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Deep learning model for efficient traffic forecasting in intelligent transportation systems

neural Computing and Applications 2024年 1-14页

作者： Khan, Shakir Alghayadh, Faisal Yousef Ahanger, Tariq Ahamed Soni, Mukesh Viriyasitavat, Wattana Berdieva, Uguloy Byeon, Haewon Riyadh Saudi Arabia University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali140413 India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Chulalongkorn Business School Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand The Department of Tax and Taxation Tashkent Institute of Finance Tashkent Uzbekistan Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of

In this paper, a novel intelligent transportation framework called graph convolutional neural network for distributed machine learning (GCN-DML) is presented. Its primary application is traffic forecasting in large-scale, dynamically-evolving traffic scenarios. One unique method in traffic forecasting, called GCN-DML, is that it allows user data to be processed locally on edge devices, removing the requirement to send all data to CS (CS). Designed specifically for edge computing, this distributed machine learning framework makes it easier to train neural networks on a variety of edge devices. In addition to protecting user privacy, this method reduces the difficulties related to centralised neural network training, minimises communication delays, lowers data transfer quantities, and improves overall data processing efficiency. GCN-LSTM, the composite graph convolutional networks used in this framework, combine edge-enhanced attention mechanisms with the feature transmission capabilities of graph convolutional neural networks to effectively handle the challenging task of traffic forecasting. Even as the number of cars in the network rises, they perform exceptionally well at quickly extracting and sending interaction information between vehicles, while preserving high prediction accuracy and low time complexity. The versatility of GCN-DML is extended to a range of traffic forecasting application scenarios, whereby edge devices can adjust the kind and scale of the neural network according to available compute and storage resources, all without sacrificing performance. The usefulness of GCN-DML in traffic forecasting is demonstrated by experimental evaluations on the NGSIM public dataset, where it outperforms other models with better computation time and prediction performance. It obtains noteworthy F1 scores of 0.9473, 0.9557, and 0.9391, respectively, for recall, precision, and precision. Even when there is an increase in the number of cars, the accuracy remains high,

关键词： Convolutional neural networks

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Precision DDoS Detection through Gaussian Noise-Augmented neural networks 15

Precision DDoS Detection through Gaussian Noise-Augmented Ne...

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15th International Conference on networks of the Future

作者： Alfatemi, Ali Oliveira, Dingo Rahouti, Mohamed Hafid, Abdelatif Ghani, Nasir Fordham Univ Comp & Informat Sci Dept Bronx NY 10458 USA Penn State Univ Coll IST Monaca PA 15601 USA ESISA ESISA Analyt Lab Fes 30000 Morocco Univ S Florida Dept Elect Engn Tampa FL 33620 USA

ISBN: (纸本)9798350377774;9798350377767

The detection of distributed Denial of Service (DDoS) attacks is a critical challenge in network security, requiring effective and efficient solutions to safeguard data and services. This paper addresses this problem by introducing a neural network model specifically designed for DDoS attack detection. The model employs a streamlined architecture to ensure rapid processing times and high performance. A key innovation is the integration of Gaussian noise, which enhances the robustness and generalization capabilities of the model. Extensive experiments validate the effectiveness and resilience of this approach, demonstrating its practicality for real-world network security applications. The findings highlight the significant role of noise regularization in improving the reliability of neural network models for detecting cyber threats.

关键词： Deep learning distributed Denial of Service Gaussian Noise neural network

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Fed-IoVIDS: Intrusion Detection based on Attack Behavior Analysis with Temporal Model on IoV Considering Privacy Protection

Fed-IoVIDS: Intrusion Detection based on Attack Behavior Ana...

