Similarity Measure Function Research Articles

A Framework for Semantic Clustering of News Articles Based on Fuzzy

Text mining is a process that uses data mining approaches to extract valuable information held in the hidden form in textual data. In this paper, we are proposing a framework for fuzzy clustering of news articles. These news articles originate on different news portals on the web. The data obtained need to be stored in a central database and then pre-processing reduces the noise. The keyword extraction is used to extract keywords from the text and then word-frequency vector is generated. On these vectors, distance measure or similarity measure function is used to find the similarity between articles. One article may belong to more than one cluster so fuzzy context vector must be generated. Mutual Information can be used to find fuzzy membership values. The threshold values are required for the identification of clusters. The proposed framework shows that fuzzy clustering does not restrict each news article to belong exactly to one cluster. Therefore this framework when applied to information retrieval systems or other application systems, system gives better performance and relevance to the users.

A similarity measure-based Pythagorean fuzzy additive ratio assessment approach and its application to multi-criteria sustainable biomass crop selection

In the modern world, the selection of an optimal sustainable biomass crop type for biofuel production can be assumed as a multi-criteria decision-making problem due to numerous conflicting criteria. Therefore, uncertainty usually arises in sustainable biomass crop selection problems, and the Pythagorean fuzzy set, an extension of the intuitionistic fuzzy set, has been shown as a prolific tool to tackle uncertain and ambiguous information. Thus, the aim of the paper is to introduce an extended additive ratio assessment methodology to assess the sustainable biomass crop selection problem on Pythagorean fuzzy sets. In this method, a new Pythagorean fuzzy similarity measure is proposed. Afterward, a weighting procedure is developed based on the introduced similarity measure and score function to compute the criteria significance degrees. The computational process of the developed framework is illustrated using a case study of sustainable biomass crop selection with uncertain information, which verifies the feasibility and efficacy of the developed approach. Further, this work makes comparative and sensitivity analyses to confirm the steadiness and robustness of the presented method. The results illustrate that the developed method is a useful, practical, and valuable way to rank the sustainable biomass crop alternatives with uncertainty.

Fuzzy fault tree analysis of chlorine gas release hazard in Chlor-Alkali industry using [formula omitted]-cut interval-based similarity aggregation method

In chemical industries, the release of toxic gases and chemicals is always a serious concern as it has the potential to harm human life and the environment, leading to significant losses. Risk assessment in chemical industries is essential to identify and prevent such type of unwanted hazards. Conventional fault tree analysis has been used effectively to identify the system failure causes and evaluate the system reliability, but it requires quantitative historical failure data of system components. In many real-world applications, it can be difficult to obtain precise and sufficient quantitative failure data of system components. This research paper proposes a novel fuzzy set theory-based fault tree analysis to evaluate system failure probability by utilizing the available qualitative data such as expert opinions/views when quantitative historical failure data of system components is unavailable or insufficient. To aggregate the different opinions of selected experts, a new similarity aggregation method is developed by using interval distance-based similarity measure function. The proposed fuzzy fault tree analysis applies system fault tree, α-cut intervals of fuzzy membership functions, and interval arithmetic operations. To validate the applicability and effectiveness of proposed approach, the basic event probabilities in fault tree analysis of chlorine release from storage and filling facility are generated using proposed approach and then compared with the known probabilities. Further, importance analysis is performed to evaluate the contribution of each basic event in top event “chlorine release” occurrence that would help the decision makers to identify the risk factor of chlorine release. The obtained results may be helpful for decision makers to decide whether and where to take protective actions in the risk management process.

DeepSimDEF: deep neural embeddings of gene products and gene ontology terms for functional analysis of genes.

