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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161-170 订阅

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BioNet and NeFF: Crop Biomass Prediction from Point Clouds to Drone Imagery

BioNet and NeFF: Crop Biomass Prediction from Point Clouds t...

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Li, Xuesong Hayder, Zeeshan Zia, Ali Cassidy, Connor Liu, Shiming Stiller, Warwick Stone, Eric Conaty, Warren Petersson, Lars Rolland, Vivien Csiro Agriculture and Food Australia Australian National University Australia Csiro Data61 Australia

ISBN: (纸本)9798331510831

Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder large-scale quantification of this trait. To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery. Our BioNet, utilizing a sparse 3D convolutional neural network (CNN) and a transformer-based prediction module, processes point clouds and other 3D data representations to predict biomass. To further extend BioNet for drone imagery, we integrate a neural feature field (NeFF) module, enabling 3D structure reconstruction and the transformation of 2D semantic features from vision foundation models into the corresponding 3D surfaces. For the point cloud modality, BioNet demonstrates superior performance on two public datasets, with an approximate 6.1% relative improvement (RI) over the state-of-the-art. In the RGB image modality, the combination of BioNet and NeFF achieves a 7.9% RI. Additionally, the NeFF-based approach utilizes inexpensive, portable drone-mounted cameras, providing a scalable solution for large field applications. © 2025 ieee.

关键词： Convolutional neural networks

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Anomaly Detection in Meteorological data Using Machine Learning Techniques

Anomaly Detection in Meteorological Data Using Machine Learn...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Rawal, Uddhav Patel, Subhas Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331529833

Anomaly detection is a very important component in weather forecasting, disaster preparedness and climate modeling in meteorology. We use Density Based Spatial Clustering of Applications with Noise, Isolation Forest, Local Outlier Factor, Elliptic Envelope and One Class Support Vector Machine to detect anomalies in Tunisian meteorological data in this study. Key meteorological parameters, such as wind speed, rainfall and temperature, was analyzed as the important indicators of atmospheric conditions. DBSCAN detected local density changes very well and is therefore a good choice for anomaly detection in areas with complex spatial patterns from the models used. Isolation Forest algorithm was able to find global anomalies with robust performance, giving very high true positive rates with minimum false positives. The use of these machine learning techniques is complementary and this dual approach shows how they can be used to detect different kinds of anomalies. The results show how machine learning can be used to make environmental monitoring and weather prediction much more accurate and reliable, with the ability to detect anomalies precisely and reliably. Having such capabilities is critical for taking proactive actions in response to extreme weather events, and to be able to intervene at the right time to minimize the impacts of the events. Additionally, these models could be integrated into real time monitoring systems to provide additional utility in optimizing their use and provide actionable insight for disaster management and climate resilience. Further research could involve the fusion of these algorithms with deep learning methods, larger datasets, and real time deployment to build intelligent systems that can learn and adapt to changing environmental conditions. This would be a large advance in applying machine learning in meteorology. © 2025 ieee.

关键词： Intelligent systems

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Immersive Urban Narratives: Augmented Reality data Visualization for Smart City Engagement

Immersive Urban Narratives: Augmented Reality Data Visualiza...

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2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Vasudev, Sneha Kumari, Prerna Sawlani, Jayana Sharma, Priyanshi Madhya Pradesh India Madhya Pradesh India Madhya Pradesh India

ISBN: (纸本)9798331529833

This paper explores the transformative role of Augmented Reality (AR) in shaping smart city systems by combining four models of AR for: Real-Time navigation system for pedestrian safety, Smart retail and commerce, construction visualization and lastly for enhancing tourism. These models utilize advanced frameworks like Unity's AR Foundation, ARCore in combination with Python in order to process the data in real time to enhance the decision-making ability of the *** pedestrian navigation system focuses on pedestrian mobility and safety, guiding them through hazardous areas and optimizing traffic light interactions using real-time simulations. In smart retail, AR data visualization enhances customer experience and involvement by providing real-time product information, improving the transparency of the supply chain and preventing product damage through interactive AR markers. Meanwhile, the construction visualization model aids architects in capturing the site-specific measurements by generating boundary sketches and selecting building templates to enhance their design accuracy and reducing their resource waste. Lastly, the AR-enhanced tourism model provides an immersive city experience by overlaying historical, environmental and logistical data on real-world settings which improves both the tourist engagement and urban *** combining these models, the paper demonstrates the potential of AR to revolutionize diverse sectors within smart cities, from infrastructure planning to retail, construction and tourism. This interdisciplinary approach highlights the versatility of AR in various sectors within a smart city environment in enhancing resource efficiency, safety and user experience, making it a critical tool future urban development. © 2025 ieee.

