High-level Data Research Articles

On-Farm Production of Renewable Energy in 2014–2022

The main purpose of this study is to present family farms as consumers and producers of renewable energies which provide them with an opportunity to reduce operating costs. The time scope of the study is 2014–2022, and the Farm Accountancy Data Network is used as the data source. The following research methods were employed: comparative and descriptive analysis, intensity indicators, ranking assignment and panel regression. Based on the values of energy output and energy costs, the rankings revealed a strong position of the Netherlands and Germany. As demonstrated by the study, energy production and consumption volumes depend on the farms’ economic size, but are not impacted by production type. Another finding is that energy production covers only one-third of its costs. Also, both production volumes and costs were on a growth path on a year-over-year basis, with similar growth ratios. The European Union’s leaders in energy consumption and production are the Netherlands, Germany, Belgium, Denmark, Czech Republic, Hungary, Luxembourg, Slovakia and Sweden. The study included the structuring of panel models for energy output and costs and identified their determinants. Energy output depends on total inputs and grows as they grow. Energy costs, in turn, are related to utilized agricultural area, total output and family farm income. An important limitation of this study is that FADN is a provider of high-level data. Hence, it is impossible to tell what specific sources of renewable energy are used by farms, and how they are affected by such exogenous factors as climate, earmarked subsidies or energy policy. The findings from this study are discussed in the context of the European Commission’s recommendations laid down in the Bioeconomy Strategy of the EU (2013), the Seventh Environment Action Program, the New Innovation Agenda of the European Union, the Report “Transforming Our World: the United Nations 2030 Agenda for Sustainable Development” and the Circular Economy Action Plan.

Society of Surgical Oncology Consensus Statement: Assessing the Evidence for and Utility of Gene Expression Profiling of Primary Cutaneous Melanoma.

Gene expression profiling (GEP) of primary cutaneous melanoma aims to offer prognostic and predictive information to guide clinical care. Despite limited evidence of clinical utility, these tests are increasingly incorporated into clinical care. A panel of melanoma experts from the Society of Surgical Oncology convened to develop recommendations regarding the use of GEP to guide management of patients with melanoma. The use of currently available GEP tests were evaluated in three clinical scenarios: (1) the utility in patient selection for sentinel lymph node biopsy; (2) the utility to guide surveillance; and (3) the utility to inform adjuvant therapy. As a basis for these recommendations, the panel performed a systematic review of the literature, including articles published from January 2012 until August 2023. After review of 137 articles,50met the inclusion criteria. These articles included evidence related to three available GEP tests: 31-GEP, CP-GEP, and 11-GEP. The consensus recommendations were finalized using a modified Delphi process. The panel found that current evidence often fails to account for known clinicopathologic risk factors and lacks high-level data. The panel recognizes that thestudy of GEP tests is still evolving. The integration of GEP into routine clinical practice for predicting sentinel lymph node status and patient prognosis in melanoma is therefore not currently recommended. At present, GEP should be considered primarily an investigational tool, ideally used in the context of clinical trials or specialized research settings.

Minimally Invasive Pauli Parastomal Hernia Repair.

Although originally described as an open procedure, the application in the setting of minimally invasive surgery of the Pauli technique for parastomal hernia repair is gaining interest among surgeons owing to encouraging early results. We aimed at combining and analyzing the results of minimally invasive Pauli repair by assessing the available evidence. A literature search in Pubmed, Embase, and Web of Science was undertaken to include all articles reporting on minimally invasive repair of parastomal hernias using the Pauli technique. Data regarding a total of 75 patients across 11 articles published between 2019 and 2023 were included. Patients received surgery either by a laparoscopic (27%) or robotic (73%) approach. A transabdominal route was chosen in 62 patients (83%), while an extraperitoneal technique was employed in the remaining 13 patients. The rate of postoperative morbidity was 34%, with 10% being the incidence of grade >II complications. The reported overall rate of recurrence was 7% at a median follow-up of 1 to 43 months. The available evidence derived from a growing number of centers suggests that minimally invasive Pauli repair is a viable option to treat parastomal hernias. Despite robust, high-level data still lacking, preliminary experiences indicate promising results.

