Radial Bias Research Articles

Multi-Fidelity Modelling of the Effect of Combustor Traverse on High-Pressure Turbine Temperatures

As turbine entry temperatures of modern jet engines continue to increase, additional thermal stresses are introduced onto the high-pressure turbine rotors, which are already burdened by substantial levels of centrifugal and gas loads. Usually, for modern turbofan engines, the temperature distribution upstream of the high-pressure stator is characterized by a series of high-temperature regions, determined by the circumferential arrangement of the combustor burners. The position of these high-temperature regions, both radially and circumferentially in relation to the high-pressure stator arrangement, can have a strong impact on their subsequent migration through the high-pressure stage. Therefore, for a given amount of thermal power entering the turbine, a significant reduction in maximum rotor temperatures can be achieved by adjusting the inlet temperature distribution. This paper is aimed at mitigating the maximum surface temperatures on a high-pressure turbine rotor from a modern commercial turbofan engine by conducting a parametric analysis and optimization of the inlet temperature field. The parameters considered for this study are the circumferential position of the high-temperature spots, and the overall bias of the temperature distribution in the radial direction. High-fidelity unsteady (phase-lag) and conjugate heat transfer simulations are performed to evaluate the effects of inlet clocking and radial bias on rotor metal temperatures. The optimized inlet distribution achieved a 100 K reduction in peak high-pressure rotor temperatures and 7.5% lower peak temperatures on the high-pressure stator vanes. Furthermore, the optimized temperature distribution is also characterized by a significantly more uniform heat load allocation on the stator vanes, when compared to the baseline one.

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine.

Identifying and diagnosing faults is a critical task in process industries to maintain effective monitoring of process and plant safety. Minimizing process downtime is critical for enhancing the quality of the product and minimizing production costs. Real-time categorization of issues across several levels is essential for the monitoring of processes. However, there are still notable obstacles, that must be addressed, such as the existence of robust correlations, the complexity of the data, and the lack of linearity. This study introduces a novel fault identification technique in batch reactor experimental trials that employs multikernel support vector machines (SVMs) to categorize internal and external issues, specifically reactor temperature, coolant temperature, and jacket temperature. The data set was obtained from empirical research. The classification has been conducted using a multikernel SVM. This article identified that the nonlinear classifier using the radial bias function results in an accuracy that is at least 22.08% superior to other methods.

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

The assessment of water quality assumes a position of utmost significance as it plays a critical role in upholding ecological balance and safeguarding the well-being of human populations. To achieve these goals, an in-depth consideration of water quality trends is essential, as it offers comprehension into the intricate interplay between various elements within aquatic ecosystems. As a consequence, the proposed work investigates the water quality trends specifically within the Valliyar sub-basin, which encompasses the geographical areas of Kattathurai, Colachal, Thuckalay, and Villukuri. The temporal scope of investigation spans from the year 2000 to 2018 using the dependent variable of water quality parameters with dependent variables of climate data. Recognizing the need for advanced methodologies to tackle the multifaceted nature of water quality dynamics, this research harnesses the power of pioneering machine learning techniques. Two notable approaches, the Radial Bias Function Neural Network (RBFNN) and the DenseNet-121-based Convolutional Neural Network (CNN), are brought into performance. The primary objective is to leverage these techniques to forecast water quality trends for the next twenty-two years. The effectiveness of various machine learning models in predicting water quality is evaluated using the following key performance metrics: the Mean-Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE). Notably, the DenseNet CNN model exhibits accurate prediction among the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning (DL) models. This research underscores the significance of machine learning techniques, with DenseNet CNN model emerging as a particularly promising tool in this domain.

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.

Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling.

Primate visual cortex exhibits key organizational principles: cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells, and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.

