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

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Automated Inter-Hospital Communication for Bed Allocation 4

Automated Inter-Hospital Communication for Bed Allocation

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

作者： Akila Priya Varshini, M. Kashvi, R. Pushpa Varshini, A. Nirmala, V. Vidhya, V. Dept of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331523923

This paper presents an automated inter-hospital communication system for efficient bed allocation during pandemics, featuring role-based access for secure data handling. The system leverages real-time data integration, predictive analytics, and AI-driven optimization to streamline bed allocation across hospitals. The role-based access makes sure that only authorized personnel can access information, enhancing data security and operational transparency. This approach addresses common inefficiencies in current systems, reducing patient wait times and improving resource management in high-demand scenarios, ultimately enhancing crisis response capabilities. © 2025 ieee.

关键词： data integration

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A Robust Online Miscalibration Detection and Correction Method for LiDAR-Camera

A Robust Online Miscalibration Detection and Correction Meth...

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

作者： Pan, Feng Wang, Wei Zhang, Jianing College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350368741

The increasing reliance on multi-sensor systems to enhance the robustness and accuracy of robotic systems introduces new challenges, particularly in calibrating multiple sensors. Traditional offline calibration techniques, which use targets like checkerboards, assume that extrinsic parameters between sensors remain constant during operation. However, this assumption does not account for drift caused by external factors such as vibration and deformation over time. In this letter, we introduce CalibOnline, a novel method for the online detection and correction of miscalibration in multi-sensor setups. First, we propose a unified representation of LiDAR and camera data as depth maps to reduce calibration uncertainties due to data modality discrepancies. We then explore the robust characteristic of depth discontinuity edges to facilitate efficient matching between depth maps. The impact of extrinsic variations on edge-matching constraints is analyzed, leading to the design of a miscalibration detection module to monitor extrinsic parameters. Finally, we frame the correction of extrinsic parameters as an on-manifold optimization problem, enhancing the convergence of the estimated parameters. Experimental results across various scenarios demonstrate the robustness and accuracy of our proposed method. Code is open-sourced at: https://***/cchester25/***. © 2025 ieee.

关键词： calibration miscalibration correction miscalibration detection on-manifold optimization

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Federated Smoothing ADMM for Quantile Regression with Non-Convex Sparse Penalties

Federated Smoothing ADMM for Quantile Regression with Non-Co...

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

作者： Mirzaeifard, Reza Werner, Stefan Department of Electronic Systems Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350368741

In decentralized systems like the Internet of Things (IoT) and cyber-physical networks, where data are distributed across multiple nodes, ensuring accurate and robust data analysis is crucial. Existing methods for penalized quantile regression often struggle with asynchronous operations and multiple updates per node, leading to inconsistencies across these distributed nodes. To address these challenges, we propose the Federated Smoothing ADMM (FSAD) algorithm, which integrates non-convex sparse penalties - specifically, the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)-to effectively identify significant predictors while retaining sparsity. By incorporating a total variation norm within a smoothing ADMM framework, FSAD supports asynchronous updates and ensures model consistency across nodes, thereby overcoming traditional convergence limitations in non-convex, federated settings. Our theoretical analysis provides rigorous convergence guarantees, and extensive simulations confirm that FSAD outperforms existing methods in terms of both accuracy and computational efficiency. © 2025 ieee.

关键词： Federated learning non-convex and non-smooth sparse penalties quantile regression smoothing techniques

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Explainable AI-Powered Networking Intrusion Detection systems 3

Explainable AI-Powered Networking Intrusion Detection System...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Senthamizh, G. Anunya, G. Malathy, C. Jagadesh, R. Prithiv Sri Prasanth, C.S. Department of Networking and communication Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Networking Intrusion Detection systems (NIDS) are essential for identifying and mitigating cyberthreats in modern Networking infrastructures. Modern NIDS still struggle with scalability, data imbalance, and high false positive rates despite significant advancements. This paper proposes a system that integrates state-of-the-art machine learning (ML) methods with Explainable AI (XAI) approaches to improve the efficacy, accuracy, and interpretability of NIDS. The study evaluates existing systems, identifies their flaws, and proposes an enhanced architecture that includes real-time threat adaptation, scalable processing, and an intuitive dashboard for security analysts. The experiment’s results demonstrate a significant drop in false positives, an increase in detection rates, and improved user insights into system selections. © 2025 ieee.

关键词： Network intrusion

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Scalable Performance Testing for Distributed Big data Frameworks 1

Scalable Performance Testing for Distributed Big Data Framew...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Ramdass, Karthikeyan Mulka, Arun Agarwal, Nitin Avvaru, Sugeetha Gupta, Gopal Kumar Chauhan, Sansar Singh Anna University Chennai India Kakatiya University Telangana Warangal India Galgotia's College of Engineering and Technology Uttar Pradesh Technical University Uttar Pradesh India Nagpur Campus Pune440008 India G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798331518578

The development of distributed Big data frameworks relies heavily on performance testing because this part is what decides the robustness and scalability of systems. There is no scale performance testing methods that could support the enormous volume of data in the new era of Big data frameworks. Thus, a scalable performance testing approach can monitor the system's performance and expose potential bottlenecks. The suggested solution is a Scalable Performance Testing framework to test big-scale available systems. The framework is built on a distributed architecture that models real-world scenarios and creates a high-volume workload for testing. Think of it as a driver-assist that uses one of the most advanced data generation techniques to generate data sets that look like real-world data sets, making it easier to measure performance. SPT is flexible, extensible, and will be out-of-the-box compatible with most of the Big data frameworks and deployment scenarios. Such scalability guarantees that the framework is adapted for the volatility of Big data environments. Also, it remains a reliable tool for performance measurements in the long run when the system scales. © 2025 ieee.

关键词： Development Framework Generation Performance Techniques

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AES Efficient Encryption of Multimedia data using Threads for 5G System 2

AES Efficient Encryption of Multimedia Data using Threads fo...

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

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Kumar, Nathi Kranthi Venkateswaramma, Jarabhala Nirosha, Chelumala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India St. Martin's Engineering College Dept. of Artificial Intelligence and Machine Learning Hyderabad India Hyderabad India Secunderabad India St. Martin's Engineering College Department of EEE Telangana Secunderabad500 100 India

ISBN: (纸本)9798331505745

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method. © 2025 ieee.

关键词： Sensitive data

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An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 ieee.

关键词： Java programming language

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Deep Support Vein Machine for Lung Parcellation

Deep Support Vein Machine for Lung Parcellation

引用

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

作者： Peng, Haichao Fang, Hao Fan, Wenkang Wang, Yong Ke, Sunkui Luo, Jie Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China Zhongshan Hospital Xiamen University Xiamen361004 China Massachusetts General Hospital Harvard Medical School Boston02114 United States

ISBN: (纸本)9798350368741

Pulmonary segments parcellation is essential to thoracoscopic segmentectomy. Surgeons manually outline pulmonary segments from preoperative images before surgery, which is a time-consuming, labor-intensive and mental-stress procedure. This work proposes a novel small learning model of deep support vein machine without using annotated pulmonary segments data for automatic lung parcellation. Specifically, this machine can learn anatomical structures of pulmonary lobe, bronchus, artery, and vein by two cascade multilayer perceptrons to automatically divides the lung into eighteen segments. The perceptron module typically smooths the boundary of the pulmonary segment to attain robust and precise parcellation. Additionally, three new metrics are defined to quantitatively evaluate the quality of lung parcellation. We validate our methods on 108 clinical pulmonary computed tomography scans, with the experimental results showing that our proposed machine certainly outperforms current methods and provides a promising way to fully automated lung parcellation. Particularly, the dice similarity coefficient of lung parcellation was significantly improved from 0.886 to 0.918. © 2025 ieee.

关键词： Lung Segmentectomy Multilayer Perceptron Pulmonary Segments Video-Assisted Thoracoscopic Surgery

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Research on Mechanical Automation Control based on Bidirectional Long Short-Term Memory 1

Research on Mechanical Automation Control based on Bidirecti...

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

作者： Song, Xiaolu Zaozhuang Vocational College of Science & Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9798331529246

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability. © 2025 ieee.

关键词： Three term control systems

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Design and Implementation of a data Encryption Framework Simulink Model for Protecting Sensitive Organizational data 1

Design and Implementation of a Data Encryption Framework Sim...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Patchigolla, Veera Lakshmayya Clarksburg MD United States

ISBN: (纸本)9798331518578

Internet-of-Medical-Things (IoMT) based sensitive data protection has become a critical problem lately. IoMT-sensitive data are used to connect patients with adults. Since every record, data, and healthcare signal is transmitted over these systems, they need strong and safe access methods for individuals to transmit their essential statistics or documents. Because of this, breaching these systems could have negative impacts on patients. An emerging pattern in biometric verification is to prevent using authentic biometrics for security purposes. Cancelable digital fingerprints can be created using a data encryption framework or non-invertible transformations. This study presents an integrated structure for cancelable ECG signal detection that is suitable for use in IoMT networking access steps. The suggested system uses fuzzy reasoning to perform a non-invertible transition on ECG data, altering its shifting spectrum. Cancellable biometric platforms aim to retrieve original ECG data from processing variants, but the procedure is non-invertible and hinders this goal. Next, customized trends are used to build compact encryption using XOR. This eliminates the complication of comprehensive encryption systems requiring significant computing resources. By combining encryption techniques and non-invertible transformations, the encryption phase improves the safety of cancelable biometrics features and permits the mixed nature of the suggested cancelable biometrics architecture. For the actual adoption of the suggested ECG-based cancelable biometrics identification structure, hardware adaptation is also shown. According to empirical findings, the suggested structure performs well, with an Equivalent Error Rate (EER) of 0.059%. © 2025 ieee.

关键词： Electrocardiography

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