Classification Error Research Articles

Selection of OAM signal constellations for atmospheric channels using optimal transport theory

We describe a method for determining optimal selections of orbital angular momentum (OAM) superpositions for OAM signal modulation in free-space optical communications using a measure of distance in the context of the Optimal Transport theory. Within the range of topological charges ℓ = −20 to ℓ = 20 we design OAM constellations using 16 to 128 symbols consisting of solos, duets, trios, and quartets of OAM modes. We propose a classification strategy requiring relatively low complexity to evaluate the performance of these constellations, achieving a classification error smaller than 1/1000 in weak to strong turbulence conditions for the 16-OAM constellation. We have found that the optimal set shows some dependence on the receiver’s architecture, so we offer results for optical detectors based on the conjugate projection, the mode sorter, and the Shack-Hartmann sensor.

Identifying the Urban Development Pattern of Subway Cities Using Predictive Analysis

Starting from the hypothesis that there are structural differences in urban development between cities that have implemented underground transport and those that have not, the paper aims to provide a predictive analysis model to identify cities that show the pattern for the development of the subway network. In order to identify the predictive analysis model capable of classifying urban settlements, the predictive analysis part consisted of two stages. In the first stage, a Random Forest classification model will be estimated on a sample of the 27 capitals of the European Member States. Subsequently, based on this model, 9 cities in Romania will be classified from the perspective of the existence of the urban development pattern for the development of underground public transport. In order for the classification error of the predictive model to be as low as possible, variables from all areas specific to urban development (economic development, population density, circular economy, green city, digitalization, urban mobility) were used. The element of originality of the paper consists in calculating variables on the area of urban mobility, urban infrastructure and circular economy, using GIS coordinates. Thus, in addition to official international data sources, variables calculated by using geospatial technology (average distances, densities of green spaces, etc.) were included in this research. The main results of the research show that there are cities in Romania that present the same pattern of development similar to the capitals of the Member States of the European Union that have implemented underground public transport. Therefore, it can be said that for these cities it is necessary to implement this type of transport, the depreciation of the investment representing an easy process, which can be achieved in a medium or short term.

Dementia Prevalence, Incidence, and Mortality Trends Among U.S. Adults Ages 72 and Older, 2011-2021.

U.S.-focused studies have reported decreasing dementia prevalence in recent decades, but have not yet focused on the implications of the coronavirus disease 2019 (COVID-19) pandemic for trends. We use the 2011-2021 National Health and Aging Trends Study (N = 48 065) to examine dementia prevalence, incidence, and mortality trends among adults ages 72 and older, and the contribution to prevalence trends of changes in the distribution of characteristics of the older population ("compositional shifts") during the full and prepandemic periods. To minimize classification error, individuals must meet dementia criteria for 2 consecutive rounds. The prevalence of probable dementia declined from 11.9% in 2011 to 9.2% in 2019 and 8.2% in 2021 (3.1% average annual decline). Pre-pandemic declines continued for women and non-Hispanic White individuals and emerged over the 2011-2021 period for men and those ages 80-89. Declines in dementia incidence were stronger for the 2011-2021 period than for the prepandemic period, while mortality among those with dementia rose sharply with the onset of the COVID-19 pandemic. Shifts in the composition of the older population accounted for a smaller fraction of the decline over the full period (27%) than over the prepandemic period (45%). Declines in dementia prevalence continued into years marked by onset of the COVID-19 pandemic, along with declines in incidence and sharp increases in mortality among those with dementia. However, declines are no longer largely attributable to compositional changes in the older population. Continued tracking of dementia prevalence, incidence, and mortality among those with and without dementia is needed to understand long-run consequences of the pandemic.

Mapping schistosomiasis risk landscapes and implications for disease control: A case study for low endemic areas in the Middle Paranapanema river basin, São Paulo, Brazil.

Schistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. In the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed. This study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors among disorders with commonly co-occurring features to examine this heterogeneity. Classification analyses were conducted with the University of California, Los Angeles Phenomics Study database using a support-vector classifier to differentiate disorders via whole brain task-based functional connectivity, predicting that model misclassifications would provide insight about brain connectivity characteristics shared across disorders. Whether symptoms and specific brain networks could account for misclassification rates was also explored. The classification model performed better than chance (44% accuracy, p = .01) and revealed that misclassification of schizophrenia (SCZ) as bipolar disorder (BD; 38%) and BD as SCZ (36%) was symmetrical. Attention-deficit/hyperactivity disorder (ADHD) was misclassified as BD at the highest rate (46%) and higher than the converse (17%). SCZ and ADHD were misclassified least (15% SCZ as ADHD and 22% ADHD as SCZ). Considerable variance in misclassification of SCZ as BD (R2 = .83) and BD as SCZ (R2 = .71) could be accounted for by symptoms of both SCZ and BD. Permutation testing revealed disorder- and network-specific effects, with certain networks improving classification accuracy and others hindering it for specific disorders. An approach focused on classification errors replicated known disorder overlap, producing errors in the expected configuration. Further, it identified clinical and neural features within and across diagnostic categories that contribute to disorder misclassification and within-disorder heterogeneity. This approach may facilitate neurobiologically informed phenotypic differentiation within diagnostic groups. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Maximizing Steel Slice Defect Detection: Integrating ResNet101 Deep Features with SVM via Bayesian Optimization

Accurate detection of defects on steel surfaces is crucial for maintaining quality standards in steel production. This paper addresses the challenge of classifying steel sheets into distinct defect categories by presenting a robust method that leverages deep learning and advanced optimization techniques. We propose a novel approach that utilizes the ResNet101 model to extract deep features, which are then classified using a support vector machine (SVM). To enhance the SVM's performance, Bayesian optimization is employed for hyperparameter tuning. Our method is validated using the "Severstal: Steel Defect Detection" dataset from Kaggle, achieving a validation accuracy of 89.1% and a test accuracy of 90.6%, with a classification error of 0.10934. Additionally, the area under the curve (AUC) for each class exceeds 0.95 in both the validation and test sets, demonstrating excellent discriminatory power. Further evaluation on the DAGM dataset achieved flawless results, with an accuracy of 100%, AUC of 1, sensitivity of 100%, specificity of 100%, precision of 100%, MCC of 100%, F1 score of 1, and kappa of 100%. On the NEU dataset, our method achieved an accuracy of 97.92%, sensitivity of 97.92%, specificity of 99.58%, precision of 98.06%, F1 score of 0.9791, MCC of 97.55%, and kappa of 92.50%. These results demonstrate the robustness and adaptability of the proposed method, offering an efficient and reliable solution for automating steel defect detection and surface defect classification in industrial applications.

A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation

AbstractSnowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%.

Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception

Point cloud segmentation is essential for scene understanding, which provides advanced information for many applications, such as autonomous driving, robots, and virtual reality. To improve the accuracy and robustness of point cloud segmentation, many researchers have attempted to fuze camera images to complement the color and texture information. The common fusion strategy is the combination of convolutional operations with concatenation, element-wise addition or element-wise multiplication. However, conventional convolutional operators tend to confine the fusion of modal features within their receptive fields, which can be incomplete and limited. In addition, the inability of encoder–decoder segmentation networks to explicitly perceive segmentation boundary information results in semantic ambiguity and classification errors at object edges. These errors are further amplified in point cloud segmentation tasks, significantly affecting the accuracy of point cloud segmentation. To address the above issues, we propose a novel self-attention multi-modal fusion semantic segmentation network for point cloud semantic segmentation. Firstly, to effectively fuze different modal features, we propose a self-cross fusion module (SCF), which models long-range modality dependencies and transfers complementary image information to the point cloud to fully leverage the modality-specific advantages. Secondly, we design the salience refinement module (SR), which calculates the importance of channels in the feature maps and global descriptors to enhance the representation capability of salient modal features. Finally, we propose the local-aware anisotropy loss measure the element-level importance in the data and explicitly provide boundary information for the model, which alleviates the inherent semantic ambiguity problem in segmentation networks. Extensive experiments on two benchmark datasets demonstrate that our proposed method surpasses current state-of-the-art methods.

Applications of AI in public health: Improving access to high-quality evidence syntheses

Abstract Issue Using the best available evidence is crucial for effective public health practice. Health Evidence™ provides access to high-quality synthesis evidence relevant to public health. As the volume of peer-reviewed published literature grows, maintaining a database of this magnitude is increasingly resource-intensive. Artificial intelligence (AI) can be used to reduce the maintenance burden thereby ensuring decision makers can easily access high-quality synthesized public health evidence. These innovative strategies may be transferable to enhance efficient maintenance of large curated databases of published literature. Description of the problem In 2020, The Health Evidence™ team conducted extensive training and testing of a supervised machine learning application to explore the accuracy of AI-assisted reference de-duplication and relevance screening. Finding promising results, the team implemented these AI-assisted strategies in August 2020. To assess the impact on the overall screening burden and time saved, implementation data was analyzed between November 2020 to 2023. Results For these 3+ years, AI assisted de-duplication and relevance screening was applied to 394,903 search results. The AI assisted de-duplication application removed 31% (n = 123,903) of references as duplicates. From the remaining reference sets (n = 272,253), the AI assisted screening application removed 70% (n = 190,966) of references as not relevant. Quality assurance spot testing found minimal classification errors (n = 1). In total, AI assisted approaches reduced the need for manual screening by 80%, saving approximately 626 hours of manual screening time over three years (or approximately 17 hours/month). Lessons With the reality of limited public health resources, continued access to high-quality synthesis evidence is critical. Innovative strategies using AI-assisted applications improves the feasibility of maintaining a large database of quality-appraised public health synthesis evidence. Key messages • Access to high quality synthesis evidence is critical for evidence-informed decision making. • Artificial Intelligence can be used to efficiently identify evidence syntheses relevant for public health.