关键词： Sales

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Attention Mechanisms in Semantic Convolutional Neural Networks for Flood Area Segmentation

Attention Mechanisms in Semantic Convolutional Neural Networ...

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

作者： Basit, Muhammad Banin Shidqi, Muhammad Dzaki Prasetyo, Simeon Yuda Purwanto, Eko Setyo School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Flood area segmentation remains a significant challenge in remote sensing, primarily due to complex textures, lighting variations, and shadow effects in flood imagery. This study introduces Attention-SCNN, a novel semantic convolutional neural network framework enhanced with spatial and channel attention mechanisms. These modules refine feature maps by prioritizing relevant regions while suppressing noise, addressing critical segmentation challenges. Using the Flood Segmentation Area dataset, the proposed model achieved superior performance metrics, including an IoU of 67.04%, Dice Coefficient of 80.26%, and Accuracy of 84.89%, outperforming state-of-the-art models such as Fast-SCNN, Mobile Fast-SCNN, and PSPNet. Comparative analyses revealed that AttentionSCNN effectively delineates flood boundaries, reduces false positives, and demonstrates robust segmentation capabilities under adverse conditions. Furthermore, its lightweight architecture ensures computational efficiency, enabling realtime flood monitoring in resource-constrained environments. These findings underscore the potential of Attention-SCNN for advancing disaster management systems and highlight directions for future research, including the integration of multi-modal data for enhanced segmentation performance. © 2025 ieee.

关键词： Convolutional neural networks

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Predicting LinkedIn Post Reactions: A Comparative Analysis of Machine Learning Models

Predicting LinkedIn Post Reactions: A Comparative Analysis o...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Jayashree, S. Livedha, S. Ragupathi, T. Hariharan, Kumaran Sanjana, R. Sagar, Shrddha Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India Galgotias University Scse India

ISBN: (纸本)9798331508685

Social media platforms have become a critical space for businesses to interact with their audience. LinkedIn, a professional networking platform, offers metrics like reactions, comments, and shares to gauge post engagement. This paper investigates a data-driven approach for predicting the number of responses to a LinkedIn post. Various features such as post content, company statistics (e.g., followers and connections), and media type are analyzed using machine learning models. The study compares the performance of multiple regression models and explores the importance of features, yielding valuable insights for optimizing LinkedIn post engagement. © 2025 ieee.

关键词： Contrastive Learning

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Fault-Tolerant Architectures for Distributed Big data Analytics 1

Fault-Tolerant Architectures for Distributed Big Data Analyt...

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

作者： Sudhakaran, Sunil Shah, Samarth Mishra, Manas Kumar Natarajan, Vignesh Bhatt, Jay Goel, Om Mahatma Gandhi University Kerala Kottayam India University at Albany AlbanyNY12222 United States G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India Arizona State University AZ United States Northeastem University BostonMA United States ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331518578

It has created challenges in the processing of data and analysis, which need to go beyond the traditional centralized architecture to solve the current data volume, speed, and types. To address these challenges, fault-tolerant architectures have become one of the keys to meeting the needs of distributed big data analytics. Failure is a normal part of systems;these architectures are meant to accommodate failures of individual components so that the entire system does not need to stop working or, worse, give false results. They make use of several nodes to operate and retain data in a distributed fashion, which supplies excessive scalability and fault tolerance. This means that even if some nodes fail, a fault-tolerant architecture can still provide redundancy by replicating data across many nodes, giving the system redundancy. Fault-tolerant architectures also utilize efficient data processing techniques like partitioning and data parallel processing, to achieve high performance. This allows real-time and near-real-time analysis of streaming data. © 2025 ieee.

关键词： data Analytics

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Integrative Automation of Cybersecurity systems through Advanced data science Methodologies: Enhancing Predictive Analytics and Real-Time Threat Mitigation 4

Integrative Automation of Cybersecurity Systems through Adva...

