Feature Vector Research Articles

Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network-HistoXGAN-capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy."

Mask-guided network for finger vein feature extraction and biometric identification

The problems of complex background, low quality of finger vein images, and poor discriminative features have been the bottleneck of feature extraction and finger vein recognition. To this end, we propose a feature extraction algorithm based on the open-set testing protocol. In order to eliminate the interference of irrelevant areas, this paper proposes the idea of segmentation-assisted classification, that is, using the rough mask of the finger vein to constrain the feature learning process so that the network can focus on the vein area and learn greater weight for the vein. Specifically, the feature maps of the shallow layers of the network are first sent to the feature pyramid module to fuse the primary features of different scales, which are then sent to the spatial attention module to obtain the spatial weight map of the image. Based on the results of several classical vein skeleton extraction algorithms, a weighting method is used to obtain a more accurate mask to constrain the learning of the spatial weight map. Finally, a hybrid loss function combining triplet loss and cross-entropy loss is used to reduce the distance between feature vectors of the same categories and increase the distance between feature vectors of different categories in the Euclidean space, thereby improving feature discriminability. Good recognition results were achieved on the three public data sets of SDUMLA, MMCBNU, and FVUSM, and the values of equal error rate (EER) on them are as low as 2.50%, 0.20%, and 0.14%, respectively.

Deep metric learning with in-batch feature vector constraints and unsupervised label integration

Deep metric learning with in-batch feature vector constraints and unsupervised label integration

MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction.

Considering that the traditional deep learning event extraction method ignores the correlation between word features and sequence information, it cannot fully explore the hidden associations between events and events and between events and primary attributes. To solve these problems, we developed a new framework for event extraction called the masked attention-guided dynamic graph aggregation network. On the one hand, to obtain effective word representation and sequence representation, an interaction and complementary relationship are established between word vectors and character vectors. At the same time, a squeeze layer is introduced in the bidirectional independent recurrent unit to model the sentence sequence from both positive and negative directions while retaining the local spatial details to the maximum extent and establishing practical long-term dependencies and rich global context representations. On the other hand, the designed masked attention mechanism can effectively balance the word vector features and sequence semantics and refine these features. The designed dynamic graph aggregation module establishes effective connections between events and events, and between events and essential attributes, strengthens the interactivity and association between them, and realizes feature transfer and aggregation on graph nodes in the neighborhood through dynamic strategies to improve the performance of event extraction. We designed a reconstructed weighted loss function to supervise and adjust each module individually to ensure the optimal feature representation. Finally, the proposed MaskDGNets framework is evaluated on two baseline datasets, DuEE and CCKS2020. It demonstrates its robustness and event extraction performance, with F1 of 81.443% and 87.382%, respectively.

Deep Learning using Spatial Distances, Normalized Coordinates, Scaling and Visual Words for Large Datasets

Image Retrieval has become popular and crucial task and the number of digital images on (web servers) amplified, it became increasingly very difficult to classify and track images. Many methods have been used to make image exploration effective and reliable, such as search based on the file name, image tagging, etc. but none have proved a good idea to work in real scenario. Our proposed methodology applies deep learning using spatial distances, normalized coordinates, scaling and visual words for large data sets for retrieval of images with highest accuracy. The proposed methodology has three basic steps: the first is Content Analysis. The image is passed through coarser intervolving phase, second is CNN, third is RGB color evaluation, fourth is Retrieved feature vectors and fifth is results derivation. Proposed methodology was applied on the three famous datasets namely, Cifar-100, FTVL and Fashion. Experiments conducted on these datasets have shown outstanding results.

