Similarity Measurement Techniques Research Articles

Clustering algorithm based on DINNSM and its application in gene expression data analysis.

Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

A novel IFN-CSM-CoCoSo approach for multiple-attribute group decision-making with intuitionistic fuzzy sets: An application in assessing corporate social responsibility performance

With the rapid growth of the economy, enterprises have encountered a series of problems while pursuing economic benefits, such as food safety and environmental pollution issues, resource shortages and energy consumption issues, which affect the sustainable development of enterprises. Establishing a corporate performance evaluation system from the perspective of social responsibility, based on stakeholder theory and the importance of overall goals reflected in the weight of social responsibility indicators, is a very effective measure to achieve corporate social responsibility (CSR) goals through CSR motivation and stakeholders. The performance evaluation of CSR from the perspective of environmental accounting is a MAGDM. Recently, the CoCoSo technique and cosine similarity measure (CSM) technique was utilized to conduct the MAGDM. The intuitionistic fuzzy sets (IFSs) are utilized as a technique for conducting uncertain information during the performance evaluation of CSR from the perspective of environmental accounting. In this study, the intuitionistic fuzzy CoCoSo based on the CSM (IFN-CSM-CoCoSo) technique is built for MAGDM with IFSs. Finally, a numerical example for performance evaluation of CSR from the perspective of environmental accounting is conducted to verify the IFN-CSM-CoCoSo technique.

A new approach to computing the distances between research disciplines based on researcher collaborations and similarity measurement techniques

The measurement of distance between research disciplines involves various approaches, with a focus on publication citation analysis. However, calculating discipline distance requires more than just selecting relevant information; it also involves choosing suitable quantification methods and similarity measures. In this paper, we introduce a novel approach to measuring the distance between research disciplines, referred to as a distance matrix. This approach is particularly useful when there is limited availability of citation data, providing an alternative method for quantifying the distance between disciplines. Our method counts co-occurrences of disciplines based on researcher collaborations in projects and evaluates various similarity measures to convert the co-occurrence matrix into a similarity matrix. We analyze the behavior of different similarity measures and propose functions to transform the similarity matrix into a distance matrix, capturing research discipline dissimilarity effectively. Additionally, we establish evaluation criteria for distance matrix quality. We implement our approach on the Flanders Research Information Space dataset, showing promising results. The distance matrix demonstrates satisfactory density scores, outperforming traditional approaches in skewness and deviation. The probability density functions of distances remain consistent over time, indicating stability. Furthermore, the distance matrix proves valuable for visualizing discipline profiles associated with the dataset, providing valuable insights.

Enterprise technological innovation capability evaluation using a spherical fuzzy number based CSM-EDAS model

With the rapid development of science and technology, the flow of information has become more convenient, and society has entered the era of knowledge economy. In this era, technological innovation capability is becoming increasingly important and has become an important weapon for enterprises to survive in fierce competition, especially for technology-based small and medium-sized enterprises. Nowadays, technology-based small and medium-sized enterprises have developed many technological innovation achievements through continuous technological innovation, and have created a large number of high-tech products and services. Technological innovation has been proven to effectively improve the core competitiveness and economic benefits of technology-based small and medium-sized enterprises. Therefore, evaluating the technological innovation capabilities of technology-based small and medium-sized enterprises has both theoretical and practical significance. The enterprise technological innovation capability evaluation from a low carbon perspective could be deemed as the multiple attribute group decision making (MAGDM) problem. Recently, the evaluation based on distance from average solution (EDAS) technique and cosine similarity measure (CSM) technique has been employed to manage MAGDM issues. The spherical fuzzy sets (SFSs) are used as an efficient tool for portraying uncertain information during the enterprise technological innovation capability evaluation from a low carbon perspective. In this paper, the spherical fuzzy number EDAS based on the CSM (SFN-CSM-EDAS) technique is cultivated to manage the MAGDM under SFSs. Finally, a numerical study for enterprise technological innovation capability evaluation from a low carbon perspective is supplied to validate the proposed technique. The main contributions of this paper are outlined: (1) the EDAS and CSM technique was extended to SFSs; (2) the CRITIC technique is used to derive weight based on CSM technique under SFSs. (3) the SFN-CSM-EDAS technique is founded to manage the MAGDM under SFSs; (4) a numerical case study for enterprise technological innovation capability evaluation from a low carbon perspective and some comparative analysis is supplied to validate the SFN-CSM-EDAS technique.