Context matters: The importance of investigating random effects in hierarchical models for early childhood education researchers

This paper highlights the importance of examining individual, classroom, and school-level variables simultaneously in early childhood education research. While it is well known that Hierarchical Linear Modeling (HLM) in school-based studies can be used to account for the clustering of students within classrooms or schools, less known is that HLM can use random effects to investigate how higher-level factors (e.g., effects that vary by school) moderate associations between lower-level factors. This possible moderation can be detected even if higher-level data are not collected. Despite this important use of HLM, a clear resource explaining how to test this type of effect is not available for early childhood researchers. This paper demonstrates this use of HLM by presenting three analytic examples using empirical early childhood education data. First, we review school-level effects literature and HLM concepts to provide the rationale for testing cross-level moderation effects in education research; next we do a short review of literature on the variables that will be used in our three examples (viz., teacher beliefs and student socioemotional behavior); next we describe the dataset that will be analyzed; and finally we guide the reader step-by-step through analyses that show the presence and absence of fixed effects of teacher beliefs on student social outcomes and the erroneous conclusions that can occur if school-level moderation (i.e., random effects) tests are excluded from analyses. This paper provides evidence for the importance of testing for how teachers and students impact each other as a function of school differences, shows how this can be accomplished, and highlights the need to examine random effects of clustering in educational models to ensure the full context is accounted for when predicting student outcomes.

5G-Cloud-based real-time robotic part repairing for advanced manufacturing via computer vision

In this paper, we delve into the transformative capabilities of 5G wireless communication technology within the realm of robotics, particularly in the context of advanced manufacturing. Leveraging the power of computer vision and cloud/edge computing, we unveil the boundless possibilities that this technology heralds. Leveraging the ultra-reliable low latency communication (uRLLC) of 5G, we establish a distributed system showcasing its potential to revolutionize industrial processes. Additionally, to validate our framework, we developed a testbed tailored for real-world applications, focusing on milling operations using an industrial robot. Through high-level lidar data processing, we identify defects and create toolpaths, seamlessly to execute milling tasks. This research highlights 5G’s potential to redefine industrial processes while elucidating operational requirements for achieving uRLLC. Empirical testing underscores the benefits of 5G-robotics integration, enhancing efficiency and innovation in advanced manufacturing.

Butterfly species vary in sex‐specific sodium accumulation from larval diets

Abstract Sodium is essential for animals, and its heterogeneous distribution can cause a range of phenomena, from sodium‐seeking behaviours to impacting their performance. Although sodium content in soils and plants is relatively well documented, data for higher trophic levels are limited. Knowledge of the variation in sodium in lower trophic levels could have implications for understanding the behaviour and physiology of species at higher levels. We investigated the variation in tissue sodium concentration between males and females of four butterfly species. Puddling behaviour of Lepidoptera suggests sodium needs of males are generally greater than females, thus, we predicted males would accumulate more sodium than females on a given diet. Larvae were reared on plants (for Battus philenor, Chlosyne lacinia and Danaus plexippus) and an artificial diet (for Pieris rapae) under Low Na (no added sodium) and High Na (sodium added) conditions. Among species and sexes, we quantified and compared adult absolute tissue sodium concentrations and bioconcentration factors, which indicate net sodium accumulation or excretion relative to individuals' diets. On average, individuals on low‐sodium diets had higher bioconcentration values across all species. Male butterflies accumulated significantly higher sodium concentrations than females in two sodium treatments for B. philenor, and P. rapae and only in the High Na treatment for C. lacinia. However, in D. plexippus, individuals accumulate sodium in the High Na treatment, but males and females responded in the same way. Our study revealed sex‐ and species‐specific patterns of butterfly sodium accumulation, which could be linked to variations in behaviour and/or performance. Differences in sodium content across species have implications for variation in predation and trophic‐level interactions, an interesting avenue for future ecological and evolutionary research.

Sacral neuromodulation for urinary incontinence

Sacral neuromodulation (SNM) was first approved in Europe in 1994 and the United States in 1997. Since its initial approval, indications for SNM have expanded to include urinary urgency/frequency, urgency urinary incontinence (UUI), non-obstructive urinary retention, and fecal incontinence. In the realm of idiopathic overactive bladder (OAB) and UUI multiple studies have highlighted the effectiveness of this therapy with the most recent data reporting a success rate of 93% at 2 year follow-up. Additionally, SNM has been compared to various other therapies for OAB/UUI. SNM demonstrated superiority in terms of symptom improvement when compared to anticholinergic medication and similar efficacy to 200 units of intradetrusor onabotulinum toxin. SNM has also demonstrated similar effectiveness in management of neurogenic patients although there remains a paucity of high level data in these patients. With the introduction of systems with improved MRI conditionality ongoing studies aim to evaluate effectiveness within this population.