Advanced Intrusion Detection in Vehicular Networks: Empowering Security through Hybrid Off-loading Techniques and Enhanced Radial Bias Neural Network

Over the last several decades, the implementation of ITS has shown to be the most efficient and successful strategy for expanding the variety of current transportation networks. Vehicle-based offloading of data going to be essential for forthcoming networking innovations like D2D and 5G due to the substantial contribution it makes to efficiently using network capability while wasting minimal power. Information transmissions that would normally need a cellular network's infrastructure may instead be made using alternative networking mechanisms including Bluetooth, WiFi, and opportunistic communications. Data offloading has the ability to significantly increase the efficiency with which network resources are used. The offloading of data from vehicles has a considerable impact on the strain on cellular networks. It helps the network achieve higher throughput by facilitating the simultaneous reception of data by a large number of users. First, we must establish that the problem of Vehicular data offloading is an NP-hard target set selection (TSS) issue before we can even begin to characterize it. Using a combination of Hybrid PSO and GWO, TSS selects a small group of nodes to do the redundant data exchange (Particle Swarm Optimization with Gray Wolf Optimization). Collaboration between individuals and ISPs to identify effective aim sets may provide useful insights. If malicious users are present in the target group, they may slow down network activity by spoofing or by reducing the network's offloading capacity. It is possible that the whole network's performance would suffer as a direct result of these malicious users. In this study, we suggest a hybrid approach to communication for specifying the intended audience. We take use of the characteristics of opinion dynamics amongst users to get around the issue of overlapping community detection. Trust-based metrics inferred from users' activities are used to ensure the safety of the target set. In order to call 911, the suggested work additionally incorporates a method of sorting and classifying the offload limitations through Radial Bias Neural Network (RBNN). The following may be determined with the use of the proposed work's performance indicators: precision, entropy, and delay.

Software Testing Using Cuckoo Search Algorithm with Machine Learning Techniques

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

Forecasting Bitcoin Price Utilizing Evolutionary Radial Bias Function Networks

A semi-parametric evolutionary method called the Fireworks method (FWA) and a radial bias function neural network (RBFN) are combined in this study to make a new type of network called RBFN-FWA. Effectively adjusting the RBFN's biases and weights is the goal of integrating FWA. We next used this proposed methodology to predict how Bitcoin's price will go in the future. The solo RBFN was trained using various optimization techniques such as GA, PSO, and GD for comparison purposes. This resulted in three new models: RBFN-GA, RBFN-PSO, and RBFN-GD. We also use all the other models that can do the same thing. Utilizing error metrics like Mean Absolute Percentage Error (MAPE) and Normalized Mean Squared Error (NMSE), we evaluated the models' performance. In terms of NMSE and MAPE, the experimental findings show that RBFNN-FWA is the best comparison model, demonstrating its higher predictive potential.A semi-parametric evolutionary method called the Fireworks method (FWA) and a radial bias function neural network (RBFN) are combined in this study to make a new type of network called RBFN-FWA. Effectively adjusting the RBFN's biases and weights is the goal of integrating FWA. We next used this proposed methodology to predict how Bitcoin's price will go in the future. The solo RBFN was trained using various optimization techniques such as GA, PSO, and GD for comparison purposes. This resulted in three new models: RBFN-GA, RBFN-PSO, and RBFN-GD. We also use all the other models that can do the same thing. Utilizing error metrics like Mean Absolute Percentage Error (MAPE) and Normalized Mean Squared Error (NMSE), we evaluated the models' performance. In terms of NMSE and MAPE, the experimental findings show that RBFNN-FWA is the best comparison model, demonstrating its higher predictive potential.

Automated Spam Detection Using Sandpiper Optimization Algorithm-Based Feature Selection with the Machine Learning Model

The email has become an online communication tool and an important part of daily life. Spam mails take up a lot of space and bandwidth, and spam filtering algorithms have flaws that cause them to mistake legitimate emails for spam (false positives). These issues are becoming a bigger difficulty for the online world. This work proposes the use of a sandpiper optimization (SPO) algorithm, which is applied for the feature selection process which minimizes the training complexity and maximizes the classification accuracy and Radial Bias Neural Network (RBNN) for classifying emails as genuine email and spam email. The Enron email dataset and Spam Assassin datasets were used. The outcomes show that the rotation forest algorithm after feature selection with SPO accurately classifies the emails as genuine email and spam email with 3.88%, 5.75%, and 6.16% higher accuracy for Genuine Email, 2.31%, 8.47%, and 7.23% higher accuracy for spam email compared with existing Universal Spam Detection using Transfer Learning of the BERT Model (USD-TL-BERT), Hybrid Learning Approach for E-mail Spam Detection and Classification (HLA-ESDC), and the Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method (ESD-HAHD), respectively. This demonstrates that the proposed method significantly outperforms existing methods.

