Assign Class Labels Research Articles

Generative dynamical models for classification of rsfMRI data

ABSTRACT The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone—although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.

A virtual reality data visualization tool for dimensionality reduction methods

In this paper, we present a virtual reality interactive tool for generating and manipulating visualizations for high-dimensional data in a natural and intuitive stereoscopic way. Our tool offers support for a diverse range of dimensionality reduction (DR) algorithms, enabling the transformation of complex data into insightful 2D or 3D representations within an immersive VR environment. The tool also allows users to include annotations with a virtual pen using hand tracking, to assign class labels to the data observations, and to perform simultaneous visualization with other users within the 3D environment to facilitate collaboration.

Learning Hierarchical Task Structures for Few-shot Graph Classification

The problem of few-shot graph classification targets at assigning class labels for graph samples, where only limited labeled graphs are provided for each class. To solve the problem brought by label scarcity, recent studies have proposed to adopt the prevalent few-shot learning framework to achieve fast adaptations to graph classes with limited labeled graphs. In particular, these studies typically propose to accumulate meta-knowledge across a large number of meta-training tasks, and then generalize such meta-knowledge to meta-test tasks sampled from a disjoint class set. Nevertheless, existing studies generally ignore the crucial task correlations among meta-training tasks and treat them independently. In fact, such task correlations can help promote the model generalization to meta-test tasks and result in better classification performance. On the other hand, it remains challenging to capture and utilize task correlations due to the complex components and interactions in meta-training tasks. To deal with this, we propose a novel few-shot graph classification framework FAITH to capture task correlations via learning a hierarchical task structure at different granularities. We further propose a task-specific classifier to incorporate the learned task correlations into the few-shot graph classification process. Moreover, we derive FAITH+, a variant of FAITH that can improve the sampling process for the hierarchical task structure. The extensive experiments on four prevalent graph datasets further demonstrate the superiority of FAITH and FAITH+ over other state-of-the-art baselines.

Multiple instance learning for medical image classification based on instance importance

Multiple Instance Learning (MIL) is designed to classify instances where class labels are associated with sets of instances, a common occurrence in biomedical data, especially when multiple images are derived from a single object measurement. Among the various MIL techniques, instance-based classification emerges as the main approach. Individual instances are assigned class labels, and the final bag-level classification is determined through an aggregation process, usually based on average or maximum values. While instance-based models may exhibit slightly lower performance compared to more general methods, they offer the unique advantage of interpretability, allowing the decision-making process to be traced to each individual instance. This article presents an innovative instance-based approach that not only assigns class labels to instances, but also gives them meaning. The final aggregation uses a weighted average, allowing the model to identify instances carrying valuable information while distinguishing those that are redundant in the classification process. This enhancement gives the model the ability to recognize the meaning of individual instances, thereby increasing both its overall interpretability and classification performance.

Data-driven prediction of possible quality deterioration in injection molding processes

We propose an approach for the automated prediction of possible quality deteriorations at injection molding machines using data-driven models. This approach relies on data solely recorded during the regular production phase without the need to (i.) collect data from anomalous phases, which may be cost-intensive due to production waste, (ii.) perform time-intensive (manual) annotation cycles for assigning class labels, and (iii.) know specific fault modes in advance. Our approach embeds two main concepts: the first is the establishment of causal relations between process variables, which serve as online residual generators. The multi-variate residual signals are analyzed using an advanced independent component analysis, whose reconstruction characteristics serve as a control signal indicating a potential anomaly of any type/mode, upon violation of the internal dependence structure. No parameters are needed for the online analysis, which in turn avoids the use of (pre-collected) data from anomalous phases, which is typically required to tune the parameters properly. The second concept performs a direct prediction of quality criteria, which are permanently by-measured through the usage of an optical inspection system of the produced/molded parts. Time series-based trends of the process data are used to establish quality prediction models based on a specific time series transformation technique. It preserves the local structure of the data to ensure it can be effectively combined with predictive fuzzy systems training. To account for system dynamics, the prior trained prediction models can be automatically updated on the fly with new measurements. To further increase flexibility in the case of higher dynamics, as was the case in our application, a dynamic lazy learning (dLL) approach was developed in combination with partial least squares. This technique is able to permanently update the reference data base and establish a local model per new query sample with reduced input dimensionality. Our approach was successfully evaluated during the real injection molding processes of bottle caps: several real-occurring anomalies and significant changes in material and production parameters were successfully found over a horizon of 7162 production cycles. Furthermore, three essential quality criteria were predicted with a correlation of 0.7, 0.89 and 0.98 between predicted and observed trend lines, with mean absolute error rates below 5%. This makes the detection of quality deteriorations possible at an early stage.

