Ada-STGMAT: An adaptive spatio-temporal graph multi-attention network for intelligent time series forecasting in smart cities

Expert Systems with Applications, Год журнала: 2025, Номер 269, С. 126428 - 126428

Опубликована: Янв. 10, 2025

Язык: Английский

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An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture

Agriculture, Год журнала: 2023, Номер 13(3), С. 567 - 567

Опубликована: Фев. 26, 2023

In modern agriculture and environmental protection, effective identification of crop diseases pests is very important for intelligent management systems mobile computing application. However, the existing mainly relies on machine learning deep networks to carry out coarse-grained classification large-scale parameters complex structure fitting, which lacks ability in identifying fine-grained features inherent correlation mine pests. To solve problems, a pest method based graph pyramid attention, convolutional neural network (GPA-Net) proposed promote agricultural production efficiency. Firstly, CSP backbone constructed obtain rich feature maps. Then, cross-stage trilinear attention module extract abundant discrimination portions objects as much possible. Moreover, multilevel designed learn multiscale spatial graphic relations enhance recognize diseases. Finally, comparative experiments executed cassava leaf, AI Challenger, IP102 datasets demonstrates that GPA-Net achieves better performance than models, with accuracy up 99.0%, 97.0%, 56.9%, respectively, more conducive distinguish applications practical smart protection.

Язык: Английский

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A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2023, Номер 25(3), С. 2966 - 2975

Опубликована: Май 24, 2023

Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite prediction making travel plans. The speed of can be affected by roads condition, weather, holidays, etc. Moreover, sensors to catch information about will interfered with environmental factors such as illumination, collection time, occlusion, Therefore, in practical system complicated, uncertain, and challenging predict accurately. Motivated from aforementioned issues challenges, this paper, we propose deep encoder-decoder framework based on variational Bayesian inference. A neural network designed combining inference Gated Recurrent Units (GRU) which used unit mine intrinsic dynamics flow. Then, introduced into multi-head attention mechanism avoid noise-induced deterioration accuracy. proposed model achieves superior performance Guangzhou urban dataset over benchmarks, particularly when long-term prediction.

Язык: Английский

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Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities

IECE transactions on intelligent systematics., Год журнала: 2024, Номер 1(2), С. 79 - 90

Опубликована: Сен. 27, 2024

Accurate predictions of traffic flow are very meaningful to city managers. With such information, systems can better coordinate signals and reduce congestion. By understanding patterns, navigation provide real-time routing suggestions that avoid jams, save time, fuel consumption. However, will be interfered with by multiple factors as collection time place. In this paper, we propose use stochastic configuration networks (SCNs) predict the flow. The network is trained through stepwise construction, parameters effectively optimized based on approximation theorem convergence analysis optimization mechanism. proposed automatically adjusts its structure according complexity adapt complex non-linearity We observed model achieves prediction performance overall greater flexibility in length period compared benchmarks using Guangzhou urban dataset. It's worth noting SCNs consistently outperform other models across different intervals. They yield RMSE improvements up 10.73% for 10-minute predictions, 5.02% 30-minute 11.21% 60-minute least effective models. R-value also exhibits steady enhancement, increasing 0.78%, 0.65%, 2.33% 10-minute, 30-minute, respectively. These notable advancements, combined model's computational efficiency, especially short-term underscore effectiveness practicality tasks.

Язык: Английский

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Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Март 21, 2024

Abstract Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across farmlands necessitates distributed transparent automation. We developed an edge computing framework complemented auction mechanisms fuzzy optimizers that connect various supply chain stages. Specifically, powerful capabilities enable real-time monitoring decision-making in smart agriculture. propose tailored to agricultural needs ensure through a renewable solar energy supply. Although the manages crop data collection, market-based mechanisms, such as auctions optimization models, support for smooth operations. formulated invisible hide actual bid values regulate information flows, combined with machine learning techniques robust predictive analytics. While rule-based systems encode domain expertise decision-making, adaptable training algorithms help optimize model parameters from data. A two-phase hybrid approach is formulated. Fuzzy models were using three key decision problems. Auction markets discover optimal demand–supply balancing pricing signals. incorporate knowledge into interpretable crop-advisory models. An integrated evaluation of 50 farms over five cycles demonstrated high performance proposed computing-oriented auction-based neural network compared benchmarks.

