检索结果-内蒙古大学图书馆

Advanced Wildfire prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations

IEEE ACCESS 2024年 12卷 191733-191747页

作者： Jadouli, Ayoub El Amrani, Chaker Abdelmalek Essaadi Univ Fac Sci & Technol Dept Comp Sci & Smart Syst Tangier 90000 Morocco

This study introduces a novel dataset for wildfire prediction in Morocco, integrating multisource observations to address the country's unique geographical and climatic challenges. We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. Our best-performing models achieve an accuracy of up to 90%, significantly improving upon traditional approaches. The key contributions include: 1) A localized dataset tailored to Morocco's conditions;2) benchmarking of advanced ML and DL algorithms;and 3) open sharing of the dataset and codebase. We discuss the potential applicability of our methodology to other regions and highlight future research directions. This work advances dataset creation techniques and emphasizes the importance of localized research for effective wildfire management strategies in underrepresented areas.

关键词： Wildfires Deep learning Predictive models Feature extraction Data models Accuracy Population density Meteorology Vegetation mapping prediction algorithms machine learning wildfire prediction satellite observations ground stations Morocco NDVI soil moisture

来源：评论

学校读者我要写书评

暂无评论

Driving Behavior prediction Based on Combined Neural Network Model

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第3期11卷 4488-4496页

作者： Li, Runmei Shu, Xiaoting Li, Chen Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Accurate behavior prediction of surrounding vehicles can greatly improve the operating safety of autonomous vehicles. However, in real traffic scence, the complexity and uncertainties of traffic flow bring great challenges to driving behavior prediction. This article proposes a driving behavior prediction model using a wide-deep framework that combines gradient boosting decision tree (GBDT), convolutional neural network (CNN), and long short-term memory network (LSTM) algorithm to fully mine driving behavior characteristics while improve interpretability of the CNN-LSTM model. The GBDT algorithm can quantitatively describe the interaction between the autonomous vehicle and its surrounding vehicles during the driving process, obtaining a series of driving behavior rules, and integrating the driving behavior rule features into the CNN-LSTM neural network. The CNN-LSTM neural network model is constructed to find the spatial features in driving trajectory by CNNs and the temporal features by LSTM networks. The accuracy of the driving behavior prediction model is further improved. Simulation experiments show the rationality and validity of themodel and algorithm.

关键词： Behavioral sciences Predictive models Convolutional neural networks Hidden Markov models Feature extraction Neural networks prediction algorithms driving behavior prediction gradient boosting tree algorithm long short-term memory neural network wide-deep

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer prediction Using Shapely and Game Theory in Federated Learning Environment

引用

IEEE ACCESS 2024年 12卷 123018-123037页

作者： Supriya, Y. Chengoden, Rajeswari Vellore Inst Technol Vellore 632014 Tamil Nadu India

Breast cancer is a critical health issue affecting the well-being of women. Breast cancer is one of the most common causes of the increase in the mortality rate of women around the world. Early detection of breast cancer can, to some extent, decrease the number of deaths caused and also improve treatment outcomes and patient survival rates. Traditional Machine learning (ML) and Deep learning (DL) approaches have proven to be more successful in predicting breast cancer. In terms of privacy and early detection, Federated Learning (FL), a decentralized ML approach, offers a promising solution for training predictive models on distributed healthcare data while ensuring privacy and security. This paper proposes a novel framework that combines the benefits of integrating Shapley values and game theory concepts with FL for breast cancer prediction. The framework uses Shapley values for feature selection from 30 features of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine machine learning repository (UCI ML). The framework also addresses the issue of poor-performing clients by introducing a payoff mechanism based on individual client accuracy. Clients with higher accuracy are given greater influence in the model aggregation process, encouraging client competition and improving the overall model performance. Our framework proves to be promising by achieving a prediction accuracy of 94.73% in the FL environment. The proposed approach provides a privacy-preserving solution for breast cancer prediction in an FL environment, by combining Shapley values and game theory. The results of this study can help in the development of more accurate and robust breast cancer prediction models, contributing to improved patient outcomes and healthcare decision-making.

