Xue Tie Liu School of Electrical Engineering Shenyang University of Technology Shenyang China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Flexible tactile sensors have attracted significant attention in the fields of industrial robotics and healthcare. However, the fabrication of low-cost, high-stability tactile sensors remains challenging. this study proposes a tactile sensor based on a conductive rubber array, which is composed of a conductive rubber array embedded with an intermediate support layer, encapsulated by flexible printed circuits (FPCs). the sensor employs a 4×4 layout (16 channels in total) to capture time-series voltage signals as inputs for a deep neural networe. Furthermore, using a convolutional neural network (CNN) + long short-term memory network (LSTM) + Attention hybrid model, the tactile sensing array achieves a classification accuracy of 97.2 % across diverse objects. this demonstrates promising application potential in complex scenarios and fulfills the integration and scalability requirements for robotic electronic skins.

关键词： Accuracy Time series analysis Tactile sensors Voltage Feature extraction Stability analysis Rubber Convolutional neural networks Long short term memory sensor arrays

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Improving Reliability in Wireless sensor Network for sensor Nodes using Vice Cluster Head

Improving Reliability in Wireless Sensor Network for Sensor ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Amit Raikar Sahana V R Souparnika S Sukruthi Bharath V R Nakshatra Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Enhancing the operational lifespan of Wireless sensor networks (WSNs) is crucial in energy-constrained environments where recharging or replacing nodes is not feasible. Network longevity is typically evaluated based on the time to initial node failure and application-specific parameters. Over the time, Clustering has emerged as an effective strategy to improve WSN durability, minimizing communication loads by grouping nodes under a central Cluster Head (CH). However, traditional probabilistic clustering protocols often result in suboptimal CH placement, leading to imbalanced energy consumption and lacking a backup lead. this paper proposes a LEACH with Cluster Head and Vice cluster head (LEACH-CHV) method with centralized clustering approach utilizing the LEACH algorithm and fuzzy logic that employs election and implementation of the Cluster Head (CH) and Vice Cluster Head (VCH) which enhances the reliability of WSNs by ensuring continuous network operation, which mitigates the risk of communication failures due to unexpected CH depletion, promoting equitable energy distribution, extending network endurance and it's future work.

关键词： Fuzzy logic Wireless sensor networks Protocols Voting Materials reliability Routing Probabilistic logic Market research Sustainable development Optimization

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Gearbox Fault Diagnosis Using Stacked Multisensor Image Fusion and Self-Normalizing Convolutional Neural networks

Gearbox Fault Diagnosis Using Stacked Multisensor Image Fusi...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Boubker Najdi Mohammed Benbrahim Mohammed Nabil Kabbaj Systems Engineering Modeling and Analysis Laboratory Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the accurate diagnosis of gearbox faults in industrial machinery is essential for ensuring operational efficiency and preventing costly downtime. this paper introduces a novel diagnostic framework that employs advanced signal processing and deep learning techniques to elevate fault detection accuracy and reliability in gearbox systems. By leveraging stacked Synchrosqueezing Wavelet Transforms (SSWT) for multi-sensor image fusion, our approach begins with transforming raw vibration signals from multiple sensors into high-resolution Time-Frequency (TF) representations, essential for detecting subtle mechanical anomalies. these images are then processed using Self-Normalizing Convolutional Neural networks (SCNNs), which exploit their inherent normalization capabilities to maintain consistency across network layers and mitigate the vanishing gradient problem. the spatial features extracted are further analyzed through Gated Recurrent Units (GRUs), adept at modeling long-term dependencies and capturing fault progression dynamics. Enhancement of this process with a Multihead Attention layer allows selective focusing on salient features, significantly improving diagnostic precision. Validated on two public gearbox datasets, our method achieved 100% accuracy on both, advancing gearbox fault diagnostics and setting a new benchmark for industrial fault detection methods.

