Cosine Similarity Measure Research Articles

A cosine similarity measures between hesitancy fuzzy graphs and its application to decision making

<abstract><p>A new cosine similarity measure between hesitancy fuzzy graphs, which have been shown to have greater discriminating capacity than certain current ones in group decision making problems by example verification. This study proposes a novel method for estimating expert-certified repute scores by determining the ambiguous information of hesitancy fuzzy preference relations as well as the regular cosine similarity grades from one separable hesitancy fuzzy preference relation to some others. The new approach considers both "objective" and "subjective" information given by experts. We construct working procedures for assessing the eligible reputational scores of the experts by applying hesitancy fuzzy preference relations. In an evaluation in which multiple conflicting factors are taken into consideration, this can be applied to increase or reduce the relevancy of specified criteria. Applying the two effective methods, the newly developed cosine similarity measure, the energy of hesitancy fuzzy graph, and we provide a solution to a decisional issue. Finally, the two working procedures and examples are given to verify the practicality and dominance of the proposed techniques.</p></abstract>

Classification of Stakeholders of an Information System using Fuzzy based Adjusted Cosine Similarity Measure during Requirements Elicitation Process

Requirements elicitation process is employed for the identification of stakeholders of an information system. A large number of stakeholders from different domains across the globe participate during the requirements elicitation process. Based on our review, we found that existing methods for the analysis of stakeholders do not support how to classify the stakeholders based on the similarity measures. These measures play an important role for the recommendation of an item/user in a recommendation system. Thus, the objective of this paper is to classify the stakeholders of an information system using fuzzy-based adjusted cosine similarity measure. We have identified the stakeholders of library information system and their opinions for different requirements are recorded. The identified stakeholders have been classified and analysed based on the following similarity measures under fuzzy environment, i.e., Cosine similarity measure, Euclidean distance, Pearson coefficient correlation etc., so that stakeholders having similar requirements can be recommended to strengthen the requirements elicitation process.

A novel multi-viewpoints based cosine similarity visual technique for an effective assessment of clustering tendency

Data clustering is an unsupervised technique that can be used to partition the data into groups based on the similarities of the retrieved objects using different distance metrics like Euclidean, cosine, etc. In contrast to Euclidean, the cosine computes the object's similarity by considering both the magnitude and direction of the data vectors. As a result, it performed far better than a standard Euclidean distance metric in applications involving real-time data clustering. The initial k-value (clustering tendency) is required by top clustering techniques like k-means and hierarchical approaches to determine the clusters' quality. Users with knowledge can assign the k-value. However, sometimes the right k-value in such algorithms may need to be assigned. After a thorough review of the work, it was discovered that the visual technique known as visual assessment of (cluster) tendency (VAT) effectively addresses the clustering tendency issue. It uses the Euclidean metric to find the similarity features in its algorithm. Another enhanced visual technique, cosinebased VAT (cVAT), outperformed the VAT for text data and speech clustering applications. However, the similarity features are extracted about a single viewpoint in cVAT. This paper develops the multi-viewpoints-based cosine similarity measure (MVPCSM) for a more informative assessment. Instead of using a single reference point like a typical cosine measure, the MVPCSM generates precise similarity characteristics using several views. The performance of the existing and proposed technique (MVPCSM-VAT) is evaluated using clustering accuracy (CA) and normalized mutual information (NMI). It has been demonstrated that the proposed MVPCSM-VAT is 15-25% more efficient than VAT and cVAT in terms of the parameters of CA and NMI. The proposed method successfully obtains more quality data clusters than MVS-VAT.

Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique

The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.

Circular Pythagorean Fuzzy Sets and Applications to Multi-Criteria Decision Making