ThP1.11 - Harnessing digital consent analytics to highlight multicentre operative and consent trends in breast surgery

Abstract Introduction Paper-based consent is difficult to audit at scale, therefore hospital episode statistics data is used to provide insights into national breast surgical practice. However, as digital consent is becoming routine, analysis of amalgamated data is possible to provide a better understanding of consent practice. Methods Between 22/04/2020-29/11/23, patient anonymised digital consent data was analysed retrospectively using Concentric Health analytics (Audit ID:1123ABS). Inclusion criteria required the responsible clinician to be a breast surgeon, with non-breast surgical procedures excluded. Results n=6860 digital consent episodes were performed across 10 NHS Trusts and 4 private institutions (n=890 unique operations. n=82 unique responsible clinicians, n=191 consenting clinicians). 45.6% (n=3125/6860) of the cases were consented by consultants. 18.7% (n=1286/6860) underwent a mastectomy; 78.5% simple & 21.5% reconstruction (of which 82.6% implant and 17.4% autologous reconstruction). 40.1% (n=2750/6860) underwent breast conserving surgery (79.2% WLE, 18.9% therapeutic mammoplasty, 1.9% Intercostal perforator). Re-excision of margins accounted for 16% of BCS cases (n=441/2750). SLNB was performed in 40.6% and ANC in 11.5% of all breast surgery cases. Regarding localisation; 44.5% Seed, 30.6% Wire, 21.7% palpable/other, 3.2% USS skin mark. Conclusion As well as providing benefits in terms of information sharing and improved patient satisfaction, digital consent also provides an opportunity to provide high-level data, regarding which operations are being consented to, when and by whom.

Fourth-Order Algebraic Diagrammatic Construction for Electron Detachment and Attachment: The IP- and EA-ADC(4) Methods.

We present a non-Dyson fourth-order algebraic diagrammatic construction formulation of the electron propagator, featuring the distinct IP- and EA-ADC(4) schemes for the treatment of ionization and electron attachment processes. The algebraic expressions have been derived automatically using the intermediate state representation approach and implemented in the Q-Chem quantum-chemical program package. The performance of the novel methods is assessed with respect to high-level reference data for ionization potentials and electron affinities of closed- and open-shell systems. While only minor improvements over the corresponding third-order methods are observed for one-hole ionization and one-particle electron attachment processes from closed-shell systems (MAEIP-ADC(4) = 0.27 eV and MAEEA-ADC(4) = 0.05 eV), a significantly enhanced performance is found in case of open-shell reference states (MAEIP-ADC(4) = 0.11 eV and MAEEA-ADC(4) = 0.02 eV). A particularly appealing feature of the novel methods is their accurate treatment of satellite transitions. For closed-shell reference states, we obtain accuracies of MAEIP-ADC(4) = 0.81 eV and MAEEA-ADC(4) = 0.27 eV, while in case of open-shell reference states, mean absolute errors of MAEIP-ADC(4) = 0.15 eV and MAEEA-ADC(4) = 0.27 eV are found.

Management of Patients With a Negative Multiparametric Prostate MRI Examination: AJR Expert Panel Narrative Review.

Multiparametric MRI (mpMRI) of the prostate aids risk stratification of patients with elevated PSA levels. Although most clinically significant prostate cancers are detected by mpMRI, insignificant cancers are less evident. Thus, multiple international prostate cancer guidelines now endorse routine use of prostate MRI as a secondary screening test before prostate biopsy. Nonetheless, management of patients with negative mpMRI results (defined as PI-RADS category 1 or 2) remains unclear. This AJR Expert Panel Narrative Review summarizes the available literature on patients with an elevated screening PSA level and a negative prostate mpMRI result and provides guidance for these patients' management. Systematic biopsy should not be routinely performed after a negative mpMRI examination in patients at average risk but should be considered in patients at high risk. In patients who undergo PSA screening rather than systematic biopsy after negative mpMRI, clear triggers should be established for when to perform a repeat MRI examination. Patients with a negative MRI result followed by negative biopsy should follow their health care practitioners' preferred guidelines concerning subsequent PSA screening for the patient's risk level. Insufficient high-level data exist to support routine use of adjunctive serum or urine biomarkers, artificial intelligence, or PSMA PET to determine the need for prostate biopsy after a negative mpMRI examination.