关键词： Fault diagnosis Deep learning Wavelet transforms Vibrations Accuracy Fault detection Signal processing Feature extraction Convolutional neural networks Image fusion

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Research and Design of an Intelligent Connected sensor Training Platform

Research and Design of an Intelligent Connected Sensor Train...

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sensors and Information Technology (ICSI), international conference on

作者： Lanlan Li Yongkui Zhu Jiajia Li Ting Zhang Nanchong Vocational College of Science and Technology Nanchong Sichuan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

this paper addresses the issues of limited functionality and insufficient scalability in intelligent connected vehicle sensor training equipment within vocational education. A sensor training platform is designed, integrating traditional and intelligent sensors, which is applicable for competitions, teaching, innovation, and research. the platform utilizes the STM32 embedded system as the main control core, combined with LabVIEW upper computer software, to construct a multi-modal data acquisition, high-precision environmental perception, and intelligent processing system. through modular hardware design, optimization of the embedded real-time system, and LabVIEW interactive design, the platform not only meets the needs of basic teaching but also supports innovative practices in engineering fields such as intelligent connected vehicles. It provides technical support for the integration of theory and practice in vocational education.

关键词： Training Technological innovation Connected vehicles three-dimensional displays Scalability Robot sensing systems Real-time systems Hardware Software Design optimization

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Survey of AI and Optimization Methods for DoS Attack Detection in WSNs

Survey of AI and Optimization Methods for DoS Attack Detecti...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Kamparapu V V Satya Trinadh Naidu S. Lakshmana Pandian Dept. of Computer Science and Engineering Puducherry Technological University Puducherry India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the use of Wireless sensor networks (WSNs) in modern IoT applications is very important, as they can monitor and transmit data in different environments. Although their open communication nature, limited energy, and computational capacity render them highly vulnerable to Denial-of-Service (DoS) attacks that will deteriorate network performance and reliability significantly. this survey covers all aspects of recent developments using artificial intelligence (AI) based techniques for the detection and prevention of DoS attacks in wireless sensor networks (WSNs). It gives particular attention to the integration of deep learning models and metaheuristic optimization strategies in terms of making their detection accuracy better, minimizing predication of false positives, and improving the model’s adaptability in the way of constraints of resource limits. Real time DoS detection has been studied under the perspective of different ensemble learning strategies, hybrid deep learning frameworks, and feature selection methods. the paper also examines the efficiency of the dimensionality reduction and heuristic tuning techniques in improving model performance. It also discusses future research directions, challenges and open issues that are meant for guiding future innovation in this domain.

关键词： Deep learning Surveys Wireless sensor networks Adaptation models Accuracy Metaheuristics Intrusion detection Denial-of-service attack Real-time systems Artificial intelligence

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Flood Forecasting System based on Water Monitoring using IoT-Machine Learning

Flood Forecasting System based on Water Monitoring using IoT...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： R. Ramya Mohamed thanseem K Vimalraj V Vinith Kumar S Electronics and Communication Engineering K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Floods are among the most devastating natural disasters, resulting in massive loss of life, widespread property damage, and overall economic disruption Conventional flood monitoring systems, which depend on manual data gathering and old models, tend to lag behind interventions this project suggests an IoT-based flood forecasting system for real-time monitoring, precise predictions, and timely warnings. IoT sensors monitor water level, rainfall, and moisture in flooded regions and send information to a cloud platform for processing. Machine learning models analyze the data, combining hydrological, meteorological, and geographic variables to improve prediction. the system employs low-power IoT sensors with communication protocols such as LoRa WAN, Zigbee, or cellular networks for real-time monitoring. Modular and scalable, it adjusts to varied environments. Visualization interfaces and mobile/web interfaces offer decision-makers and community’s clear data and timely notifications, enabling proactive disaster management, evacuation planning, and resource allocation. Simulated and tested through real-life applications, the system proves to have efficient early flood detection, minimizing damage and enhancing resilience in limited-resource areas.

关键词： Wide area networks Accuracy Rain Biological system modeling Predictive models sensor systems Real-time systems sensors Floods Forecasting

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