Analyzing complex networks: Extracting key characteristics and measuring structural similarities

SummaryThis paper discusses the importance of feature extraction and structure similarity measurement in the analysis of complex networks. Social networks, biological systems, and transportation networks are just a few examples of the many phenomena that have been modeled using complex networks. However, analyzing these networks can be challenging due to their large size and complexity. Feature extraction techniques can help to simplify the network by identifying key nodes or substructures. Structure similarity measurement techniques can be used to compare different networks and identify similarities and differences between them. Previous research has suggested that real‐world complex networks are influenced by multiplex features and either local or global features. However, the interaction between these characteristics is not well understood. The proposed approach outperforms other graph similarity methods on publicly available datasets, with accurate estimations of overall complex network structures. Specifically, the approach based on cosine similarity outperforms as compared to existing methods. Overall, this study highlights the importance of considering various graph features–local and global features and their interactions in the analysis of complex networks.

Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier

Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality worldwide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based feature selection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K = 3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe) = 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%. The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.

Measuring flight-destination similarity: A multidimensional approach

E-tourism websites offer users a vast array of travel destinations and opportunities, necessitating tools that enable destination comparison and intelligent search capabilities. One key requirement for such tools is the ability to measure the similarity between destinations. Over the years, various similarity measurement techniques have been proposed, including user-based and content-based approaches. However, many of these techniques require data preparation or prior domain knowledge from experts. In contrast, this study proposes an innovative approach that requires no prior domain knowledge of flight destinations or their relationships, and utilizes only readily available data. Our approach draws upon concepts from image recognition and natural language processing (NLP) to extract hidden aspects of destinations. Using data from a flight-search website as a testbed, we analyze similarity metrics based on state-of-the-art methods for image recognition, NLP, and product-network analysis. We then compare these metrics to those obtained by human subjects. Our findings suggest that no single method dominates in all aspects, leading us to propose a hybrid method that leverages the strengths of each. The proposed method can be readily applied to measure product similarity in other domains.

A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10,000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.

XL-BPMN Model-Based Service Similarity Measurement Technique

In service-oriented developments, existing studies do not give lots of efforts on a formalized and systematic method for measuring similarities between services for their reuse in business models. This deteriorates the reusability of the constructed service due to the developers’ intuition and informal service analyses. In this paper, we propose a technique for measuring similarity of services by analyzing syntax and semantics between services in the eXtended Layered business process modeling notation (XL-BPMN) model. First of all, the profiles of the formalized attributes for specifying services are defined, and the criteria for determining service similarities are established. To measure similarity between services, a technique both a syntactic similarity analysis facilitated by the XL-BPMN model-based edge counting method and a semantic similarity analysis based on meta data registry (MDR)-applied service attributes is specified. To automate analysis, a tool that can support the semantic similarity analysis technique is implemented. An online shopping mall system is investigated and evaluated to verify the effectiveness of the proposed technique. The similarity measurement technique, which is further formalized at upper business levels, can improve the accuracy of service analyses and enhance service reusability by distinguishing services with high similarity levels as common services.

