Search Results (96)

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

Microsporidia MB (MB), a promising biological control agent, suppresses Plasmodium falciparum transmission in Anopheles mosquitoes. This study examined the spatial distribution of MB infection in natural populations of An. gambiae s.l. mosquitoes collected in Nigeria and Niger Republic, and its association with insecticide susceptibility in the mosquitoes. Microsporidia MB has wide geographic distribution across Nigeria and Niger Republic. The overall prevalence of MB in F₀ mosquitoes was 12.25% (95% CI: 7.76–16.75%); 25 mosquitoes out of 204 were positive. Geographic variation was observed, with a higher prevalence (5/15 mosquitoes) in Ebonyi State (33.33%, CI: 9.48–57.19%, Fisher’s exact test, p = 0.008). Infection rates were higher in An. coluzzii mosquitoes (21/133 mosquitoes), estimated at 15.79% (CI: 9.59–21.99%) compared to An. gambiae s.s. mosquitoes (4/71), with approximately 5.63% (CI: 0.27–11.00%, χ² = 4.44; df = 1, p = 0.035). Resistant mosquitoes had a significantly higher prevalence of MB infection than susceptible mosquitos at 28.57% (CI: 16.74–40.40%) with an odds ratio of 3.33 (CI: 1.23–9.03, p = 0.017). These findings suggests that MB can be exploited as an alternative for vector control in Nigeria and Niger, but its possible association with pyrethroid resistance suggests that it should be taken into account as a potential confounder when designing insecticide resistance management strategies. Full article

This study addresses the side effect of the rapid development of the Internet, positioning botnets within digital ecosystems as a very serious potential threat to the Internet users. Malicious web robot might facilitate Web/data scraping, DDoS attacks, and data theft yielding serious cybersecurity threats. Modern botnets are advanced and have unique browser fingerprints, making their detection a real challenge. Traditional feature extraction methods heavily depend on expert knowledge. They also struggle with dimensional inconsistency when processing sessions of varying lengths, failing to counter evolving camouflage attacks. To approach such challenges, we propose Session2vec, a session representation framework based on multi-instance learning (MIL), which pioneers the MIL approach for Web session modeling. In this approach, we treat each request as an instance and the entire session as an instance collection, and then we use the FastText model to convert each URL request into a vector representation. Then, we utilize two innovative multi-instance aggregation methods: SARD (Session-level Aggregated Residual Descriptors) and SFAR (Session-level Fisher Aggregated Representation) to aggregate variable-length sessions into fixed-dimensional vectors capturing spatiotemporal features and distributional information within sessions. Simulation results of the proposed SARD and SFAR methods on public datasets show accuracy improvement of 5.2% and 16.3% on average, respectively, compared to state-of-the-art baselines. They also enhance F1 scores by 8.5% and 19.7%, respectively. Full article

In this article, we present a model for analyzing the co-occurrence count data derived from practical fields such as user–item or item–item data from online shopping platforms and co-occurring word–word pairs in sequences of texts. Such data contain important information for developing recommender systems or studying the relevance of items or words from non-numerical sources. Different from traditional regression models, there are no observations for covariates. Additionally, the co-occurrence matrix is typically of such high dimension that it does not fit into a computer’s memory for modeling. We extract numerical data by defining windows of co-occurrence using weighted counts on the continuous scale. Positive probability mass is allowed for zero observations. We present the Shared Parameter Alternating Tweedie (SA-Tweedie) model and an algorithm to estimate the parameters. We introduce a learning rate adjustment used along with the Fisher scoring method in the inner loop to help the algorithm stay on track with optimizing direction. Gradient descent with the Adam update was also considered as an alternative method for the estimation. Simulation studies showed that our algorithm with Fisher scoring and learning rate adjustment outperforms the other two methods. We applied SA-Tweedie to English-language Wikipedia dump data to obtain dense vector representations for WordPiece tokens. The vector representation embeddings were then used in an application of the Named Entity Recognition (NER) task. The SA-Tweedie embeddings significantly outperform GloVe, random, and BERT embeddings in the NER task. A notable strength of the SA-Tweedie embedding is that the number of parameters and training cost for SA-Tweedie are only a tiny fraction of those for BERT. Full article

