A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image features.There are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient records.Several methods are applied to enhance the retrieval performance of CBMIR systems.Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems.This study proposes the relative difference-based similarity measure(RDBSM)for CBMIR.The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features.Furthermore,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)models.Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval measures.The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

The Golden Ratio Theorem: A Framework for Interchangeability and Self-Similarity in Complex Systems

The Golden Ratio Theorem, deeply rooted in fractal mathematics, presents a pioneering perspective on deciphering complex systems. It draws a profound connection between the principles of interchangeability, self-similarity, and the mathematical elegance of the Golden Ratio. This research unravels a unique methodological paradigm, emphasizing the omnipresence of the Golden Ratio in shaping system dynamics. The novelty of this study stems from its detailed exposition of self-similarity and interchangeability, transforming them from mere abstract notions into actionable, concrete insights. By highlighting the fractal nature of the Golden Ratio, the implications of these revelations become far-reaching, heralding new avenues for both theoretical advancements and pragmatic applications across a spectrum of scientific disciplines.

Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems

Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user’s rating scale. Accordingly, many efforts have been done to introduce weights to the similarity measures of CRSs. This paper proposes fuzzy weightings for the most common similarity measures for memory-based CRSs. Fuzzy weighting can be considered as a learning mechanism for capturing the preferences of users for ratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, effective and does not require any more space. Moreover, fuzzy weightings based on the rating deviations from the user’s mean of ratings take into account the different rating scales of different users. The experimental results show that fuzzy weightings obviously improve the CRSs performance to a good extent.

Measurement of Characteristic Self-Similarity and Self-Diversity for Complex Mechanical Systems

Based on similarity science and complex system theory,a new concept of characteristic self-diversity and corre- sponding relations between self-similarity and self-diversity for complex mechanical systems are presented in this paper.Methods of system self-similarity and self-diversity measure between main system and sub-system are studied.Numerical calculations show that the characteristic self-similarity and self-diversity measure method is validity.A new theory and method of self-similarity and self- diversity measure for complexity mechanical system is presented.

Ontology-based similarity measure for text clustering

A method that combines category-based and keyword-based concepts for a better information retrieval system is introduced. To improve document clustering, a document similarity measure based on cosine vector and keywords frequency in documents is proposed, but also with an input ontology. The ontology is domain specific and includes a list of keywords organized by degree of importance to the categories of the ontology, and by means of semantic knowledge, the ontology can improve the effects of document similarity measure and feedback of information retrieval systems. Two approaches to evaluating the performance of this similarity measure and the comparison with standard cosine vector similarity measure are also described.

An Approach to Unsupervised Character Classification Based on Similarity Measure in Fuzzy Model

This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.

Similarity measure design and similarity computation for discrete fuzzy data

The similarity computations for fuzzy membership function pairs were carried out.Fuzzy number related knowledge was introduced,and conventional similarity was compared with distance based similarity measure.The usefulness of the proposed similarity measure was verified.The results show that the proposed similarity measure could be applied to ordinary fuzzy membership functions,though it was not easy to design.Through conventional results on the calculation of similarity for fuzzy membership pair,fuzzy membership-crisp pair and crisp-crisp pair were carried out.The proposed distance based similarity measure represented rational performance with the heuristic point of view.Furthermore,troublesome in fuzzy number based similarity measure for abnormal universe of discourse case was discussed.Finally,the similarity measure computation for various membership function pairs was discussed with other conventional results.

Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

Similarity measure on intuitionistic fuzzy sets

Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of many entropy definitions. By considering different fuzzy entropy definitions, fuzzy entropy on IFSs is designed and discussed. Similarity measure was also presented and its usefulness was verified to evaluate degree of similarity.

Similarity measure application to fault detection of flight system

Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is constructed explicitly. The designed distance-based similarity measure is applicable to general fuzzy membership functions including non-convex fuzzy membership function, whereas fuzzy number-based similarity measure has limitation to calculate the similarity of general fuzzy membership functions. The applicability of the proposed similarity measure to general fuzzy membership structures is proven by identifying the definition. To decide fault detection of flight system, the experimental data （pitching moment coefficients and lift coefficients） are transformed into fuzzy membership functions. Distance-based similarity measure is applied to the obtained fuzzy membership functions, and similarity computation and analysis are obtained with the fault and normal operation coefficients.