In this paper, we introduce the concept of circular Pythagorean fuzzy set (value) (C-PFS(V)) as a new generalization of both circular intuitionistic fuzzy sets (C-IFSs) proposed by Atannassov and Pythagorean fuzzy sets (PFSs) proposed by Yager. A circular Pythagorean fuzzy set is represented by a circle that represents the membership degree and the non-membership degree and whose centre consists of non-negative real numbers μ and ν with the condition ${\mu ^{2}}+{\nu ^{2}}\leqslant 1$. A C-PFS models the fuzziness of the uncertain information more properly thanks to its structure that allows modelling the information with points of a circle of a certain centre and a radius. Therefore, a C-PFS lets decision makers to evaluate objects in a larger and more flexible region and thus more sensitive decisions can be made. After defining the concept of C-PFS we define some fundamental set operations between C-PFSs and propose some algebraic operations between C-PFVs via general triangular norms and triangular conorms. By utilizing these algebraic operations, we introduce some weighted aggregation operators to transform input values represented by C-PFVs to a single output value. Then to determine the degree of similarity between C-PFVs we define a cosine similarity measure based on radius. Furthermore, we develop a method to transform a collection of Pythagorean fuzzy values to a C-PFS. Finally, a method is given to solve multi-criteria decision making problems in circular Pythagorean fuzzy environment and the proposed method is practiced to a problem about selecting the best photovoltaic cell from the literature. We also study the comparison analysis and time complexity of the proposed method.

Visual Assessment of Cluster Tendency with Variations of Distance Measures

Finding the cluster structure is essential for analyzing self-organized networking structures, such as social networks. In such problems, a wide variety of distance measures can be used. Common clustering methods often require the number of clusters to be explicitly indicated before starting the process of clustering. A preliminary step to clustering is deciding, firstly, whether the data contain any clusters and, secondly, how many clusters the dataset contains. To highlight the internal structure of data, several methods for visual assessment of clustering tendency (VAT family of methods) have been developed. The vast majority of these methods use the Euclidean distance or cosine similarity measure. In our study, we modified the VAT and iVAT algorithms for visual assessment of the clustering tendency with a wide variety of distance measures. We compared the results of our algorithms obtained from both samples from repositories and data from applied problems.

New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach.

The most straightforward approaches to checking the degrees of similarity and differentiation between two sets are to use distance and cosine similarity metrics. The cosine of the angle between two n-dimensional vectors in n-dimensional space is called cosine similarity. Even though the two sides are dissimilar in size, cosine similarity may readily find commonalities since it deals with the angle in between. Cosine similarity is widely used because it is simple, ideal for usage with sparse data, and deals with the angle between two vectors rather than their magnitude. The distance function is an elegant and canonical quantitative tool to measure the similarity or difference between two sets. This work presents new metrics of distance and cosine similarity amongst Fermatean fuzzy sets. Initially, the definitions of the new measures based on Fermatean fuzzy sets were presented, and their properties were explored. Considering that the cosine measure does not satisfy the axiom of similarity measure, then we propose a method to construct other similarity measures between Fermatean fuzzy sets based on the proposed cosine similarity and Euclidean distance measures and it satisfies the axiom of the similarity measure. Furthermore, we obtain a cosine distance measure between Fermatean fuzzy sets by using the relationship between the similarity and distance measures, then we extend the technique for order of preference by similarity to the ideal solution method to the proposed cosine distance measure, which can deal with the related decision-making problems not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate the reasonableness and effectiveness of the proposed method, which is also compared with other existing methods.

Contrastive study of minimum edit distance and cosine similarity measures in the context of word suggestions for misspelled Marathi words

Contrastive study of minimum edit distance and cosine similarity measures in the context of word suggestions for misspelled Marathi words

Application of spectral small-sample data combined with a method of spectral data augmentation fusion (SDA-Fusion) in cancer diagnosis

