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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141-150 订阅

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Comparative Analysis of Traditional and Ensemble Learning Models for Network Security System

Comparative Analysis of Traditional and Ensemble Learning Mo...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Verma, Anand Rathod, Maya Oriental University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331512965

In this paper use CIC-IDS2017 dataset to illustrate a comparative analysis of traditional and proposed models for intrusion detection in network security systems. The comparison includes DT, RF, ET and XGBoost classifiers. Normalize class, SMOTE data preprocessing to compensate for missing values and of imbalance classes From the results of evaluation metrics, we conclude that proposed methods show a significant improvement on its model performances in detecting network intrusions as compared to traditional models XGboost specifically this work. Out of all the algorithms, the accuracy for XGBoost stands out, as it is mostly known to cater to complex attack patterns. The results indicate that ensemble base techniques offer the best solutions which can prove to be both effective and efficient as an intrusion detection systems for current network technologies. © 2025 ieee.

关键词： Network intrusion

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 ieee.

关键词： Smart manufacturing

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Machine Learning in Healthcare: A Review of Applications, Opportunities and Challenges 2

Machine Learning in Healthcare: A Review of Applications, Op...

引用

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

作者： Thute, Trupti Saratkar, Saniya Chaudhari, Aarti Raut, Rohini Thakare, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This article explores the potential of machine learning (ML) in transforming healthcare. It provides an overview of ML and its various categories, highlighting how supervised and unsupervised learning algorithms can be utilized for tasks like disease diagnosis, treatment personalization, and resource allocation. The article delves into specific applications of ML in healthcare, including image analysis for early disease detection, visualization of complex medical data, and personalized medicine approaches. It further explores the exciting opportunities presented by ML, such as improved diagnostics and treatment, increased efficiency and cost reduction, and advancements in research and development. However, the article also acknowledges the challenges associated with implementing ML in healthcare, including data privacy concerns, real-time data requirements, and the complexity of certain models. Overall, the article emphasizes the multifaceted landscape of ML in healthcare, showcasing its potential to revolutionize the industry while acknowledging the hurdles that need to be addressed for successful integration. © 2025 ieee.

关键词： Theranostics

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Alam, Mahfooz Ashraf, Zubair Singh, Priya Pandey, Bishwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems. © 2025 ieee.

关键词： Network intrusion

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Blockchain for Patient-Centric Solutions Using Distributed Ledger Technology 2

Blockchain for Patient-Centric Solutions Using Distributed L...

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

作者： Sandhya, G. Vaishnavi, T. Subashree, M. Naveen Kumar, A. Sri Krishna College of Technology Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Blockchain technology, with its decentralized and tamper-resistant nature, has the potential to revolutionize healthcare by improving data security, reliability, and interoperability. By leveraging encryption methods such as Attribute-Based Encryption (ABE), patient records can be securely stored and protected from unauthorized modifications or breaches. Additionally, the integration of smart contracts and standardized data formats enables seamless communication across diverse healthcare systems, fostering a more connected and efficient ecosystem. This patient-focused model enhances data privacy while granting individuals greater control over their medical information. However, the adoption of blockchain in healthcare encounters several challenges, including regulatory hurdles, scalability issues, implementation costs, and acceptance by users. Moreover, integrating blockchain with existing infrastructures and ensuring cooperation among stakeholders adds further complexity. Despite these obstacles, research underscores blockchain's potential to redefine healthcare data management. Addressing these challenges can pave the way for a more secure, interoperable, and patientdriven healthcare system in the future. © 2025 ieee.

关键词： Information management

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USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions

USV-AUV Collaboration Framework for Underwater Tasks under E...

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

作者： Xu, Jingzehua Xie, Guanwen Wang, Xinqi Ding, Yimian Zhang, Shuai Tsinghua Shenzhen International Graduate School Tsinghua University China College of Information Science & Electronic Engineering Zhejiang University China Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)9798350368741

Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone often face challenges in harsh and extreme sea conditions. This study introduces a unmanned surface vehicle (USV)-AUV collaboration framework, which includes high-precision multi-AUV positioning using USV path planning via Fisher information matrix optimization and reinforcement learning for multi-AUV cooperative tasks. Applied to a multi-AUV underwater data collection task scenario, extensive simulations validate the framework's feasibility and superior performance, highlighting exceptional coordination and robustness under extreme sea conditions. To accelerate relevant research in this field, we have made simulation code (demo version) available as open-source. © 2025 ieee.

