Coefficient Similarity Measure Research Articles

Correction to: Investigation of the brain carcinoma based on generalized variation coefficient similarity measures using complex q-rung orthopair fuzzy information

Correction to: Investigation of the brain carcinoma based on generalized variation coefficient similarity measures using complex q-rung orthopair fuzzy information

Investigation of the brain carcinoma based on generalized variation coefficient similarity measures using complex q-rung orthopair fuzzy information

Brain carcinoma is one of the massive dangerous diseases in the human body, and certain intellectuals have been affected by them. Additionally, by using the complex q-rung orthopair fuzzy set, which is the massive important, and dominant technique to manage indeterminate and ambiguous information in genuine life troubles. This study aims to employ the principle of variation coefficient similarity measures and generalized variation coefficient similarity measures under the complex q-rung orthopair fuzzy sets and illustrated their properties. Certain special cases of the elaborated measures are investigated to expand the superiority of the investigated works. Moreover, by using the presented generalized variation coefficient similarity measures under the complex q-rung orthopair fuzzy information, a medical diagnosis is illustrated to determine the most dangerous sorts of brain carcinoma in the human body to determine the supremacy and dominance of the elaborated measures. Lastly, certain examples are illustrated based on proposed measures under a complex q-rung orthopair fuzzy set to find the advantages and sensitive analysis of the initiated measures to illustrate the rationality and dominance of the developed measures.

An Approximate Memory Based Defense Against Model Inversion Attacks to Neural Networks

Diverse and comprehensive training data is critical in building robust machine learning (ML) models. However, model inversion attacks (MIA) have demonstrated that an ML model can leak important information about its training dataset. This work examines the existing MIAs and proposes a hardware-oriented solution to protect the training data from such attacks. Our proposed solution – MIDAS: Model Inversion Defenses with an Approximate memory System – intentionally introduces memory faults to thwart MIA without compromising the original ML model. We use detailed SPICE simulations to build the DRAM fault model with voltage overscaling, implement the state-of-the-art MIAs, and evaluate our proposed solution. Our experiments demonstrate that MIDAS can effectively protect training data from run-time adversarial attacks. In terms of the Pearson Correlation Coefficient (PCC) similarity measure (between the original and the recovered training data), MIDAS reduces the PCC value for shallow and deep neural networks.

Consistency Fuzzy Sets and a Cosine Similarity Measure in Fuzzy Multiset Setting and Application to Medical Diagnosis

The main purpose of this study is to construct a base for a new fuzzy set concept that is called consistency fuzzy set (CFS) which expresses the multidimensional uncertain data quite successfully. Our motive is to reduce the complexity and difficulty caused by the information contained in the truth sequence in a fuzzy multiset (FMS) and to present the data of the truth sequence in a more understandable and compact manner. Therefore, this paper introduces the concept of CFS that is characterized with a truth function defined on a universal set 0,1 2 . The first component of the truth pair of a CFS is the average value of the truth sequence of a FMS and the second component is the consistency degree, that is, the fuzzy complement of the standard deviation of the truth sequence of the same FMS. The main contribution of a CFS is the reflection of both the level of the average of the data that can be expressed with the different sequence lengths and the degree of the reasonable information in data via consistency degree. To develop this new concept, this paper also presents a correlation coefficient and a cosine similarity measure between CFSs. Furthermore, the proposed correlation coefficient and cosine similarity measure are applied to a multiperiod medical diagnosis problem. Finally, a comparison analysis is given between the obtained results and the existing results in literature to show the efficiency and rationality of the proposed correlation coefficient and cosine similarity measure.

Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application.

One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts. Likewise, a lot of examples are exposed under the invented measures based on PHF data in the environment of medical diagnosis to demonstrate the stability and elasticity of the explored works. Finally, the sensitive analysis of the presented works is also implemented and illuminated their graphical structures.

The Fourth Axiom of Similarity Measures

In this research, the fourth axiom to improve the well-defined examination of similarity measures is studied, where the measures have a symmetric structure. We first provide a theoretic enhancement of three correlation coefficient similarity measures that were proposed by a source paper. Second, we use the same numerical example proposed by the source paper for pattern recognition problems to illustrate that the weighted correlation coefficient similarity measure is dependent on the set of weights. Finally, we demonstrate that the correlation coefficient similarity measure in the intuitionistic fuzzy set environment can address the issue of practical fault diagnosis when solving the turbine engine problems using similarity measures with symmetric characteristics.

