Statistical Filtering Research Articles

Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission

Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

Constraining Present‐Day Anthropogenic Total Iron Emissions Using Model and Observations

AbstractIron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long‐term, near‐source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub‐sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under‐ or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting‐related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long‐term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates.

Novel autoantibodies help diagnose anti-SSA antibody negative Sjögren disease and predict abnormal labial salivary gland pathology

ObjectivesSjögren disease (SjD) diagnosis often requires either positive anti-SSA antibodies or a labial salivary gland biopsy with a positive focus score (FS). One-third of patients with SjD lack anti-SSA antibodies...

A Study on the 3D Reconstruction Strategy of a Sheep Body Based on a Kinect v2 Depth Camera Array.

Non-contact measurement based on the 3D reconstruction of sheep bodies can alleviate the stress response in sheep during manual measurement of body dimensions. However, data collection is easily affected by environmental factors and noise, which is not conducive to practical production needs. To address this issue, this study proposes a non-contact data acquisition system and a 3D point cloud reconstruction method for sheep bodies. The collected sheep body data can provide reference data for sheep breeding and fattening. The acquisition system consists of a Kinect v2 depth camera group, a sheep passage, and a restraining pen, synchronously collecting data from three perspectives. The 3D point cloud reconstruction method for sheep bodies is implemented based on C++ language and the Point Cloud Library (PCL). It processes noise through pass-through filtering, statistical filtering, and random sample consensus (RANSAC). A conditional voxel filtering box is proposed to downsample and simplify the point cloud data. Combined with the RANSAC and Iterative Closest Point (ICP) algorithms, coarse and fine registration are performed to improve registration accuracy and robustness, achieving 3D reconstruction of sheep bodies. In the base, 135 sets of point cloud data were collected from 20 sheep. After 3D reconstruction, the reconstruction error of body length compared to the actual values was 0.79%, indicating that this method can provide reliable reference data for 3D point cloud reconstruction research of sheep bodies.

A Hybrid Improved SAC-IA with a KD-ICP Algorithm for Local Point Cloud Alignment Optimization

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment.

Modelowanie cyfrowe w celu przewidywania zagrożenia osuwania się gruntu na wybranym obszarze; Przypadek testowy Ponzano (Włochy)

Creating a digital model is one of the aims of the geotechnical engineers, to predict the land sliding hazard which occur in different regions in the world. In February 2017, an extensive landslide occurred in the hamlet of Ponzano as a sloped area in the Abruzzo region in Italy. In this regard, predicting the land sliding hazard is one of the important issues to prevent hazard to the civilizations. In this project we created a model in the 3D dimension through the Plaxis 3D numerical solution software from the data based of the region Abruzzo in 2007 to evaluate the land sliding hazard before happened and then compare the results with the data from the drone data surveyed recently in the 2022. In this regard, the data from the Abruzzo resources from 2007 imported into the QGIS as the open-source cross-platform software to analysis the geospatial data and then imported into the Recap software to work on the point cloud data and then imported into the Civil 3D software to create a solid surface from the TIN surface and finally since the solid surface contains a large number of irrelative details, they were imported into the Rhinoceros software to create a NURBS surface to be smoothed for better performance in the analysis. The NURBS surface imported into the Plaxis and all of the geometry and geotechnical engineering parameters by considering the investigated geotechnical survey that was conducted in parallel in the area, defined for the model. The “Hardening Soil” model considered for the 1st layer as the “clay and lime” and the “Hoek-Brown” model defined for the 2nd layer as the “marl-flysch”. A fine mesh elements distribution assessed also for the model. The phases defined as the “gravity” to define the unit weight of the soil layers, the “plastic” phase to calculate the instant deformations and the “consolidation” to analyze the plastic deformations in the sloped area of the model. In parallel, the drone data achieved in 2022, were imported into the CloudCompare as the 3D point cloud processing software and different methods such as the “segment”, “statistical outlier Filter”, “CSF filter”, “noise filter”, “cross section” etc. were performed to clean the data and then imported into the Recap software to work on data and then imported into the Civil 3D software to create solid surface of the current data after the land sliding. In this regard, to evaluate the displacement occurred from the year 2007 toward the 2022, a TIN volume surface as the colored map created through the Civil 3D software to show the displacements in the z direction and all of the results were compared with the Plaxis 3D numerical solution software. The results showed that the colored map with the displacement in the positive and negative direction of the z is the same of the analyzed model and the values match each other’s and we created a digital model of the selected area to predict the land sliding hazard in the region in the following.