Real-time Actuator Fault Detection and Isolation for Quadrotor UAV via Directional Residuals

Abstract Fault diagnosis is critical for the safe and reliable operation of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce its negative impact. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying (LPV) model is established to describe quadrotor UAV dynamics, which considers measurement noise and external disturbance. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and Η_/L∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L∞ performance condition, and actuator fault is detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residuals and the defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Fault Diagnosis Model of Hydraulic Motor Based on Fuzzy Neural Network

When the hydraulic motor fault occurs,it is not easy to be detected,and the leakage degree will gradually increase.In order toavoid bigger accidents causedby thehydraulic motor fault,the accident isexcluded in theembryonic stage,and the hydraulic motor fault prediction method based onfuzzy neural network isused topredict thehydraulic motor fault.The feature vector is output inthe global meanpooling layer,andthe feature vectormatrix between thehealth state feature vector library and the samples to be measured is constructed.The dynamic cluster graph isobtained by fuzzy clustering,so as to realize the fault diagnosis of thehydraulic motor.The results show that the accuracy of training set,verification set andtest set ishigher than99.8%.The accuracy of diagnosis classification is99.00%,which is better than othercomparison models.In this study,the number of training samples can be appropriately increased ordecreased according to thecurve complexity of the detection target,so as to improve thefeature extraction capability of the convolutional layer andincrease the classification accuracy.

PYRAMID CHANNEL-BASED FEATURE ATTENTION NETWORK WITH ENSEMBLE LEARNING-BASED UAV IMAGE CLASSIFICATION ON IoT-ASSISTED REMOTE SENSING ENVIRONMENT

Advances in Unmanned Aerial Vehicles (UAVs), otherwise recognized as drones, have tremendous promise in improving the wide-ranging applications of the Internet of Things (IoT). UAV image classification using deep learning (DL) is an amalgamation to modernize data analysis, collection, and decision-making in a variety of sectors. IoT devices collect information in real time, while remote sensing captures data afar without direct contact. UAVs equipped with sensors offer high-quality images for classification tasks. DL techniques, especially the convolutional neural networks (CNNs), analyze data streams, extracting complicated features for the accurate classification of objects or environmental features. This synergy enables applications including urban planning and precision agriculture, fostering smarter disaster response, decision support systems, and efficient resource management. This paper introduces a novel Pyramid Channel-based Feature Attention Network with an Ensemble Learning-based UAV Image Classification (PCFAN-ELUAVIC) technique in an assisted remote sensing environment. The PCFAN-ELUAVIC technique begins with the contrast enhancement of the UAV images using the CLAHE technique. Following that, the feature vectors are derived by the use of the PCFAN model. Meanwhile, the hyperparameter tuning procedure is executed by the inclusion of a vortex search algorithm (VSA). For image classification, the PCFAN-ELUAVIC technique comprises an ensemble of three classifiers like long short-term memory (LSTM), graph convolutional networks (GCNs), and Hermite neural network (HNN). To exhibit the improved detection results of the PCFAN-ELUAVIC system, an extensive range of experiments are carried out. The experimental values confirmed the enhanced performance of the PCFAN-ELUAVIC model when compared to other techniques.

Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model.

Cancer is a life-threatening disease resulting from a genetic disorder and a range of metabolic anomalies. In particular, lung and colon cancer (LCC) are among the major causes of death and disease in humans. The histopathological diagnoses are critical in detecting this kind of cancer. This diagnostic testing is a substantial part of the patient's treatment. Thus, the recognition and classification of LCC are among the cutting-edge research regions, particularly in the biological healthcare and medical fields. Earlier disease diagnosis can significantly reduce the risk of fatality. Machine learning (ML) and deep learning (DL) models are used to hasten these cancer analyses, allowing researcher workers to analyze a considerable proportion of patients in a limited time and at a low price. This manuscript proposes the Predictive Analytics of Complex Healthcare Systems Using the DL-based Disease Diagnosis Model (PACHS-DLBDDM) method. The proposed PACHS-DLBDDM method majorly concentrates on the detection and classification of LCC. At the primary stage, the PACHS-DLBDDM methodology utilizes Gabor Filtering (GF) to preprocess the input imageries. Next, the PACHS-DLBDDM methodology employs the Faster SqueezeNet to generate feature vectors. In addition, the convolutional neural network with long short-term memory (CNN-LSTM) approach is used to classify LCC. To optimize the hyperparameter values of the CNN-LSTM approach, the Chaotic Tunicate Swarm Algorithm (CTSA) approach was implemented to improve the accuracy of classifier results. The simulation values of the PACHS-DLBDDM approach are examined on a medical image dataset. The performance validation of the PACHS-DLBDDM model portrays the superior accuracy value of 99.54% over other DL models.

