Network Data Research Articles

Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts

Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.

Optimizing IOT Network Performance: A Study on Low-Power Wide-Area Network (LPWAN) Technologies for Smart City Applications

Abstract: This research explores with a specific focus on smart city applications, how to optimise the execution of Web of Things (IoT) networks by utilising breakthroughs in Low-Powered Wide Area Networks (LPWAN). As IoT devices proliferate and generate vast amounts of data, having a competent network becomes essential, especially when dealing with low device thickness and limited power availability. This analysis provides a detailed energy consumption model tailored to LoRaWAN, based on a deeper understanding of its functional components in Internet of Things scenarios. The review uses MATLAB for replication and looks at two clear scenarios: the standard allocation of spreading factors without optimisation and an improved method that uses particle swarm optimisation (PSO) for dynamic spreading factor work. The results show that the network energy consumption dropped significantly, from 6.3997×10^ (- 17) Joules to 2.5230×10^(- 17) Joules, along with increased throughput, decreased parcel misfortune, and decreased idleness. Through a detailed analysis of several parameters, such as network density and data transfer efficiency, this study demonstrates the feasibility of the suggested optimisation model in terms of improving battery efficiency and overall network performance. Finally, the findings demonstrate how efficient LPWAN systems may provide robust and useful IoT environments, which are essential for the successful implementation of smart city initiatives.

Double standards in status ascriptions? The role of gender, behaviors, and social networks in status orders among adolescents

Abstract We examine the gendered distribution of peer-ascribed status in schools. Using network data from more than 14,000 students in 676 classrooms, we explore gender differences in the ascription of status and the types of behavior rewarded with status. On average, girls receive slightly fewer status ascriptions than boys, and students tend to grant status more frequently within the same gender. Contextual analyses show that classroom demographics can moderate some of these patterns. We also uncover gender-specific differences and similarities in status-related behaviors. Notably, girls engaging in substance use are awarded with slightly more status ascriptions than boys. However, network models reveal that most behaviors affect peer status similarly for both genders, suggesting that previous findings of gender-behavioral differences based on regression analysis may be conflated with network processes. Our study updates long-held notions regarding gendered status orders in schools and highlights the value of a multidimensional approach to status processes. We discuss implications for future social network research on status ascriptions and other relational cognitions and consider how school-based interventions might benefit from our findings.

2D Echocardiographic aortic root dimensions and longitudinal analysis with artificial intelligence using an open dataset from a UK-wide collaborative

Abstract Background Surveillance of aortic dimensions requires reproducible measurements, and knowledge of the rate of progression in those with dilatation. Purpose To develop an open machine-learning method for measuring the aortic root and proximal ascending aorta on echocardiograms, validate it through a multi-expert panel from the Unity UK Echocardiography AI Collaborative, and applying the AI to derive the rate of progression in historical databases. Methods The neural network was trained on 1478 parasternal long-axis images. Each image was labelled with key points for the aortic annulus, sinus and sinotubular junction, and proximal ascending aorta dimensions. Labels accommodated inner-to-inner and leading-to-leading conventions. For validation, the end-diastolic and mid-systolic images were identified (accommodating different guidelines recommendations) from 100 studies, and 10 expert echocardiographers made the aortic measurements. The consensus of experts defined the reference standard, and the variation between individual experts defined the acceptable variation for the AI. The AI was then applied to 724 echocardiograms of 102 patients under surveillance spanning 12 years (ranging from 4 to 17 scans per patient) without intervention. Training data labels and networks are made freely available on our project website. Results The median absolute deviations between the AI and the expert consensus ranged from 0.06cm to 0.17cm across the 16 measurements. For the individual expert opinions this was from 0.08cm to 0.19cm. For 7/16 measurements, there was no statistically significant difference between the AI deviation and the individual-expert deviation. The AI’s confidence level was a useful indicator of the reliability of the AI measure. For the top 9 deciles of AI confidence level, the AI measurements had significantly smaller deviations than the individual experts. The rates of progression averaged across the three aortic measurements was 2.0 mm/decade (95% CI 1.0 to 3.0; p =0.007). Conclusion Expert consensus can be used to both define the reference standard and also the range of acceptable deviation from consensus. Overall, the AI performs similarly to experts and more importantly, provides an automated estimate of confidence. The validated AI is consistent over time, and when applied retrospectively to patients with dilated aortic roots, found the rate of progression was more rapid than in previously reported healthy patients. This approach could be applied generally in developing medical AI to not only make reproducible measurements but to automate large scale longitudinal studies with little input, and ultimately serves as a useful research and clinical tool.Figure: AI(red) vs Expert (blue)Table: AIDeviations from Consensus