Land covers associated with forest expansion hot spots in the Nepal Himalaya

Abstract Many regions of the Himalaya are experiencing forest expansion, signifying a widespread forest transition across this fragile mountain system. In the Nepal Himalaya, forest expansion is increasingly attributed to the narrative of cropland abandonment driven by rural outmigration. Understanding forest change dynamics as consequences of land use changes is critical in the context of rural mountain societies such as Nepal, which exhibit complex interactions between forest, agriculture, and livelihoods. We analysed forest gain in Nepal from 2000–2019 to inquire where and when forests have expanded and what land covers transitioned into forests. We integrated spatiotemporal analysis of national land cover maps and photointerpretation of very high resolution (VHR) imagery to improve analytical estimates. The Middle Mountain physiographic region experienced the strongest hot spots of forest gains relative to the high mountains or lowlands. Visual interpretation of VHR showed that 68.8% of forest gains since 2000 occurred on shrubland, and 26.5% on cropland. The dominance of shrubland-forest transitions implied that shrubland is an intermediate stage in a multi-decadal succession process, or a semi-permanent state such as arrested succession. Notably however, we also detected cropland-forest transitions occurring as fast as 6–10 years, indicating the potential for rapid forest succession if biophysical conditions permit. Thus, variations in land cover transitions to forests in the hills implicated multiple pathways of forest regeneration, reflecting a variety of spatiotemporally explicit drivers. Our results differ from previous studies because VHR image analysis accounted for land cover classification errors and improved land cover quantification. Further research on spatially explicit mechanisms and drivers of forest gain are needed to understand the synergies of forest, agriculture, and livelihoods to inform land use land cover policies that could be leveraged to enhance rural mountain livelihoods.

Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering

The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (θ-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved θ-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of θ-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show θ-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy.

Development of a neural network with a layer of trainable activation functions for the second stage of the ensemble classifier with stacking

One of the promising directions for improving the quality of object recognition in images and parallelizing calculations is the use of ensemble classifiers with stacking. A neural network at the second level makes it possible to achieve the resulting quality of classification, which is significantly higher than each of the networks of the first level separately. The classification quality of the entire ensemble classifier with stacking depends on the efficiency of the neural networks at the first stage, their number, and the quality of the classification of the neural network of the second stage. This paper proposes a neural network architecture for the second stage of the ensemble classifier, which combines the approximating properties of traditional neurons and learning activation functions. Gaussian Radial Basis Functions (RBFs) were chosen to implement the learned activation functions, which are summed with the learned weights. The experimental studies showed that when working with the CIFAR-10 data set, the best results are obtained when six RBFs are used. A comparison with the use of multilayer perceptron (MLP) in the second stage showed a reduction in classification errors by 0.45–1.9 % depending on the number of neural networks in the first stage. At the same time, the proposed neural network architecture for the second degree had 1.69–3.7 times less learning coefficients than MLP. This result is explained by the fact that the use of an output layer with ordinary neurons allowed us not to enter into the architecture many learning activation functions for each output signal of the first stage, but to limit ourselves to only one. Since the results were obtained on the CIFAR-10 universal data set, a similar effect could be obtained on a large number of similar practical data sets.