Background: Alzheimer’s disease is a progressive neurological condition marked by a decline in cognitive abilities. Early diagnosis is crucial but challenging due to overlapping symptoms among impairment stages, necessitating non-invasive, reliable diagnostic tools. Methods: We applied information geometry and manifold learning to analyze grayscale MRI scans classified into No Impairment, Very Mild, Mild, and Moderate Impairment. Preprocessed images were reduced via Principal Component Analysis (retaining 95% variance) and converted into statistical manifolds using estimated mean vectors and covariance matrices. Geodesic distances, computed with the Fisher Information metric, quantified class differences. Graph Neural Networks, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, were utilized to categorize impairment levels using graph-based representations of the MRI data. Results: Significant differences in covariance structures were observed, with increased variability and stronger feature correlations at higher impairment levels. Geodesic distances between No Impairment and Mild Impairment (58.68, $p<0.001$) and between Mild and Moderate Impairment (58.28, $p<0.001$) are statistically significant. GCN and GraphSAGE achieve perfect classification accuracy (precision, recall, F1-Score: 1.0), correctly identifying all instances across classes. GAT attains an overall accuracy of 59.61%, with variable performance across classes. Conclusions: Integrating information geometry, manifold learning, and GNNs effectively differentiates AD impairment stages from MRI data. The strong performance of GCN and GraphSAGE indicates their potential to assist clinicians in the early identification and tracking of Alzheimer’s disease progression. Full article

Background/Objectives: To evaluate the visual and refractive outcomes of keratorefractive lenticule extraction (KLEx) surgery and refractive lens exchange (RLE) surgery in moderate to high myopia patients. Methods: A retrospective cohort study was performed, and patients receiving KLEx or RLE surgeries with myopia within −3.00 to −10.00 diopter (D) were enrolled. A total of 19 and 35 patients were put into the RLE and KLEx groups after exclusion. The main outcomes are postoperative uncorrected visual acuity (UDVA), the spherical equivalent (SE), and residual astigmatism via vector analysis. Fisher’s exact test and the Mann–Whitney U test were utilized for the statistical analysis. Results: The percentages of patients who reached UDVA results of more than 20/25 and 20/20 were statistically similar between groups (both p > 0.05), and the percentages of patients who reached SE results within ±0.50 D and ±1.00 D were statistically similar between groups (both p > 0.05). The change in SE in the KLEx group was lesser compared to that in the RLE group (p = 0.021). The vector analysis showed a lower DV and ME and a higher CoI in the KLEx group than in the RLE group (all p < 0.05). The percentage of patients who reached specific UDVA and SE thresholds were statistically similar between groups with different myopia degrees (all p > 0.05). Conclusions: The postoperative visual and refractive outcomes between RLE and KLEx surgeries are grossly comparable, while the KLEx may have a slight advantage in astigmatism correction. Full article

In this work, we introduce an extension of the so-called beta autoregressive moving average ($\beta$ARMA) models. $\beta$ARMA models consider a linear dynamic structure for the conditional mean of a beta distributed variable. The conditional mean is connected to the linear predictor via a suitable link function. We propose modeling the relationship between the conditional mean and the linear predictor by means of the asymmetric Aranda-Ordaz parametric link function. The link function contains a parameter estimated along with the other parameters via partial maximum likelihood. We derive the partial score vector and Fisher’s information matrix and consider hypothesis testing, diagnostic analysis, and forecasting for the proposed model. The finite sample performance of the partial maximum likelihood estimation is studied through a Monte Carlo simulation study. An application to the proportion of stocked hydroelectric energy in the south of Brazil is presented. Full article

In this study, an in-depth analysis is presented on forecasting aggregated wind power production at the regional level, using advanced Machine-Learning (ML) techniques and feature-selection methods. The main problem consists of selecting the wind speed measuring points within a large region, as the wind plant locations are assumed to be unknown. For this purpose, the main cities (province capitals) are considered as possible features and four feature-selection methods are explored: Pearson correlation, Spearman correlation, mutual information, and Chi-squared test with Fisher score. The results demonstrate that proper feature selection significantly improves prediction performance, particularly when dealing with high-dimensional data and regional forecasting challenges. Additionally, the performance of five prominent machine-learning models is analyzed: Long Short-Term Memory (LSTM) networks, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Extreme-Learning Machines (ELMs). Through rigorous testing, LSTM is identified as the most effective model for the case study in northern Italy. This study offers valuable insights into optimizing wind power forecasting models and underscores the importance of feature selection in achieving reliable and accurate predictions. Full article

High-dimensional sparse matrix data frequently arise in various applications. A notable example is the weighted word–word co-occurrence count data, which summarizes the weighted frequency of word pairs appearing within the same context window. This type of data typically contains highly skewed non-negative values with an abundance of zeros. Another example is the co-occurrence of item–item or user–item pairs in e-commerce, which also generates high-dimensional data. The objective is to utilize these data to predict the relevance between items or users. In this paper, we assume that items or users can be represented by unknown dense vectors. The model treats the co-occurrence counts as arising from zero-inflated Gamma random variables and employs cosine similarity between the unknown vectors to summarize item–item relevance. The unknown values are estimated using the shared parameter alternating zero-inflated Gamma regression models (SA-ZIG). Both canonical link and log link models are considered. Two parameter updating schemes are proposed, along with an algorithm to estimate the unknown parameters. Convergence analysis is presented analytically. Numerical studies demonstrate that the SA-ZIG using Fisher scoring without learning rate adjustment may fail to find the maximum likelihood estimate. However, the SA-ZIG with learning rate adjustment performs satisfactorily in our simulation studies. Full article