The Influence of Dimensions on the Complexity of Computing Decision Trees

A decision tree recursively splits a feature space \mathbb{R}^d and then assigns class labels based on the resulting partition. Decision trees have been part of the basic machine-learning toolkit for decades. A large body of work considers heuristic algorithms that compute a decision tree from training data, usually aiming to minimize in particular the size of the resulting tree. In contrast, little is known about the complexity of the underlying computational problem of computing a minimum-size tree for the given training data. We study this problem with respect to the number d of dimensions of the feature space \mathbb{R}^d, which contains n training examples. We show that it can be solved in O(n^(2d + 1)) time, but under reasonable complexity-theoretic assumptions it is not possible to achieve f(d) * n^o(d / log d) running time. The problem is solvable in (dR)^O(dR) * n^(1+o(1)) time, if there are exactly two classes and R is an upper bound on the number of tree leaves labeled with the first class.

An IoT framework for quality analysis of aquatic water data using time-series convolutional neural network.

Water quality monitoring and analysis in fish farms are of paramount importance for the aquaculture sector; however, traditional methods can pose difficulties. To address this challenge, this study proposes an IoT-based deep learning model using a time-series convolution neural network (TMS-CNN) for monitoring and analyzing water quality in fish farms. The proposed TMS-CNN model can handle spatial-temporal data effectively by considering temporal and spatial dependencies between data points, which allows it to capture patterns and trends that would not be possible with traditional models. The model calculates the water quality index (WQI) using correlation analysis and assigns class labels to the data based on the WQI. Then, the TMS-CNN model analyzed the time-series data. It produces high accuracy of 96.2% in analysis of water quality parameters for fish growth and mortality conditions. The proposed model accuracy is higher than the current best model MANN, which has only had an accuracy of 91%.

Application of linear and nonlinear pattern recognition techniques for discrimination of fluoroquinolones using modified AgNPs-based colorimetric sensor array

Surface plasmon resonance-based colorimetric sensor arrays made up of metallic nanoparticles (NPs) are powerful tools that are mostly used for pattern recognition and quantification purposes, especially in the case of pharmaceutical and biologically active substances. Ease of fabrication, simplicity, cost-effectiveness, and huge amounts of collectible data besides the various fabrication techniques, expand their applications in analytical chemistry, especially when combined with chemometrics data analysis tools. In this study, a colorimetric sensor array is fabricated using unmodified citrate-capped together with the modified AgNPs for the discrimination of three fluoroquinolone drugs ofloxacin, ciprofloxacin, and moxifloxacin in real serum samples. The aggregation-induced color changes of the AgNPs were monitored during the time and the collected kinetic-spectrophotometric huge data in the ranges 300–800 nm was compressed by Discrete Wavelet Transform (DWT) to remove the redundant variables and analyzed with different unsupervised and supervised pattern recognition techniques like Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA) and XY-Fused neural networks (XYF). The best classification performance was achieved using the genetic algorithm-optimized XY-Fused networks with 92% accuracy and an error of cross-validation equal to zero. In addition, all of the spiked real serum samples were classified correctly using the generated model according to the assigned class labels.

Automated tumor immunophenotyping and response to immunotherapy in non-small cell lung cancer using a spatial statistics approach.

