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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86302 条记录，以下是251-260 订阅

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Traffic and Pollution Control Using IoT-Enabled Smart Routing Algorithm 2

Traffic and Pollution Control Using IoT-Enabled Smart Routin...

引用

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

作者： Hammed, S Shahul Preethi, C. Haripriya, K. Pavalarajan, S. Gowtham, M.R. Krithick, N. Akshaay PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

Urbanization has led to increased traffic congestion and air pollution, primarily from vehicle emissions, posing risks to public health and the environment. Existing traffic management systems are inefficient in integrating real-time pollution data, leading to reactive control measures. This project proposes an IoT-based smart traffic management system that integrates real-time air quality monitoring with dynamic traffic control. IoT sensors collect pollution and vehicle data, which is processed using cloud computing and machine learning to optimize traffic flow and reduce emissions. AI techniques provide transparency, helping city authorities understand and trust the system's decisions. The system also offers predictive analytics for proactive pollution management and a user-friendly dashboard for real-time visualization. The solution aims to reduce urban emissions, and enhance traffic efficiency, offering a sustainable approach for modern cities. © 2025 ieee.

关键词： Traffic congestion

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 ieee.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces

Cross-Channel Unlabeled Sensing over a Union of Signal Subsp...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Koka, Taulant Tsakiris, Manolis C. Haro, Benjamín Béjar Muma, Michael Robust Data Science Group Technische Universität Darmstadt Darmstadt Germany Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Swiss Data Science Center Paul Scherrer Institute Villigen Switzerland

ISBN: (纸本)9798350368741

Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex signal structures and broadens the framework to tasks like compressed sensing. These mismatches between samples and channels often arise in applications such as whole-brain calcium imaging of freely moving organisms or multi-target tracking. We improve over previous models by deriving tighter bounds on the required number of samples for unique reconstruction, while supporting more general signal types. The approach is validated through an application in whole-brain calcium imaging, where organism movements disrupt sample-to-neuron mappings. This demonstrates the utility of our framework in real-world settings with imprecise sample-channel associations, achieving accurate signal reconstruction. © 2025 ieee.

关键词： Cross-Channel Unlabeled Sensing Sampling Sparsity Unlabeled Sensing

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An IoT-Driven Performance Evaluation Framework for Secure Healthcare Monitoring systems 4

An IoT-Driven Performance Evaluation Framework for Secure He...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aswathy, R.H. Suresh, P. Mani, C. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai Erode India

ISBN: (纸本)9798331523923

Today, all countries have a socio-economic concern about the healthcare services. Advanced healthcare systems leverage technology, such as the Internet of Things (IoT), to enhance quality and reduce costs. IoT integrates smart devices, medical professionals, and patients into a connected network. The advanced healthcare trend extends the possibilities for doctors to monitor the patient remotely and give medication during emergencies. Since it is a critical application security plays a vital role as the data transferred through the wireless environment and it is maintained digitally as a digital patient record. A major challenge in IoT healthcare includes security, privacy and power consumption. In this proposed work the secure cryptographic based MIAS server is applied for establishing the data transmission and tested over the known security attacks such as DDoS, replay attack, password guessing, sensor node spoofing etc. In contrast to alternative approaches, the proposed scheme is better suited to ensuring security and minimizing power consumption within the limited confines of IoT environments. © 2025 ieee.

关键词： Electronic health record

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Food Hazines and Tracking System for Farmers using Blockchain Technologies 1

Food Hazines and Tracking System for Farmers using Blockchai...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Jananipriya, R. Ramya, P. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of individuals. There are a number of significant issues with the current agricultural food tracking systems, including a large number of subjects, challenging interactions brought on by lengthy logistics phases, and user and centrally managed distrust of data. The traceability issue in agricultural food tracking systems is successfully resolved by the development of blockchain technology. In order to reduce the requirement for central organizations and departments and enhance the dependability, safety, and authenticity of records of transactions, this work suggests a structure based on smart contracts and consortiums to monitor and document the process of agricultural food tracking system, implement transparency and accessibility, and dismantle data centers between businesses. Farmers simultaneously store file hashes in smart contracts and record environmental and agricultural development information in the InterPlanetary File System (IPFS), which improves the safety of information and mitigates the issue of blockchain storage overflow. The proposed system demonstrated superior performance with reduced delay in response (0.035) and increased transmission speed (265), reception speed (305) and processing power (17.65) compared to Ethereum and Hyperledger Fabric. © 2025 ieee.

关键词： Blockchain

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Eco-Friendly Fabrication of Suspended 1D Nanoheaters for Ultralow Power TCD-Type Gas Sensors 38

Eco-Friendly Fabrication of Suspended 1D Nanoheaters for Ult...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Cho, Wootaek Yoo, Jihyeon Kwak, Jong-Hyun Shin, Heungjoo Korea Republic of

ISBN: (纸本)9798331508890

We developed a simple and eco-friendly fabrication method of a suspended 1D nanoheater for ultralow power thermal conductivity detector (TCD)-type gas sensors. The nanoheater was fabricated through selective metal deposition on the unique suspended carbon nanogrid, which acted as a shadow mask during the metal deposition process. Consequently, the nanoheaters can be fabricated using only a simple four-step microscale batch process (photolithography-pyrolysis-photolithography-metallization) without using any harmful etching. Due to the high surface-to-volume ratio and small size of the nanoheater, the nanoheater-based TCD-type gas sensor enabled ultrafast gas detection, facilitating pulse-width modulation-based sensor operation at 300 nW. © 2025 ieee.

