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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201-210 订阅

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Controlling Bias in Generative AI: Techniques for Fair and Equitable data Generation in Socially Sensitive Applications 3

Controlling Bias in Generative AI: Techniques for Fair and E...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Bhardwaj, Nimit Bhardwaj, Anvit Garg, Lavanya The Data Family Ltd. Department of Data Science London United Kingdom Jaypee University of Information Technology Department of Data Science Solan India University of York Department of Economics London United Kingdom

ISBN: (纸本)9798331508456

Generative AI models have transformed content creation by enabling high-quality image and data synthesis. However, these models often inherit and amplify societal biases present in their training datasets, leading to concerns about fairness in their applications. This paper addresses this issue by proposing a comprehensive framework for identifying, measuring and reducing bias in generative AI systems. The framework uses data reweighting, fairness-constrained training, and post-processing to enhance fairness across demographics like age, gender, and ethnicity. Using the FFHQ dataset, the framework demonstrates substantial improvements in fairness metrics, with demographic parity improving from 0.42 to 0.15 and equalized odds from 0.35 to 0.12, while maintaining competitive image quality, as indicated by a slight increase in FID from 7.21 to 8.12. Qualitative analysis confirms the generated outputs closely align with ideal demographics, highlighting the framework's effectiveness in addressing fairness without sacrificing performance, thus offering a practical solution for socially responsible AI development. © 2025 ieee.

关键词： Economic and social effects

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Anomaly Detection on Gas Pipeline Operational data Using Classic Seasonal Decomposition and Level Shift Anomaly Detection Architecture

Anomaly Detection on Gas Pipeline Operational Data Using Cla...

引用

2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Bakir, Akmal Muzakki Ihsan, Aditya Firman Astuti, Widi School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Gas pipeline infrastructure is critical to energy distribution, but its operational existence is often threatened by anomalies such as leaks, malfunctions, and system disruptions. Early detection of these anomalies is critical to prevent safety hazards, operational inefficiencies, and economic losses. This paper proposes an anomaly detection framework using Classic Seasonal Decomposition and Level Shift Anomaly Detection methods for frequency selection and labeling processes, as well as LSTM and VAE-GAN algorithms for anomaly detection. The dataset consists of time-series sensor measurements, including pressure, temperature, energy flow rate, and gas composition. Classic Seasonal Decomposition isolates trend, seasonal, and residual components, enhancing periodic anomaly detection. Meanwhile, Level Shift Anomaly Detection identifies abrupt changes in data levels that signal critical events. LSTM leverages temporal dependencies to detect anomalies with high accuracy in data with strong sequential patterns, while VAE-GAN is effective in modeling complex data distributions and capturing anomalies in datasets with less obvious patterns. Experimental results demonstrate the effectiveness of the proposed approach, with model performance evaluated using F1-Score for each LSTM and VAE-GAN model. The proposed framework shows great potential for peering into real-time monitoring systems, improving operational efficiency, supporting data-driven decision making, and significantly reducing the risk of system failure. © 2025 ieee.

关键词： Gas pipelines

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Enhancing Battery Durability: Early Life Cycle Prediction Through Machine Learning 2

Enhancing Battery Durability: Early Life Cycle Prediction Th...

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

作者： Vinod, Dasari Naga Kapileswar, N. Simon, Judy Aarthi Elaveini, M. Polasi, Phani Kumar Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India SRM Institute of Science and Technology Ramapuram Department of ECE Chennai India Chalapathi Institute of Technology Department of ECE Guntur India

ISBN: (纸本)9798331505745

In this paper, the battery life using Machine Learning Algorithms was predicted using MATLAB Software. Early and accurate predictions are important for electric vehicles, electronics, and energy storage. Traditional methods need a lot of real-time data, leading to inefficiencies. This article uses machine learning to predict battery performance early, based on factors like temperature, voltage, and current during initial charge-discharge cycles. Mechanisms such as Gradient Boosting, Random Forest, SVM, as well as Neural Networks, are tested with historical battery data to find patterns of degradation. The study also explores how well these models work for different types of batteries. © 2025 ieee.

关键词： Lithium-ion batteries

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Natural Language Processing in Geographic Information systems 3

Natural Language Processing in Geographic Information System...

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

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 ieee.

关键词： Structured Query Language

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Heart Disease Classification using Adaptive Recurrent Neural Network Method 1

Heart Disease Classification using Adaptive Recurrent Neural...

引用

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

作者： Divya, M. Karthikeyan, A. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Cardiovascular diseases are still among the leading causes of mortality, which require fast and reliable classification methods for diagnosing and preventing the disease. This work thus presents an elaborate framework involving an ARNN for heart disease categorization. First, the technique of Variational Autoencoders (VAE) is used to detect and remove irrelevant or undesirable data records, purging the dataset. Subsequent to this, the Information Gain Feature Selection (IGFS) technique is employed to select the most informative features that lower dimensionality and increase the model's accuracy. Finally, the ARNN is used for classification using the fact that it is a dynamic network that can accommodate temporal characteristics of the data. The experimental results also show that the accuracy, precision, and recall of the approach are higher and higher than those of the conventional method. The proposed framework demonstrates tremendous promise for real-world implementation in medical diagnostics and is a sound and scalable approach to heart disease prediction. Even more striking, these results stress the meaning of applying modern techniques to machine learning in healthcare-sector development. The ARNN achieve 95.6% accuracy for predict the cardiovascular disease. © 2025 ieee.