Язык: Английский

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A Comprehensive review on technological breakthroughs in precision agriculture: IoT and emerging data analytics

European Journal of Agronomy, Год журнала: 2024, Номер 163, С. 127440 - 127440

Опубликована: Ноя. 29, 2024

Язык: Английский

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Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Energies, Год журнала: 2024, Номер 17(2), С. 415 - 415

Опубликована: Янв. 15, 2024

Load forecasting is a research hotspot in academia; the context of new power systems, prediction and determination load reserve capacity also important. In order to adapt forms day-ahead automatic generation control (AGC) demand method based on Fourier transform attention mechanism combined with bidirectional long short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) proposed. Firstly, response time, used refine distinction between various types demand, AGC band calculated using Parseval’s theorem obtain sequence. The maximum mutual information coefficient explore relevant influencing factors sequence concerning data characteristics Then, historical daily sequences features are input into Attention-BiLSTM model, automatically find optimal hyperparameters better results. Finally, arithmetic simulation results show that proposed this paper has best performance upper (0.8810) lower (0.6651) bounds (R2) higher than other models, it smallest mean absolute percentage error (MAPE) root square (RMSE).

Язык: Английский

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Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 912 - 912

Опубликована: Янв. 17, 2025

Mental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents groundbreaking IoT-based system integrates big data analytics, fuzzy logic, machine learning to revolutionise mental monitoring. In contrast existing solutions, proposed uniquely incorporates environmental factors, such as temperature humidity in enclosed spaces—critical yet often overlooked contributors emotional well-being. By leveraging IoT devices collect process large-scale ambient data, provides real-time classification personalised visualisation tailored individual sensitivity profiles. Preliminary results reveal high accuracy, scalability, potential generate actionable insights, creating dynamic feedback loops for continuous improvement. innovative approach bridges gap between conditions healthcare, promoting transformative shift from reactive proactive care laying groundwork predictive systems.

Язык: Английский

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Attention mechanism‐based ultralightweight deep learning method for automated multi‐fruit disease recognition system

Agronomy Journal, Год журнала: 2025, Номер 117(2)

Опубликована: Март 1, 2025

Abstract Automated disease recognition plays a pivotal role in advancing smart artificial intelligence (AI)‐based agriculture and is crucial for achieving higher crop yields. Although substantial research has been conducted on deep learning‐based automated plant systems, these efforts have predominantly focused leaf diseases while neglecting affecting fruits. We propose an efficient architecture effective fruit with state‐of‐the‐art performance to address this gap. Our method integrates advanced techniques, such as multi‐head attention mechanisms lightweight convolutions, enhance both efficiency performance. Its ultralightweight design emphasizes minimizing computational costs, ensuring compatibility memory‐constrained edge devices, enhancing accessibility practical usability. Experimental evaluations were three diverse datasets containing multi‐class images of disease‐affected healthy samples sugar apple ( Annona squamosa ), pomegranate Punica granatum guava Psidium guajava ). proposed model attained exceptional results test set accuracies weighted precision, recall, f1‐scores exceeding 99%, which also outperformed pretrain large‐scale models. Combining high accuracy represents significant step forward developing accessible AI solutions agriculture, contributing the advancement sustainable agriculture.

Язык: Английский

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A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications

Chinese journal of information fusion., Год журнала: 2025, Номер 2(1), С. 38 - 58

Опубликована: Март 22, 2025

With the progressive advancement of remote sensing image technology, its application in agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing images to ensure adequate food supply. However, such often exist harsh environments with many gaps dense distribution, which poses major challenges traditional target detection methods. The frequent missed detections inaccurate bounding boxes severely constrain further analysis within sector. This study presents an enhanced version YOLO algorithm, specifically tailored achieve high-efficiency densely distributed small targets images. We replaced convolutions a convolution kernel size 3 last two ELAN modules DeformableConvNetsv2 so that backbone better extract various objects. proposed detector introduces Bi-level Routing Attention module pooled pyramid SPPCSPC network YOLOv7, thereby intensifying attention towards areas concentration augmenting network's capacity features related through effective feature fusion. Additionally, our approach employs dynamic non-monotonic WIoUv3 loss function network, enabling allocation most appropriate gradient gain strategy at each instant enhancing ability focus on detecting accurately. Finally, comparative experimentation DIOR dataset, YOLOv7-bw exhibits superior performance higher [email protected] [email protected]: 0.95, achieving rates 85.63% 65.93%, surpassing those YOLOv7 by 1.93% 2.03%, respectively, thus substantiating effectiveness algorithmic approach.

Язык: Английский

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