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

Maximizing energy efficiency in HetNets through centralized and distributed sleep strategies under QoS constraint

This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

BackgroundGene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis.ResultsIn this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data.ConclusionThe proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein.

Spatiotemporal implicit neural representation as a generalized traffic data learner

Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employed to directly map coordinates to traffic variables. To unravel the entangled spatial–temporal interactions, the variability is decomposed into separate processes. We further enable modeling in irregular spaces such as sensor graphs using spectral embedding. Through continuous representations, our approach enables the modeling of a variety of STTD with a unified input, thereby serving as a generalized learner of the underlying traffic dynamics. It is also shown that it can learn implicit low-rank priors and smoothness regularization from the data, making it versatile for learning different dominating data patterns. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales. Empirical results not only indicate that our model has significant superiority over conventional low-rank models, but also highlight that the versatility of the approach extends to different data domains, output resolutions, and network topologies. Comprehensive model analyses provide further insight into the inductive bias of STTD. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks. PyTorch implementations of this project is publicly available at:https://github.com/tongnie/traffic_dynamics.

Integrating Graph Structures into Kernel Regression Models

Kernel methods are highly flexible and powerful tools for capturing complex, nonlinear relationships in data. In this paper, we propose a substantial extension of existing network-based regression models by integrating kernel methods with graph-theoretic constraints. Our approach builds upon the foundational work of Li et al. [1], who incorporated network cohesion into generalized linear models (GLMs). We extend their framework by introducing a kernelized regression model that allows for the modeling of nonlinear interactions while leveraging network data. This kernelized framework relaxes the linearity constraints of GLMs, making the model more versatile and capable of capturing complex patterns in high-dimensional spaces. We demonstrate the effectiveness of our approach using simulated and real-world datasets, such as the Teenage Friends and Lifestyle Study. Results show that our kernelized network regression model not only outperforms traditional linear and generalized linear models in predictive accuracy but also scales efficiently to larger datasets. Our work represents a significant advancement in the modeling of network-linked data, providing a robust, scalable, and interpretable framework that extends the application of kernel methods beyond their traditional constraints. Future directions include exploring more complex graph structures, such as weighted and directed graphs, and developing optimized algorithms for even larger datasets.

Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study.

Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions. We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union. We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category. In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001). Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes.

Robustness of wavelet features for damage detection with neural network and digital twin

Recent studies demonstrate that the wavelet energy–based features are highly sensitive to local damages, and accurate damage identification could be achieved by integrating such features with neural networks. However, the robustness of the features in practical applications and the feasibility of generating damage information with a digital twin to facilitate training of neural networks have not been systematically investigated. This paper aims to provide new insight into the practicality of using wavelet energy–based features for accurate damage detection of structures. A systematic investigation into the tolerance of the normalised wavelet packet node energy features with neural network (NWPNE-NN) approach against measurement noises, excitations uncertainties and limited frequency range in the measured responses is carried out, with consideration of data fusion from multiple measurement points. The feasibility of a digital twin to simulate damage scenarios and generate training data for neural networks is explored, and the use of relative WPNE as a new feature is proposed. The results show that the NWPNE-NN approach is capable of detecting and quantifying the main damage in a structure. The neural networks trained by data generated from a well-calibrated digital twin is capable of identifying damage in the actual structure.