Robust Decision Support System for Stress Prediction Using Ensemble Techniques

The prevalence of stress among students has become a significant concern, impacting academic performance and overall well-being. Low-level stress can be beneficial as it helps us maintain focus on our daily routines, enabling us to perform tasks efficiently. However, severe stress can lead to heart disease, diabetes, aches, pains, and numerous other serious health issues. To address this issue, this research presents a comprehensive Decision Support System (DSS) tailored for predicting student stress levels using ensemble techniques. The goal of this project is to develop an ensemble model method of voting to classify the student dataset. An ensemble model is a combination of individual machine-learning algorithms that enhance the accuracy of the model by taking different approaches to the problem and their summary output through base classifiers. Extensive experimentation and validation are conducted using real-world student data to assess the effectiveness of the proposed DSS. In this research, we employed an ensemble modeling voting approach to classify the student dataset. Ensemble models integrate individual machine learning algorithms to enhance accuracy by leveraging the combined output of the base classifiers. Gradient Boosting Classifier (GBC), Random Forest (RF), Bagging Classifier (BC), and Extra Trees Classifier (ETC) served as base learners to construct a voting ensemble model for student dataset classification. Our findings revealed that the voting classifier consisting of Support Vector Machine (SVM), Random Forest (RF), Extra Trees Classifier (ETC), and Bagging Classifier (BC) yielded the highest accuracy at 94.78%, while GBC achieved 84.39% accuracy, RF attained 94.26% accuracy, ETC achieved 94.02% accuracy, and BC obtained 93.65% accuracy. The ensemble modeling approach significantly outperformed individual machine learning algorithms, demonstrating superior accuracy by mitigating variance and classification errors. Implementing such a system not only enhances the student assessment process in the education sector but also fosters interdisciplinary research collaboration across diverse fields within the region.

Machine learning classification of archaea and bacteria identifies novel predictive genomic features

BackgroundArchaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria (N=2546\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=2546$$\\end{document}) and archaea (N=109\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=109$$\\end{document}). From these, a set of genomic features (nucleotide frequencies and proportions, coding sequences (CDS), non-coding, ribosomal and transfer RNA genes (ncRNA, rRNA, tRNA), Chargaff’s, topological entropy and Shannon’s entropy scores) was extracted and used as input data to develop machine learning models for the classification of archaea and bacteria.ResultsThe classification accuracy ranged from 0.993 (Random Forest) to 0.998 (Neural Networks). Over the four models, only 11 examples were misclassified, especially those belonging to the minority class (Archaea). From variable importance, tRNA topological and Shannon’s entropy, nucleotide frequencies in tRNA, rRNA and ncRNA, CDS, tRNA and rRNA Chargaff’s scores have emerged as the top discriminating factors. In particular, tRNA entropy (both topological and Shannon’s) was the most important genomic feature for classification, pointing at the complex interactions between the genetic code, tRNAs and the translational machinery.ConclusionstRNA, rRNA and ncRNA genes emerged as the key genomic elements that underpin the classification of archaea and bacteria. In particular, higher nucleotide diversity was found in tRNA from bacteria compared to archaea. The analysis of the few classification errors reflects the complex phylogenetic relationships between bacteria, archaea and eukaryotes.

Multi-Task Diffusion Learning for Time Series Classification

Current deep learning models for time series often face challenges with generalizability in scenarios characterized by limited samples or inadequately labeled data. By tapping into the robust generative capabilities of diffusion models, which have shown success in computer vision and natural language processing, we see potential for improving the adaptability of deep learning models. However, the specific application of diffusion models in generating samples for time series classification tasks remains underexplored. To bridge this gap, we introduce the MDGPS model, which incorporates multi-task diffusion learning and gradient-free patch search (MDGPS). Our methodology aims to bolster the generalizability of time series classification models confronted with restricted labeled samples. The multi-task diffusion learning module integrates frequency-domain classification with random masked patches diffusion learning, leveraging frequency-domain feature representations and patch observation distributions to improve the discriminative properties of generated samples. Furthermore, a gradient-free patch search module, utilizing the particle swarm optimization algorithm, refines time series for specific samples through a pre-trained multi-task diffusion model. This process aims to reduce classification errors caused by random patch masking. The experimental results on four time series datasets show that the proposed MDGPS model consistently surpasses other methods, achieving the highest classification accuracy and F1-score across all datasets: 95.81%, 87.64%, 82.31%, and 100% in accuracy; and 95.21%, 82.32%, 78.57%, and 100% in F1-Score for Epilepsy, FD-B, Gesture, and EMG, respectively. In addition, evaluations in a reinforcement learning scenario confirm MDGPS’s superior performance. Ablation and visualization experiments further validate the effectiveness of its individual components.