Complex q-Rung Orthopair Fuzzy Variation Co-efficient Similarity Measures and their Approach in Medical Diagnosis and Pattern Recognition

The q-rung orthopair fuzzy set (q-ROFS) as a generalization of fuzzy set (FS), is characterized by membership and non-membership, and the sum of their q-powers is restricted to [0,1]. In this manuscript, a new Complex q-rung orthoapir fuzzy set (Cq-ROFS) is proposed by combining q-ROFS and complex fuzzy set (CFS). Cq-ROFS is a better way to process uncertain and imprecise information in decision making, which is characterized by complex-valued membership and complex-valued non-membership. First, some fundamental operational laws, score function and accuracy function, and comparison method are proposed. Further, because the vector similarity measures (VSMs) play a key role in statistics, physics and engineering sciences, some VSMs called Jaccard similarity measures (JSMs), dice similarity measures (DSMs) and cosine similarity measures (CSMs) for Cq-ROFSs and interval-valued complex q-rung orthopair fuzzy sets (IVCq-ROFSs) are investigated. Moreover, the hybrid vector similarity measures (HVSMs) called variation co-efficient similarity measures (VCSMs) for Cq-ROFSs and IVCq-ROFSs are also proposed and their properties are discussed. Finally, in order to demonstrate the feasibility of the investigated HVSMs, the existing similarity measures about complex Pythagorean fuzzy sets (CPFSs) and complex intuitionistic fuzzy sets (CIFSs) are compared with the proposed methods by numerical examples of medical diagnosis and pattern recognition.

Collaborative Filtering Recommendation Model Based on k-means Clustering

In this age of information load, it becomes a herculean task for user to get the relevant things from vast number of information. This huge number of data demand specially designed Recommender system that can plays an important role in suggesting relevant information preferred by the users. From this point, this paper presents a modest approach to enhance prediction in MovieLens dataset with high scalability by applying user-based collaborative filtering methods on clustered data. The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase. The experimental results obtained conducting K-means clustering and correlation coefficient similarity measures against MovieLens datasets lead to an increase in the scalability of recommender system.

A novel approach to text clustering using shift k-medoid

As the amount of data is growing day by day, there is a need to convert it into some effective manner so as to extract some useful information from huge data. Text mining is used to perform this task. In this paper, text clustering is used to convert the large data into different cluster forms to extract the meaningful information for the purpose of analysis so as to get the summarised data. Three partitioning-based clustering techniques, i.e., k-means, k-means fast and k-medoids are compared, and a new algorithm named shift k-medoid is proposed, which is hybrid of k-medoid and mean shift clustering algorithms. Cosine similarity, correlation coefficient and Jaccard similarity measures are used to check the performance of the algorithms and two measures, i.e., randomised feature and normalised mutual information (NMI) feature are used to test the accuracy of the algorithms. The outcomes demonstrate that the best performance is accomplished by using the proposed algorithm.

A novel approach to text clustering using shift k-medoid

As the amount of data is growing day by day, there is a need to convert it into some effective manner so as to extract some useful information from huge data. Text mining is used to perform this task. In this paper, text clustering is used to convert the large data into different cluster forms to extract the meaningful information for the purpose of analysis so as to get the summarised data. Three partitioning-based clustering techniques, i.e., k-means, k-means fast and k-medoids are compared, and a new algorithm named shift k-medoid is proposed, which is hybrid of k-medoid and mean shift clustering algorithms. Cosine similarity, correlation coefficient and Jaccard similarity measures are used to check the performance of the algorithms and two measures, i.e., randomised feature and normalised mutual information (NMI) feature are used to test the accuracy of the algorithms. The outcomes demonstrate that the best performance is accomplished by using the proposed algorithm.

Non–fuzzy sets for intuitionistic fuzzy sets

We examine an important paper of Gerstenkorn and Mańko that was published in Fuzzy Sets and Systems. Their paper is the pioneer for correlation coefficient similarity measure. We point out several questionable results and then provide our improvement that will useful to develop theoretical structure for similarity measures under intuitionistic fuzzy sets environment.

A novel method for comfort assessment in a supine sleep position using three-dimensional scanning technology

Mechanical interaction behavior between human body and mattress is one of the crucial physical factors affecting the sleep comfort and quality. This paper proposes a novel method for sleep (dis)comfort level assessment in a supine posture without interfering with sleep in an attempt to improve mattress design. Three-dimensional (3D) back surface model is constructed by scanning human body in an upright standing position. Based on the mechanical property of mattress and body pressure distribution measured by Tactilus system, finite element (FE) models are established to numerically calculate mattress upper surface indentation. Finally, Pearson correlation coefficient similarity measure is used to evaluate sleep (dis)comfort by compared back surface with mattress upper surface indentation. The experimental process is validated with two distinct types of mattress, namely palm fiber mattress and latex foam/palm fiber mattress. Results show that all the participants feel more comfortable when lying on latex foam/palm fiber mattress, which are in excellent agreement with the results obtained by body pressure distribution and spinal alignment.

Similarity analysis between chromosomes of Homo sapiens and monkeys with correlation coefficient, rank correlation coefficient and cosine similarity measures.

In this paper, we consider correlation coefficient, rank correlation coefficient and cosine similarity measures for evaluating similarity between Homo sapiens and monkeys. We used DNA chromosomes of genome wide genes to determine the correlation between the chromosomal content and evolutionary relationship. The similarity among the H. sapiens and monkeys is measured for a total of 210 chromosomes related to 10 species. The similarity measures of these different species show the relationship between the H. sapiens and monkey. This similarity will be helpful at theft identification, maternity identification, disease identification, etc.

Power coefficient as a similarity measure for memory-based collaborative recommender systems

E-commerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. However, choosing appropriate similarity measure is a key to the recommender system success. Based on this measure, a set of neighbors for the current active user is formed which in turn will be used later to recommend unseen items to this active user. Pearson correlation coefficient, the most popular similarity measure for memory-based collaborative recommender system (CRS), measures how much two users are correlated. However, statistic’s literature introduced many other coefficients for matching two sets (vectors) that may perform better than Pearson correlation coefficient. This paper explores Jaccard and Dice coefficients for matching users of CRS. A more general coefficient called a Power coefficient is proposed in this paper which represents a family of coefficients. Specifically, Power coefficient gives many degrees for emphasizing on the positive matches between users. However, CRS users have positive and negative matches and therefore these coefficients have to be modified to take negative matches into consideration. Consequently, they become more suitable for CRS research. Many experiments are carried out for all the proposed variants and are compared with the traditional approaches. The experimental results show that the proposed variants outperform Pearson correlation coefficient and cosine similarity measure as they are the most common approaches for memory-based CRS.

Studying the Role of Protein Flexibility in Allosteric and Evolutionary Changes as Seen in PyrR Protein Family

Oligomerisation is essential for the function of some proteins. The PyrR family of proteins are involved in pyrimidine operon attenuation. They are regulated by the presence of certain nucleotides such as guanosine monophosphate (GMP), which stabilises the tetrameric state. Notably, some members of this family can adopt a tetrameric oligomerisation state regardless of the presence of the GMP. Perica et al. (2012) previously found that this family, among several others, have differences in sequence outside the oligomeric interfaces and these are sufficient to explain the changes to their subunit geometry and oligomerisation states. Here, we compared the differences in structural flexibility linked to the changes in oligomeric state caused by a) specific mutations and, b) by the presence of bound GMP. We calculated the coarse-grained normal modes of dimeric units of the PyrR dimers and tetramers using an Elastic Network Model implemented in the Molecular Modelling ToolKit. We conducted several analyses including comparing the normal modes of these proteins with each other using the Bhattacharyya Coefficient similarity measure, correlations matrices, and the conformational overlap analysis, by which the contribution of these modes to the transition from one state to another can be quantified. Firstly, we found that while the dynamics were very similar between all the structures, there was a noticeable difference between the dimeric and tetrameric units. We also show that both sets of proteins transition from tetrameric to dimeric states similarly, indicating some overlap between the effects of allostery and evolution on oligomerisation in PyrR.