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

Measurement of outer diameter parameters of manual grinding cable joints based on 3D point cloud processing

For the problems associated with low measurement accuracy caused by surface shrinkage, uneven grinding, and irregular shapes in manual grinding cable joints, as well as the limitations of existing machine vision measurement techniques for the outer diameter parameters, this paper proposes a non-contact outer diameter parameters measurement method of manual grinding cable joints based on three-dimensional (3D) point cloud processing. Firstly, statistical filtering is used to remove discrete noise, and coordinate transformation preprocessing is conducted to ordered the point cloud. Secondly, a bidirectional point cloud slicing method is proposed based on the cable joint structure and point cloud distribution to preserve the original features for effective measurement. Subsequently, the normalized mean curvature is utilized as the weight of point cloud coordinate averaging to construct key points for surface slices, which reduces the amount of data in the sliced point cloud and improves the matching efficiency. Finally, the key points are matched according to the position of the surface slices, and the distance of point pairs is calculated to complete the measurement of the outer diameter parameters. Experiments and error analysis are conducted on multiple manual grinding 110 kV and 500 kV real cable joints. The absolute error in the measurement of the outer diameter is less than 0.2 mm, with a relative error within 1 %. The absolute error of the XY-axis outer diameter deviation is less than 0.1 mm. The experiments demonstrate that the proposed method exhibits superior performance in cable joint outer diameter measurement.

A 3D reconstruction-based method using unmanned aerial vehicles for the representation and analysis of road sections

Due to the fast growth of cities worldwide, roads are increasing daily, and pavement maintenance has become very heavy and costly. Despite all efforts made under the pavement management system to keep the road surface in good shape, several road sections need to be in better condition, which presents a danger for drivers and pedestrians. This paper proposes a novel pavement 3D reconstruction and segmentation approach using the structure from motion technique, unmanned aerial vehicle, and digital camera. The method consists of the 3D modeling of the road by using images taken from different perspectives and the structure from motion technique. In this method, points cloud is sampled and cleaned using statistical outlier removal and noise filters. After that, duplicated and isolated points are eliminated to retain only significant data. The normal road plane is estimated using the principal component analysis technique and the remaining points. This plan presents a root mean square less than 0.85 cm. Finally, distances from those points to the normal plane are calculated and clustered to segment the road into distressed and non-distressed areas. The proposed approach presents a similarity rate to the survey measurement passed 95%. It has demonstrated promising results and has the potential for further improvement by optimizing various steps.

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm.

Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.

System identifiability in a time-evolving agent-based model.

Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.

Feasibility evaluation of radiotherapy positioning system guided by augmented reality and point cloud registration.

To develop a radiotherapy positioning system based on Point Cloud Registration (PCR) and Augmented Reality (AR), and to verify its feasibility. The optimal steps of PCR were investigated, and virtual positioning experiments were designed to evaluate its accuracy and speed. AR was implemented by Unity 3D and Vuforia for initial position correction, and PCR for precision registration, to achieve the proposed radiotherapy positioning system. Feasibility of the proposed system was evaluated through phantom positioning tests as well as real human positioning tests. Real human tests involved breath-holding positioning and free-breathing positioning tests. Evaluation metrics included 6 Degree of Freedom (DOF) deviations and Distance (D) errors. Additionally, the interaction between CBCT and the proposed system was envisaged through CBCT and optical cross-source PCR. Point-to-plane iterative Closest Point (ICP), statistical filtering, uniform down-sampling, and optimal sampling ratio were determined for PCR procedure. In virtual positioning tests, a single registration took only 0.111 s, and the average D error for 15 patients was 0.015±0.029mm., Errors of phantom tests were at the sub-millimeter level, with an average D error of 0.6±0.2mm. In the real human positioning tests, the average accuracy of breath-holding positioning was still at the sub-millimeter level, where the errors of X, Y and Z axes were 0.59±0.12mm, 0.54±0.12mm, and 0.52±0.09mm, and the average D error was 0.96±0.22mm. In the free-breathing positioning, the average errors in X, Y, and Z axes were still less than 1mm. Although the mean D error was 1.93±0.36mm, it still falls within a clinically acceptable error margin. The AR and PCR-guided radiotherapy positioning system enables markerless, radiation-free and high-accuracy positioning, which is feasible in real-world scenarios.