Optimizing New Technology Implementation Through Fuzzy Hypersoft Set: A Framework Incorporating Entropy, Similarity Measure, and TOPSIS Techniques

Optimizing New Technology Implementation Through Fuzzy Hypersoft Set: A Framework Incorporating Entropy, Similarity Measure, and TOPSIS Techniques

Measuring lane-changing trajectories by employing context-based modified dynamic time warping

The spatial lane-changing (LC) behavior should be analyzed for the vehicles in transportation systems in order to identify the patterns of vehicles’ movements using the similarities detected in their lane-changing trajectories. The trajectory of an LC vehicle is a function of its context. The present paper utilized spatial footprints and external/internal contexts to contextualize a measure applicable to the similarities found between LC trajectories. While only the external context of the previous investigation was constrained to the surrounding vehicles on the road, this study has investigated the idea of the contribution of solar radiation to the lane-changing trajectory patterns. The similarities found between multi-dimensional trajectories were determined by offering context-based modified dynamic time warping (CMDTW), and the CMDTW technique with the Next Generation Simulation (NGSIM) dataset was carefully analyzed. The weighting framework used for each dimension made it possible to quantify the similarities between lane-changing trajectories using the AHP technique. The obtained results showed that not only the lane-changing procedure depends on the conditions of the lane changer, but this procedure also depends on the solar radiation and the surrounding vehicles offered as the external contexts. Additionally, by including different dimensions, both internal and external contexts, the similarity results of LC trajectories turn into a more realistic phenomenon. The potential of the context-based modified dynamic time warping algorithm in the detection of a trajectory with the maximum similarity is also enhanced. Furthermore, in order to determine the LC trajectories, we used the Fuzzy C-means (FCM) clustering technique. We utilized Cohen’s kappa for the evaluation of the Fuzzy C-means results, and since the calculated Kappa score exceeds 0.8, the clustering algorithm has an excellent performance. The results obtained by comparing the suggested technique with commonly used similarity measurement techniques indicated that the accuracy of the CMDTW technique outperforms other techniques in the detection of the lane-changing trajectory patterns. The suggested CMDTW method has therefore been effective in the identification of the patterns of lane-changing trajectory.

A Multifocus Image Fusion Scheme Based on Similarity Measure of Transformed Isosceles Triangles Between Intuitionistic Fuzzy Sets

Intuitionistic fuzzy set (IFS) theory is widely used to solve imprecise and uncertain problems, and the similarity measure between IFSs can describe the similarity or dissimilarity degree of objects in the real word. In image fusion, it is a key issue to measure and extract the features of multifocus images to fuse them. This research proposes a similarity measure technique of IFSs, which is introduced into image fusion to measure the feature similarity of adjacent pixels. In this work, we present an available similarity measure method between IFSs and propose an image fusion method based on this measure and also design an edge reserved method based on the Canny edge detection to reinforce the image fusion effect. First, the proposed similarity measure is introduced and proved. Second, a focus detection method that can detect the focused regions of an image is designed based on the proposed measure method of IFSs to obtain a preliminary decision map. Third, morphological operations are employed to generate the secondary decision map. Fourth, a Canny edge model-based technique is designed to preserve the edges of the source images. Extensive experiments show that the presented similarity measure method of IFSs is reliable, and the fuzzy measure-based image fusion scheme is effective compared with current methods.

A dynamic time warping approach for handling class imbalanced medical datasets with missing values: A case study of protein localization site prediction

Class imbalanced medical datasets, such as cancer prediction, contain imbalanced numbers of data in different classes leading to skewed class distribution, which makes it very difficult for a classifier to distinguish between minority (i.e. cancer) and majority (i.e. non-cancer) classes. Related studies in the literature have proposed different types of solutions for the class imbalance problem including data level, algorithmic level, and cost-sensitive learning approaches. However, none of these potential solutions have considered the issue of missing attribute values residing in the class imbalanced medical datasets, especially for the minority class. Missing value imputation is commonly used for the construction of some models where statistical or machine learning techniques are used to produce estimations to replace the missing values. However, the existing imputation methods require a certain number of observed data to produce their estimations, the major challenge for them being that the amount of observed data (with no missing values) in the minority class is very limited, or that some data are not complete. In this paper, we proposed a novel approach, namely Dynamic Time Warping-based Imputation (DTWI), to handle class imbalanced datasets with missing values. Based on the similarity measurement technique of DTW, all of the data (with or without missing values) in the minority class can be used for missing value imputation. The experimental results based on 10 different class imbalanced medical datasets show that when the missing rates in the minority classes are smaller than 30%, DTWI performs similarly to the baseline K-NN imputation method and better than the mean/mode imputation and case deletion methods. When the missing rates are larger than 30%, DTWI significantly outperform the other techniques.