High-Order Multimodal Interaction Network for Efficient Prediction of Drug-Drug Interaction

The pharmaceutical industry places a lot of focus on preventing drug-drug interactions (DDIs). Medication interaction detection has been the primary emphasis of machine learning-based DDI prediction methods. Since studies have shown that DDIs can cause different future occurrences, it is more beneficial to predict DDI connected events while examining the mechanism behind combination medicine consumption or adverse reactions. The areas of medication development and illness diagnostics are seeing increased usage of a developing approach that predicts DDIs-associated occurrences. We examine potential interactions between the two medications as well as the kinds of interactions that can occur in this research. Additionally, we provide a strategy that relies on learning and use High-Order Multimodal Interaction Network (HOMIN) to forecast DDIs by learning feature representations. The purpose of this work is to provide HOMIN-DDI, a new method for drug-drug interaction predictions that we have developed utilizing a HOMIN architecture. First, we develop feature vectors from medication categories, targets, paths, and digestive enzymes, and we use the Jaccard similarity to evaluate how similar drugs appear. After that, we build a new HOMIN using the feature representation to predict DDIs. The results of the experiments show that the drug categories feature type, when used to the HOMIN -DDI approach, is effective. Furthermore, compared to employing only one feature, combining numerous features yields more useful and informative results. Compared to other algorithms, HOMIN-DDI performs better when it comes to predicting DDIs.

Generalization Limits of Data-Driven Turbulence Models

AbstractMany industrial applications require turbulent closure models that yield accurate predictions across a wide spectrum of flow regimes. In this study, we investigate how data-driven augmentations of popular eddy viscosity models affect their generalization properties. We perform a systematic generalization study with a particular closure model that was trained for a single flow regime. We systematically increase the complexity of the test cases up to an industrial application governed by a multitude of flow patterns and thereby demonstrate that tailoring a model to a specific flow phenomenon decreases its generalization capability. In fact, the accuracy gain in regions that the model was explicitly calibrated for is smaller than the loss elsewhere. We furthermore show that extrapolation or, generally, a lack of training samples with a similar feature vector is not the main reason for generalization errors. There is actually only a weak correlation. Accordingly, generalization errors are probably due to a data-mismatch, i.e., a systematic difference in the mappings from the model inputs to the required responses. More diverse training sets unlikely provide a remedy due to the strict stability requirements emerging from the ill-conditioned RANS equations. The universality of data-driven eddy viscosity models with variable coefficients is, therefore, inherently limited.

A novel epilepsy detection approach using intrinsic multiscale entropy analysis and DSEAM-enhanced 1D-ResNets

Abstract Epilepsy, a prevalent neurological disorder, typically requires a complex diagnostic process involving medical history inquiry, physical examination, head computed tomography, and electroencephalogram (EEG) visual examination. Among the existing epilepsy automated detection algorithms, machine learning methods require manual feature selection. Most of deep learning algorithms for automatic detection have high complexity and computational complexity. To address this issue, this study proposes a high-precision, robust, and low computational epilepsy automatic detection algorithm based on EEG signal processing. The study utilizes ensemble empirical mode decomposition to preprocess the original EEG signal, breaking it down into intrinsic mode functions (IMFs) across various frequency bands. These IMFs contain information about epilepsy occurrence within the signal at different frequency bands. To enhance computational efficiency and reduce data dimension, the refined composite multiscale dispersion entropy of each IMF is further computed at different scales, referred to as intrinsic multiscale entropy (IME) analysis. IME analysis consolidates epilepsy occurrence information from EEG signals across different frequency bands and scales, linking entropy values to generate feature vectors. Drawing inspiration from successful deep residual networks and Squeeze-and-Excitation networks, the study introduces a double Squeeze-and-Excitation attention module enhanced one-dimensional residual network to classify one-dimensional feature vectors. The proposed method was tested on epilepsy dataset from University of Bonn, and the results demonstrated superior classification performance. In the experiment, the distinction between normal and epileptic EEG signals achieved 100% accuracy rate, while distinguishing between normal, epileptic interval, and epileptic EEG signals achieved accuracy rate of 99.41%.