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4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Das, Subhranil Kumari, Rashmi Singh, Raghwendra Kishore School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of CS & AI SR University Warangal India

ISBN: (纸本)9798331530402

Due to emergence in Corona disease, the digital revolution become more intensified which makes more prevalent in day-to-day activities. As a result, cloud service providers become more frequent. Therefore, system integration is essential throughout a partner ecosystem. Unbelievably many edge devices are sending IoT data to the cloud. The system becomes automated due to the organizational structures which resulted in developing new technologies. This new" Integration ecosystem" is where there is just one platform forall the system. According tothe survey of 4, 500 decisionmakers, data protection-rather than business process optimization or processautomation-was the primary argument for employing AI withinorganizations. Using ML algorithmsto automatically identify cyberattacks is referred to as automating cyber security systems. By leveraging automation, cyber security systems can swiftly identify and respond to attacks, reducing the amount of time needed to avert possible harm. With the considerations in mind, we proposed Automating Cyber Security systems with data science Practices in which we will be talking about how various cyber security systems can effectively utilize current data science practices to automate their workflow. © 2025 ieee.

关键词： Predictive analytics

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Liver Disease Prediction Using Novel Ensemble Techniques with Catboost and LightGBM

Liver Disease Prediction Using Novel Ensemble Techniques wit...

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

作者： Udhaya, T. Moulitharan, M. Arockia Jegan, S. Akash, M. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Using Stream lit, this project proposes a web application to predict liver disease based on patient demographics and a number of biochemical markers. The developed program makes use of a CatBoost classifier that was trained on an extensive dataset that include characteristics like age, sex, and particular blood test results. The application allows users to enter their data, processes it, and then uses the learnt model to forecast the risk of liver disorders. Users can easily grasp their liver health condition because to the results' clear and easy-to-understand style. By improving early diagnosis and knowledge of liver illnesses, this technology hopes to improve health outcomes by facilitating prompt intervention. After they submit their data, the model analyses the inputs and makes predictions about the probability of different liver disorders. © 2025 ieee.

关键词： Prediction models

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Neural Network-Driven Base Station Selection in Next-Generation Cellular Networks 17

Neural Network-Driven Base Station Selection in Next-Generat...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Harrithha, B. Kotagi, Vijeth J. Thakur, Rahul Indian Institute of Technology Dharwad Department of Computer Science and Engineering Karnataka Dharwad India Indian Institute of Technology Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798331531195

With the exponential increase in data consumption from activities like streaming, gaming, and IoT applications, existing network infrastructure is under significant strain. To address the growing demand for higher data rates and better coverage, increasing base station density and exploring data offloading in unlicensed spectrum have become essential. While higher base station density reduces the distance between devices and network nodes, enhancing signal strength and data throughput, it also complicates the process of base station selection for users, particularly for those at the cell edge. Thus, ever-increasing demand for high-speed, low-latency communication services in 5G and 5G advanced New Radio (NR) unlicensed (NR-U) networks necessitates intelligent and dynamic approaches for user admission control and resource *** paper presents a novel solution employing neural network technology to address these critical challenges. We first propose an efficient algorithm for admission control and resource allocation. Upon generation of training data using the above algorithm, we propose a neural network-based framework for cellular networks in unlicensed spectrum such as LTE/LTE-U and 5G/NR-U networks. © 2025 ieee.

关键词： Base stations

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Research on Medical Efficiency of Beijing-Tianjin-Hebei Based on SBM Model

Research on Medical Efficiency of Beijing-Tianjin-Hebei Base...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Bai, Yanxin Dong, Yaru Ding, Haozhe Zhao, Chengyu Yang, Zixi Liu, Shuo Zhang, Xinpeng Song, Weiyou School of Economics and Management Hebei University of Technology 300131 China School of Artificial Intelligence and Data Science Hebei University of Technology 300131 China School of Mathematics and Statistics Hebei University of Economics and Trade 050064 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Based on the data of Beijing-Tianjin-Hebei region from 2011 to 2019,this paper measures the medical efficiency of Beijing-Tianjin-Hebei region in recent ten years by using the super efficiency SBM model, and uses the ML index decomposition method to conduct a dynamic analysis of medical efficiency, and further discusses the calculation results through convergence analysis, The medical efficiency in Beijing-Tianjin-Hebei region was studied. The study found that the overall medical efficiency in Beijing-Tianjin-Hebei region showed an increasing trend, but the increase was not large, and there was greater room for progress;The convergence of ML index changes is also high, indicating the overall development of Beijing-Tianjin-Hebei region. © 2025 SPIE.

关键词： Medical research data modeling Analytical research data acquisition Medical equipment Diseases and disorders systems modeling Biomedical applications Education and training Energy efficiency

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