2626 Background: Based on the clinical success of immuno-oncology therapeutics, tissue-based, biomarker analyses have shifted from a focus on tumor cell phenotypes to spatial and functional analyses of the tumor immune environment. Distribution and composition of immune infiltrates have shown prognostic or predictive value in various studies; however, standardized approaches to categorize tumors into “Desert”, “Excluded” or “Inflamed” immunophenotypes based on the density and pattern of immune infiltrates are missing. This categorization is typically based on visual inspection of a stained tissue section; it is labor-intensive and associated with poor inter-observer concordance. To eliminate these barriers we developed an automated approach that relies on a set of derived spatial features and named this analysis pipeline LATIS (metric Learning based Automated Tumor Immunophenotyping with Spatial statistics). Methods: We used two clinical trials, POPLAR (phase II; n=258) and OAK (phase III; n=623) that compared anti-PD-L1 treatment vs chemotherapy in patients with advanced non-small cell lung cancer to develop and validate this approach. LATIS utilizes scans of slides stained immunohistochemically for CD8 and cytokeratin (CK); images were tiled and classified according to CK status (positive/negative). First, CD8 positive and CK density was calculated for each tile. Twenty six spatial features were then extracted for each tiled image scan. We used a labeled (manual immunophenotype calls) POPLAR dataset to learn a metric that separates classes of labeled data best with respect of spatial features provided for each data point. Then, to co-embed OAK dataset with POPLAR dataset into the same space, we used that learned metric as a measure of distance between new unlabeled points (OAK data set). K-means clustering in the embedded space was used to identify clusters of data points followed by QFMatch to assign class labels (“Inflamed”, “Excluded”, “Desert”) to each data point. Results: LATIS successfully relates spatial features to both, manual reads and patient response to therapy. In OAK, anti-PD-L1 treated patients experienced longer median overall survival (OS) when tumors showed intra-epithelial CD8+ cells (Inflamed) compared to tumors with a stromal pattern of CD8+ cells (Excluded) or a low density of CD8+ cells (Desert): 17.6 months vs 10.3 (Excluded) vs 12.1 (Desert) [p=0.0061]. Median progression-free survival (PFS) was 3.2 months (Inflamed) vs 2.6 (Excluded) vs 1.4 (Desert) [p=0.00058]. OS and PFS were not significantly different for the three categories in chemotherapy only arm. Conclusions: We suggest that tumor immunophenotype categories generated in an automated fashion by LATIS can serve as predictive biomarker for cancer immunotherapy.

Odia Text Classification Using Naïve Bayes Algorithm : An Empirical Study

Text classification is recognized as one of the key techniques basically used for classifying the text in different classes including positive, negative and neutral. This paper illustrates the Odia text classification process using Naïve Bayes Algorithm, which is a very fast growing discipline in computer science. This classification algorithm is suitable for binary and multiclass classification in machine learning concept and belongs to supervised classification category used to classify future objects by assigning class labels to instances using conditional probability. In this paper, an auxiliary feature method for Odia text is proposed. It determines features by an existing feature selection method of Naïve bayes algorithm, and selects an auxiliary feature which can classify the text at the selected features, and then the chosen conditional probability is used to improve high classification accuracy. Illustrative examples are shown that the proposed method increases the performance of Naïve Bayes classifier. Around one thousand sentences are taken to be considered as both training and tested for empirical diagnosis of the proposed work. Accuracy is depending on mainly training the corpus size which is designed by own and may be increased to some extend depend on the parameters as well as the large size of corpus further. The result shows that Naïve Bayes technique significantly outperform many other technique like HMM (Hidden Markov Model), CRF (Conditional Random Field) and KNN (K Nearest Neighbourhood). Text classification plays an important role in Sentiment Analysis, Information Extraction, Text Summarization, Text Retrieval, and Question Answering.

Ranking of attributes—comparative study based on data from stylometric domain

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors.

Interpolation sub-pixel mapping based on multiple subpixel shifted images based on a consideration of the point spread function effect

ABSTRACT In this paper, the interpolation sub-pixel mapping (ISM) based on multiple sub-pixel shifted images (MSSIs) based on a consideration of the point spread function (PSF) effect, named as MSSI-PSF, is proposed. In the proposed MSSI-PSF, MSSIs are first unmixed to produce the coarse fractional images. Second, under considering the PSF effect, the improved coarse fractional images are obtained by applying the area-to-point kriging (ATPK) and then the ideal square-wave filter to the coarse fractional images. Third, the improved coarse fractional images are upsampled by interpolation to obtain the upsampled fractional images, and then the upsampled fractional images are integrated to obtain a fine fractional image. Finally, according to the fine fractional images from all MSSIs, the class allocation method is utilized to assign class labels to sub-pixels to obtain the final mapping result. The experimental results show that the proposed MSSI-PSF model produces better mapping result than the existing ISM models.

Ways to build text collections for training classifiers

We report on solving the problem of forming a Russian-language text collection (dataset) consisting of bibliographic descriptions of scientific articles for training classifiers. Various approaches to creating such collections are considered. The expediency of using expert estimates for assigning class labels is assessed. The known datasets are analyzed, the requirements for the generated text array are formulated, and the choice of the subject area (Computer Science) is justified. We propose a technology of forming collection in conditions of the shortage of Russian-language articles. To do this we use automated translation of publications (bibliographic descriptions) from available English-language electronic libraries (ACM digital library, IEEE Xplore digital library, CiteSeerX) with additional expert quality control of the translation. The bibliographic collection thus formed was studied using methods of clustering (Latent Semantic Analysis) and visualization (Principal Component Analysis). Training and test samples were compiled and «standard» classifiers (K-Nearest Neighbor Method, Logistic Regression, Random Forest) were used. Then we calculated standard quality measures (accuracy, precision, recall). The rigid and soft classification were carried out. For rigid and soft classification all calculated measures (for the studied classifiers) ranged within [0.79; 0.87], and [0.91; 0.95], respectively. The experiments showed almost identical results for Russian and English bibliographic descriptions (the difference did not exceed 2%). The proposed method of forming text collections reduces the complexity of the labeling process compared to the expert approach, solves the problem of the lack of Russian-language documents, allows formation of sufficiently large balanced bibliographic datasets for training and testing classifiers.

CeCILE - An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells.

The fundamental basis in the development of novel radiotherapy methods is in-vitro cellular studies. To assess different endpoints of cellular reactions to irradiation like proliferation, cell cycle arrest, and cell death, several assays are used in radiobiological research as standard methods. For example, colony forming assay investigates cell survival and Caspase3/7-Sytox assay cell death. The major limitation of these assays is the analysis at a fixed timepoint after irradiation. Thus, not much is known about the reactions before or after the assay is performed. Additionally, these assays need special treatments, which influence cell behavior and health. In this study, a completely new method is proposed to tackle these challenges: A deep-learning algorithm called CeCILE (Cell Classification and In-vitro Lifecycle Evaluation), which is used to detect and analyze cells on videos obtained from phase-contrast microscopy. With this method, we can observe and analyze the behavior and the health conditions of single cells over several days after treatment, up to a sample size of 100 cells per image frame. To train CeCILE, we built a dataset by labeling cells on microscopic images and assign class labels to each cell, which define the cell states in the cell cycle. After successful training of CeCILE, we irradiated CHO-K1 cells with 4 Gy protons, imaged them for 2 days by a microscope equipped with a live-cell-imaging set-up, and analyzed the videos by CeCILE and by hand. From analysis, we gained information about cell numbers, cell divisions, and cell deaths over time. We could show that similar results were achieved in the first proof of principle compared with colony forming and Caspase3/7-Sytox assays in this experiment. Therefore, CeCILE has the potential to assess the same endpoints as state-of-the-art assays but gives extra information about the evolution of cell numbers, cell state, and cell cycle. Additionally, CeCILE will be extended to track individual cells and their descendants throughout the whole video to follow the behavior of each cell and the progeny after irradiation. This tracking method is capable to put radiobiologic research to the next level to obtain a better understanding of the cellular reactions to radiation.