A method for calculating and simulating phenotype of soybean based on 3D reconstruction

In plant phenotypic research, accurate organ segmentation and crop simulation are crucial for optimizing crop planting and increasing yield. In this study, an efficient method for soybean organ segmentation and phenotypic growth simulation was explored based on 3D reconstruction technology. Taking Dongnong252 soybean as the research object, the soybean phenotype acquisition system based on Kinect sensor was used to achieve high-precision and non-destructive acquisition of soybean canopy images during the whole growth period. First, conditional filtering and statistical filtering were used to remove noise. Then, combined with the Intrinsic Shape Signatures-Coherent Point Drift (ISS-CPD) and the Iterative Closest Point (ICP) algorithms, the multi-view three-dimensional (3D) canopy structure of soybean was reconstructed, which provided a reliable basis for plant stem and leaf segmentation. On this basis, the average accuracy of plant stem and leaf segmentation was 79.99% by using the Distance-field-based segmentation pipeline (DFSP) algorithm. Furthermore, the accurate information was provided for extracting and calculating 3D phenotypic parameters such as plant height, crown width, stem thickness, leaf length and leaf width from three scales of whole plant, stem and leaf. The calculated values were highly consistent with the measured values, with an average coefficient of determination of 0.9654 and an average percentage error of 3.4862%. Meanwhile, by analyzing the quantitative relationship between phenotypic parameters and physiological development time, the data-driven Richards growth simulation model was introduced to accurately predict the growth process of soybean plants. The coefficient of determination R2 values of each phenotypic simulation model reached above 0.9357, which improved the goodness of fit of the model by 0.03 compared with the Logistic model, and its root mean square error (RMSE) ranged from 0.0020 to 0.1112. The research results indicated that this method had high accuracy and reliability in 3D reconstruction of soybean canopy, phenotype calculation, and growth simulation. It could provide quantitative indicators for soybean variety selection, planting, and management, and provide technical support and reference for high-throughput phenotype acquisition and analysis of field crops.

Innovative design of digital sculpture art under 3D modeling technology

Abstract 3D modeling technology is one of the most important research directions in computer vision, which is increasingly linked with digital sculpture art. Combined with the point cloud, a new type of image data, this paper innovatively proposes a multi-view 3D digital sculpture reconstruction model to realize the 3D digital sculpture reconstruction of point cloud data.3D digital sculpture reconstruction is mainly divided into two parts: point cloud data processing and 3D reconstruction. The former uses radius, statistical filtering, and bilateral filtering algorithms for point cloud data denoising and proposes an NDT alignment algorithm to integrate point cloud data. The latter triangulates the unreconstructed observation points introduced by the new image, uses beam method leveling to globally optimize the point cloud structure, and proposes incremental recovery and global recovery reconstruction methods, respectively. In the point cloud data alignment performance test, the reconstruction plotting time of the reconstruction model in this paper possesses a large improvement, and the proposed NDT algorithm outperforms the traditional algorithm in terms of accuracy and execution efficiency. In the reconstruction of architectural digital sculpture, the average error of the parts of the digital sculpture is 0.00775, and the maximum residual difference is at about 4.5 pixels. Hence, the sculpture’s accuracy and precision performance are better.

Proposed Combined Technique of Statistical Filter and Machine Learning for Exploratory Data Analytics and Features Selecting of Telecommunication Customer Churn

This study can determine a customer churn based on his historical data and behavior. It indicates that an efficient churn prediction model should employ a significant volume of historical data to identify churners. However, existing models have several limitations that make it difficult to do churn prediction reasonably and accurately. To solve this issue this study proposed new combined technique of statistical filter and machine learning preprocessing is used. Furthermore, statistical methods are utilized to generate models, resulting in poor prediction performance. Also, benchmark datasets are not employed in the literature for model evaluation, resulting in a poor representation of the actual visual representation of data. Without benchmark datasets, it is impossible to compare different models fairly. An intelligent model can be utilized to relieve current issues and deliver more accurate churn prediction. Keywords: Customer churn, Data Acquisition, Exploratory Data Analytics, Feature selecting technique, Filter statistical technique, Machine learning.