MotivationThere is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein–protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application-specific metrics to quantify the degree of shared information between two genes using their Gene Ontology (GO) annotations.ResultsWe introduce deepSimDEF, a deep learning method to automatically learn FS estimation of gene pairs given a set of genes and their GO annotations. deepSimDEF’s key novelty is its ability to learn low-dimensional embedding vector representations of GO terms and gene products and then calculate FS using these learned vectors. We show that deepSimDEF can predict the FS of new genes using their annotations: it outperformed all other FS measures by >5–10% on yeast and human reference datasets on protein–protein interactions, gene co-expression and sequence homology tasks. Thus, deepSimDEF offers a powerful and adaptable deep neural architecture that can benefit a wide range of problems in genomics and proteomics, and its architecture is flexible enough to support its extension to any organism.Availability and implementationSource code and data are available at https://github.com/ahmadpgh/deepSimDEFSupplementary information Supplementary data are available at Bioinformatics online.

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

In content-based image retrieval (CBIR), primitive image signatures are critical because they represent the visual characteristics. Image signatures, which are algorithmically descriptive and accurately recognized visual components, are used to appropriately index and retrieve comparable results. To differentiate an image in the category of qualifying contender, feature vectors must have image information's like colour, objects, shape, spatial viewpoints. Previous methods such as sketch-based image retrieval by salient contour (SBIR) and greedy learning of deep Boltzmann machine (GDBM) used spatial information to distinguish between image categories. This requires interest points and also feature analysis emerged image detection problems. Thus, a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary. In this study, a technique called CBIR-similarity measure via artificial neural network interpolation (CBIR-SMANN) has been presented. By collecting datasets, the images are resized then subject to Gaussian filtering in the pre-processing stage, then by permitting them to the Hessian detector, the interesting points are gathered. Based on Skewness, mean, kurtosis and standard deviation features were extracted then given to ANN for interpolation. Interpolated results are stored in a database for retrieval. In the testing stage, the query image was inputted that is subjected to pre-processing, and feature extraction was then fed to the similarity measurement function. Thus, ANN helps to get similar images from the database. CBIR-SMANN have been implemented in the python tool and then evaluated for its performance. Results show that CBIR-SMANN exhibited a high recall value of 78% with a minimum retrieval time of 980 ms. This showed the supremacy of the proposed model was comparatively greater than the previous ones.

Some Linear Diophantine Fuzzy Similarity Measures and Their Application in Decision Making Problem

The uncertainty in the data information for decision making is a most challenging and critical fear. In order to reduce the uncertainty in the decision making expert information for decision making problem, the Linear Diophantine fuzzy number is taking more critical part in reducing the uncertainty in information. Therefore the primary aim of this paper is to develop some different types of similarity and distance measures for linear Diophantine fuzzy numbers. With the frequent occurrence of emergency events, emergency decision making (EDM) plays a significant role in the emergency situations. It is essential for decision makers to make reliable and reasonable emergency decisions within a short time period since inappropriate decisions may result in enormous economic losses and chaotic social order. Accordingly, to ensure that EDM problems can be solved effectively and quickly, this paper proposes a new EDM method based on the novel distance and similarity measures under Linear Diophantine fuzzy (LDF) information. The similarity measure is one of the beneficial tools to determine the degree of similarity between objects. It has many crucial applications such as decision making, data mining, medical diagnosis, and pattern recognition. In this study, some novel distances and similarity measures of linear Diophantine fuzzy sets are presented. Then, the Jaccard similarity measure, exponential similarity measure, Cosine and Cotangent function based on similarity measures for LDFSs were proposed. The newly defined similarity measures are applied to medical diagnosis problem for COVID-19 virus and the results are discussed. A comparative study for the new similarity measures is established, and some advantages of the proposed work are discussed.

A Theoretical Framework of the Graph Shift Algorithm

Since no theoretical foundations for proving the convergence of Graph Shift Algorithm have been reported, we provide a generic framework consisting of three key GS components to fit the Zangwill’s convergence theorem. We show that the sequence set generated by the GS procedures always terminates at a local maximum, or at worst, contains a subsequence which converges to a local maximum of the similarity measure function. What is more, a theoretical framework is proposed to apply our proof to a more general case.

