Real-valued Features Research Articles

Leaf Moisture Content Detection Method Based on UHF RFID and Hyperdimensional Computing

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model’s Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs.

Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

Cancelable template protection techniques are indispensable to provide essential security and privacy privileges in biometric systems. This paper introduces an efficient cancelable palmprint recognition technique based on multi-level transformations. Gabor filtering with feature remapping is introduced for extracting highly discriminative features. Histogram remapping is applied to nonlinearly transform the downsampled Gabor features to be normally distributed. This feature-remapping step is proposed to enhance the discriminatory power and alleviate the effect of feature variability and image artifacts. Comb filtering is applied to the mapped features as a first protection layer. To provide security guarantees against linkability attacks, index-based locality-sensitive hashing (LSH) is introduced as a second protection layer to transform the comb-filtered mapped real-valued features into maximum-ranked indices. Recognition is performed in the secured domain to accelerate matching and to preserve the user’s privacy. Results show that the proposed scheme provides a large re-issuance ability, protects templates from being inverted, and demonstrates strong unlinkability across different databases. In addition, favorable verification/identification accuracy is obtained and the system satisfies the needs for real-time applications. A global measure D↔sys\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${D}_{\\leftrightarrow }^{sys}$$\\end{document} value of 0.01 is obtained, and thus, correlation attacks are mitigated. The recognition results for the legitimate scenario are 100% identification accuracy and 0% EER. For the worst case (same token scenario), the corresponding results are 99.752% and 0.31%, respectively.

Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist.

This study introduces a novel cheminformatic read-across approach designed to identify potential environmental obesogens, substances capable of disrupting metabolism and inducing obesity by mainly influencing nuclear hormone receptors (NRs). Leveraging real-valued two-dimensional features derived from chemical fingerprints of 8435 Tox21 compounds, cluster analysis and subsequent statistical testing revealed 385 clusters enriched with compounds associated with specific NR targets. Notably, one cluster exhibited selective enrichment in peroxisome proliferator-activated receptor γ (PPARγ) agonist activity, prominently featuring methoxy cinnamate ultraviolet (UV) filters and obesogen-related compounds. Experimental validation confirmed that 2-ethoxyethyl 4-methoxycinnamate, an organic UV filter cinoxate, could selectively bind to PPARγ (Ki = 18.0 μM), eliciting an obesogenic phenotype in human bone marrow-derived mesenchymal stem cells during adipogenic differentiation. Molecular docking and further experiments identified cinoxate as a potent PPARγ full agonist, demonstrating a preference for coactivator SRC3 recruitment. Moreover, cinoxate upregulated transcription levels of genes encoding lipid metabolic enzymes in normal human epidermal keratinocytes as primary cells exposed during clinical usage. This study provides compelling evidence for the efficacy of cheminformatic read-across analysis in prioritizing potential obesogens, showcasing its utility in unveiling cinoxate as an obesogenic PPARγ agonist.

Fault detection and identification in induction motor using weightless neural network.

This paper presents a novel multiclass, multiparameter real-valued weightless neural network-based classifier for fault detection and identification. In contrast to primitive variants of weightless neural nets, the proposed method is capable of multiclass identification with real-valued input features and has improved recognition and generalization capabilities. The major accomplishments of the proposed method include an improved input-to-address mapping strategy that is suitable for address assignment within discriminator units and an effective memory expansion scheme based on similarity metric-based membership value. The developed classifier is utilized for fault detection and identification in single-phase and three-phase induction motors. The sensitivity analysis of the method is investigated for design parameter variation and the fault diagnosis is performed for multiple faults of the motor. The proposed method achieves the highest accuracy of 99.6% and 89.25% for single-phase and three-phase induction motor fault identification, respectively.

Deep Progressive Asymmetric Quantization Based on Causal Intervention for Fine-Grained Image Retrieval

In the field of computer vision, fine-grained image retrieval is an extremely challenging task due to the inherently subtle intra-class object variations. In addition, the high-dimensional real-valued features extracted from large-scale fine-grained image datasets slow the retrieval speed and increase the storage cost. To solve above issues, existing fine-grained image retrieval methods mainly focus on finding more discriminative local regions for generating discriminative and compact hash codes, which achieve limited fine-grained image retrieval performance due to the large quantization errors and the confounding granularities and context of discriminative parts, i.e., the correct recognition of fine-grained objects mainly attribute to the discriminative parts and their context. To learn robust causal features and reduce the quantization errors, we propose a deep progressive asymmetric quantization (DPAQ) method based on causal intervention to learn compact and robust descriptions for fine-grained image retrieval task. Specifically, we introduce a structural causal model to learn robust casual features via causal intervention for fine-grained visual recognition. Subsequently, we design a progressive asymmetric quantization layer in the feature embedding space, which can preserve the semantic information and reduce the quantization errors sufficiently. Finally, we incorporate both the fine-grained image classification and retrieval tasks into an end-to-end deep learning architecture for generating robust and compact descriptions. Experimental results on several fine-grained image retrieval datasets demonstrate that the proposed DPAQ method performs the best for fine-grained image retrieval task and surpasses the state-of-the art fine-grained hashing methods by a large margin.

Graph signal processing is combined with deep learning for detection of damaged wind turbine blades

For early warning the damaged blade of wind turbines, an emission noise processing framework is proposed based on combination of Graph signal processing and Deep Learning. A microphone array is utilized to receive the noise emitted by the wind turbine blades. The weak abnormal signal from the damaged blade is enhanced by beamforming techniques. The enhanced signal is transformed into the graph domain by Graph Fourier Transform, from which the Mel filter bank features are extracted as inputs of a Multi-scale Feature Aggregation Conformer (MFA-Conformer) for damage detection. The MFA-Conformer combines Transformers and convolution neural networks (CNNs) to capture global and local features from the frequency or Graph domain. And the multi-stage aggregation strategy is utilized to exploit hierarchical context information. The reduction in the computational cost is achieved in the CNNs-based damage detection due to the real-valued features extracted from graph domain. The MFA-Conformer neural network is trained on the dataset which is created by applying data augmentation to the training samples. With the Mel filter bank features extracted from the frequency and graph domains, the MFA-Conformer neural network performs well in the five wind-farm data tests, with 2.55 % improvement in accuracy over the residual networks.

Data generation and verification of pipeline transportation based on integration of simulation and DBSCAN algorithm

Through the analysis and mining of historical data, machine learning method can be used to obtain high accuracy prediction effect without establishing a complex physical model in equipment fault diagnosis, operation condition prediction, and pipeline energy consumption analysis. In the oil and gas pipeline system, the machine learning model unable to gain an ideal training effect with the data set, because of confidentiality of data, imperfect data acquisition technology, low frequency of abnormal working conditions, and other factors. In this paper, aiming at the operation energy consumption of a crude oil pipeline, the power consumption of oil pump unit is simulated by software, which can expand the data. The quality of simulation samples has a great effect on the training results. A DBSCAN algorithm based on Mahalanobis distance is proposed to evaluate the reliability of simulation samples and identify abnormal simulation samples, given the characteristics of virtual samples in pipeline transmission simulation, such as no real value control, feature correlation, and high dimension. Examples have shown that the fitting ability of the model can be improved after the simulation samples for eliminating abnormal data are added to the training set, which provides a new method for the generation and verification of simulation samples.

Similarity Graph-correlation Reconstruction Network for unsupervised cross-modal hashing

Existing cross-modal hash retrieval methods can simultaneously enhance retrieval speed and reduce storage space. However, these methods face a major challenge in determining the similarity metric between two modalities. Specifically, the accuracy of intra-modal and inter-modal similarity measurements is inadequate, and the large gap between modalities leads to semantic bias. In this paper, we propose a Similarity Graph-correlation Reconstruction Network (SGRN) for unsupervised cross-modal hashing. Particularly, the local relation graph rebasing module is used to filter out graph nodes with weak similarity and associate graph nodes with strong similarity, resulting in fine-grained intra-modal similarity relation graphs. The global relation graph reconstruction module is further strengthens cross-modal correlation and implements fine-grained similarity alignment between modalities. In addition, in order to bridge the modal gap, we combine the similarity representation of real-valued and hash features to design the intra-modal and inter-modal training strategies. SGRN conducted extensive experiments on two cross-modal retrieval datasets, and the experimental results effectively validated the superiority of the proposed method and significantly improved the retrieval performance.

Fully-Dynamic Decision Trees

We develop the first fully dynamic algorithm that maintains a decision tree over an arbitrary sequence of insertions and deletions of labeled examples. Given ε>0 our algorithm guarantees that, at every point in time, every node of the decision tree uses a split with Gini gain within an additive ε of the optimum. For real-valued features the algorithm has an amortized running time per insertion/deletion of O((d·log³n)/ε²), which improves to O((d·log²n)/ε) for binary or categorical features, while it uses space O(n·d), where n is the maximum number of examples at any point in time and d is the number of features. Our algorithm is nearly optimal, as we show that any algorithm with similar guarantees requires amortized running time Ω(d) and space Ω(n·d/polylog(nd)). We complement our theoretical results with an extensive experimental evaluation on real-world data, showing the effectiveness of our algorithm.

Learning binary codes for fast image retrieval with sparse discriminant analysis and deep autoencoders

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods.

Rotational Voxels Statistics Histogram for both real-valued and binary feature representations of 3D local shape

3D Local feature description is an active and fundamental task in 3D computer vision. However, most of the existing descriptors fail to simultaneously achieve satisfactory performance among descriptiveness, robustness, efficiency, and compactness. To address these limitations, we first propose a real-valued descriptor named Rotational Voxels Statistics Histogram (RoVo), which exploits the novel 3D multi-pose processing mechanism proposed in this paper to calculate the 3D voxel density distribution in different 3D poses. Moreover, through well-designed binary encoding algorithms, we conduct the seamless extension of the real-valued RoVo descriptor to three binary representations that have different performance characteristics. Extensive evaluation experiments validate the superiority of the real-valued and three binary RoVo descriptors concerning descriptiveness, robustness, and efficiency. Furthermore, the three binary RoVo descriptors extend the performance of high compactness. Lastly, we perform the experiments of 3D scene registration and 3D object recognition to intuitively present the effectiveness of the four proposed RoVo descriptors.

Multi-Level Cross-Modal Semantic Alignment Network for Video–Text Retrieval

This paper strives to improve the performance of video–text retrieval. To date, many algorithms have been proposed to facilitate the similarity measure of video–text retrieval from the single global semantic to multi-level semantics. However, these methods may suffer from the following limitations: (1) largely ignore the relationship semantic which results in semantic levels are insufficient; (2) it is incomplete to constrain the real-valued features of different modalities to be in the same space only through the feature distance measurement; (3) fail to handle the problem that the distributions of attribute labels in different semantic levels are heavily imbalanced. To overcome the above limitations, this paper proposes a novel multi-level cross-modal semantic alignment network (MCSAN) for video–text retrieval by jointly modeling video–text similarity on global, entity, action and relationship semantic levels in a unified deep model. Specifically, both video and text are first decomposed into global, entity, action and relationship semantic levels by carefully designing spatial–temporal semantic learning structures. Then, we utilize KLDivLoss and a cross-modal parameter-share attribute projection layer as statistical constraints to ensure that representations from different modalities in different semantic levels are projected into a common semantic space. In addition, a novel focal binary cross-entropy (FBCE) loss function is presented, which is the first effort to model the unbalanced attribute distribution problem for video–text retrieval. MCSAN is practically effective to take the advantage of the complementary information among four semantic levels. Extensive experiments on two challenging video–text retrieval datasets, namely, MSR-VTT and VATEX, show the viability of our method.

Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures

The current research on fuzzy rough sets (FRSs) for feature selection has two major problems. On the one hand, most existing methods employ multiple intersection operations of fuzzy relations to define fuzzy dependency functions applied to feature selection. These operations can make the evaluation of the significance of feature subsets less identifiable in high-dimensional data space. On the other hand, the classical FRS implemented for feature selection is highly sensitive to noisy information. Thus, improving the robustness of the FRS model is critical. To address the above issues, first, we propose a radial basis function kernel-based similarity measure for computing fuzzy relations. The value difference metric and Euclidean metric are utilized to measure the distance values of the mixed symbolic and real-valued features. Hereafter, a novel robust FRS model is proposed by introducing the relative classification uncertainty (RCU) measure. k-nearest neighbours and Bayes rules are employed to yield an RCU level. Relative noisy information is detected in this way. Finally, extensive experiments are conducted to illustrate the effectiveness and robustness of the proposed model.

Rapid Person Re-Identification via Sub-space Consistency Regularization

Person Re-Identification (ReID) matches pedestrian across disjoint cameras. Existing ReID methods adopting real-value feature descriptors have achieved high accuracy, but they are low in efficiency due to the slow Euclidean distance computation as well as complex quick-sort algorithms. Recently, some works propose to yield binary encoded person descriptors which instead only require fast Hamming distance computation and simple counting-sort algorithms. However, the performances of such binary encoded descriptors, especially with short code (e.g, 32 and 64 bits), are hardly satisfactory given the sparse binary space. To strike a balance between the model accuracy and efficiency, we propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by 0.25 times than real-value features under same dimensions whilst maintain a competitive accuracy, especially under short codes. SCR transforms real-value features vector (e.g, 2048 float32) with short binary codes (e.g, 64 bits) by first dividing real-value features vector into M sub-spaces, each with C clustered centroids. Thus the distance between two samples can be expressed as the summation of respective distance to the centroids, which can be sped up by offline calculation and maintained via a look-up-table. On the other side, these real-value centroids help to achieve significantly higher accuracy than using binary code. Lastly, we convert the distance look-up-table to be integer and apply the counting-sort algorithm to speed up the ranking stage. We also propose a novel consistency regularization with an iterative framework. Experimental results on Market-1501 and DukeMTMC-reID show promising and exciting results. Under short code, our proposed SCR enjoys Real-value-level accuracy and Hashing-level speed.

Machine learning-assisted prediction of pneumonia based on non-invasive measures.

BackgroundPneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases via non-invasive measures such as physical and laboratory parameters is gaining momentum and has been proposed to decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight machine learning models to predict pneumonia based on biomarkers, laboratory parameters, and physical features.MethodsWe perform machine-learning analysis on 535 different patients, each with 45 features. Data normalization to rescale all real-valued features was performed. Since it is a binary problem, we categorized each patient into one class at a time. We designed three experiments to evaluate the models: (1) feature selection techniques to select appropriate features for the models, (2) experiments on the imbalanced original dataset, and (3) experiments on the SMOTE data. We then compared eight machine learning models to evaluate their effectiveness in predicting pneumoniaResultsBiomarkers such as C-reactive protein and procalcitonin demonstrated the most significant discriminating power. Ensemble machine learning models such as RF (accuracy = 92.0%, precision = 91.3%, recall = 96.0%, f1-Score = 93.6%) and XGBoost (accuracy = 90.8%, precision = 92.6%, recall = 92.3%, f1-score = 92.4%) achieved the highest performance accuracy on the original dataset with AUCs of 0.96 and 0.97, respectively. On the SMOTE dataset, RF and XGBoost achieved the highest prediction results with f1-scores of 92.0 and 91.2%, respectively. Also, AUC of 0.97 was achieved for both RF and XGBoost models.ConclusionsOur models showed that in the diagnosis of pneumonia, individual clinical history, laboratory indicators, and symptoms do not have adequate discriminatory power. We can also conclude that the ensemble ML models performed better in this study.

Elastic-Link for Binarized Neural Networks

Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1 × 1 convolutional layers, for which standard binarization methods do not work well. Because 1 × 1 convolutions are common in the design of modern architectures (e.g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an “Elastic-Link” (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also aids con-vergence in the training of binarized MobileNet, for which a top-1 accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.