Opportunities for developing artificial intelligence in tuning the quality of internal auditing: An applied study on the Egyptian banking sector

This study aims to find out the possibility of using artificial intelligence techniques to control the quality of internal auditing, through a field study on Egyptian public and private banks, by distributing a questionnaire to 330 items that were calculated according to the Stephen Thompson statistical equation and the statistical package program SPSS was used. The study concluded that the use of artificial intelligence in auditing operations plays an important role in improving the quality and reliability of information in financial statements and helps in verifying errors in the auditing processes of the banks under study, as artificial intelligence affects the quality of internal auditing with a value of (0.897), which indicates The great importance of artificial intelligence in controlling the quality of internal auditing in the banks under study. This means that whenever the banks under study had an effective strategy for applying artificial intelligence in their internal audit departments, this led to the effectiveness of the banks’ internal audit quality. Keywords: artificial intelligence, internal audit quality, technological programs, knowledge management, objectivity.

An improved parameterless empirical wavelet transform for incipient fault identification of wheelset bearing.

The empirical wavelet transform (EWT), along with its adaptable spectrum segmentation technique, finds extensive application in the incipient detection of rolling bearing faults. However, determining mode boundaries adaptively under strong noise interference remains a substantial challenge. Herein, an improved parameterless EWT based on the order statistics filter (OSF) is proposed to overcome this shortcoming. This approach replaces the Fourier spectrum with its envelope spectrum through OSF, and the local minima of the envelope spectrum are selected as the initial boundary to obtain the initial empirical modes. Furthermore, the adjacent initial empirical modes are combined using Pearson's correlation coefficient, and the final number and boundaries of empirical modes are automatically determined using the mean envelope entropy. The advantages of the proposed method are demonstrated through an accelerated degradation bearing test bench and a wheelset-bearing test bench, as well as by comparing it with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and Autogram.

Immune-Related Gene Expression Signatures Identify a Patient's Subset Achieving Durable Complete Response to Brentuximab Vedotin in Relapsed/Refractory Hodgkin Lymphoma

Background: In classical Hodgkin lymphoma (cHL), 20%-30% of patients experience relapse or progressive disease after standard chemotherapy, requiring salvage treatment strategies including targeted immunotherapy with brentuximab vedotin (BV). Molecular mechanisms underlying treatment resistance to BV in cHL are still poorly understood. The unique characteristics of cHL microenvironment, which represents >90% of the cellular infiltrate, make targeted gene expression profiling (T-GEP) a suitable technique to investigate immune-related determinants of BV sensitivity. Methods: 31 consecutive patients (pts) affected by relapsed-refractory (R/R) cHL treated at 2 italian Institutions from 2013 to 2018 with available formalin-fixed paraffin embedded (FFPE) tissue collected right before the start of BV therapy, were included in this retrospective study. FFPE tissue was profiled by T-GEP on the Nanostring platform, using the PanCancer Immune Profiling panel, encompassing 730 genes regulating the most relevant immunologic pathways. Disease response after BV treatment was evaluated by FDG-PET after 4, 8, 12 and 16 cycles. Results: Median age of the whole cohort was 38 years (y) (range 18-76). 18 pts (58%) were male. Median number of BV cycles was 8 (range 2-16). After a median follow-up of 3.5 y from the start of Brentuximab treatment, the overall survival (OS) and progression-free survival (PFS) rates were 87% and 32% respectively. Pts were classified as BV-responders in case of complete metabolic response (CMR) lasting more than 6 months (m); pts achieving less than CMR or short lasting (< 6 m) CMR were classified as non-responders (NR). By applying these criteria, 12 pts (39%) were classified as responders and 19 as non-responders. Differential expression (DE) analyses were performed and differentially expressed (DE) genes were selected by applying two statistical filters: (log2FC > 1 and p-value < 0.05). 61 genes were differentially expressed between responders and non-responders. The 61-gene signature included genes involved in JAK-STAT signaling, IL-17 signaling and cytokine-cytokine receptor interaction. Unsupervised clustering analyses identified two different pts clusters with clearly different BV-sensitivity profiles: cluster1 (22 pts) and cluster2 (9 pts). Among the 22 pts of cluster 1, only 4 pts were responders while 18 were non-responders. As opposite, 8 of 9 patients of Cluster2 were responders, while only 1 was non-responder (p=0.0005). In line with this, 4-y PFS rate of the 22 pts included in cluster1 was 19%, while the 4-y PFS rate of the 9 pts included in cluster2 was 66% (p=0.007). Median PFS of Cluster1 was only 5.7 months. The composition of the two clusters was not significantly different in terms of number of prior therapies and disease stage at the time of BV treatment. After achieving a CMR 6 of 9 pts were consolidated with autologous stem cell transplantation (ASCT) in cluster2. In cluster1, 1 patient was consolidated with ASCT after CMR, and 2 pts received allogeneic stem cell transplantation (allo-SCT) after BV-failure. Conclusions: With the limitation of the small sample size, these data suggest that the composition of cHL microenvironment could influence the activity of single agent BV. Pre-treatment microenvironment profiling with T-GEP could be a useful tool for selecting patients who may derive maximal benefit from BV in cHL.