Speed prediction and nearby road impact analysis using machine learning and ensemble of explainable AI techniques

Traffic speed prediction is an important topic in Intelligent Transport Systems (ITS). Although traffic speed prediction for real-life applications is burgeoning, the study of explaining and interpreting AI-based speed prediction is still in its initial stage. In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and LASSO to predict traffic speed more accurately in the subsequent time windows. The experiment with prediction methods was conducted using the traffic speed data of the Seoul metropolitan road network. Each road segment represented as a node in the network is associated with neighboring roads within a configurable range. We picked heavily congested nodes as prediction targets. Then, we evaluated nearby road influences to determine critical contributions to the situation of the target nodes. We interpreted the model’s output and extracted the topmost influential neighboring nodes by using an ensemble of explainable artificial intelligence (XAI) techniques such as feature importance assessment using the GINI entropy function, Recursive Feature Elimination, Shapely Additive Explanation, and a method of measuring the impact of masked nodes. We validated the XAI interpretations through traffic flow simulation by tuning the topmost influential nearby roads’ speed and observing the effect on the roads’ traffic congestion relief correspondingly. We also proved our solution through local explanation techniques such as Local Interpretable Model-Agnostic Explanations. Our methods are applicable to any transport network and open the door to new strategies for controlling the specific nearby roads for effective congestion relief.

The Impact of Generative Artificial Intelligence on Education: A Comparative Study

Generative Artificial Intelligence (GAI), also known as creative AI, is a technology capable of independently producing original and creative content, such as text, images, videos, music, or code. It uses machine learning algorithms to analyze and learn from a vast amount of data to generate similar or even innovative content. This type of AI is used in several fields, such as health for the production of new drugs, design to generate adaptive designs, video games to create immersive environments, and artificial intelligence to generate training data for new networks. Seizing the opportunities offered by AI innovations in education is crucial. We cannot afford to let time pass without doing anything and without benefiting from this technique. It is time to enhance traditional learning methods and adopt more innovative approaches that take advantage of the potential of AI, like content and exam generation, and also improve students' learning experience by exploiting their closeness to the technology. This paper aims to answer the question of when and how to use artificial intelligence responsibly, considering it as a technology and limiting its excessive use. Additionally, we present the impact of GAI through a comparative study of student performance in three exam types: a classical MCQ exam written by the teacher, an adaptive MCQ exam generated by GAI from a topic, and a third MCQ exam generated by GAI based on the content of the provided documents. This study is conducted on students undergoing a software testing training program. By evaluating their performance in the AI-generated adaptive MCQ exams and the traditional MCQ exam, we can gain insights into the potential of this technology to enhance educational practices and provide personalized learning experiences. This research will contribute to the ongoing discussion on responsible and effective utilization of GAI in education, paving the way for future advancements in the field.

Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations

We present a proof-of-concept simulation-based inference on Ωm and σ 8 from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ Northern Galactic Cap (NGC) catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid light-cone simulations L-picola, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network, Minkowski-PointNet . We also add relatively more accurate Gadget mocks to obtain robust and generalizable neural networks. By explicitly learning the representations that reduce the discrepancies between the two different data sets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find Ωm = 0.339 ± 0.056 and σ 8 = 0.801 ± 0.061, inferred only from the distribution of galaxies in the light-cone slices without relying on any indirect summary statistics. A single machine that best adapts to the Gadget mocks yields a tighter prediction of Ωm = 0.282 ± 0.014 and σ 8 = 0.786 ± 0.036. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data.

SPLANG—a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms

Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

Predicting 5G throughput with BAMMO, a boosted additive model for data with missing observations

Abstract To deliver on the promise of 5G, network providers and application developers need to understand the factors impacting millimetre wave (mmWave) 5G throughput. Missing data, however, pose significant challenges for modelling throughput. Even in controlled settings, signal strength data may be only intermittently observed when a device’s connection is weak, leading to missing predictor values in model training. In addition, users may choose not to share their data once the model is deployed, meaning that key predictors may be missing when we want to predict throughput for their devices. To address these challenges, we introduce boosted additive model for data with missing observation (BAMMO), a novel additive model estimator obtained via a componentwise boosting algorithm that naturally incorporates data with missing values in model fitting. We validate BAMMO’s approach to handling missing data by comparing it with competing methods on real 5G network data with a high proportion of missing values and in simulations, finding that it delivers more accurate predictions and takes less time to compute. To identify key predictors of mmWave 5G throughput, we develop a novel extension of sparsity oriented importance learning for BAMMO, giving us a measure of variable importance based on the entire boosting solution path rather than a single selected model.