Analytical Neural Network System for the Helicopter Turboshaft Engines Operating Modes Classification

A novel neural network-based analytical system has been developed for classifying helicopter turboshaft engine operating modes during flight operations. This system utilizes a neural network architecture comprising an input layer with three neurons, two fully connected hidden layers, and an output layer with two neurons. The proposed approach demonstrates an exceptional recognition accuracy of 0.997 (99.7%) across steady-state, unsteady, and transient operating modes of helicopter turboshaft engines. A new method for training this neural network has been introduced, employing forward propagation, loss calculation, backpropagation, and weight updates, enhanced by an adaptive learning rate and the cross-entropy function as the loss criterion. The method also incorporates a novel modified Smooth ReLU activation function for hidden layer neurons. This innovation led to a near-perfect accuracy in network training and reduced the loss to 0.025 (2.5%), highlighting the high quality and reliability of the neural network in classifying engine operating modes during flight. Furthermore, it has been empirically shown that the application of this neural network significantly reduces type I errors by a factor of 2.09 to 2.14 and type II errors by 2.05 to 2.21 times compared to traditional classifiers based on ART-1 and BAM networks. This advancement marks a substantial improvement in classification accuracy and error minimization for helicopter turboshaft engine operating modes.

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image.

Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition

BackgroundGesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human–machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications.MethodsThis study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects.ResultsThe results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training.ConclusionsThe study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

A-212 Comparison of formulas for measuring LDL in cancer patients with hypertriglyceridemia

Abstract Background Treatments in recent years in cancer patients have been very favorable. However, some data suggest that dyslipidemia plays a role in carcinogenesis through the deregulation of the lipid signal, and this possibly leads to the development of cardiovascular diseases. A marker for the diagnosis and treatment of dyslipidemias is low density lipoproteins (LDL), and currently, the primary care level still calculates LDL due to the high costs of the direct method; Friedewald has been used since 1972, and in recent years, new equations have appeared such as Sampson and Martin - Hopkins (MH). These formulas use total cholesterol (TC), high-density lipoproteins (HDL), and triglycerides (TG). Objective was to compare the formulas for measuring LDL in cancer patients with hypertriglyceridemia. Methods It´s an observational, cross-sectional - and analytical study. The sample size was 880 lipid profiles processed in an automated dry chemistry analyzer between January and March 2023 at an Oncology Institute in Lima, Peru. The population was divided into several groups of triglyceride levels (&amp;lt;200 mg/dl, 200- 399 mg/dl, ≥ 400 mg/dl), and LDL was stratified according to ATP III to evaluate the classification of patients with respect to LDL formulas. The statistical analysis was performed by descriptive analysis of the categorical and numerical variables; The correlation was performed using Pearson, and a comparison of the LDL formulas was used by Bland Altman to determine the bias and percentage bias. To clinical relevance, it was compared with the biological variation of the Westgard table. A p-value &amp;lt;0.05 was considered significant, and the data were processed in MedCalc v20.027. Results The average age of the patients was 55.02 ± 13.92 years, and the frequency of female sex was 77.39% (n = 681). The average measured TC, HDL, and LDL were 208.95 ± 54.84 mg/dl, 41.53 ± 13.83 mg/dl, and 115.70 ±45.53 mg/dl, respectively, while the calculated average LDL in Friedewald was 120.40 ± 48.57, MH was 130.49 ± 44.15 mg/dl and Sampson was 125.97 ± 45.53 mg/dl. The correlations of measured versus calculated LDL were strong (r &amp;gt; 0.89) at different TG levels; Statistical (p&amp;lt;0.05) and clinical relevance was observed in the Friedewald formula when TG ≥ 400 mg/dl, while the MH and Sampson formulas presented clinical and significant relevance at all triglyceride levels. Patients with an LDL level between 130 and 159 mg/dl presented a more substantial classification error according to the Friedewald, MH, and Sampson formulas. Conclusions Friedewald formula presented a clinically acceptable bias up to 400 mg/dl, unlike the MH and Sampson formulas, which were affected by triglycerides ≥ 150 mg/dl.

Evaluation of 1064 nm Raman for meat and bone meal species discrimination: From raw form to chemical and physical components

The 1064 nm Raman spectroscopy technique was employed to characterize raw, bone fragments (BF), meat fragments (MF), lipid, and residue of meat and bone meal (MBM) samples. Employing three chemometric algorithms, species discrimination models were optimized for the aforementioned sample types, indicating that the presence of BF favored discriminant analysis for poultry samples, yielding a classification error of 0.000. The species discrimination model constructed using Raman spectra combined with PLS-DA algorithm for residue samples was found to be overall optimal, with classification error for four species samples all below 0.061. A detailed analysis of the BF related spectral variables contributing significantly to PLS-DA discriminant analysis was conducted. Finally, this study established the complementarity of Raman spectroscopy in species discrimination analysis from both a physical and chemical components perspective, laying the theoretical groundwork for the development of a high-precision 1064 nm Raman spectroscopic analysis method for MBM species discrimination.