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

共 86325 条记录，以下是291-300 订阅

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

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

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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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Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning

Enhancing Energy Efficiency in Wireless Sensor Networks usin...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Raskar, Sandeep Dhasmana, Gulshan Lakshminarayana, M. Patil, Harshal Bhagya Shree, S. Natrayan, L. Terna Engineering College Department of Artificial Intelligence and Data Science Nerul Maharashtra Navi Mumbai400706 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India M S Ramaiah Institute of Technology Department of Medical Electronics Engineering Karnataka Bengaluru560054 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201 306 India Brindavan College of Engineering Department of Physics Karnataka Bengaluru560063 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations. © 2025 ieee.

关键词： Deep reinforcement learning

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Developing Scalable and Fault Tolerant Distributed Architecture for Big Unstructured data systems 6

Developing Scalable and Fault Tolerant Distributed Architect...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Saranya, G. Kumaran, K. Pratheep, V. Dhanush, S.R. School of Computer Science and Engineering Vellore Institue of Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the era of big data, efficiently processing and retrieving insights from unstructured data presents a critical challenge. This paper introduces a scalable leader-worker distributed data pipeline designed to handle large sets of unstructured data blobs. Leveraging data parallelism, the system distributes workload across worker nodes, ensuring high performance for embarrassingly parallel tasks with minimal inter-process communication. Fault tolerance is achieved through an optimized next-in-line leader re-election strategy, addressing cascading failures and mitigating the thundering herd effect. The use of Apache Zookeeper for service discovery ensures seamless coordination within the cluster while maintaining a loosely coupled architecture for enhanced scalability. Google Protocol Buffers are employed for secure data transmission, offering tamper resistance against eavesdropping and man-in-the-middle attacks. The system also integrates the TF-IDF algorithm for efficient information retrieval, demonstrating the ability to achieve data parallelism while preserving task uniformity across nodes. Performance evaluation highlights significant reductions in response times with increasing cluster size, validating the system’s scalability, efficiency, and robustness. The proposed architecture is well-suited for real-world applications requiring secure, fault-tolerant, and scalable solutions for unstructured data analysis. © 2025 ieee.

关键词： Big data

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A pyDAOS Approach for Enabling Efficient data Handling in DAOS 4

A pyDAOS Approach for Enabling Efficient Data Handling in DA...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Prasad, Chandrika Geetha, J. Vinay, M. Manoj, V.L. Vamsi, Bharath Dileepa, D. Malavika Department of Computer Science and Engineering Ramaiah Institute of Technology MSR Nagar Mattikere Bangalore560054 India

High-Performance Computing (HPC) systems require efficient storage solutions to handle vast amounts of data. Distributed Asynchronous Object Storage (DAOS) is designed for such environments, offering scalable and high performance storage. Our study initially explores the capabilities and performance of DAOS using pyDAOS when integrated with a key-value store (KV store). The existing methodologies are analyzed to identify inefficiencies, particularly in handling large I/O operations. This paper proposes an enhanced approach that incorporates chunking for large I/O operations, automated resource selection, and simplified system administration via server discovery and by fetching configuration details. This results show significant improvements in efficiency, scalability, and usability of DAOS using pyDAOS, making it a robust solution for modern data intensive applications. © 2025 The Authors. Published by Elsevier B.V.

关键词： Storage efficiency

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Blockchain-Enabled Federated Learning systems with Explainable AI: A Review 2

Blockchain-Enabled Federated Learning Systems with Explainab...

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

作者： Potdukhe, Nayan Gourshettiwar, Palash Zade, Sujal Waghale, Atharva Datta Meghe Institute of Higher Education and Research Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Meghe Computer Science and Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The convergence of Blockchain with Federated Learning (FL) technology and Explainable Artificial Intelligence (XAI) serves as a new framework to solve privacy protection problems, security issues, and interpretability needs. Model training collaborations supported by FL technology allow different entities to develop models while safeguarding raw data from exposure during training processes, thus addressing privacy needs in healthcare and finance. The combined challenges of model vulnerability to malicious attacks, data poisoning vulnerabilities, and explainability issues block broad industrial adoption. Blockchains strengthen FL security systems by providing decentralised solutions that produce unchangeable audit trails through their proof of transparency, immutability, and distributed consensus models. By enabling model interpretability, XAI methods build stakeholder trust and ensure system accountability. The study combines the technologies to analyse current systems and identify gaps that integrated systems should resolve. A detailed review examines explainable blockchain models and privacy preservation methods to tackle performance and implementation limitations within the framework. Combining these technologies builds a transformative process to create secure AI systems that maintain interpretability functions and protect data privacy during system development as researchers develop frameworks for wider application deployment. © 2025 ieee.

关键词： Federated learning

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Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 ieee.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

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Cybersecurity Threat Detection in Smart Cities using Box Plot Sampling Isolation Forest 1

Cybersecurity Threat Detection in Smart Cities using Box Plo...

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

作者： Shankari, S. Uma Mohameed, Haeedir Kulkarni, Megha Aravindh, S. Purushotham, N. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as Distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT-IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively. © 2025 ieee.

关键词： Bot (Internet)

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