Crop health assessment through hierarchical fuzzy rule-based status maps

Precision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year.

Visible light activated time dependent phosphorescence colors from carbon dots based materials

To endow carbon dots (CDs)-based materials with time dependent phosphorescence colors (TDPCs) activated by visible light will benefit high-level data encryption and anticounterfeiting. It needs multiple emission centers of triplet state stabilized by noncovalent bonds. In this work, levofloxacin and ethanolamine were applied to synthesize CDs (LEE-CDs), in which, aromatic moieties and multiple emissive centers were formed by the reaction between fluorinated quinolone moieties and ethanolamine. Melamine (MA) and cyanuric acid (CA) with large π systems were selected as matrices, and urea and biuret without π systems were selected as the comparison matrices. Interestingly, LEE-CDs encapsulated in MA (LEE-CDs@MA) exhibited visible-light-activated TDPCs varying from red to yellow green, while LEE-CDs@CA displayed TDPCs from red to yellow green after excited by visible light, yellow to green after excited by UV light. However, LEE-CDs@Urea and LEE-CDs@Biuret could only generate a single color RTP after excited by visible or UV light. It can be inferred that π-π stacking interactions combining with hydrogen bonds activated the O-related triplet state with a short lifetime, and hydrogen bonds only retained the N-related triplet state in LEE-CDs with a long lifetime and the emissive center of matrices with an intermediate lifetime. Therefore, the dynamic ratio of the RTP emissive bands from the three centers changing over time resulted in TDPCs.

TWO-LEVEL CONCEPT FOR SIMULATING UNIFORM INTERFERENCE-RESISTANT DIGITAL DATA TRANSMISSION

The article formalizes, creates and provides for consideration the concept of a single secure interference-resistant data transmission channel. In modern cybersecurity theory and practice, there is the NIST Cybersecurity Framework, which is a set of recommendations for reducing risks for organizations. In order for high-level data to be secure, there are requirements for the SIA triad, namely confidentiality, integrity and availability of information. Therefore, in the future, the expediency of the work and its result will directly depend on the satisfaction of the SIA conditions. As you know, high-level data: such as e-mail, visual data in the GUI of various applications, etc., are transmitted over low-level communication channels: such as cables, wireless radio communication channels, and others. At each of the levels for safe transmission of information, there are specific pests. At high levels, the main threat to information is man and the human factor. The lower the level of information transmission becomes, the more nature, natural obstacles and random short phenomena begin to influence. In order for the user to be able to use various devices without a threat to the confidentiality, integrity and availability of information, it is necessary to actively and continuously develop, improve and improve the existing methods of data protection, restoration, transmission and storage. Each aspect in this struggle for security is both an advantage and a disadvantage: excessive security is not appropriate for mass traffic, complexity does not always provide adequate security, and so on. Therefore, the optimality and expediency of the methods used becomes an important factor. For these reasons, the paper proposes a relatively simple, but no less effective approach to maintaining SIA requirements.

Rewetting impact on the hydrological function of a drained peatland in the boreal landscape

There is a growing interest in peatland restoration as a nature-based solution to mitigate hydrological extremes. To counter the impacts of past peatland degradation and ongoing climate change trajectories, governmental authorities propose rewetting of drained peatlands as a key tool to enhance landscape resilience against floods and droughts by improving water storage. Despite a growing body of literature on this topic, the effectiveness of rewetting to enhance peatland hydrological functions remains insufficiently documented, especially in the boreal region. Therefore, this study utilized high temporal resolution groundwater table level and streamflow data to investigate the impact of peatland rewetting using a before-after-control-impact (BACI) approach. This investigation was conducted on a historically drained peatland located at the Trollberget Experimental Area (TEA) in northern Sweden. The primary aim of the experimental study was to examine the impact of rewetting on (1) the groundwater table level response, (2) runoff dynamics, and (3) water storage and hydrological buffer capacity. Our results showed that peatland rewetting led to a significant increase in the groundwater table level by 60 mm compared to the control. Flowdurationcurve(FDC) analysis demonstrated that the low-flow threshold increased by up to 150% at the rewetted sites. Furthermore, our findings suggested that rewetting resulted in an increase in the groundwater table level threshold at which stream runoff is generated. Additionally, our findings showed a noteworthy shift in the monthly runoff coefficient, with an increase during dry months and a decrease during wet periods. Combined, these observations point towards an enhancement in the peatland’s water storage and hydrological buffer capacity as a positive outcome of the rewetting efforts, but also highlight that within the first three years, full hydrological restoration did not occur.