Model-based neuroadaptive event-triggered tracking consensus control for nonlinear multiagent systems with input delay

In this article, a neuroadaptive event-triggered control methodology is proposed to tackle the leader-following tracking consensus issue of uncertain nonlinear multiagent systems (MASs) with input delay and external disturbances. The proposed approach incorporates radial bias function neural networks (RBF NNs) and the integral compensation technique, effectively addressing the issues of nonlinear uncertainty and input delay, respectively. For the sake of reducing the transmission of information, we adopt the strategy that the neural network (NN) weight update law is only changed at the event-triggered instant, and a novel adaptive model and related controller are constructed. Simultaneously, this paper establishes an impulsive dynamic system to analyze the stability of the closed-loop system by extending the Lyapunov theorem to continuous and reset dynamics. Sufficient conditions for achieving leader-following tracking consensus are derived without the Zeno behavior. Finally, a numerical example is presented to illustrate the validity of the proposed method.

UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN.

To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.

Imposition recognition in vehicular networks using hybrid offloading techniques with improved radial bias neural network

Imposition recognition in vehicular networks using hybrid offloading techniques with improved radial bias neural network

Tactile localization on stretched skin.

The ability to localize touch on the skin is an important aspect of tactile perception. As our limbs move, the skin stretches flexibly, and research has found that signals specifying stretch affect perception of limb posture. Skin stretch also distorts the relative spatial position of different locations on the skin, posing potential problems for tactile localization. Here, we investigated the effects of skin stretch using an established test of tactile localization on the hand. Twenty participants completed a tactile localization task in no-stretch and stretched conditions, respectively, after giving informed consent. The current study found a clear distal and radial bias in both the no-stretch condition and the stretched condition. Indeed, the distal bias was even larger in the stretched condition than at baseline. Critically, however, this change in distal bias was entirely accounted for by changes in the actual location of stimulus as a result of skin stretch, with no corresponding change in the judged location. Thus, the somatosensory system appears to disregard stretch when calculating the location of tactile stimuli. These results mirror recent findings showing that tactile distance perception also fails to take skin stretch into account. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Strong radial bias, but no evidence of oblique effect from high-resolution data in primary visual cortex

Strong radial bias, but no evidence of oblique effect from high-resolution data in primary visual cortex

Radial bias alters perceived motion direction

Radial bias alters perceived motion direction

Radial bias in face identification

Human vision in the periphery is most accurate for stimuli that point towards the fovea. This so-called radial bias has been linked with the organization and spatial selectivity of neurons at the lowest levels of the visual system, from retinal ganglion cells onwards. Despite evidence that the human visual system is radially biased, it is not yet known whether this bias persists at higher levels of processing, or whether high-level representations are invariant to this low-level orientation bias. We used the case of face identity recognition to address this question. The specialized high-level mechanisms that support efficient face recognition are highly dependent on horizontally oriented information, which convey the most useful identity cues in the fovea. We show that face selective mechanisms are more sensitive on the horizontal meridian (to the left and right of fixation) compared to the vertical meridian (above and below fixation), suggesting that the horizontal cues in the face are better extracted on the horizontal meridian, where they align with the radial bias. The results demonstrate that the radial bias is maintained at high-level recognition stages and emphasize the importance of accounting for the radial bias in future investigation of visual recognition processes in peripheral vision.

Trending Ability of End-Tidal Capnography Monitoring During Mechanical Ventilation to Track Changes in Arterial Partial Pressure of Carbon Dioxide in Critically Ill Patients With Acute Brain Injury: A Monocenter Retrospective Study.