BackgroundCancer is one of the most life-threatening diseases to human life, whose accurate diagnosis is the prerequisite for precise treatment. The detection technology with computer-aided vibrational spectroscopy has achieved gratifying results in intelligent cancer diagnosis. However, limited by factors such as the number of cancer instances in clinical practice and the cost of spectral acquisition, it is difficult to obtain a large amount of spectral data, which ultimately puts constraints on the performance optimization and improvement of diagnostic models. MethodFaced with the above challenges, we adopted the different data augmentation strategies in this study to obtain more available training data. In addition to the augmentation methods commonly used in vibrational spectroscopy, such as adding random noise, adding random variations from offset, multiplication and slope, and synthetic minority over-sampling technique (SMOTE), two generative adversarial networks with different architectures were selected for comparison. One is based on artificial neural networks (ANN) and the other on convolutional neural networks (CNN). In the experiments, t-distributed stochastic neighbor embedding (t-SNE) visualization and cosine similarity (CS) measure were opted to evaluate the quality of generated new spectra. New spectra with different manifestations were produced by dissimilar augmentation tactics. Effective merging of heterogeneous data information generated by different augmentation techniques can further enlarge the sample space and increase the diversity of samples. With these factors in mind, we proposed a new spectral data augmentation fusion (SDA-Fusion) method to acquire more available instances. This method is carried out by fusing the new data generated by the five different data augmentation techniques mentioned before. Finally, three groups of experiments, with the original training data, the augmented training data, and the fused training data as input, were designed. Support vector machines (SVM) with different kernel functions, CNN as well as ResNet were used as classification models. Group five-fold (Group5Fold) cross-validation was utilized to assess model performance. ResultsWe applied the augmentation methods and experimental ideas mentioned above to two real datasets – the Raman spectral dataset of lung cancer and the mid-infrared spectral dataset of glioma, respectively. The results illustrate that the generative adversarial networks working through adversarial learning concepts can produce new data approximate to the original. This technique can be a complementary means for expanding the size of the vibrational spectroscopy data. Moreover, by introducing different augmentation strategies, the classification accuracies of most classifiers were higher than the original training set. In addition, a more extensive and heterogeneous dataset can be yielded using our proposed SDA-Fusion method. We have trained more robust models that provided better predictive performance for both spectral datasets on the foundation of these data. ConclusionThis research aims to address the lack of data volume of vibrational spectra from cancer at the data level. It can provide the solution ideas to be consulted by other researchers in the future when faced with the small-sample learning tasks for vibrational spectra.

Feature level fusion framework for multimodal biometric system based on CCA with SVM classifier and cosine similarity measure

ABSTRACT The multimodal biometric system using feature level fusion offers more accurate and reliable recognition performance than the unimodal system. But in practice, feature level fusion is challenging to perform when biometric modalities have heterogeneous and incompatible feature representation and enforce the final decision more confidently. One of the main concerns in the fusion of features is to drive the highly discriminatory representation amongst different biometric modalities. This paper aims to design a framework for an efficient feature level fusion based on canonical correlation analysis (CCA) with a support vector machine (SVM) classifier to get a highly discriminant and affine invariant fused feature vector. The principal component analysis (PCA) + CCA subspace approach is used to achieve dimensionality reduction and feature fusion in a coherent manner, This approach eliminates the need for a complex matcher/classifier design to process a fused feature vector and also reduces computational complexity. The experimental findings for the SDUMLA-HMT multimodal database demonstrate that CCA on the extracted feature sets of iris and fingerprint modalities results in reasonably better multimodal classification accuracy with a substantial reduction in the feature dimensions. Using SVM, we achieved a classification accuracy of 100%. In this paper furthermore, three different distance measures are explored to test the efficacy of the proposed CCA-based feature level fusion approach. The best recognition performance is achieved in terms of an equal error rate (EER) of 0.176% for the cosine similarity measure. We also compared the proposed approach with the match score level fusion method. The proposed feature level fusion approach excels the recognition performance in contrast to the other literature approaches.

Multicriteria Decision-Making Problem via Weighted Cosine Similarity Measure and Several Characterizations of Hypergroup and (Weak) Polygroups under the Triplet Single-Valued Neutrosophic Structure

Polygroups are an extended form of groups and a subclass of hypergroups that follow group-type axioms. In this paper, we define a triplet single-valued neutrosophic set, which is a generalization of fuzzy sets, intuitionistic fuzzy sets, and neutrosophic sets, and we combine this novel concept with hypergroups and polygroups. Firstly, the main goal of this paper is to introduce hypergroups, polygroups, and anti-polygroups under a triplet single-valued neutrosophic structure and then present various profound results. We also examine the interaction and properties of level sets of triplet single-valued neutrosophic polygroups and (normal) subpolygroups. Secondly, we rank the alternatives and select the best ones in a single-valued neutrosophic environment using the weighted cosine similarity measure between each alternative and the ideal alternative. Finally, we provide an example that clearly shows how the proposed decision-making method is applied.