A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images

Background/Objectives: Despite recent advances in research, cancer remains a significant public health concern and a leading cause of death. Among all cancer types, lung cancer is the most common cause of cancer-related deaths, with most cases linked to non-small cell lung cancer (NSCLC). Accurate classification of NSCLC subtypes is essential for developing treatment strategies. Medical professionals regard tissue biopsy as the gold standard for the identification of lung cancer subtypes. However, since biopsy images have very high resolutions, manual examination is time-consuming and depends on the pathologist’s expertise. Methods: In this study, we propose a hybrid model to assist pathologists in the classification of NSCLC subtypes from histopathological images. This model processes deep, textural and contextual features obtained by using EfficientNet-B0, local binary pattern (LBP) and vision transformer (ViT) encoder as feature extractors, respectively. In the proposed method, each feature matrix is flattened separately and then combined to form a comprehensive feature vector. The feature vector is given as input to machine learning classifiers to identify the NSCLC subtype. Results: We set up 13 different training scenarios to test 4 different classifiers: support vector machine (SVM), logistic regression (LR), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). Among these scenarios, we obtained the highest classification accuracy (99.87%) with the combination of EfficientNet-B0 + LBP + ViT Encoder + SVM. The proposed hybrid model significantly enhanced the classification accuracy of NSCLC subtypes. Conclusions: The integration of deep, textural, and contextual features assisted the model in capturing subtle information from the images, thereby reducing the risk of misdiagnosis and facilitating more effective treatment planning.

Scalable multimodal approach for face generation and super-resolution using a conditional diffusion model

Multimodal Conditioned face image generation and face super-resolution are significant areas of research. To achieve optimal results, this paper utilizes diffusion models as the primary engine for these tasks. This paper presents two main contributions: (1) “Speaking the Language of Faces” (SLF): a flexible, modular, fusion-less and architecturally simple multimodal system. (2) A Scalability scheme and a sensitivity analysis which can assist practitioners in system parameter estimation and feature selection. SLF consists of two main components: a feature vector generator (encoder), and an image generator (decoder) utilizing a conditional diffusion model. SLF can accept various inputs, including low-resolution images, speech signals, person attributes (age, gender, ethnicity), or any combination of these. Moreover, Scalability based on conditional scale values is utilized. The implementation of SLF has confirmed its versatility (e.g., speech to face image generation, conditioned face super-resolution). We trained multiple system versions to conduct a sensitivity analysis and to determine the influence of each individual feature on the output image. Consequently, speaker embeddings have proven to be sufficient audio features for our task. It was also found that the effects of audio signals are profound and are more pronounced than those of the low resolution images (8 × 8), whose effects are still significant. The effect of gender, ethnicity and age were found to be moderate. On another note, conditional scale values significantly impact the system’s behavior and performance.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

The multidimensional coagulation and collisional breakage equation with unbounded kernel

Many scientific and technical fields have benefited from the understanding of the Coagulation and Collisional Breakage Equation (CCBE). In one-dimensional CCBE, only one property of the particle (like size or mass of the particle) is considered. However, there are other factors (like including volume, enthalpy, porosity, mole number, binder content, and more) that affect how particle ensembles behave. Therefore the study of multidimensional CCBE is more accurate. For the multi-dimensional (CCBE), we denote the property characteristics vector as x → = ( x 1 , x 2 , … , x d ) ∈ R + d . In this research work, the existence of a continuous solution is investigated where collision rate satisfies C ( x → , y → ) ≤ c ∏ i = 1 d ( 1 + x i ) ν ( 1 + y i ) ν , where c>0 is a constant, 0 ≤ ν ≤ θ and the coagulation kernel satisfies the following, K ( x → , y → ) ≤ k ∏ i = 1 d ( 1 + x i ) α ( 1 + y i ) α , where k is a positive constant, 0 ≤ α ≤ θ and 0 < θ ≤ 1 . Additionally, the uniqueness of the solution is shown. To prove the result, we have defined a new norm and examined the equicontinuity with respect to time and the property characteristics vector and Arzelà–Ascoli theorem is used.