关键词： Autonomous underwater vehicle Extreme sea conditions Fisher information matrix Reinforcement learning Underwater tasks Unmanned surface vehicle

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Real-Time Blood Pressure Monitoring and Hypertension Prediction based on Multitask Temporal Convolutional Network 1

Real-Time Blood Pressure Monitoring and Hypertension Predict...

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

作者： Musnmusawi, Muntather Deepa, R. Shankar, Smitha Sharath Nirupashri, G. Khanam, P. Katheeja The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq St.Joseph's Institute of Technology Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Bio Medical Engineering Mamallapuram India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In recent days, blood pressure monitoring and hypertension prediction have become essential for effective measurement and management. Continuously tracking blood pressure helps in making informed decisions like changing medication or dosage. However, some challenges are raised while monitoring such as blood pressure readings being prone to errors, variability in measurements if a patient moves and environmental noise. Therefore, this research proposes Multitask Temporal Convolutional Network (MTCN) for realtime blood pressure monitoring and hypertension prediction. Initially, data is collected from Medical Information Mart for intensive Care III (MIMIC-III) and preprocessed by using Min- Max Normalization which rescales numeric data to definite range. After that, features like time frequency coefficients, power spectral density, Scalogram features are extracted by using Continuous Wavelet Transform (CWT). Finally, blood pressure monitoring and hypertension prediction is done by using proposed MTCN which provides accurate hypertension prediction and personalized risk assessments. The proposed MTCN achieved better results in terms of Mean Absolute Error (MAE) of 2.01, Root Mean Square Error (RMSE) of 3.26 when compared with existing Random Forest Regression model. © 2025 ieee.

关键词： Wavelet transforms

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An Interactive Evaluation Framework for Empathetic Response Generation

An Interactive Evaluation Framework for Empathetic Response ...

引用

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

作者： Lei, Xixi Li, Changqun He, Liang Lin, Xin School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Empathetic response generation is a significant domain in Natural Language Processing (NLP). Its development is a critical step toward achieving humanized AI systems. However, current evaluations of empathetic dialogue models are primarily single-turn and static, leading to bias between evaluation results and real-world multi-turn interaction performance. To overcome the longstanding challenge, we propose a novel Interactive Empathy Evaluation Framework (IEEF). It eliminates the bias by facilitating a human-free multi-turn interaction evaluation. Specifically, for human-free interaction, we design a user simulator using reinforcement learning, leveraging a reward model based on LLM scoring. For evalution, we introduce a series of empathy-related metrics based on LLM. The experiments show that IEEF's evaluation results are highly correlated with real-world multi-turn interaction performance, demonstrating its alignment with human preferences in empathy evaluation. © 2025 ieee.

关键词： Dialogue System Empathetic Dialogue Empathy Metrics LLM Scoring Natural Language Processing

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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 ieee.

关键词： Convolutional neural networks

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Density-Adaptive Fuzzy Clustering with Isolation Kernel

Density-Adaptive Fuzzy Clustering with Isolation Kernel

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

作者： Tiwari, Paritosh Kachroo, Rankit Rathore, Punit Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bengaluru India

ISBN: (纸本)9798350368741

Fuzzy clustering algorithms have become essential tools in data analysis, yet they face significant challenges when dealing with complex and real-world datasets. Fuzzy C-Means (FCM) and its variants, such as Kernel FCM, Robust FCM, and Robust Kernel FCM struggle with clusters of varying densities and shapes, often due to their reliance on fixed distance metrics and sensitivity to initial conditions. These limitations underscore the need for a more versatile and adaptive clustering approach. This paper introduces the Isolation Kernel Fuzzy C-Means (IK-FCM) algorithm, a novel method designed to address these shortcomings. IK-FCM innovatively integrates the Isolation Kernel's density-adaptive similarity measure with FCM's soft clustering capabilities, enabling more accurate and flexible clustering across diverse data distributions. Through comprehensive evaluations against standard FCM, Kernel FCM, Robust FCM, and Robust Kernel FCM, IK-FCM demonstrates superior clustering quality and accuracy on both synthetic and real datasets, as measured by Normalised Mutual Information (NMI). The algorithm's effectiveness is further validated through its application to brain tissue segmentation, highlighting its potential in critical real-world tasks. By effectively addressing the limitations of existing fuzzy clustering techniques, IK-FCM represents a significant advancement in unsupervised learning, offering improved accuracy and adaptability across a wide range of data challenges. © 2025 ieee.

关键词： Clustering Fuzzy C-Means (FCM) Isolation Kernel Segmentation

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