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International Joint Conference on neural networks (IJCNN)

作者： Chen, Rui Zhao, Jing Dalian Univ Technol Sch Software Technol Dalian 116024 Liaoning Peoples R China

ISBN: (纸本)9798350359329;9798350359312

With advancements in distributed communications for the IoV, security threats expose significant challenges. While current IoV intrusion detection systems demonstrate high accuracy, they rely heavily on private or easily forged data. Moreover, the training process incurs increased network communication costs, fails to protect user privacy, and distorted data degrades detection performance. To address these limitations, we proposes a distributed federated learning-based intrusion detection model for IoV using non-private behavior features. Firstly, we design a data processing algorithm that groups and slices IoV communication messages into time series. Then, behavior vectors are extracted using an attention-based time series model designed in this work. Attacks are detected by spatially transforming the residuals with a neural network. Finally, we use a federated learning algorithm for data processing and training of the model, effectively reduce communication burden and protect privacy training data on the vehicle-side. Extensive experiments on two datasets validate the proposed model, achieving F1 scores of 91.66% and 90.25% respectively, outperforming state-of-theart methods. We publicly release the model and algorithms to improve reproducibility and accessibility of effective IoV intrusion detection solutions.

关键词： distributed Communication Attack Behavior analysis Intrusion Detection Privacy Protection federated learning

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Multiple Attention Mechanisms-Based Convolutional neural network for Desert Seismic Denoising

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PURE AND APPLIED GEOPHYSICS 2023年第6期180卷 2135-2155页

作者： Li, Juan Qu, Ruolan Lu, Changgang Jilin Univ Dept Informat Engn Changchun 130026 Peoples R China Jilin Univ Dept Coll Automot Engn Changchun 130026 Peoples R China

For seismic exploration, excellent seismic records are of extreme significance. However, because of extensive mining over a long period, spare natural resources are primarily distributed in sophisticated areas with weak signals and low signal-to-noise ratios (SNRs). Besides, the incoherent noise overlaps with signals in both the time and frequency domains. All these characteristics place a greater demand on denoising methods. In this article, we propose a multiple attention mechanism-based modular convolutional neural network for desert seismic denoising. To extract weak signals from noisy recordings, two types of attention modules are chosen in this network. The channel-wise mechanism in the enhanced attention module is added to the network to extract detailed information from noisy records. Then, the supervised attention module can learn from the input noisy records again, emphasizing the significant features again to further enhance the denoising capability of our network. Besides, different types of dilated convolution layers are added to the network to extend the receptive field to cover minute information as much as possible. Both synthetic and real seismic denoising experiments show that the proposed network framework can suppress the random noise and recover signals even in low SNR situations.

关键词： Data processing Noise Seismic data processing Signal processing Signal-to-noise ratio

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A 3D Hybrid Optical-Electrical NoC Using Novel Mapping Strategy Based DCNN Dataflow Acceleration

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第7期35卷 1139-1154页

作者： Zhang, Bowen Gu, Huaxi Zhang, Grace Li Yang, Yintang Ma, Ziteng Schlichtmann, Ulf Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tech Univ Munich Chair Elect Design Automat D-80333 Munich Germany Xidian Univ Inst Microelect Xian 710071 Peoples R China

A large number of multiply-accumulate operations and memory accesses required in deep convolutional neural networks (DCNN) leads to high latency and energy consumption (EC), that hinder their further applications. Dataflow-based acceleration schemes reduce memory accesses by leveraging reusable data in DCNNs. Row Stationary (RS) dataflow is a more advanced dataflow. In the convolutional layer acceleration of RS dataflow, the flexibility of mapping from logical processing element (LPE) sets to physical PE sets is relatively poor. The utilization of processing elements (PEs) is low. In this article, a novel mapping strategy based on genetic algorithm (GAMS) with the goal of optimizing EC is proposed. GAMS is designed to address the energy inefficiencies faced when mapping RS dataflow. A 3D hybrid optical-electrical network-on-Chip (3DHOENoC) is proposed to further improve the communication efficiency, energy efficiency and the processing speed of DCNN. Simulation and evaluation results show that GAMS can achieve better mapping flexibility, higher PEs utilization and 15.9% improvement of execution speed on average. In addition, the execution time (ET) performance of processing the DCNN can be further improved by adopting the 3DHOENoC architecture with better communication parallelism.