Increase of Reliability of Anomalies Detection on Images at Formation of Their Feature Vectors in Wavelet Bases

The method of detection of life rafts and lifeboats in the water area of seas and oceans after shipwrecks based on the recognition of anomalies in the processed images, which increases the probability of recognition of monitoring objects, is proposed. The approach to solving such a problem is substantiated. The formulation of the problem of object recognition from the perspective of binary classification in the detection of anomalies is presented. The analytical expression for the decision-making algorithm is obtained. The possibility of formalization of image matrices in the form of histograms of color (brightness) intensity distributions is considered. The contrast of the feature space on their basis is estimated. It is suggested that the contrast of feature spaces be increased due to the secondary processing of histograms of distributions in the basis of multiple-scale wavelet decomposition. The possibility of realization of wavelet transformations on the basis of Haar functions and Gauss wavelets of the 1st and 2nd orders is considered. The mechanism of formation of secondary feature vectors from three-dimensional wavelet transforms by averaging their coefficients along the time shift axis is substantiated. It is shown that at the same dimensionality of histograms of brightness distribution with newly formed feature vectors, the latter provide higher contrast of feature spaces. It is recommended to use a Gaussian wavelet of the 2nd order for the formalization of images in jpeg format, which provides, other things being equal, a greater magnitude of differences for images containing anomalies. An approach to probabilistic evaluation of the algorithm for automatic image recognition is developed. The analytical expression is obtained and its constituent elements are justified. Graphical dependences of the probability of correct detection (recognition) of anomalies, depending on the size in relation to the total area of the frame and the dispersion of the underlying background are given. The results of the experiment on image recognition with a lifeboat in the ocean water area are presented. The directions of further research are defined.

Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks**This paper is significantly revised from an earlier version [] presented at the Explainable Machine Learning for Sciences Workshop (XAI4Sci) in conjunction with the 38th AAAI Conference on Artificial Intelligence (AAAI 2024) held in Vancouver, Canada, in February 2024.

Abstract The impact of meteorological observations on weather forecasting varies with the sensor type, location, time, and other environmental factors. Thus, the quantitative analysis of observation impacts is crucial for the effective and efficient development of weather forecasting systems. However, existing impact analysis methods are dependent on specific forecast systems, because system-specific adjoint models are used and the sensitivity of the observation to the forecast is measured. This study investigates the impact of observations on atmospheric state estimation in weather forecasting systems by developing a novel graph neural network (GNN) model specialized for analyzing the heterogeneous relations between observations and atmospheric states. The observation impact can then be assessed by applying explainable methods to the proposed GNN model, which is independent of forecasting systems. Further, we develop a novel application called ‘CloudNine,’ a system that provides impact analysis for individual observations with visualization. Our GNN model comprises hierarchical message-passing modules that separately analyze spatial correlations between observations at close locations and atmospheric states at close locations and then examine correlations between observations and atmospheric states. To consider the different factors influencing these correlations, we utilized geo-coordinates and types of observations in the attention mechanism of the modules with their feature vectors. We then applied gradient-based explainability methods to quantify the significance of the different observations in the estimation. Evaluated using data from 11 satellites and land-based observations, the results highlight the effectiveness of the proposed model and the visualization of observation impacts, enhancing the understanding and optimization of observational data in weather forecasting.

Fault diagnosis of wind turbine gears based on OCSSA-VMD and WOA-CNN-BiLSTM

Abstract The accuracy of wind turbine gearbox fault diagnosis will be compromised if the fault feature data is not adequately extracted during operation. To enhance fault identification efficiency and mitigate human interference in parameter setting, this paper introduces an optimized mode decomposition algorithm OCSSA-VMD, derived from variational mode decomposition (VMD) and further optimized by osprey-Cauchy-sparrow search algorithm (OCSSA). This algorithm offers two key advantages: (1) automatic optimization of parameters such as the number of modes k and penalty factor α; (2) reduction of feature dimensionality through mean impact value (MIV) algorithm based on minimum envelope entropy principle, resulting in a multi-fault feature vector set from 13 time-domain features in the intrinsic mode function (IMF) optimal component of wind turbine gearbox vibration data. Additionally, a fault diagnosis model WOA-CNN-BiLSTM is proposed based on whale optimization algorithm (WOA) and convolutional neural network-bidirectional long-short-term-memory (CNN-BiLSTM), which demonstrates improved fault classification accuracy to 98.3333% and diagnosis accuracy to 98.3853% under conditions of insufficient data when compared with other models.

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.