A Choquet Integral Based Cosine Similarity Measure for Interval-Valued Intuitionistic Fuzzy Sets and an Application to Pattern Recognition

A Choquet Integral Based Cosine Similarity Measure for Interval-Valued Intuitionistic Fuzzy Sets and an Application to Pattern Recognition

Cosine similarity measures between q-rung orthopair linguistic sets and their application to group decision making problems

The q-rung orthopair linguistic set (q-ROLS), a combined version of linguistic term sets and q-rung orthopair fuzzy set, is an efficient mathematical tool to accomplish the imprecise information while solving the decision-making problems. Under this environment, we propose additional operations and relations to deal with the decision information, and some properties are well proved. Furthermore, we propound some cosine similarity measures and weighted cosine similarity measures for q-ROLSs based on the traditional cosine similarity measures with a brief study of related properties. In the proposed similarity measures, various linguistic scale functions are utilized in order to take into account the semantics of linguistic terms. Besides this, we employ the stated q-rung orthopair linguistic similarity measures to multi-criteria group decision making problems, in which the weights of DMs are delineated by the projection of individual decisions on the ideal decision results. At last, a numerical example is used to demonstrate the practicality of the suggested technique, and its efficacy is validated by comparison with prevailing techniques. The suggested method’s sensitivity and stability analyses are also provided.

A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines

Intelligent fault diagnosis technology, as a promising approach, is gradually playing an irreplaceable role in ensuring the safety, reliability, and efficiency of mechanical equipment. However, in real-world industrial scenarios, obtaining adequate high-quality label information is typically challenging and unrealistic, resulting in the performance degradation of most existing supervised learning-based diagnosis models, and necessitating the development of unsupervised intelligent diagnostic models. In addition, the sample independence hypothesis is widely used in existing studies, which significantly ignores the further mining of relevant auxiliary information between samples and its positive effect on performance improvement. To overcome these challenges, a novel intelligent fault diagnosis framework, called the convolutional capsule auto-encoder-based unsupervised directed hierarchical graph network with clustering representation (CCAE-UDHGN-CR), is established and employed in unlabeled scenarios. First, a novel convolutional capsule auto-encoder (CCAE), which combines reconstruction loss and semantic clustering loss, is constructed and used to extract deep coding features that contain attribute information of the sample itself. Then, with the assistance of cosine similarity measurement strategy, the internal correlation between samples is fully mined, and on this basis, the conversion of deep coding features to the graph sample set is realized, which serves as the input of the subsequent unsupervised directed hierarchical graph network (UDHGN). Finally, the deep representation features extracted by the UDHGN are further fed into the density-based spatial clustering of applications with noise (DBSCAN) model to complete the determination of category information. A total of three cases based on key functional components and manipulator are employed for performance verification. The comprehensive diagnosis results all show that the proposed CCAE-UDHGN-CR model can effectively alleviate the dependence on label information while maintaining excellent diagnosis performance.

A new intrusion detection system based on Moth–Flame Optimizer algorithm

This study relies on using a Moth–Flame Optimization (MFO) method as a search algorithm and a Decision Tree (DT) as an evaluation algorithm to generate an efficient feature subset for intrusion detection systems (IDS). The target is to find a feature subset using the minimum number of traffic network features that later obtains the maximum performance by the machine algorithms used in the classification task. This depends on enhancing the MFO by adopting new operators besides the embedded spiral operator to balance the exploration and exploitation alleviating the local minima problem. The main contribution of this work is the adoption of the cosine similarity measure to binarize the continuous MFO into a binary problem. Cosine similarity overcomes the limitations of the commonly used sigmoid function that depends on using a threshold value for conversion. However, cosine similarity computes the similarity ratio between the current solution and the optimal solution. The augmented MFO wrapper framework is applied as an IDS to detect anomalous traffic in the network. The proposed method is compared against several well-known state-of-the-art algorithms on three network datasets (KDDCUPP9, NSL-KDD, and UNSW-NB15), using IDSACC, IDSTPR, IDSFPR, IDSF-score, and convergence evaluation measures to assess the performance of the proposed method. The experimental results show the superiority of the proposed cosine similarity method compared to other algorithms with an accuracy of 97.8%, F-score of 99%, TPR of 99.6%, and FPR of 8.1% using only five selected features from the KDDCUPP99 dataset. It achieved the accuracy of 89.7%, TPR of 89.1%, FPR of 2.9%, when four selected features from the NSL-KDD dataset are used. And finally, it achieved an accuracy of 92.4%, TPR of 92.3%, FPR of 3%, and F-score 94.2% when the UNSW-NB15 dataset is used.