A comparison of photographic and spectrometric methods to quantify the colours seen by animal eyes

Abstract Quantifying how colours stimulate animal eyes is an essential first step in many areas of biology, from behavioural and visual ecology to neuroethology. Although multiple methods exist, their relative accuracies remain untested, leaving researchers unsure which methods are most accurate and which might be unacceptably inaccurate. Here, I compare measurements from reflectance spectrometry and four camera‐based techniques to ground‐truth spectroradiometric measurements of radiance, using eight diverse visual systems. Because errors may compound through successive calculations, I calculate not only relative quantum catches but also chroma and colour contrasts. I find that filters mimicking photoreceptor spectral sensitivity curves, through either custom fabrication or weighted additive combinations of bandpass filters (‘computational filters’), as well as reflectance spectrometry, are the most accurate techniques, with most R2 > 0.98. Statistical filters trained on the library of reflectance spectra in the multispectral image calibration and analysis (MICA) toolbox lag behind in accuracy, particularly for dark colours, with R2 values dipping as low as 0.23. However, the performance of these filters is vastly improved (all R2 > 0.96) if one (a) uses the full UV–VIS range of camera and animal vision, even for animals lacking UV photoreceptors and (b) expands the library of training spectra to include simulated low‐reflectance spectra. Statistical filters trained on either a 24‐ or 45‐colour chart (an alternative method offered by the MICA toolbox) vary wildly in accuracy, with R2 values ranging from 0.11 to 0.94, and should not be trusted. For those requiring point measurements only, reflectance spectrometry is highly accurate within the constraints described in the main text. Those using photography and desiring the highest levels of accuracy should opt for custom‐fabricated or computational filters. Statistical filters trained on the MICA toolbox's library of natural spectra can be expected to be reasonably accurate for many combinations of visual systems and stimuli, but only if (a) the full UV–VIS spectrum is used and (b) the training library is expanded to include simulated low‐reflectance spectra. Statistical filters trained on a colour chart are highly unreliable and their use should be discontinued.

Normalized effect size (NES): a novel feature selection model for Urdu fake news classification

Social media has become an essential source of news for everyday users. However, the rise of fake news on social media has made it more difficult for users to trust the information on these platforms. Most research studies focus on fake news detection in the English language, and only a limited number of studies deal with fake news in resource-poor languages such as Urdu. This article proposes a globally weighted term selection approach named normalized effect size (NES) to select highly discriminative features for Urdu fake news classification. The proposed model is based on the traditional inverse document frequency (TF-IDF) weighting measure. TF-IDF transforms the textual data into a weighted term-document matrix and is usually prone to the curse of dimensionality. Our novel statistical model filters the most discriminative terms to reduce the data’s dimensionality and improve classification accuracy. We compare the proposed approach with the seven well-known feature selection and ranking techniques, namely normalized difference measure (NDM), bi-normal separation (BNS), odds ratio (OR), GINI, distinguished feature selector (DFS), information gain (IG), and Chi square (Chi). Our ensemble-based approach achieves high performance on two benchmark datasets, BET and UFN, achieving an accuracy of 88% and 90%, respectively.