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Analysis of Smart City Sustainability Based on Big Data Integrated Edge Computing Modeling with neural network

Analysis of Smart City Sustainability Based on Big Data Inte...

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2024 International conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Abdulkareem, Sarah Ali N.abdulhussain, Zahraa Hussam, Ragheed Farhan, Khaled Al-Farouni, Mohammed Al-Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

The fact that urban areas are preferred places to live is evidence of a sophisticated system in which authorities can effectively satisfy citizens' needs and address economic, social, and environmental sustainability issues while facing several obstacles. The smart city idea improves sustainable development, human flourishing, and economic expansion. The smart city concept can potentially enhance a wide range of municipal operations, including transportation, energy, and healthcare, education, and resource management. Many network quality, stability, and security issues have arisen from developing smart cities and IoT applications. The dispersed nature of smart cities and IoT technology has led to problems with service reliability, longevity, and safety. This paper proposes Big Data Integrated Edge Computing Modeling (BDIECM) to improve data management interfaces' security and sustainability elements in various smart city applications to eliminate hazards. Edge computing allows for distributed processing and communication, useful in various contexts, including intelligent transportation systems, health and social services. Edge computing's ability to reduce latency is particularly useful for mission-critical applications. The system coordinates between dispersed edge servers and security mediator nodes for smart city use cases. The simulation results validate the suggested approach can significantly enhance the security ratio of95.2%, sustainability ratio of94.1 %,reliability ratio of 91.4 %, scalability ratio of 93.5 %, latency ratio of 21.1 %, and data transmission ratio of 96.2 % compared to other methods. © 2024 IEEE.

关键词： Sustainable city

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Federated Learning with LSTM neural network for Regional Load Forecasting

Federated Learning with LSTM Neural Network for Regional Loa...

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International conference on Harmonics and Quality of Power

作者： Bo You Cheng Xing Qiao Yu Maolin Zhang Zhaoran He Lizhen Zeng Chen Liu Datang Chongqing Energy Marketing Co. LTD Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (数字)9798350382563

ISBN: (纸本)9798350382570

With the increasing penetration of distributed energy resources at the grid edge, including renewable energy generation, flexible loads, and energy storage devices, accurately predicting consumer-level distributed generation and consumption has become crucial. However, conventional centralized processing and machine learning approaches are impractical because of data security and privacy issues. This paper proposes a load forecasting method based on federated learning with LSTM neural network which updates model parameters by having clients responsible for model training and a server responsible for model aggregation, allowing multiple clients to collaboratively train a machine learning model without sharing raw data. Simulation results show that this method can achieve accurate predictions while preserving privacy.

关键词： Data privacy Accuracy Load forecasting Federated learning neural networks Predictive models Data models distributed power generation Long short term memory Load modeling

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Aperiodic Local SGD: Beyond Local SGD 51

Aperiodic Local SGD: Beyond Local SGD

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51st International conference on Parallel processing (ICPP)

作者： Zhang, Hao Wu, Tingting Cheng, Siyao Liu, Jie Harbin Inst Technol Harbin Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9781450397339

Variations of stochastic gradient decedent (SGD) methods are at the core of training deep neural network models. However, in distributed deep learning, where multiple computing devices and data segments are employed in the training process, the performance of SGD can be significantly limited by the overhead of gradient communication. Local SGD methods are designed to overcome this bottleneck by averaging individual gradients trained over parallel workers after multiple local iterations. Currently, both for theoretical analyses and for practical applications, most studies employ periodic synchronization scheme by default, while few of them focus on the aperiodic schemes to obtain better performance models with limited computation and communication overhead. In this paper, we investigate local SGD with an arbitrary synchronization scheme to answer two questions: (1) Is the periodic synchronization scheme best? (2) If not, what is the optimal one? First, for any synchronization scheme, we derive the performance boundary with fixed overhead, and formulate the performance optimization under given computation and communication constraints. Then we find a succinct property of the optimal scheme that the local iteration number decreases as training continues, which indicates the periodic one is suboptimal. Furthermore, with some reasonable approximations, we obtain an explicit form of the optimal scheme and propose Aperiodic Local SGD (ALSGD) as an improved substitute for local SGD without any overhead increment. Our experiments also confirm that with the same computation and communication overhead, ALSGD outperforms local SGD in performance, especially for heterogeneous data.