DOTS STRATEGIC WITH EXTREME MACHINE LEARNING METHOD IN THE CLASSIFICATION OF DISEASE TRANSMISSION IN TB PATIENTS

Machine Learning Model The type of data to be grouped determines the type similarity measurement techniques are used. The exact image measurements are used for determine the similarity of the two data points in the dataset. Therefore, we studied an image dataset of TB patients is an important step in its treatment to anticipate early detection with the aim of helping the DOTS plan strategy with the community. The results showed that there were 9 men with DOTS and EML algorithms (73.3%). While for women, the DOTS and EML algorithms were 5 people (26.4%). The age was determined between 12 people (25.3%) while those and 7 people (55.5). Sufficient knowledge of DOTS and EML algorithms among 30 people (66.7%). Minimum knowledge is 2 people and middle knowledge is 13 people (34.3%). Based on the final results of the DOTS and EML algorithm, there were 26 people (46.9%) and tall as many as 6 people (54.1%).

Semantic integration of traditional and heterogeneous data sources (UML, XML and RDB) in OWL2 triplestore

With the success of the internet and the expansion of the amount of data in the web, the exchange of information from various heterogeneous and classical data sources becomes a critical need. In this context, researchers must propose integration solutions that allow applications to simultaneously access several data sources. In this perspective, we propose a semi-automatic integration approach of classical data sources via a global schema located in database management systems of RDF or OWL data, called triplestore. Our contribution is subdivided into three axes: 1) an automatic mapping solution that converts classical data sources such as UML, XML and RDB to local ontologies based on OWL2 language; 2) an alignment system of local ontologies based on syntactic, semantic and structural similarity measurement techniques in order to increase the probability of having real correspondences and real differences; 3) a fusion system of pre-existing local ontologies into a global ontology based on the alignment found in the previous step.

Bipolar quadripartitioned single valued neutrosophic sets

The notion of simple bipolar quadripartition is presented valuable neutrosophic set. Some basic set theoretic terminologies, operations and properties of bipolar quadripartitioned single valued neutrosophic set are given here. Also different types of distances, similarity measures and entropy measure are discussed. Finally a decision making problem using the similarity measure technique of bipolar quadripartitioned single valued neutrosophic sets has been solved.

Subspace transform induced robust similarity measure for facial images

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

A new approach to similarity measure for generalized trapezoidal fuzzy numbers and its application to fuzzy risk analysis

A new similarity measure has been proposed in this paper for generalized trapezoidal fuzzy numbers (GTrFNs). Here, the proposed similarity measure has been devised based on exponent distance, area, perimeter and height of a GTrFN. Most of the researchers described the similarity measure between two GTrFNs whose components belong to [0, 1] only. But the components of GTrFNs should be any real numbers belonged to R. For this reason, in this paper a similarity measure technique has been framed newly on GTrFN whose components are any real number which is the most important consideration here. Depending on the proposed method of similarity measure, some essential properties have been illustrated in this paper. Also, this method has been compared with some existing techniques of similarity measure taking twenty five different sets of GTrFNs. Henceforth, it is obtained that our proposed technique is better than other existing techniques. Finally, the proposed similarity measure has been applied in fuzzy risk analysis in a production system which has been described with numerical illustration.

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires.