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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321-330 订阅

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Learning Non-cooperative Driving Practices and its Impact on Road Traffic Dynamics 17

Learning Non-cooperative Driving Practices and its Impact on...

引用

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

作者： Osho Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Cooperative driving that prioritizes global safety and efficiency over self-interest is crucial in ensuring smooth traffic flow and enhanced road safety. However, experiencing noncooperative driving is common under heterogeneous and chaotic on-road traffic conditions. While non-cooperativeness is often context-dependent and assessed during direct vehicle interactions, it can be inferred through longitudinal analysis of driving patterns from vehicular kinematics data. This behavior not only adversely affects the vehicle itself but also has a cascading impact on nearby vehicles, ultimately disrupting overall traffic flow. In this study, we quantify a vehicle's non-cooperativeness using kinematic data and further model its cascading effects on nearby cooperative vehicles. Experimental analysis was conducted using the SUMO traffic simulator and the TraCI interface to assess the impact of non-cooperativeness on overall traffic dynamics. This research is among the first to examine the cascading effects of a vehicle's non-cooperativeness on its cooperative nearby vehicles. © 2025 ieee.

关键词： Motor transportation

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Sugar Cane Yield Prediction Using Drone data Processed by LSTM Algorithm 3

Sugar Cane Yield Prediction Using Drone Data Processed by LS...

引用

3rd international conference on Mechatronics, Control and Robotics, ICMCR 2025

作者： Arakawa, Kotetsu Shimizu, Ryusei Kikuchi, Shota Capi, Genci Hosei University Graduate School of Science and Engineering Dept. of Mechanical Eng. Tokyo Japan

ISBN: (纸本)9798331544546

Sugarcane is an essential crop in southwestern Japan, particularly in Okinawa and Kagoshima, where it sustains local economies and supports large-scale sugar production. Traditional yield prediction relies on labor-intensive field surveys and is increasingly challenged by a shortage of skilled labor, resulting in potential inaccuracies. This work proposes an innovative approach to sugarcane yield prediction by combining Long Short-Term Memory (LSTM) models with Genetic Algorithms (GA) to optimize model performance. Drone-based data collection methods are also explored, leveraging aerial imagery to provide additional predictive features. The proposed model demonstrates the effectiveness of integrating time-series and spatial data, offering a scalable and accurate solution for improving yield forecasting and supporting efficient operations in sugarcane production. © 2025 ieee.

关键词： 3D Map building Drone navigation Sugar cane yield prediction

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A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust Sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 ieee.

关键词： Gallium nitride

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A Comprehensive Review on Deep Learning Based Fall Detection in Elderly People 2

A Comprehensive Review on Deep Learning Based Fall Detection...

引用

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

作者： Suryanarayana, S. Venkata Lakshmi, H.N. Chiranjeevi Swamy, Posimsetti T Mahesh, D. Bhanu Hyderabad India Hyderabad India Swarnandhra College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Andhra Pradesh Narsapuram India KMIT Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505745

Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based systems, face limitations in accuracy, comfort, and practicality. Recent advancements in deep learning have significantly improved fall detection capabilities by leveraging powerful computational models for data analysis and feature extraction. With an emphasis on important methodologies, architectures, and datasets utilized in the area, this paper offers a thorough literature analysis of deep learning-based fall detection strategies. It investigates several deep learning techniques applied to sensor-based and video-based fall detection systems, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. The review also emphasizes the difficulties encountered in practical application, including data privacy and computing cost, and the need for robust datasets. Finally, future research directions are proposed to address current limitations and improve fall detection systems' dependability and effectiveness for elderly care. © 2025 ieee.

关键词： Convolutional neural networks

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GMatchBERT: BERT-Enhanced Relation Learning for Knowledge Graphs 14

GMatchBERT: BERT-Enhanced Relation Learning for Knowledge Gr...

引用

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

作者： Jain, Neelam Mehta, Krupa Department of Computer Science SVKM’s Mithibai College Mumbai India Faculty of Computer Application and IT GLS University Ahmedabad India

ISBN: (纸本)9798331531935

Learning the relations between two items is an important step in completing the knowledge graph. Knowledge graph completion typically requires numerous training examples for each relation to increase their coverage. In KGs, long-tail relations are more prevalent, and freshly inserted links generally don’t have enough training triplets. One-shot relation learning is a transformational approach in the field of knowledge graphs, tackling the issues given by data scarcity and the necessity for fast reasoning mechanisms. Existing one-shot relation learning approaches rely on embedding-based methods based on the continuous vector-space. This study proposes GMatchBERT, a one-shot relation learning method with BERT embedding. The approach uses information from BERT embeddings and integrates the embeddings with one-hop graph topologies to create a matching metric. Experiments on two sets of data show that our suggested framework surpasses the existing approach for one-shot relation learning. © 2025 ieee.