Abstract PD6-04: Deep-learning based prediction of homologous recombination deficiency (hrd) status from histological features in breast cancer; a research study

Abstract BackgroundHomologous recombination deficiency (HRD), originally described in tumors from patients with germline mutations in BRCA1/2 genes, renders cells sensitive to poly-ADP ribose polymerase inhibitors (PARPi) (1), but can be caused by mutations in other genes and is prevalent across multiple cancer types (2). HRD status is of clinical interest because it can indicate patient eligibility for treatment with PARPi. Currently, HRD status is determined by sequencing to identify BRCA mutations or genomic instability, but this has a high rate of failure (3). In this research study, we apply a deep-learning based computational approach to directly infer HRD status from digitized images of hematoxylin and eosin (H&E) stained histology samples in breast cancer tumors. MethodsDigitized whole slide images (WSI) of 931 H&E stained, formalin-fixed and paraffin-embedded (FFPE) breast adenocarcinoma (BRCA) tumor biopsies from the cancer genome atlas (TCGA) were used to train machine learning (ML) models to identify patients that are HRD based on human-interpretable features (HIFs) and end-to-end (E2E) modeling. To train the models, samples were split into training and validation sets designated either HRD or homologous recombination proficient (HRP) based on a previously generated aggregate HRD score (calculated from regions of loss of heterozygosity, large scale genomic instability, and telomeric allelic imbalance) by genomic analysis of the PanCancerAtlas (2). We applied an untuned HRD score threshold of 45 to assign class labels resulting in 142/931 (15.3%) HRD cases. Board certified pathologists (N=93) annotated tissue regions and cellular foci on the PathAI research platform yielding 65,477 annotations. ML models based on convolutional neural networks were trained to recognize breast cancer cells, lymphocytes, macrophages, plasma cells, fibroblasts, and tissue compartments including cancer epithelium, cancer stroma and necrosis within the H&E stained breast cancer samples. Two pipelines constructed H&E histology-based classifiers of HRD status. A weakly-supervised “end-to-end” model using ResNets extracted features from small image patches with an attention module to aggregate across patches and directly predict HRD status. The HIF-based approach used the tissue segmentation and cell identification classifiers to quantify histological features in the WSI. From the labeled images, we extracted 600 HIFs that capture complex relationships between cell and tissue types. HIFs and patient clinical covariates were applied as input to a Sparse Group Lasso model to predict the HRD status of the associated patients.ResultsML models predicted HRD status from H&E stained WSI. The area under the receiver operating characteristics curve (AUROC) was 0.87 for the HIF model and 0.80 for the E2E model. Both classifiers achieved high sensitivity for HRD status (0.86) with more moderate precision (F1 score HIF: 0.80 and E2E: 0.72). Our HIF with clinical covariates model revealed morphological features that were significantly associated with HRD compared with HRP. HRD samples were enriched for areas of necrosis, stromal fibroblasts, and tumor infiltrating lymphocytes (p< 0.001, Mann-Whitney U test). Conclusions Computational models built with the PathAI research platform identified HRD positive patients directly from routinely collected H&E stained WSIs and identified a histological basis for how mutational signatures impact the tumor microenvironment. Disclaimer: The PathAI platform and HRD model are not intended for diagnostic purposes. 1 Farmer et al., 2005. Nature 14;434(7035):917-212 Knijnenburg et al., 2018. Cell Rep 23, 239–2543 Hoppe et al., 2018. JNCI 110(7): djy0854 Coudray et al., 2018 Nat Med 24:1559-15675 Kather et al., 2019. Nat Med 25: 1054–1056pages Citation Format: Amaro Taylor-Weiner, Aryan Pedawi, Wan Fung Chui, James Diao, Jason Wang, Victoria Mountain, Benjamin Glass, Hunter Elliott, Ilan Wapinski, Michael Montalto, Aditya Khosla, Andrew H. Beck. Deep-learning based prediction of homologous recombination deficiency (hrd) status from histological features in breast cancer; a research study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-04.