A reduced-dimension (RD) method based on the proper orthogonal decomposition (POD) technology and the linearized Crank–Nicolson mixed finite element (CNMFE) scheme for solving the 2D nonlinear extended Fisher–Kolmogorov (EFK) equation is proposed. The method reduces CPU runtime and error accumulation by reducing the dimension of the unknown CNMFE solution coefficient vectors. For this purpose, the CNMFE scheme of the above EFK equation is established, and the uniqueness, stability and convergence of the CNMFE solutions are discussed. Subsequently, the matrix-based RDCNMFE scheme is derived by applying the POD method. Furthermore, the uniqueness, stability and error estimates of the linearized RDCNMFE solution are proved. Finally, numerical experiments are carried out to validate the theoretical findings. In addition, we contrast the RDCNMFE method with the CNMFE method, highlighting the advantages of the dimensionality reduction method. Full article

The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness. In this study, a Csk-means algorithm based on contour similarity is proposed to overcome the drawbacks of the traditional k-means algorithm. For the traditional k-means algorithm, which results in local optimality due to the influence of outliers or noisy data and random selection of the initial clustering centers, the Csk-means algorithm overcomes both drawbacks by combining data lattice transformation and dissimilar interpolation. In particular, the Csk-means algorithm employs Fisher optimal partitioning of the similarity vectors between samples for the process of determining the number of clusters. To improve the robustness of the k-means algorithm to the shape of the clusters, the Csk-means algorithm utilizes contour similarity to compute the similarity between samples during the clustering process. Experimental results show that the Csk-means algorithm provides better clustering results than the traditional k-means algorithm and other comparative algorithms. Full article

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC. Full article

In this paper, new path optimization algorithms are developed for uncrewed aerial vehicle (UAV) self-localization and target tracking, exploiting beacon (landmark) bearings and angle-of-arrival (AOA) measurements from a manoeuvring target. To account for time-varying rotations in the local UAV coordinates with respect to the global Cartesian coordinate system, the unknown orientation angle of the UAV is also estimated jointly with its location from the beacon bearings. This is critically important, as orientation errors can significantly degrade the self-localization performance. The joint self-localization and target tracking problem is formulated as a Kalman filtering problem with an augmented state vector that includes all the unknown parameters and a measurement vector of beacon bearings and target AOA measurements. This formulation encompasses applications where Global Navigation Satellite System (GNSS)-based self-localization is not available or reliable, and only beacons or landmarks can be utilized for UAV self-localization. An optimal UAV path is determined from the optimization of the Bayesian Fisher information matrix by means of A- and D-optimality criteria. The performance of this approach at different measurement noise levels is investigated. A modified closed-form projection algorithm based on a previous work is also proposed to achieve optimal UAV paths. The performance of the developed UAV path optimization algorithms is demonstrated with extensive simulation examples. Full article

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively. Full article

Remote Sensing plays a fundamental role in acquiring crucial information about the Earth’s surface from a distance, especially through fully polarimetric data, which offers a rich source of information for diverse applications. However, extracting meaningful insights from this intricate data necessitates sophisticated techniques. In addressing this challenge, one predominant trend that has emerged is known as target decomposition techniques. These techniques can be broadly classified into coherent and non-coherent methods. Each of these methods provides high-quality information using different procedures. In this context, this paper introduces innovative feature fusion techniques, amalgamating coherent and non-coherent information. While coherent techniques excel in detailed exploration and specific feature extraction, non-coherent methods offer a broader perspective. Our feature fusion techniques aim to harness the strengths of both approaches, providing a comprehensive and high-quality fusion of information. In the first approach, features derived from Pauli coherent decomposition, Freeman–Durden non-coherent technique, and the Symmetry criterion from Cameron’s stepwise algorithm are combined to construct a sophisticated feature vector. This fusion is achieved using the well-established Fisher Linear Discriminant Analysis algorithm. In the second approach, the Symmetry criterion serves as the basis for fusing coherent and non-coherent coefficients, resulting in the creation of a new feature vector. Both approaches aim to exploit information simultaneously extracted from coherent and non-coherent methods in feature extraction from Remote Sensing data through fusion at the feature level. To evaluate the effectiveness of the feature generated by the proposed fusion techniques, we employ a land cover classification procedure. This involves utilizing a basic classifier, achieving overall accuracies of approximately 82% and 86% for each of the two proposed techniques. Furthermore, the accuracy in individual classes surpasses 92%. The evaluation aims to gauge the effectiveness of the fusion methods in enhancing feature extraction from fully polarimetric data and opens avenues for further exploration in the integration of coherent and non-coherent features for remote sensing applications. Full article