Energy and Spectrum Efficient Blind Equalization With Unknown Constellation for Air-to-Ground Multipath UAV Communications

In unmanned aerial vehicle (UAV) communications, frequency-selective fading can severely deteriorate the quality of transmitted signal by generating undesired and disordered constellation diagrams due to scatters in the air-to-ground (ATG) mutipath channels. In this paper, we propose a low-overhead blind equalization method to combat frequency-selective fading in air-ground multipath UAV channels. Specifically, a pre-equalization method is proposed based on a constant modulus algorithm to restore the contour of the constellation diagram. Moreover, the similarity measure function and the difference measure function are derived using template matching to identify the constellation of M-ary quadrature amplitude modulation. Furthermore, we propose a weighted constant cross algorithm (WXA) to reduce the residual mean square error and construct a cross-shaped modulus value, by utilizing the statistical information of the identified normalized standard constellation diagrams and the equalizer output decision symbols' weighting value. The proposed method requires less information and no training sequences and pilots, therefore, if achieves energy and spectrum efficient ATG multipath UAV communications. Simulation results show that the proposed WXA algorithm can reduce the residual mean square error convergence value between -22dB and -25dB, making it very useful for the equalization of the frequency-selective fading channel in typical UAV communication scenarios.

An effective hybrid framework for content based image retrieval (CBIR)

In recent years, we have witnessed a massive growth in the generation of images on the cyberspace which demands to develop automated solutions for effective content management. Content-based image retrieval (CBIR) systems have been proposed to reduce the dependency on textual annotations-based image retrieval systems. There exists a variety of features-classifier combinations based CBIR methods to analyze the content of query image for relevant images retrieval. Although, these methods provide better retrieval performance in single-class scenario, however, we experience a significant performance drop in multi-class search environments due to semantics similarity among the images of different classes. CBIR methods based on the hybrid classification model offer better retrieval accuracy, however, we experience a biased classification towards the negative class due to the class imbalance problem when we experience an increase in the number of negative samples due to highly correlated semantic classes. Thus, multiple classifiers based CBIR models become unstable especially in one-against-all classification settings. To address the aforementioned problem, we proposed a CBIR method based on a hybrid features descriptor with the genetic algorithm (GA) and SVM classifier for image retrieval in multi-class scenario. More specifically, we employed the first three color moments, Haar Wavelet, Daubechies Wavelet and Bi-Orthogonal wavelets for features extraction, refine the features using GA and then train the multi-class SVM using one-against-all approach. L2 Norm is used as a similarity measurement function between the query image and retrieved images against the query image from the image repository. The proposed technique successfully addresses the class imbalance problem in CBIR. Performance of the proposed method is evaluated on four standard datasets i.e. WANG, Oxford Flower, CIFAR-10, and kvasir and compared with 25 different CBIR methods. Experimental results illustrate that our method outperforms the existing state-of-the-art CBIR methods in terms of image retrieval.

Privacy Enhanced Cloud-Based Facial Recognition

Homomorphic encryption is a significant method to protect user privacy in cloud computing environment. Due to the computation efficiency issue, there is still not many homomorphic encryption applications for common users. In this paper,we try to use homomorphic encryption to enhance the privacy in cloud-based face recognition system. By balancing the workload between client and server,and reimplementing the similarity measurement function, our homomorphic encryption version’s performance is almost the same as the original version in terms of accuracy and time consumption. Our work is especially beneficial to many face recognition methods that are using Euclidian distance as their similarity metric.