关键词： distributed learning local SGD data heterogeneity federated learning computation and communication cost

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Evaporation Parameter-Ant Colony Optimization (EP-ACO) Algorithm With Elam neural network (ENN) for Sentiment Analysis

Evaporation Parameter-Ant Colony Optimization (EP-ACO) Algor...

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distributed Computing and Optimization Techniques (ICDCOT), International conference on

作者： L Malliga V Malathy Srikanth Ramappa Zaid Alsalami Bala Dhandayuthapani V Electronics and Communication Engineering Department Mallareddy Engineering College for Women ( Autonomous) Hyderabad Telangana India Department of ECE SR University Warangal India Lead Data Scientist o9 solutions Bengaluru India The Islamic university Najaf Iraq Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Oman UAE

In recent times, online shopping is major stream that refers to users for purchasing and also consume along development of internet technology. The user satisfaction is maximized efficiently through performing Sentiment Analysis (SA) of huge amount of user reviews on e-commerce platforms. That is still difficult for predicting a correct sentiment polarities of user reviews due to modifications in sequence length, order of textual with difficult logic. In this research, proposed an Evaporation Parameter-Ant Colony Optimization (EP-ACO) algorithm with Elam neural network (ENN) for sentiment analysis of amazon product reviews. The reviews from amazon platform are taken for sentiment analysis and then the pre-processing and word embedding process are performed. The features are selected by using EP-ACO algorithm which selects the relevant features for sentiment analysis. Then the sentiment analysis id performed by ENN network, that classifies the reviews as good or bad. Proposed technique obtained 98.26% accuracy, 97.73% precision, 97.03% recall and 97.41% f1-score that is better than previous methods like Convolutional neural network (CNN) – Long Short-Term Memory (LSTM).

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Audio Public opinion Analysis Model based on heterogeneous neural network

Audio Public opinion Analysis Model based on heterogeneous N...

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IEEE International conference on Consumer Electronics and Computer Engineering (ICCECE)

作者： Jiang, Haikun Wu, Xu Xie, Xiaqing Wu, Jingchen BUPT Key Lab Trustworthy Distributed Comp & Serv Sch Cyberspace Secur Minist Educ Beijing Peoples R China Beijing Univ Posts & Telecommun Lib Sch Cyberspace Secur Key Lab Trustworthy Distributed Comp & Serv BUPTMinist Educ Beijing Peoples R China

ISBN: (纸本)9781728183190

With the rapid development of the information age, the main body of public opinion information is no longer just structured data such as text and numbers, but the proportion of unstructured data such as audio and video has increased significantly. Therefore, the analysis and processing of unstructured public opinion data has become the key to deal with the problem of public opinion. Based on this, this paper designs an audio public opinion early warning model based on heterogeneous neural network. Heterogeneous neural network refers to a variety of different types of neural networks to participate in the completion of specified tasks under the premise of data sharing, while retaining the autonomy of each neural network. In this paper, the audio data is converted into a spectrum as an input feature. The convolution neural network can obtain the feature representation of the audio in the spectrum graph, which is used as the input of the recurrent neural network, and then the features related to the text representation are obtained. In this paper, Bert is used to modify the semantic results combined with public opinion domain knowledge, and finally the results are introduced into the shallow neural network Fasttext for public opinion early warning processing. In this paper, experiments are carried out based on the open source voice data set ST-CMDS-20170001-1 Chinese mandarin corpus and Pytorch platform. The results show that this model can effectively analyze and process public opinion of unstructured audio and video data.

关键词： Public opinion analysis heterogeneous neural networks domain knowledge BERT

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Data Allocation with neural Similarity Estimation for Data-Intensive Computing 22nd

Data Allocation with Neural Similarity Estimation for Data-I...

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22nd Annual International conference on Computational Science (ICCS)

作者： Vamosi, Ralf Schikuta, Erich Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9783031087578;9783031087561

Science collaborations such as ATLAS at the high-energy particle accelerator at CERN use a computer grid to run expensive computational tasks on massive, distributed data sets. Dealing with big data on a grid demands workload management and data allocation to maintain a continuous workflow. Data allocation in a computer grid necessitates some data placement policy that is conditioned on the resources of the system and the usage of data. In part, automatic and manual data policies shall achieve a short time-to-result. There are efforts to improve data policies. Data placement/allocation is vital to coping with the increasing amount of data processing in different data centers. A data allocation/placement policy decides which locations sub-sets of data are to be placed. In this paper, a novel approach copes with the bottleneck related to wide-area file transfers between data centers and large distributed data sets with high dimensionality. The model estimates similar data with a neural network on sparse and uncertain observations and then proceeds with the allocation process. The allocation process comprises evolutionary data allocation for finding near-optimal solutions and improves over 5% on network transfers for the given data centers.