2DLIF-PROMETHEE methods for multiple attribute decision making under 2-dimension linguistic intuitionistic fuzzy environments

<h2>Abstract</h2> In this paper, how to run 2DLIF-PROMETHEE methods are introduced to deal with the multiple attribute decision making (MADM) problems evaluated by 2-dimension linguistic intuitionistic fuzzy variables (2DLIFVs). Firstly, the Euclidean distance and the cosine similarity measures for 2DLIFSs are introduced respectively. Then a hybrid distance for 2DLIFSs is introduced by integrating these two measures. Based on the hybrid distances for 2DLIFSs, six improved preference functions are designed to form the kernel part of 2DLIF-PROMETHEE methods. Finally, the partial ranking order of alternatives or the total ranking order of alternatives may be obtained by 2DLIF-PROMETHEE methods.

Text Similarity Detection Between Documents Using Case Based Reasoning Method with Cosine Similarity Measure (Case Study SIMNG LPPM Universitas Sriwijaya)

LPPM Universitas Sriwijaya is an institution that coordinates academic research and community service inside Universitas Sriwijaya. In carrying out the duty, LPPM assesses every proposal’s originality which would be impossible to do manually in the future due to massive data growth. Thus, automatization for the proposal's originality check is needed. The Case Based Reasoning method is used in this research because it allows the system to reuse the information that has been obtained to find documents that are similar to the test document. In this study, the data is represented in the form of the Vector Space Model and uses Cosine Similarity to measure document to document similarity. The data is represented by giving weight for each part of the tested documents. In this study, four formulas from previous research will be used for term weighting then the final result will be compared. The process begins by extracting data, separating parts of the document, figuring the similarity value of the test document to the case base utilizing Cosine Similarity Measure, results filtering with a certain threshold, summarizing the calculation results, and finally preserving the results obtained to be reused in the next calculation. The results of this study indicate that the text-similarity detection between documents has been successfully carried out using the proposed method with the best sensitivity level and the fastest computation time achieved in configuration II.

Maximal overlap discrete wavelet packet transforms-based bipolar neutrosophic cross entropy measure for identification of rotor defects

This study propounds a novel methodology for automatic identification of rotor defects severity, when the machine is operated at constant speed, through maximal overlap discrete wavelet packet transforms (MODWPT) and proposed cross entropy measures of bipolar neutrosophic sets, single valued neutrosophic sets and fuzzy sets respectively. After the 3rd level of decomposition of raw vibration signals into eight frequency bands (called as WPT3 bands) through MODWPT, the energy reading in each WPT3 band is monitored. Thereafter, the lower (here called as truth membership degrees) and upper energy bounds from the training and testing samples of various rotor defect conditions at each WPT3 band are extracted and then normalized to lie in [0,1]. Finally, the energy intervals are constructed and thereafter converted into the desired forms of fuzzy sets, bipolar neutrosophic sets and single valued neutrosophic sets consecutively. The minimum cross entropy measure values between the fuzzy sets (Method 1), single valued neutrosophic sets (Method 2) and bipolar neutrosophic sets (Method 3) of training and testing samples of rotor defect conditions are computed and utilized to identify defect conditions such as angular/parallel misalignment of 10 Mils, unbalance, rub and defect free respectively. The novelty of the proposed rotor defect identification methodology lies in the fact that all the proposed variants of bipolar neutrosophic cross entropy measure have been found skilled enough to identify the desired rotor defects under study. On another hand, the existing cosine similarity measure [1] does not possess the necessary capability of identifying all rotor defects, especially, the rub defect condition. Furthermore, like the existing cosine similarity measure, our proposed variants of bipolar neutrosophic cross entropy measures have been found equally compatible for fairly furnishing the consistent and feasible results under intuitive analysis.

An MCDM Technique Using Cosine and Set-Theoretic Similarity Measures for Neutrosophic hypersoft set

An MCDM Technique Using Cosine and Set-Theoretic Similarity Measures for Neutrosophic hypersoft set

Cosine and Set-Theoretic Similarity Measures for Generalized Multi-Polar Neutrosophic Soft Set with Their Application in Decision Making

Cosine and Set-Theoretic Similarity Measures for Generalized Multi-Polar Neutrosophic Soft Set with Their Application in Decision Making