Fault Diagnosis for Electro-Mechanical Actuators Based on STL-HSTA-GRU and SM

The electro-mechanical actuators (EMAs) play an important role in the new-generation aircraft, which makes the fault diagnosis of EMA become a hot topic in the industry. However, the EMA signals usually have nonlinear characteristics and seasonal tendency, which bring great challenge to the fault diagnosis. Furthermore, detecting faults in the early stage helps reduce the risk of serious damage to EMA, but most studies are focusing on the situation that the EMA faults are well-developed. To tackle the challenge, we present an innovative algorithm which combines a hybrid-spatial and temporal attention-based gated recurrent unit (HSTA-GRU) with Seasonal-Trend decomposition procedures based on Loess (STL) to predict multiple time-series data for more failure information. The STL extracts the seasonal factor for mitigating the influence of seasonal fluctuation, and the HSTA-GRU captures the spatio-temporal relationships among multivariate EMA sensors for a long-term prediction of multiple time-series data. Then, for the predicted time series, a similarity measure (SM) function based on dynamic time warping (DTW) is used to classify the fault types without training, so as to reduce the accumulated error and enhance the efficiency of classification. Ultimately, the analysis result on an experimental EMA fault dataset demonstrates that the proposed arithmetic can provide a superior performance not only in the time series prediction, but also for EMA fault diagnosis.

A survey on regional level set image segmentation models based on the energy functional similarity measure

Image segmentation is an important field of computer vision and has attracted significant research attention in the recent years. In this paper, we provide a survey of regional level set image segmentation models based on the energy functional similarity measure. Our survey begins with an introduction to region-based level set image segmentation and an overview of its general steps. Then the different segmentation models are summarized. We define and survey six categories of regional level set image segmentation models based on energy functional similarity measures. For every category, we present the mainstream approaches from the literature as examples. Experimental analyses are conducted to compare the segmentation performance of various methods, which allow us to draw meaningful conclusions about their mutual advantages and disadvantages. Finally, we conclude this survey by highlighting several promising directions which need to be further explored by the research community in the future.

Dynamic Fusion Algorithm of Building Surface Data in Heterogeneous Environment

The existing building surface data fusion algorithms do not extract the segmented data features, resulting in inaccurate fusion results. In heterogeneous environment, a Clustering Fusion Algorithm Based on mutual information and fractal dimension is proposed. The regression coefficient is used to express the sequence, and the data feature representation and data dimension reduction are realized. The dynamic data series are processed by similarity measure function method. For the long dynamic data series, the piecewise aggregation approximation method is used to segment the data and then extract the features. Through the incremental clustering processing data based on fractal dimension clustering algorithm, the research of data fusion algorithm is realized. The experimental results show that the accuracy of building surface data fusion is greatly improved by using the dynamic data fusion algorithm, the highest is 0.98, the sum of square error is reduced, and the lowest is only 90.44.

Extreme point bias compensation: A similarity method of functional clustering and its application to the stock market

Functional clustering is based on functional similarity measures that are adapted to functional data. However, the existing functional similarity measures account either for the position (value) or temporal deviation (bias) of extreme points of the functional curves. This may lead to erroneous conclusions on the similarities of the curves. In this case, most functional clustering measures underperform in (for example) the analysis of stock market data. To address this methodological limitation, a new similarity measure that is based on extreme point bias compensation is proposed in this paper. By penalizing the curves with the temporal deviation of extreme points and rewarding the curves that are close to each other, the new similarity measure better reflects the shape of the curve. In addition, the proposed method overcomes the difficulty of unifying the dimensions of the horizontal and vertical axes (i.e., time and function value) when calculating the distance between two adjacent extreme points. Finally, an empirical example of stock return analysis verifies the validity of this new measure.

Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations

AbstractAccurately identifying the soil map unit component at a specific point‐location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone‐based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non‐soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling‐location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth‐wise variability (i.e., depth‐support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert‐Plus) representing different levels of detail and three types of depth‐support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46–53%) as that achieved with an Expert (48–57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert‐Plus dataset (53–60%). This study also showed minimal effect from the type of depth‐support used to represent depth‐wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65–89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.