关键词： Spatio-temporal data

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Integration of smart Sensors and Machine Learning Algorithms for Automated Pest Management 1

Integration of smart Sensors and Machine Learning Algorithms...

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1st international conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Yadav, Krrish Roy, Sritama Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798331520540

This paper presents a new IoT-based approach for agricultural pest control, incorporating machine learning techniques. Traditional pest monitoring methods are labor-intensive and often result in the overuse of pesticides. The proposed system solves this problem by predicting pest populations using environmental data collected via IoT sensors. We use random forest regression and random search cross-validation to predict pests and identify important factors. The system calculates the amount of pesticides based on forecasts, helping farmers optimize use and reduce environmental impact. The experimental results demonstrate the effectiveness of the system in terms of accurate prediction and optimal pesticide application, and thereby providing a scalable and cost-effective solution for sustainable agriculture. © 2025 ieee.

关键词： Smart agriculture

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Hybrid AI-Electronic systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 ieee.

关键词： Adversarial machine learning

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Decentralized Intrusion Detection in Peer-to-Peer Networks using Stacking Ensemble Model 1

Decentralized Intrusion Detection in Peer-to-Peer Networks u...

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

作者： Devi, R. Renuga Riadhusin, Rame Anu, D. Swarnateja, R. Revathi, R. 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 Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, due to the rise in Peer to Peer (P2P) networks has brought a significant way in which the data is shared and services are offered over the internet. These P2P networks make use of decentralized architecture in which nodes directly interact and exchange resources. However, due to the use of decentralized architecture, the P2P networks are exposed to various security threats such as data breaches, and other malicious attacks. In this research, Stacking Ensemble Model (SEM) is proposed for network intrusion detection in P2P networks. Initially, the data is considered from network intrusion detection dataset and then preprocessed with data imputation and min-max normalization effectively handles the missing values and rescales the data into significant range. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features from preprocessed data based on feature importance. Finally, the proposed SEM effectively detects the intrusion in P2P networks which enhances the security. From the results, the proposed SEM model obtained a better outcome in terms of accuracy, precision, recall, and F1-score compared to the existing K-Nearest Neighbor (KNN) by accomplishing 97.20%, 94.71%, 96.62%, and 95.65%, respectively. © 2025 ieee.

关键词： Network intrusion

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Revolutionizing Healthcare: The Role of AI and Bigdata Analytics in Disease Prevention and Management 4

Revolutionizing Healthcare: The Role of AI and BigData Analy...

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

作者： Gatkine, Vaishnavi Khode, Saksham N. Gourshettiwar, Palash Computer Science & Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha440001 India

ISBN: (纸本)9798331523923

Artificial Intelligence (AI) and Big data Analytics are greatly transforming the healthcare scenario by facilitating the early detection of diseases, personalized medicine, and improved public health interventions. AI, by employing machine learning and deep learning, processes big and complex datasets, whereas Big data detects patterns and predicts risks, enhancing resource utilization and readiness for health emergencies. Yet, the technologies are faced with a range of challenges that include data security concerns, matters of integrating many systems, and the requirement of real-time processing in ever-changing healthcare environments. Although recent technological advancements such as sophisticated deep learning algorithms and decentralized data processing have been promising solutions to some of these challenges, issues of easy integration and privacy remain. This paper suggests an objective-oriented framework to enhance the application of AI and Big data solutions in healthcare to overcome current limitations and establish a more efficient, personalized, and predictive healthcare system. © 2025 ieee.

关键词： Adversarial machine learning

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Hybrid Deep Learning for Proactive Driver Risk Prediction and Safety Enhancement

Hybrid Deep Learning for Proactive Driver Risk Prediction an...

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

作者： Sakthivel, K. Kowsalya, A. Durgadevi, M. Dhaneswar, R.C. K.S.Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

A more advanced deep learning architecture that aims to improve in situ driver safety by perceiving the driving behavior and monitoring the surrounding road environment will be proposed in this study. It will employ data provided by in-car cameras and sensors to extract critical spatial features like nearby vehicles, pedestrians, and road signs in addition to temporal patterns reflecting vehicle movement, speed variations, and proximity to potential hazards. The framework integrates spatial and temporal data to identify unsafe driving behaviors such as lane deviations, abrupt speed changes, and unsafe following distances. It further predicts future risks and provides real-time alerts to the driver to prevent accidents. Extensive testing on real-world datasets demonstrates that the system significantly improves hazard detection accuracy, driver behavior monitoring, and performance with low latency, making it a highly practical solution for enhancing safety in modern vehicles. The system plays a vital role in promoting safer roadways and reducing the likelihood of accidents because it can process data in real time. © 2025 ieee.

关键词： Deep learning

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