A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Information, Journal Year: 2024, Volume and Issue: 15(3), P. 153 - 153

Published: March 9, 2024

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of systems. It provides various integration capabilities collection, display, analysis sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular widely used applications including IoT. To support growth, AI into is essential to enhance its after identifying current trends applicable technologies applications. first provide comprehensive review using techniques literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), collaborative AI. Next, identify characteristics each technique by considering key parameters, such as software requirements, input/output (I/O) types, processing methods, computations. Third, design based findings. Finally, discuss use cases with techniques. The implementation proposed will be future works.

Language: Английский

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A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 16

Published: May 26, 2022

Diseases and pests are essential threat factors that affect agricultural production, food security supply, ecological plant diversity. However, the accurate recognition of various diseases is still challenging for existing advanced information intelligence technologies. Disease pest typically a fine-grained visual classification problem, which easy to confuse traditional coarse-grained methods due external similarity between different categories significant differences among each subsample same category. Toward this end, paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) handle image diseases. In our approach, improved CSP-stage backbone first formed offer massive channel-shuffled features in multiple granularities. Secondly, relying on multilevel attention mechanism, module designed exploit distinguishable representing discriminating parts. Meanwhile, graphic convolution constructed analyse graph-correlated representation part-specific interrelationships by regularizing semantic into tensor space. With collaborative learning three modules, approach can grasp robust contextual details better identification. Extensive experiments several public disease datasets demonstrate proposed GHA-Net achieves performances accuracy efficiency surpassing other models more suitable identification applications complex scenes.

Language: Английский

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Blockchain-Based Information Supervision Model for Rice Supply Chains

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 17

Published: March 29, 2022

Rice is a major food crop around the world, and its various quality safety problems are closely related to human health. As an important area of research, rice supply chain has attracted increasing attention. Based on blockchain technology, this study investigated data privacy circulation efficiency caused by complex networks, long cycles, risk factors in each link. First, we deconstructed link at information level established key classification table for On that basis, built supervision model based blockchain. Various encryption algorithms used secure sensitive enterprises meet regulators' needs efficient supervision. Moreover, propose practical Byzantine fault-tolerant consensus algorithm scores credit enterprise nodes, optimizes selection strategy master ensures high low cost. Then, prototype system open-source framework hyperledger fabric, analyzed model's viability, implemented using cases. The results indicated proposed can optimize process regulators provide feasible solution grain oil.

Language: Английский

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Using ARIMA to Predict the Growth in the Subscriber Data Usage

Eng—Advances in Engineering, Journal Year: 2023, Volume and Issue: 4(1), P. 92 - 120

Published: Jan. 1, 2023

Telecommunication companies collect a deluge of subscriber data without retrieving substantial information. Exploratory analysis this type will facilitate the prediction varied information that can be geographical, demographic, financial, or any other. Prediction therefore an asset in decision-making process telecommunications companies, but only if retrieved follows plan with strategic actions. The exploratory was implemented research to predict usage trends based on historical time-stamped data. predictive outcome unknown approximated using at hand. We have used 730 points selected from Insights Data Storage (IDS). These were collected hourly statistic traffic table and subjected growth usage. Auto-Regressive Integrated Moving Average (ARIMA) model forecast. In addition, we normal Q-Q, correlogram, standardized residual metrics evaluate model. This showed p-value 0.007. result supports our hypothesis predicting increase growth. ARIMA predicted 3 Mbps maximum 14 Gbps. experimentation, compared Convolutional Neural Network (CNN) achieved best results UGRansome performed better execution speed by factor 43 for more than 80,000 rows. On average, it takes 0.0016 s execute one row, 0.069 CNN same thus making 43× (0.0690.0016) faster provide road map so telecommunication productive improving their Quality Experience (QoE). study provides understanding seasonality stationarity involved usage’s growth, exposing new network concerns facilitating development novel models.

Language: Английский

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Weakly Supervised Temporal Sentence Grounding with Uncertainty-Guided Self-training

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Journal Year: 2023, Volume and Issue: unknown, P. 18908 - 18918

Published: June 1, 2023

The task of weakly supervised temporal sentence grounding aims at finding the corresponding moments a language description in video, given video-language correspondence only video-level. Most existing works select mismatched pairs as negative samples and train model to generate better positive proposals that are distinct from ones. However, due complex structure videos, ones may correspond several video segments but not necessarily correct ground truth. To alleviate this problem, we propose an uncertainty-guided self-training technique provide extra self-supervision signal guide weakly-supervised learning. process is based on teacher-student mutual learning with weak-strong augmentation, which enables teacher network relatively more reliable outputs compared student network, so can learn teacher's output. Since directly applying methods easily causes error accumulation, specifically design two techniques our selftraining method: (1) construct Bayesian leveraging its uncertainty weight suppress noisy supervisory signals; (2) leverage cycle consistency brought by data augmentation perform between networks. Experiments demonstrate method's superiority Charades-STA ActivityNet Captions datasets. We also show experiment method be applied improve performance multiple backbone methods.

Language: Английский

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Attention based long-term air temperature forecasting network: ALTF Net

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 252, P. 109442 - 109442

Published: July 17, 2022

Language: Английский

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Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle

International Journal of Robust and Nonlinear Control, Journal Year: 2022, Volume and Issue: 32(13), P. 7575 - 7593

Published: June 12, 2022

Abstract This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on hierarchical principle. first model used to identify linear parameters and unknown measurable information vector. second for identifying non‐linear parameters. By using auxiliary model, we introduce a forgetting factor improve accuracy. model‐based recursive least squares algorithm multi‐innovation are presented. simulation results indicate that proposed algorithms effective.

Language: Английский

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AutoGRN: An adaptive multi-channel graph recurrent joint optimization network with Copula-based dependency modeling for spatio-temporal fusion in electrical power systems

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102836 - 102836

Published: Nov. 1, 2024

Language: Английский

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A PM2.5 spatiotemporal prediction model based on mixed graph convolutional GRU and self-attention network

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 125748 - 125748

Published: Feb. 1, 2025

Language: Английский

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Construction of Personalized Learning Platform Based on Collaborative Filtering Algorithm

Wireless Communications and Mobile Computing, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 9

Published: March 18, 2022

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from vast resources available. Learners’ urgent need personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid and increase learners’ efficiency significantly. This paper constructs smart classroom model based on AI (artificial intelligence) by studying connotation characteristics of light current research status trend at home abroad. The merits system determined algorithm used PR system. primarily focuses developing CF (collaborative filtering) algorithm, well conducting requirements analysis, database design, functional module implementation, testing this foundation. Experiments carried out to see if optimized effective.

Language: Английский

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