关键词： Data allocation Data placement Similarity estimation Parallel computing Wide-area file transfers

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Action-modulated midbrain dopamine activity arises from distributed control policies 36

Action-modulated midbrain dopamine activity arises from dist...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Lindsey, Jack Litwin-Kumar, Ashok Columbia Univ Dept Neurosci New York NY 10027 USA

ISBN: (纸本)9781713871088

Animal behavior is driven by multiple brain regions working in parallel with distinct control policies. We present a biologically plausible model of off-policy reinforcement learning in the basal ganglia, which enables learning in such an architecture. The model accounts for action-related modulation of dopamine activity that is not captured by previous models that implement on-policy algorithms. In particular, the model predicts that dopamine activity signals a combination of reward prediction error (as in classic models) and "action surprise," a measure of how unexpected an action is relative to the basal ganglia's current policy. In the presence of the action surprise term, the model implements an approximate form of Q-learning. On benchmark navigation and reaching tasks, we show empirically that this model is capable of learning from data driven completely or in part by other policies (e.g. from other brain regions). By contrast, models without the action surprise term suffer in the presence of additional policies, and are incapable of learning at all from behavior that is completely externally driven. The model provides a computational account for numerous experimental findings about dopamine activity that cannot be explained by classic models of reinforcement learning in the basal ganglia. These include differing levels of action surprise signals in dorsal and ventral striatum, decreasing amounts of movement-modulated dopamine activity with practice, and representations of action initiation and kinematics in dopamine activity. It also provides further predictions that can be tested with recordings of striatal dopamine activity.(1)

关键词： Reinforcement learning

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Learning Dynamic Spatial Graphs and Spatial Patterns for Accurate Traffic Prediction 29

Learning Dynamic Spatial Graphs and Spatial Patterns for Acc...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Duan, Wenying Lin, Chen Zhu, Jiayi He, Xiaoxi Huang, Wei Rao, Hong Nanchang University School of Mathematics and Computer Sciences Nanchang China Nanchang University Law School Nanchang China University of Macau Faculty of Science and Technology China Nanchang University School of Software Nanchang China

ISBN: (纸本)9798350330717

Traffic prediction poses a formidable challenge due to the dynamic and intricate spatial-temporal dependencies inherent in the task. Adaptive Spatial-Temporal Graph neural networks (ASTGNNs) have emerged as a promising solution, endeavoring to discern node-specific spatial patterns and autonomously deduce the spatial graphs among disparate traffic series. Nonetheless, the efficacy of existing ASTGNNs is often compromised as they struggle to apprehend the dynamic patterns of traffic series and deduce the dynamic spatial graphs in labyrinthine road networks, primarily due to their reliance on static node embeddings. In this paper, we introduce the Adaptive Dynamic Graph Convolutional Recurrent network (ADGCRN), an innovative ASTGNN archetype adept at learning both dynamic spatial dependencies and dynamic spatial patterns from traffic data. Our model is distinguished by two instance-wise dynamic adaptive modules: i) the Instance-wise Dynamic Adaptive Graph Generation module, which integrates instance-wise state information and temporal periodicity into node embeddings;and ii) the Instance-wise Node Adaptive Parameter Learning module, which is capable of learning dynamic, instance-wise, and node-specific patterns for each traffic series. Rigorous experiments conducted on four benchmark datasets demonstrate that ADGCRN significantly outperforms the state-of-the-art ASTGNNs, achieving an average improvement of 3.45%, 3.11%, and 8.78% in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively. © 2023 IEEE.

关键词： spatial-temporal graph neural networks traffic prediction

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MDLoader: A Hybrid Model-Driven Data Loader for distributed Graph neural network Training

MDLoader: A Hybrid Model-Driven Data Loader for Distributed ...

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High Performance Computing, networking, Storage and Analysis, SC-W: Workshops of the International conference for

作者： Jonghyun Bae Jong Youl Choi Massimiliano Lupo Pasini Kshitij Mehta Pei Zhang Khaled Z. Ibrahim Lawrence Berkeley National Laboratory USA Oak Ridge National Laboratory USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

关键词： Training Multiprocessor interconnection Loading distributed databases Stochastic processes Graph neural networks Data models Libraries Low latency communication Load modeling

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LDVNet: Lightweight and Detail-Aware Vision network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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