关键词： Graph embeddings

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Deep Learning to Effectively Detect and Eliminate Real-Time Network Intrusions 3

Deep Learning to Effectively Detect and Eliminate Real-Time ...

引用

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

作者： Harikesh, S. Sujatha, G. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

ISBN: (纸本)9798331527549

Network traffic has been growing exponentially for the past several years. As a result, it has become more and more difficult to detect cyber intrusions - especially in unbalanced environments, where the attacks are usually covered up by normal traffic. This investigation is set to deal with the problem by coming up with an intrusion detection system that uses a deep learning approach to effectively find and eliminate the unbalanced network intrusions. The process includes data preprocessing for balancing the classes, feature engineering for choosing optimal features, and comparison of CNN and RNN models for improving detection accuracy. The tests have shown precision of 93% and recall of 88% which in total brought 99.8% accuracy. Also, the real-time deployment has demonstrated the model’s ability to dynamically traffic patterns, which induced false positives to be reduced remarkably. The results help in the improvement of intrusion detection systems by providing a scalable, adaptive, and efficient way to handle constantly altering cyber threats. © 2025 ieee.

关键词： Network intrusion

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Enhancing Hydroponic Farming Using IoT Integration and Machine Learning Techniques

Enhancing Hydroponic Farming Using IoT Integration and Machi...

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

作者： Suresh, C. Selvabhuvaneswari, S. Kalaignarkarunanidhi Institute of Technology Department of Computer Science Tamil Nadu India

ISBN: (纸本)9798331509828

Hydroponic farming is a revolutionizing agricultural practice, in which nutrient-rich water solutions are used instead of traditional soil. Hydroponic farming addresses significant challenges faced by conventional agriculture, particularly in India, where land scarcity, inefficient water usage, and climate change pose urgent issues due to rapid population growth and urban expansion. By integrating the Internet of Things (IoT) and machine learning (ML), the proposed system presents a web based application designed to optimize hydroponic systems. Real-time monitoring enabled by IoT devices collects essential environmental data such as temperature, humidity, pH levels, and nutrient concentrations, which are critical for promoting healthy plant growth. Advanced ML algorithms like Random Forest Algorithm is used to analyze these environmental data, allowing for automated adjustments in nutrient delivery, light exposure, humidity levels, and irrigation schedules. Additionally, the system includes a specially curated dataset to enhance market analysis, improve management techniques, as well as suitable seed varieties for hydroponic crops. The results indicate that this comprehensive system not only improves crop yield and quality by 50% but also significantly decreases resource consumption, tackling the inefficiencies in traditional farming. © 2025 ieee.

关键词： Smart agriculture

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Energy Consumption Optimization using Big data Analytics for Smart Grid Management 5

Energy Consumption Optimization using Big Data Analytics for...

引用

5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Devi, K. Sumithra Contractor, Dipenkumar Deepa, V. Lakshmi, T.K. Santhikala, M. Mounika, Kamati Kcg College of Technology Department of Computer Science and Engineering Chennai India Government Engineering College Department of Computer Engineering Dahod India Sri Ramakrishna College of Arts and Science Department of Information Technology Tamil Nadu Coimbatore India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331522100

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems. © 2025 ieee.

关键词： Smart power grids

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Predictive Maintenance in Autonomous systems using Improved Support Vector Machine 1

Predictive Maintenance in Autonomous Systems using Improved ...

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

作者： Mamatha Bai, B.G. Divya, N. Sree Hamedshnain, Ammar Kumar, S. Senthil Vasumathi, T. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively. © 2025 ieee.

关键词： Predictive maintenance

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Hardware Assisted Low Latency NPU Virtualization Using data Prefetching Techniques

Hardware Assisted Low Latency NPU Virtualization Using Data ...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Jean, Jong-Hwan Kim, Dong-Sun Sejong University Dept. of Semiconductor Systems Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

With the rapid advancement of artificial intelligence and deep learning, the use of NPU (Neural Processing Unit) has expanded across various fields. While NPU enables faster processing of deep learning computations, model data's increasing size and complexity have increased demand for additional NPUs. NPU virtualization technology has been introduced to address this, allowing multiple deep learning applications to share a single NPU. This study proposes a method to reduce overhead in virtualized NPU environments by embedding a hardware scheduler on the NPU chip. This scheduler minimizes DRAM access latency during context switching by prefetching parts of the input and weight data for the deep learning model layers. Experimental results demonstrate that the hardware scheduler based NPU virtualization method significantly reduces memory cycles compared to conventional software-based NPU virtualization. © 2025 ieee.

关键词： Virtual environments

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