关键词： Breast cancer Game theory Predictive models prediction algorithms Data privacy Accuracy Data models Federated learning Machine learning machine learning Shapley values game theory incentive mechanism

来源：评论

学校读者我要写书评

暂无评论

RTAP: A Real-Time Model for Attack Detection and prediction in Smart Grid Systems

引用

IEEE ACCESS 2024年 12卷 130425-130443页

作者： Salehpour, Ali Al-Anbagi, Irfan Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada

One main challenge of smart grid systems is the cascading failures caused by cyber-attacks, which can affect the power and communication networks. Many testbeds have been proposed to model the impact of cyber-attacks on these two networks;however, many lack a realistic propagation model or simulate real-time behavior. In this paper, we develop a novel real-time testbed that models both the power and communication networks to analyze cyber-attacks' impacts on smart grid systems. Our proposed testbed can model various cyber-attacks on both networks and analyze the propagation of failure within the system. To create a realistic model of smart grid systems, we utilize real-time simulators and implement a failure propagation model. Using this testbed, we propose a prediction model to detect and predict failures after cyber-attacks have impacted the system. This model can detect cyber-attacks in the early stages of failure propagation and predict the state of each power and communication component following the propagation. We prove this model is realistic using the failure propagation factor and validate its effectiveness by employing an IEEE 14-bus test case, showcasing its high accuracy in detecting various types of attacks.

关键词： Smart grids Cyberattack Real-time systems Communication networks Predictive models prediction algorithms Power systems Machine learning Failure analysis Cascading failures cyber-attacks early stage failure prediction failure propagation interdependencies machine learning NS3 real-time simulator RTDS smart grid systems

来源：评论

学校读者我要写书评

暂无评论

Human Motion prediction: Assessing Direct and Geometry-Aware Approaches in 3D Space

引用

IEEE ACCESS 2024年 12卷 104643-104662页

作者： Idrees, Sarmad Kim, Jiwoo Choi, Jongeun Sohn, Seokman Yonsei Univ Sch Mech Engn Seoul 03722 South Korea Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea Korea Elect Power Res Inst Power Generat Lab Daejeon 34056 South Korea

Predicting 3D human motion is a complex task, owing to the unpredictable nature of human movements. The influx of deep learning innovations and the availability of extensive datasets have intensified research interest in this field. This survey provides an exhaustive review of human motion prediction algorithms and categorizes them according to their core architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Convolutional Networks (GCNs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, and Equivariant Neural Networks (ENNs). Our key contribution is a systematic presentation of the latest prediction methodologies, classified into direct and geometry-aware modeling. We begin with the problem formulation of human motion prediction, explore assorted techniques, and discuss data representation, accompanied by a list of accessible datasets. We also identify and analyze the ongoing challenges and limitations of the current algorithms, offering insights into potential future developments in this domain.

关键词： Task analysis Predictive models prediction algorithms Three-dimensional displays Surveys Dynamics Transformers Human motion prediction deep learning neural network equivariant models

来源：评论

学校读者我要写书评

暂无评论

Nonparametric Regression for MU-MIMO Channel prediction: From KNN to Local Linear Regression