BAYESIAN RETRIEVAL USING A SIMILARITY-BASED LEMMATIZER

The present paper describes a Bayesian network approach to Information Retrieval (IR) from Web documents. The network structure provides an intuitive representation of uncertainty relationships and the embedded conditional probability table is used by inference algorithms in an attempt to identify documents that are relevant to the user's needs, expressed in the form of Boolean queries. Our research has been directed in constructing a probabilistic IR framework that focus on assisting users to perform Ad-hoc retrieval of documents from the various domains such as economics, news, sports, etc. Furthermore, users can integrate feedback regarding the relevance of the retrieved documents in an attempt to improve performance on upcoming requests. Towards these goals, we have expanded the traditional Bayesian network IR system and tested it on several Greek web corpora on different application domains. We have developed two different approaches with regards to the structure: a simple one, where the structure is manually provided, and an automated one, where data mining is used in order to extract the network's structure. Results have depicted competitive performance against successful IR models of different theoretical backgrounds, such as the vector space utilizing tf-idf and the probabilistic model of BM25 in terms of precision-recall curves. In order to further improve the performance of the IR system, we have implemented a novel similarity-based lemmatization framework, reducing thus the ambiguity posed by the plethora of morphological variations of the languages in question. The employed lemmatization framework comprises of 3 core components (i.e. the word segregation, the data cleansing and the lemmatization modules) and is language-independent (i.e. can be applied to other languages with morphological peculiarities and thus improve Ad-hoc retrieval) since it achieves the mapping of an input word to its normalized form by employing two state-of-the-art language independent distance metric models, meaning the Levenshtein Edit distance and the Dice coefficient similarity measure, combined with a language model describing the most frequent inflectional suffixes of the examined language. Experimental results support our claim on the significance of this incorporation to Greek texts web retrieval as results improve by a factor of 4% to 11%.

A Variation Coefficient Similarity Measure and Its Application in Emergency Group Decision-making

Consider the emergency decision-making problems, we proposed a new similarity measures based on the extension of the Dice, and cosine similarity measures, which satisfy some properties in Ref. [11]. Then, we apply the variation coefficient similarity to the emergency group decision-making problems. Through the weighted similarity measures between each alternative and the ideal alternative, the ranking order of all the alternatives can be determined and the best one(s) can be easily identified as well. Finally, we give a practical example to evaluate the emergency management capability of major snow disaster in Hunan province of China. For comparison, the decision results of different similarity measures are also given, which show that our method have better similarity identification and the proposed method is applicable.

APPLYING SIMILARITY MEASURES FOR AUTOMATIC LEMMATIZATION: A CASE STUDY FOR MODERN GREEK AND ENGLISH

This paper addresses the problem of automatic induction of the normalized form (lemma) of regular and mildly irregular words with no direct supervision using language-independent algorithms. More specifically, two string distance metric models (i.e. the Levenshtein Edit Distance algorithm and the Dice Coefficient similarity measure) were employed in order to deal with the automatic word lemmatization task by combining two alignment models based on the string similarity and the most frequent inflectional suffixes. The performance of the proposed model has been evaluated quantitatively and qualitatively. Experiments were performed for the Modern Greek and English languages and the results, which are set within the state-of-the-art, have showed that the proposed model is robust (for a variety of languages) and computationally efficient. The proposed model may be useful as a pre-processing tool to various language engineering and text mining applications such as spell-checkers, electronic dictionaries, morphological analyzers etc.

Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniques

BackgroundDynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization.MethodsWe have applied principal component analysis to provide high-contrast parametric image sets of lower dimensions than the original data set separating structures based on their kinetic characteristics. Our method has the potential to constitute an alternative quantification method, independent of any kinetic model, and is particularly useful when the retrieval of the arterial input function is complicated. In independent component analysis images, structures that have different kinetic characteristics are assigned opposite values, and are readily discriminated. Furthermore, novel similarity mapping techniques are proposed, which can summarize in a single image the temporal properties of the entire image sequence according to a reference region.ResultsUsing our new cubed sum coefficient similarity measure, we have shown that structures with similar time activity curves can be identified, thus facilitating the detection of lesions that are not easily discriminated using the conventional method employing standardized uptake values.

Computationally efficient local area based imageco-registration

Area based co-registration of two or more images is typically based on maximisation of the correlation coefficient (CC) similarity measure. The authors investigate two 'nonconventional' area based similarity measures and develop a procedure for optimising their computational efficiency. The result is a computational advantage over the CC method of 2M, where M is the size of the co-registration parameter value search space. An experimental study involving radar image pairs is also included.