关键词： Batch data processing

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Enhancing GPS/IMU Localization Accuracy in Autonomous Vehicles through Deep Learning-Based Error Correction Models 9

Enhancing GPS/IMU Localization Accuracy in Autonomous Vehicl...

引用

9th international conference on Mechanical Engineering and Robotics Research, ICMERR 2025

作者： Onyema, Uchenna Charles Mahmoud, Shafik College of Science and Engineering University of Derby Derby United Kingdom

ISBN: (纸本)9798331509262

Improving the precision of GPS/IMU localisation in autonomous cars is crucial for ensuring safe and efficient navigation. Several research studies have concentrated on enhancing the precision of localisation systems by employing sensor fusion and sophisticated algorithms. This study proposes a novel approach to enhance GPS/IMU localization accuracy in autonomous vehicles using deep learning-based error correction models. Leveraging Long Short-Term Memory (LSTM) networks, the method captures and corrects inherent errors in GPS/IMU data, leading to significantly improved positional accuracy. The LSTM model was meticulously designed to process sequential data, incorporating an LSTM layer with 50 units to capture temporal relationships and a Dense output layer to predict corrected longitude and latitude values. The model was trained using the Adam optimizer and mean squared error (MSE) loss function, achieving notable reductions in prediction error across 50 epochs. Comparative analyses between actual and predicted coordinates demonstrated the model's high precision. The model was further validated through deployment in a Flask application for continuous testing and a web application for real-time tracking of autonomous vehicles. Results underscore the potential of deep learning models to substantially improve localization accuracy, thereby enhancing the reliability and performance of autonomous navigation systems. © 2025 ieee.

关键词： Deep learning

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Pattern Detection in Abnormal District Heating data 10th

Pattern Detection in Abnormal District Heating Data

引用

10th international conference on Machine Learning, Optimization, and data science, LOD 2024

作者： Mbiydzenyuy, Gideon Sundell, Håkan University of Boras Boras501 90 Sweden

ISBN: (纸本)9783031824807

data generated by industrial systems such as District Heating (DH) often lack meaningful labels for supervised Machine Learning (ML) methods in anomaly detection. Consequently, unsupervised and semi-supervised ML methods are widely used. These methods frequently uncover numerous anomalies, necessitating labor-intensive post-processing. This paper proposes an algorithm to detect topK anomaly instances with similar patterns (energy signatures) to known anomalies, and to identify clusters of similar anomalies using hierarchical clustering. Similarities between anomaly instances are computed using Dynamic Time Warping and Matrix Profiles. Generative Adversarial Networks (GANs) are employed to augment small anomaly datasets. Results demonstrate the effectiveness of the proposed algorithm in reducing the manual effort required for post-processing anomalies in a DH dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Federated Clustering Defense: Mitigating data Exposure in Collaborative Learning

Federated Clustering Defense: Mitigating Data Exposure in Co...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Radhakrishnan, Anuja Krishna, Sathya Bama Sathyabhama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331507541

The development of intelligent industrial applications is being driven globally by advancements in the next generation of Internet of Things (IoT) technologies. At the same time, artificial intelligence (AI) technologies like machine learning and deep learning are becoming more widely used. Conventional machine learning models primarily rely on large data sets. However, the vast data sets generated by network-edge devices are expensive, inefficient to acquire and handle, and they pose major privacy hazards. Federated learning (FL) is a novel paradigm for statistical model training on distributed edge networks that allows data to take part in the training of federated models without being localized. Traditional machine learning issues with low data usage, data privacy, and information security brought on by data isolation can be resolved with this method. We propose a novel Federated Clustering with Differential Privacy (FedCDP) algorithm designed to efficiently model user behavior in an unsupervised manner. This algorithm focuses on optimizing computation and storage requirements while minimizing data leakage through robust differential privacy mechanisms. © 2025 ieee.

关键词： Deep learning

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Real-Time Manufacturing data Quality: Leveraging data Profiling and Quality Metrics 10

Real-Time Manufacturing Data Quality: Leveraging Data Profil...

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10th international conference on Internet of Things, Big data and Security, IoTBDS 2025

作者： Peixoto, Teresa Oliveira, Bruno Oliveira, Óscar Ribeiro, Fillipe CIICESI School of Management and Technology Porto Polytechnic Portugal JPM Industry Portugal

ISBN: (纸本)9789897587504

Ensuring data quality in decision-making is essential, as it directly impacts the reliability of insights and business decisions based on data. data quality measuring can be resource-intensive, and it is challenging to balance high data quality and operational costs. data profiling is a fundamental step in ensuring data quality, as it involves thoroughly analyzing data to understand its structure, content, and quality. data profiling enables teams to assess the state of their data at an early stage, uncovering patterns, anomalies, and inconsistencies that might otherwise go unnoticed. In this paper, we analyze data quality metrics within Industry 4.0 environments, emphasizing various critical aspects of data quality, including accuracy, completeness, consistency, and timeliness, and showing how typical data profiling outputs can be leveraged to monitor and improve data quality. Through a case study, we validate the feasibility of our approach and highlight its potential to improve data-driven decision-making processes in smart manufacturing environments. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： data accuracy

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