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第4期23卷 2784-2795页

作者： Xiao, Zheng Sun, Jun Zhang, Zhaoyang Liu, Yingzhuang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Channel aging poses a huge challenge for the MU-MIMO (Multi-user Multiple-Input-Multiple Output) communications. To alleviate the problem, channel prediction is widely regarded as a promising mean. Existing channel prediction methods usually rely heavily on the assumption of parametric models. Severe performance degradation might occur when modeling error exists, which is unfortunately common in practice due to the complicated nature of wireless channels. To address the problem, we propose to use nonparametric regression methods for prediction. Nonparametric methods enjoy the advantage of not relying on the model assumption at all, enabling it very suit for making inference from complex channel data. In this paper, we present two nonparametric regression methods for MU-MIMO's channel prediction, i.e., k-nearest neighbors (kNN) regression and its improved version, local polynomial regression (LPR). In addition, we propose to employ the Bayesian Information Criteria (BIC) to select the parameters in LPR, whereby we get the conclusion that local linear regression is recommended in practical applications. Furthermore, for the case of predicting the right singular matrix of the channel in specific, we provide a useful preprocessing procedure. Simulation results illustrate that our proposed nonparametric regression methods can outperform significantly the conventional parametric methods for channel prediction.

关键词： Wireless communication Computational modeling Antennas prediction algorithms Predictive models Aging Wireless sensor networks MU-MIMO channel prediction nonparametric regression kNN local polynomial regression

来源：评论

学校读者我要写书评

暂无评论

Location prediction Using Bayesian Optimization LSTM for RIS-Assisted Wireless Communications

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 15156-15171页

作者： Hu, Xuejie Tian, Yue Kho, Yau Hee Xiao, Baiyun Li, Qinying Yang, Zheng Li, Zhidu Li, Wenda Xiamen Univ Technol Fujian Key Lab Commun Network & Informat Proc Xiamen 361024 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand Fujian Normal Univ Coll Photon & Elect Engn Fuzhou 350007 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

Reconfigurable intelligent surface (RIS) represent a novel form of electromagnetic metamaterial that have been extensively studied for user equipment (UE) positioning by exploiting the multipath propagation of signals. A novel RIS-assisted localization prediction (RLP) method based on Bayesian optimization and long short-term memory (BO-LSTM) has been proposed in this paper. This method capitalizes on the predictive advantages of LSTM for data sequence and RIS's flexible and controllable multidimensional feature parameters, establishing a mobile UE localization model in an RIS-assisted wireless communications system based on the interplay between time slot transmission power and user location information. In order to provide a more stable communication environment for data collection during the localization process, a power allocation optimization (PAO) method is proposed for maximizing time slot channel capacity in the RLP system based on the number of RIS reflection elements. The study conducts a thorough comparison of simulation results of BO-LSTM, convolutional neural networks (CNN)-LSTM and improved bidirectional LSTM (BiLSTM) combined with Adaptive boost, employing adaptive moment estimation (Adam) and stochastic gradient descent with momentum (SGDM) optimizers. Experimental results demonstrate that the BO-LSTM-based RLP method exhibits improved prediction accuracy. These findings suggest the effectiveness of the proposed method and highlight its potential for further enhancement.

关键词： Long short term memory Location awareness Wireless communication Optimization prediction algorithms Predictive models Machine learning algorithms Reconfigurable intelligent surface Bayesian optimization LSTM location prediction wireless communications

来源：评论

学校读者我要写书评

暂无评论

Is Single Enough? A Joint Spatiotemporal Feature Learning Framework for Multivariate Time Series prediction

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第4期35卷 4985-4998页

作者： Yuan, Kaixin Wu, Kai Liu, Jing Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China

A fuzzy cognitive map (FCM) is a simple but effective tool for modeling and predicting time series. This article focuses on the problem of multivariate time series prediction (TSP), which is essential and challenging in data mining. Although several FCM-based approaches have been designed to solve this problem, their feature extraction module designed for single mode falls short in capturing the nonlinear spatiotemporal dependencies among variates, thereby resulting in low prediction accuracy in forecasting multivariate time series, which shows that the single mode learning is not enough. Therefore, in this article, we propose a joint spatiotemporal feature learning framework for multivariate TSP, where a mix-resolution spatial module consisting of multiple sparse autoencoders (SAEs) is designed to extract the feature series with different spatial resolutions, and a mix-order spatiotemporal module concluding multiple high-order FCMs (HFCMs) is designed to model the spatiotemporal dynamics of these feature series. Finally, the outputs of the two modules are concatenated to predict future values. We refer to this framework as the spatiotemporal FCM (STFCM). Especially, an efficient learning algorithm is designed to update the integral weights of STFCM based on the batch gradient descent algorithm when it deems necessary. We validate the performance of the STFCM on four real-world datasets. Compared with the existing state-of-the-art (SOTA) methods, the experimental results not only show the advantages of the two designed modules in the STFCM but also show the excellent performance of the STFCM.