Graphene Phase Modulators Operating in the Transparency Regime.

Next-generation data networks need to support Tb/s rates. In-phase and quadrature (IQ) modulation combine phase and intensity information to increase the density of encoded data, reduce overall power consumption by minimizing the number of channels, and increase noise tolerance. To reduce errors when decoding the received signal, intersymbol interference must be minimized. This is achieved with pure phase modulation, where the phase of the optical signal is controlled without changing its intensity. Phase modulators are characterized by the voltage required to achieve a π phase shift, Vπ, the device length, L, and their product, VπL. To reduce power consumption, IQ modulators are needed with <1 V drive voltages and compact (sub-cm) dimensions, which translate in VπL < 1Vcm. Si and LiNbO3 (LN) IQ modulators do not currently meet these requirements because VπL > 1Vcm. Here, we report a double single-layer graphene (SLG) Mach-Zehnder modulator (MZM) with pure phase modulation in the transparency regime, where optical losses are minimized and remain constant with increasing voltage. Our device has VπL ∼ 0.3Vcm, matching state-of-the-art SLG-based MZMs and plasmonic LN MZMs, but with pure phase modulation and low insertion loss (∼5 dB), essential for IQ modulation. Our VπL is ∼5 times lower than the lowest thin-film LN MZMs and ∼3 times lower than the lowest Si MZMs. This enables devices with complementary metal-oxide semiconductor compatible VπL (<1Vcm) and smaller footprint than LN or Si MZMs, improving circuit density and reducing power consumption by 1 order of magnitude.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Tailoring convolutional neural networks for custom botanical data

AbstractPremiseAutomated disease, weed, and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet, and ConvNeXt often underperform on smaller, specialised datasets typical of such projects.MethodsWe address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection.ResultsResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, almost no overfitting, and an exceptionally low computation cost of 1.19 GFLOPS.ConclusionsWe show that PhytNet is a promising candidate for rapid disease or plant classification and for precise localisation of disease symptoms for autonomous systems. We also show that the most informative light spectra for detecting cocoa disease are outside the visible spectrum and that efforts to detect disease in cocoa should be focused on local symptoms, rather than the systemic effects of disease.

Integrated Genetic Algorithm and Deep Learning Approach for Effective Cyber-Attack Detection and Classification in Industrial Internet of Things (IIoT) Environments

AbstractCyber-attack detection within Industrial Internet of Things (IIoT) environments presents unique challenges due to the complex, resource-constrained, and real-time nature of these networks. Traditional detection techniques often struggle to adapt to the dynamic environment of IIoT. For instance, many existing methods rely on signature-based detection, which fails to identify evolving threats. Other approaches, such as anomaly-based detection, can generate a high rate of False Positives, leading to inefficiencies in threat management. To address these challenges, we propose a novel detection and classification model specifically tailored for IIoT environments. The proposed model integrates Genetic Algorithms (GA) and Deep Learning (DL) to enhance cyber-attack detection within IIoT environments. The GA component optimises feature selection from raw network data, ensuring the extraction of meaningful and relevant features. Leveraging these selected features, the DL component constructs a robust model capable of accurately detecting and classifying various cyber-attack patterns across IIoT devices. Through experimentation on real-world IIoT network traffic (UNSW-NB 15 dataset), the proposed approach demonstrates its efficacy in improving attack detection accuracy and adaptability. The integration of GA and DL offers a synergistic solution that addresses the complexities of IIoT cybersecurity, contributing to a more secure and resilient IIoT ecosystem. The integrated GA–DL classification model developed in this work achieved 98% precision, 96% accuracy, 94% recall, and 12% losses with only less than 50% of the features of the UNSW-NB 15 dataset. The reduction in features required for the identification and classification of cyber-attacks reduces the processing time by 50%.