关键词： Optical filters Convolutional neural networks Convolution Three-dimensional displays Optical resonators Optical network units Arrays AI DCNN acceleration dataflow mapping strategy 3DNoC ONoC

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Parallel PSO for Efficient neural network Training Using GPGPU and Apache Spark in Edge Computing Sets

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ALGORITHMS 2024年第9期17卷 378页

作者： Capel, Manuel I. Salguero-Hidalgo, Alberto Holgado-Terriza, Juan A. Univ Granada Software Engn Dept ETSIIT Granada 18071 Spain Univ Malaga Dept Comp Sci & Programming Languages ETSII Malaga 29010 Spain

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate *** paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO.

关键词： Apache Spark classification recall deep neural networks GPU parallelism optimization research particle swarm optimization (PSO) predictive accuracy

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Mode-informed complex-valued neural processes for matched field processing

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2025年第1期157卷 493-508页

作者： Liu, Yining Gao, Wei Chen, Desheng Xu, Lijun Sun Yat Sen Univ Sch Ocean Engn & Technol Zhuhai 528478 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

A complex-valued neural process method, combined with modal depth functions (MDFs) of the ocean waveguide, is proposed to reconstruct the acoustic field. neural networks are used to describe complex Gaussian processes, modeling the distribution of the acoustic field at different depths. The network parameters are optimized through a meta-learning strategy, preventing overfitting under small sample conditions (sample size equals the number of array elements) and mitigating the slow reconstruction speed of Gaussian processes (GPs), while denoising and interpolating sparsely distributed acoustic field data, generating dense field data for virtual receiver arrays. The predicted field is then integrated with the matched field processing (MFP) method for passive source localization. Validation on the SWellEx-96 waveguide shows significant improvements in localization performance and reduces sidelobes of ambiguity surface compared to traditional MFP and GP-based MFP. Moreover, the proposed kernel based on MDFs outperforms the Gaussian kernel in describing ocean waveguide characteristics. Because of the feature representation of multi-modal mapping, this kernel enhances acoustic field prediction performance and improves the accuracy and robustness of MFP. Simulated and real data are used to verify the validity.

关键词： Gaussian distribution

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RF Drone Detection System Based on a distributed Sensor Grid With Remote Hardware-Accelerated Signal processing

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IEEE ACCESS 2023年 11卷 138759-138772页

作者： Flak, Przemyslaw Czyba, Roman Silesian Tech Univ Fac Automat Control Elect & Comp Sci Dept Automat Control & Robot PL-44100 Gliwice Poland Silesian Tech Univ Fac Automat Control Elect & Comp Sci Dept Automat Control & Robot PL-44100 Gliwice Poland

Unmanned Aerial Vehicles (UAVs), sometimes known as drones, evolved from military to civilian applications, opening up novel perspectives in a variety of everyday services. The rapidly growing consumer interest in amateur drones equipped with high-end cameras compromises the everyday safety and privacy of people. In the literature, a variety of sensing techniques based on different physical phenomena have been proposed for drone detection. Among acoustic, optical, or radar detection systems, passive radiofrequency sensing is the only one that can identify a drone even before it takes off and additionally indicate the operator's location. A spectrogram-based method is developed and optimised in terms of computing location, resulting in the possibility of sensor grid deployment over a standard Ethernet network. The detection phase involves hardware-accelerated energy sensing to extract the data frames from the background noise. Drone presence is then identified using machine learning based solely on preamble pattern recognition, which reduces the computational effort. The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. The analytical energy detector stage reaches a margin of roughly -8.7 dB in the signal-to-noise (SNR) ratio. With 1.1 M parameters, the proposed neural network achieves 99.93% simulation accuracy in up to -9.5 dB SNR range. Even after quantization for embedded platform implementation, the device can be used as a stand-alone early intrusion detector or as part of a distributed sensor grid.