The role of data governance in a high-level approach of data migration to open data

Data quality issues are still a challenge in data migration. Existing methods mainly focus on technical issues. However, the problem of data quality is not only a technical problem, but the main one is the non-technical aspect. This paper aims to identify the role of data governance in the data migration process. The qualitative research method uses two primary references, namely the high-level data migration approach from Kazimir et al. and the DAMA-DMBOK framework. Both are highly compatible with data migration processes focusing on data quality and referencing governance roles. The case study was conducted in a central government working on data migration to the new One Data application. The results show that eight data governance roles are involved in 20 migration processes (65%). Non-technical activities related to data quality involve more than half of the data migration process. This paper proposes the role of data governance for non-technical aspects of the migration process. This paper presents a case study of the high-level data migration process and how data governance comes into play. This research can help the government and practitioners overcome problems that often occur in data migration, especially related to data quality.

Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds

Rapid identification of soybean seed varieties is crucial for agricultural production and seed quality. Identifying varieties of soybean seed using conventional chemical methods is time-consuming, destructive, and inappropriate for seed quality evaluation. This study utilized hyperspectral imaging technology (HSI) to identify four varieties of soybean seeds. The hyperspectral images of soybean seeds were collected in the spectral range of 400–1000 nm. A multi-level data fusion strategy based on spectral and image information was proposed to improve the accuracy of model. Subsequently, the multi-level data fusion strategy based on partial least squares discriminant analysis (PLS-DA) was used to establish the classification models of soybean seeds. Compared with the models using individual analytical sources, the results demonstrated that the models with multi-level data fusion strategy obtained better prediction performance. The high-level data fusion (HLDF) based on Bayesian consensus provided the optimal results with an accuracy (Acc) and F1-score of 93.13 % and 93.70 % in the prediction phase, respectively. Therefore, the multi-level data fusion strategy can be used as an identification method for soybean seed varieties and an effective approach to enhance the discriminatory capability of models.

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model’s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.

HBIM for Conservation of Built Heritage

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation.

Differentiation and classification of human head hair via spectroscopic analysis, data fusion, and neural networks

BackgroundAttenuated Total Reflectance – Fourier Transform Infrared (ATR-FTIR) spectroscopy and UV–visible microspectrophotometry (MSP) are common in forensic labs. These form a non-destructive method to classify hairs from different individuals using chemometrics. ResultsHair samples were collected from 14 individuals and analyzed. High-level data fusion and neural networks exhibited 100% classification accuracy. SignificanceThe forensic analysis of hair currently involves microscopic and genetic analysis. However, chemical techniques and machine learning can successfully classify hair.

A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

The increasing reliance on smart products has increased vulnerabilities in Internet of Things (IoT) traffic, which poses significant security risks. These vulnerabilities allowed some hackers to exploit them, which led to system performance degradation. Attacks can lead to these vulnerabilities to various undesirable outcomes, including data leakage, economic losses, data breaches, operational disruptions, and damage to the company's reputation. To address these security challenges, network intrusion detection alarms play a crucial role in assessing system security. In recent years, the proliferation of intelligent and soft computing-based algorithmic and structural frameworks has been evident. However, previous studies have faced challenges related to comprehensiveness, zero-day attacks, realism, and data interpretation. In light of these concerns, this study proposes to design a neural network for proactive detection of attacks. Moreover, we propose to use a hybrid system called RF-PCA to facilitate dimensionality reduction and help classifiers. Notably, this is the first application of a BOT-IoT data set in such an approach. The study also includes a discussion of relevant IoT terms in the context of our work. The proposed method uses high-level data features to represent and draw conclusive conclusions. To evaluate its effectiveness, an experiment was conducted using Python as the programming environment, achieving a remarkable detection rate of 99.73%.