CR-FPN: channel relation feature pyramid network for object detection

Object detection is an important computer vision task, which aims to locate each object and classify them correctly. Since Convolution Neural Network (CNN) high-level feature map contains more semantic information and low-level feature map contains detail information which helps to locate precisely, fusing high-level feature maps with low-level feature maps is proven to be essential for multi-scale object detection. Recent work usually direct combine feature maps from different levels of CNN. However, as each feature map has different extents of discrimination, combining them directly may turn some valuable information into noise and further degrade performance. To address this issue, we focus on the channel relationship from different level of feature maps and propose a novel network, named Channel Relation Feature Pyramid Network (CR-FPN), that captures long-range relation of channel from different level of feature maps by similarity measure function and further to magnify the most relevant channels and suppress the irrelevant channels. Extensive experiments conducted on MS COCO and Pascal VOC datasets demonstrate the effectiveness of our CR-FPN also with competitive performance. Noting that our CR-FPN can apply to several typical state-of-the-art detectors. Using CR-FPN in a basic Cascade R-CNN detector, our method achieves state-of-the-art single model results on the COCO dataset.

A novel model based on similarity measure and quality function deployment on interval neutrosophic sets for evaluation and selection market segments

A novel model based on similarity measure and quality function deployment on interval neutrosophic sets for evaluation and selection market segments

Hybrid image segmentation method based on anisotropic Gaussian kernels and adjacent graph region merging.

Image segmentation is a key technique in image analysis for object identification. In this paper, a hybrid segmentation method is proposed, which is based on the Anisotropic Gaussian Kernel (ANGK) edge detector and region adjacent graph (RAG) merging algorithm. An anisotropic directional derivative filter is constructed by angled ANGK to detect the edge contour of original images. Based on the gradient magnitude pattern of the edge contour from ANGK processing, watershed transform is adopted to produce initial partition (coarse segmentation result). Finally, combined with the RAG region merging algorithm, the proposed method performs fine segmentation by merging similar fragmented regions (initial partition) iteratively. Additionally, statistic similarity measure and shape cost function in merging cost are also introduced. They provide quantitative criteria for region merging, which have critical influences on the detection result. A series of experiments are conducted to evaluate the performance of this method, and a preferable localization accuracy as well as noise robustness is proved. Compared with conventional edge and region based methods, the proposed one has a superior segmentation effect. Another advantage is that the problem of oversegmentation can be solved effectively.

Metallographic image segmentation of GCr15 bearing steel based on CGAN

A novel deep learning segmentation method based on Conditional Generative Adversarial Nets (CGAN) is proposed, being U-GAN in this paper to overtake shortcomings of the metallographic images of GCr15 bearing steel, such as multi-noise, low contrast and difficult to segment. The results of experiment indicate that the proposed model is the most accurate comparing with the digital image processing methods and deep learning methods on carbide particle segmentation. The average Dice’s coefficient of similarity measure function is 0.9158, which is the state-of-the-art performance on dataset.

A Variational Image Segmentation Model Based on Normalized Cut with Adaptive Similarity and Spatial Regularization

Image segmentation is a fundamental research topic in image processing and computer vision. In recent decades, researchers developed a large number of segmentation algorithms for various applications. Among these algorithms, the normalized cut (Ncut) segmentation method is widely applied due to its good performance. The Ncut segmentation model is an optimization problem whose energy is defined on a specifically designed graph. Thus, the segmentation results of the existing Ncut method are largely dependent on a preconstructed similarity measure on the graph since this measure is usually given empirically by users. This flaw will lead to some undesirable segmentation results. In this paper, we propose an Ncut-based segmentation algorithm by integrating an adaptive similarity measure and spatial regularization. The proposed model combines the Parzen--Rosenblatt window method, nonlocal weights entropy, Ncut energy, and regularizer of phase field in a variational framework. Our method can adaptively update the similarity measure function by estimating some parameters. This adaptive procedure enables the proposed algorithm to find a better similarity measure for classification than the Ncut method. We provide some mathematical interpretation of the proposed adaptive similarity from multiple viewpoints, such as statistics and convex optimization. In addition, the regularizer of phase field can guarantee that the proposed algorithm has a robust performance in the presence of noise, and it can also rectify the similarity measure with a spatial priori. The well-posed theory such as the existence of the minimizer for the proposed model is given in the paper. Compared with some existing segmentation methods such as the traditional Ncut-based model and the classical Chan--Vese model, the numerical experiments show that our method can provide promising segmentation results.