AN UNSUPERVISED LABELING APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Abstract. The application of hyperspectral image analysis for land cover classification is mainly executed in presence of manually labeled data. The ground truth represents the distribution of the actual classes and it is mostly derived from field recorded information. Its manual generation is ineffective, tedious and very time-consuming. The continuously increasing amount of proprietary and publicly available datasets makes it imperative to reduce these related costs. In addition, adequately equipped computer systems are more capable of identifying patterns and neighbourhood relationships than a human operator. Based on these facts, an unsupervised labeling approach is presented to automatically generate labeled images used during the training of a convolutional neural network (CNN) classifier. The proposed method begins with the segmentation stage where an adapted version of the simple linear iterative clustering (SLIC) algorithm for dealing with hyperspectral data is used. Consequently, the Hierarchical Agglomerative Clustering (HAC) and Fuzzy C-Means (FCM) algorithms are employed to efficiently group similar superpixels considering distances with respect to each other. The distinct utilization of these clustering techniques defines a complementary stage for overcoming class overlapping during image generation. Ultimately, a CNN classifier is trained using the computed image to pixel-wise predict classes on unseen datasets. The labeling results, obtained using two hyperspectral benchmark datasets, indicate that the current approach is able to detect objects boundaries, automatically assign class labels to the entire dataset and to classify new data with a prediction certainty of 90%. Additionally, this method is also capable of achieving better classification accuracy and visual correspondence with reality than the ground truth images.

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.

MotivationMass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance.ResultsTo alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states.Availability and implementationThe data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

The representation-based classification method has become a research hotspot in recent years. Representation-based classifiers assign class labels directly to test samples based on a structured dictionary. The structured dictionary is composed of training samples. We call the samples in the dictionary as atoms. To further improve the expression ability of different training atoms in the classifier to the test samples, we propose an ensemble-enhanced sparse classification algorithm based on the correlation coefficient. The model proceeds from two aspects: the representation coefficient level and the similarity between different samples. First, a sparse dictionary is combined with the multiplicative property of training samples to build and solve the sparse and collaborative representation algorithm. The fusion representation coefficients are obtained by weighted sparse representation coefficients and collaborative representation coefficients. The test samples are reconstructed by fusing the representation coefficients, and the minimization recovery residuals for each class of samples are calculated. Second, the correlation coefficient value between the test sample and the training sample is calculated, and the maximum correlation quotient between the test sample and the training sample is obtained. Finally, the maximum correlation quotient and the minimization recovery residual are weighted, and the decision classification is carried out in the decision function to achieve the final face recognition. Experiments on AR, Extended Yale B, Georgia Tech and other general face databases show the effectiveness of the algorithm. The main contribution of this model is that it is more robust than a single sparse or collaborative representation model and can improve recognition accuracy.

Subpixel Land Cover Mapping Based on Dual Processing Paths for Hyperspectral Image

The subpixel mapping (SPM) technique can handle coarse fractional images derived by unmixing coarse original hyperspectral (HS) image to produce a fine land cover map at the subpixel scale. A popular SPM approach is a two-step model. It first increases the spatial resolution of coarse fractional images by subpixel sharpening to produce fine fractional images and then assigns class labels to each subpixel by the class allocation method. However, there is only a single processing path of the current SPM algorithm, and the information type of the fine fractional images is not rich. To enrich the information type, SPM based on dual processing paths (DPP) is proposed. DPP contains two processing paths, namely spatial–spectral path and multiscale path. First, the coarse original HS image and the high spatial resolution multispectral image are fused by component substitution to produce the fine fractional images with more spatial–spectral information in the spatial–spectral path. At the same time, deep Laplacian pyramid networks are used to obtain the fine fractional images with multiscale information in the multiscale path. The fine fractional images from the two paths are then integrated to generate the improved fraction images with multiscale spatial–spectral information. Finally, the multiscale spatial–spectral information is utilized to allocate class labels by the class allocation method. Experimental results on three real HS remote sensing data show that the proposed DPP outperforms the other SPM methods, demonstrating the effectiveness of the use of DPP in enriching the information type of the fine fractional images.

Road risk assessment using fuzzy Context-free Grammar based Association Rule Miner

Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or un-safe roads, respectively.