Changes in arterial partial pressure of carbon dioxide (Pa co2 ) may alter cerebral perfusion in critically ill patients with acute brain injury. Consequently, international guidelines recommend normocapnia in mechanically ventilated patients with acute brain injury. The measurement of end-tidal capnography (Et co2 ) allows its approximation. Our objective was to report the agreement between trends in Et co2 and Pa co2 during mechanical ventilation in patients with acute brain injury. Retrospective monocenter study was conducted for 2 years. Critically ill patients with acute brain injury who required mechanical ventilation with continuous Et co2 monitoring and with 2 or more arterial gas were included. The agreement was evaluated according to the Bland and Altman analysis for repeated measurements with calculation of bias, and upper and lower limits of agreement. The directional concordance rate of changes between Et co2 and Pa co2 was evaluated with a 4-quadrant plot. A polar plot analysis was performed using the Critchley methods. We analyzed the data of 255 patients with a total of 3923 paired ΔEt co2 and ΔPa co2 (9 values per patient in median). Mean bias by Bland and Altman analysis was -8.1 (95 CI, -7.9 to -8.3) mm Hg. The directional concordance rate between Et co2 and Pa co2 was 55.8%. The mean radial bias by polar plot analysis was -4.4° (95% CI, -5.5 to -3.3) with radial limit of agreement (LOA) of ±62.8° with radial LOA 95% CI of ±1.9°. Our results question the performance of trending ability of Et co2 to track changes in Pa co2 in a population of critically ill patients with acute brain injury. Changes in Et co2 largely failed to follow changes in Pa co2 in both direction (ie, low concordance rate) and magnitude (ie, large radial LOA). These results need to be confirmed in prospective studies to minimize the risk of bias.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review.

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

Experimental evaluation and modeling the mass and temperature of dried mint in greenhouse solar dryer; Application of machine learning method

This study is aimed to model the temperature and mass of dried mint in a Quonset type of Greenhouse Solar Dryer (GSD). The inputs including ambient air temperature (◦C), ambient air humidity (%) and solar radiation (Wm-2) and output data including temperature (◦C) and mass (gr) of dried mint were collected from a Quonset GSD. Artificial Neural Network (ANN) models including Multilayer Perceptron (MLP) and Radial Bias Function (RBF) and also, Gaussian Process Regression (GPR) by k-fold cross validation method were used for modeling. Levenberg-Marquardt (LM) learning algorithm with Sigmoid-Tangent transfer function by different combinations of neurons in the hidden layer were assessed for ANN models. The results showed that MLP and GPR have higher error than RBF model for predicting the temperature and mass of dried mint. The results of RBF optimization indicated that 3-15-1 and 3-18-1 topologies with using 60 and 50% of total dataset for training steps and having 0.4 and 0.3 spread factor values can predict the temperature and mass of dried mint with Mean Absolute Percentage Error (MAPE) of 1.4 and 1.82%, respectively. The results of t, F, and Kolmogorov–Smirnov tests indicated that there is no significant difference between actual and RBF output values.

Radial bias alters high-level motion perception

The visual system involves various orientation and visual field anisotropies, one of which is a preference for radial orientations and motion directions. By radial, we mean those directions coursing symmetrically outward from the fovea into the periphery. This bias stems from anatomical and physiological substrates in the early visual system. We recently reported that this low-level visual anisotropy can alter perceived object orientation. Here, we report that radial bias can also alter another higher-level system, the perceived direction of apparent motion. We presented a bistable apparent motion quartet in the center of the screen while participants fixated on various locations around the quartet. Participants (N = 22) were strongly biased to see the motion direction that was radial with respect to their fixation, controlling for any biases with center fixation. This was observed using a vertical-horizontal quartet as well as an oblique quartet (45° rotated quartet). The latter allowed us to rule out the contribution of the hemisphere effect where motion across the midline is perceived less often. These results extend our earlier findings on perceived object orientation, showing that low-level structural aspects of the visual system alter yet another higher-level visual process, that of apparent motion perception.