关键词： Convolutional neural network drones field programmable gate array software defined radio spectrogram surveillance unmanned aerial vehicles

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Internet of Things Enabled Smart Energy Management of distributed Energy Resources using Self-Adaptive Physics-Informed neural network Optimized with Green Anaconda Optimization

Internet of Things Enabled Smart Energy Management of Distri...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Rahul D. Ghongade J. Sri Arunaa P. S. Latha Mageshwari K. Vinoth M. Shanmugathai J. Femila Mercy Rani Department of Electronics and Telecommunication Engineering P. R. Pote Patil College of Engineering and Management Amravati Maharashtra India Department of Electronics and Communication Engineering Dr. M. G. R Educational and Research Institute Chennai Tamil Nadu India Department of S&H (Physics) R. M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of English Sri Sairam Engineering College Chennai Tamil Nadu India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Internet of Things (IoT)-enabled Smart Energy Management (SEM) in distributed Energy Resources (DERs), while crucial for optimizing energy distribution and resource management faces challenges such as data inconsistencies and the variability of renewable energy generation. These issues result in inaccurate demand forecasts, leading to suboptimal allocation of resources and inefficient Energy Management (EM). Additionally, as energy demand and generation patterns are influenced by factors like weather conditions, time of day, and energy consumption behaviors, prediction models may struggle to capture these dynamic changes, affecting the reliability of forecasts. To address these limitations, this manuscript proposes a novel approach for energy demand prediction. Data is collected from IoT-enabled sensors monitoring DERs. The data undergoes pre-processing, where the Fast Resampled Iterative Filtering (FRIF) method is used to eliminate missing values and normalize the inputs. The Self-Adaptive Physics-Informed neural network (SAPINN) model then utilizes the processed data to forecast energy demand, renewable energy generation, and storage levels. Green Anaconda Optimization (GAO) is applied to optimize the weight parameters of the SAPINN model. The proposed SAPINN-GAO method is implemented using the MATLAB platform and compared with existing models, such Stacked Convoluted Bi-Directional Gated Attention network-Hybrid Darts Seagull Optimizer (SConBGAN-HDSO), Recurrent neural network (RNN), and Support Vector Machine-Particle Swarm Optimization (SVM-PSO). The SAPINN-GAO method achieves an accuracy of 99.2%, precision of 99.2%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in energy demand prediction. The SAPINN-GAO method's higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for energy demand, renewable energy generation, and storage levels forecasting in IoT-enabled SEM syst

关键词： Energy consumption Renewable energy sources Accuracy Recurrent neural networks Green products Predictive models Internet of Things Reliability Energy management Optimization

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A High-Performance and Energy-Efficient Photonic Architecture for Multi-DNN Acceleration

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第1期35卷 46-58页

作者： Li, Yuan Louri, Ahmed Karanth, Avinash George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

Large-scale deep neural network (DNN) accelerators are poised to facilitate the concurrent processing of diverse DNNs, imposing demanding challenges on the interconnection fabric. These challenges encompass overcoming performance degradation and energy increase associated with system scaling while also necessitating flexibility to support dynamic partitioning and adaptable organization of compute resources. Nevertheless, conventional metallic-based interconnects frequently confront inherent limitations in scalability and flexibility. In this paper, we leverage silicon photonic interconnects and adopt an algorithm-architecture co-design approach to develop MDA, a DNN accelerator meticulously crafted to empower high-performance and energy-efficient concurrent processing of diverse DNNs. Specifically, MDA consists of three novel components: 1) a resource allocation algorithm that assigns compute resources to concurrent DNNs based on their computational demands and priorities;2) a dataflow selection algorithm that determines off-chip and on-chip dataflows for each DNN, with the objectives of minimizing off-chip and on-chip memory accesses, respectively;3) a flexible silicon photonic network that can be dynamically segmented into sub-networks, each interconnecting the assigned compute resources of a certain DNN while adapting to the communication patterns dictated by the selected on-chip dataflow. Simulation results show that the proposed MDA accelerator outperforms other state-of-the-art multi-DNN accelerators, including PREMA, AI-MT, Planaria, and HDA. MDA accelerator achieves a speedup of 3.6, accompanied by substantial improvements of 7.3x, 12.7x, and 9.2x in energy efficiency, service-level agreement (SLA) satisfaction rate, and fairness, respectively.

关键词： Accelerator dataflow deep neural network silicon photonics

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