关键词： Time series analysis Feature extraction Spatiotemporal phenomena Correlation Representation learning Predictive models prediction algorithms Fuzzy cognitive maps (FCMs) fuzzy neural network multivariate time series prediction (TSP) sparse autoencoder (SAE) spatiotemporal feature

来源：评论

学校读者我要写书评

暂无评论

O/S Level Interrupt prediction for Performance and Energy Management on Android

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第3期23卷 2219-2230页

作者： Lezcano, Daniel Da Costa, Georges Linaro Cambridge CB22 7GG England Toulouse Univ IRIT F-31000 Toulouse France

Billions smartphones and smart objects battery-powered use Android, i.e., on the Linux kernel. To save energy, the main kernel leverage is to put processors in a low power state as soon as they are idle. It predicts the next event to estimate the sleep duration and choose a sleep state accordingly. Several wake-up sources (interrupts, events...) impact this prediction which is usually done considering them as a single source. The resulting signal is nearly random and difficult to predict. Processors are recently supporting deeper idle states but the prediction paradigm was never challenged. We propose to predict the next event by splitting the wake-up source signal into simpler event patterns. We describe a fast and efficient algorithm and its kernel-level performance evaluation. We compare our approach with multiple reference sleep state selection algorithms on actual ARM and x86 boards using classical mobile workloads. Our proposal detects correctly (up to 20% improved correctness leading to 5% reduced energy consumption) the time of next interrupt, and thus the right sleep level for the processor. We show and discuss the energy impact of the tested prediction algorithm and we compare it with the different generations of sleep level managers in the Linux kernel.

关键词： Program processors Operating systems Kernel Linux prediction algorithms Hardware Task analysis Android deep sleep energy efficiency idle selection operating system

来源：评论

学校读者我要写书评

暂无评论

Deep Demand prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin-Destination Ride-Hailing Demand prediction

引用

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2024年第3期16卷 111-124页

作者： Lin, Hongyi He, Yixu Liu, Yang Gao, Kun Qu, Xiaobo Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China Chalmers Univ Technol Dept Architecture & Civil Engn SE-41296 Gothenburg Sweden

In intelligent transportation systems, one key challenge for managing ride-hailing services is the balancing of traffic supply and demand while meeting passenger needs within vehicle availability constraints. Accurate origin-destination (OD) demand predictions can empower platforms to execute timely reallocation of cruising vehicles and improve ride-sharing services. Nonetheless, the complexity of OD-based demand prediction arises from intricate spatiotemporal dependencies and a higher need for precision compared to zone-based predictions, which leads to many unprecedented OD pairs. To tackle this issue, we design a comprehensive set of 102 features, including travel demand, passenger count, travel volume, liveliness, weather, and cross features. We also introduce an enhanced conformer model, which is composed of a single conformer block that integrates feedforward layers, multihead self-attention mechanisms, and depth-wise separable convolution layers. To address the cold-start problem and manage large values, we design a specific algorithm for OD pairs lacking training data and apply a technique to handle larger values. Our approach demonstrates a marked improvement in prediction performance, with an 18% decrease in the total travel demand error and up to a 47% reduction for certain larger values in some cases. Through extensive experiments on a dataset collected from a city, provided by a ride-hailing platform, our proposed methods significantly outperform the most advanced models.

关键词： Predictive models Urban areas Task analysis prediction algorithms Meteorology Real-time systems Data models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：