Similarity measure design for high dimensional data

Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.

On Similarity Measures and Distance Measures of Fuzzy Soft Sets

In this paper we introduce several new similarity measures and distance measures between fuzzy soft sets, these measures are examined based on the set-theoretic approach and the matching function. Comparative studies of these measures are derived. By introducing two general formulas, we propose a new method to define the similarity measures and the distance measures between two fuzzy soft sets with different parameter sets.

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.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

Formation Principles of Similar Systems

In this paper some new concepts, i.e, Ordered Structure of Systems, Universal Information, Strength of Information Field are proposed. Based on Similarity System Theory and Similology, five principles for investigating similarity formation, i.e., Principle of Ordered Structue, Information Principle, Consanguining Principle, Coadaptation Principle and Governing Principle are introduced. The origin of similar systems, and way of similarity formation are expounded. Thus, formation principles of similar systems is presented.

Designing similarity measurement with distance measure and application on laterally directional mode flight test

Similarity measure construction has been proposed as fault detection of flight test method in order to obtain the primary control surface stuck and the combination stuck of primary control.Similarity measures were obtained through analyzing the certainty and uncertainty of fuzzy membership functions,which were designed based on well-known Hamming distance.It was applied to the fault detection of primary control surface stuck of uninhabited aerial vehicle(UAV).At post-failure control surface,if the UAV is controllable and trimmable using other control surfaces,the UAV is able to fly or return to the safety region through reconfiguration of flight control system.To detect the fault,similarity measure computations were carried out.This result could be applicable with the real-time parameter estimation method.By monitoring the value of coefficients due to the control surface deviation,it becomes aware that the control surface fault occurs or not.The control surface stuck position and value were separated by comparing the trim value with the reference value.This is the advantage of increasing in reliability without adding sensors or with additional low cost.

Shape Similarity Measures of Linear Entities

The essential of feature matching technology lies in how to measure the similarity of spatial entities.Among all the possible similarity measures,the shape similarity measure is one of the most important measures because it is easy to collect the necessary parameters and it is also well matched with the human intuition.In this paper a new shape similarity measure of linear entities based on the differences of direction change along each line is presented and its effectiveness is illustrated.

Power Aggregation Operators and Similarity Measures Based on Improved Intuitionistic Hesitant Fuzzy Sets and their Applications to Multiple Attribute Decision Making

Intuitionistic hesitant fuzzy set(IHFS)is amixture of two separated notions called intuitionistic fuzzy set(IFS)and hesitant fuzzy set(HFS),as an important technique to cope with uncertain and awkward information in realistic decision issues.IHFS contains the grades of truth and falsity in the formof the subset of the unit interval.The notion of IHFS was defined by many scholars with different conditions,which contain several weaknesses.Here,keeping in view the problems of already defined IHFSs,we will define IHFS in another way so that it becomes compatible with other existing notions.To examine the interrelationship between any numbers of IHFSs,we combined the notions of power averaging(PA)operators and power geometric(PG)operators with IHFSs to present the idea of intuitionistic hesitant fuzzy PA(IHFPA)operators,intuitionistic hesitant fuzzy PG(IHFPG)operators,intuitionistic hesitant fuzzy power weighted average(IHFPWA)operators,intuitionistic hesitant fuzzy power ordered weighted average(IHFPOWA)operators,intuitionistic hesitant fuzzy power ordered weighted geometric(IHFPOWG)operators,intuitionistic hesitant fuzzy power hybrid average(IHFPHA)operators,intuitionistic hesitant fuzzy power hybrid geometric(IHFPHG)operators and examined as well their fundamental properties.Some special cases of the explored work are also discovered.Additionally,the similarity measures based on IHFSs are presented and their advantages are discussed along examples.Furthermore,we initiated a new approach to multiple attribute decision making(MADM)problem applying suggested operators and a mathematical model is solved to develop an approach and to establish its common sense and adequacy.Advantages,comparative analysis,and graphical representation of the presented work are elaborated to show the reliability and effectiveness of the presented works.

A random forest algorithm based on similarity measure and dynamic weighted voting

The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.