QBB: Quantile-Based Binarization of 3D Point Cloud Descriptors

Local 3D point feature descriptors play an important role in many areas of computer vision, such as object recognition, registration, etc. There are many well-functioning feature descriptors, but they are typically real-valued and multidimensional vectors, leading to high computational complexity in nearest neighbor searches. To overcome this challenge, methods binarizing real-valued descriptors have emerged. In this paper, we first investigate the available binarization methods and standalone binary feature descriptors and show that existing binarization techniques cannot generally achieve good performance for arbitrary feature descriptors. To remedy this problem, we propose a new binarization method called quantile-based binarization (QBB) that can be applied to any real-valued feature descriptors. It analyses the distribution of feature descriptors that is then used to form meaningful groups along each dimension. To this end, QBB computes quantiles of the empirical distribution and the interval lengths (bin sizes) defined by quantile boundaries. Finally, it assigns a binary code to each group and concatenate them to get the final binary descriptor. QBB is able to adaptively compute the number of bits based on a capacity constraint, i.e., with the appropriate capacity setting, the resulting binary descriptor can be used on devices with lower computational power. We evaluate the descriptiveness of well-known descriptors binarized by QBB and compare them to state-of-the-art methods. According to our evaluation, QBB is able to create binary descriptors whose descriptiveness is closer to the real-valued descriptors than prior approaches. Finally, we also show that QBB can even compete with standalone binary feature descriptors.

Ensemble of Machine Learning Fusion Models for Breast Cancer Detection Based on the Regression Model

Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. The various machine learning (ML) algorithms applied include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 real-valued feature information. Among all five machine learning methods, K-Neighbors regressor had the best performance and ensemble using K-Neighbors regressor gave the best accuracy. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.

Learning Compact Multirepresentation Feature Descriptor for Finger-Vein Recognition

Due to its high anti-counterfeiting and universality, the use of finger-vein pattern for identity authentication has recently attracted extensive attention in academia and industry. Despite recent advances in finger-vein recognition, most of the hand-crafted descriptors require strong prior knowledge, which may be ineffective in expressing its distinctiveness. In this paper, we present a novel compact multi-representation feature descriptor (CMrFD) with visual and semantic consistency, for finger-vein feature representation. Given the finger-vein images, we first form two-view representations to describe the informative vein features in local patches. Then, we jointly learn a feature transformation to map the two-view representations into discriminative binary codes. For the projection function, we linearly combine multi-view information and minimize the quantization error between the projected binary features and the original real-valued features. In terms of visual consistency, we minimize the Euclidean distance of each representation from the same class, at the same time, maximize the Euclidean distance from different classes in the projected space. Semantic consistency is used to ensure that similar images have compact multi-representation combined projection features. Lastly, we calculate the block-wise histograms as the final extracted features for finger-vein recognition. Experimental results on four widely used finger-vein databases demonstrate that the proposed method outperforms the state-of-the-art finger-vein recognition methods.

DenseSENet: more accurate and robust cross-domain iris recognition

With the broad application of iris recognition systems, iris images acquired under cross-sensor and cross-spectrum conditions are increasingly common. Differences in the information stored in these images place high requirements for accuracy and robustness on cross-domain iris recognition algorithms, and current algorithms have not performed well in cross-domain iris recognition. Hence, we propose a dense squeeze and excitation network (DenseSENet) to extract common features in cross-domain iris images and use real-valued features for cross-domain matching. DenseSENet is a deep convolutional neural network based on an attention mechanism. It uses the batch normalization-rectified linear unit-Conv1 × 1 structure to increase the nonlinearity of the network and repeatedly uses features through dense connections to effectively prevent overfitting. The proposed dense squeeze and excitation structure extracts more accurate and stable features by learning the relations between channels and assigning corresponding attention values. The structure of global average pooling-fully connected-softmax is utilized to obtain compact and normalized real-valued feature templates to effectively reduce the size of the iris template. The proposed cross-domain iris recognition framework based on DenseSENet is end-to-end, which transforms the cross-domain matching problem to a many-to-one classification problem. Experiments are conducted on publicly available cross-sensor and cross-spectral iris databases. The results show that the proposed real-valued feature matching based on DenseSENet has better matching accuracy and robustness than the state-of-art cross-domain matching algorithm on two publicly available cross-sensor and cross-spectral iris databases.