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The similarity measure is used to tackle many issues that include indistinct as well as blurred information excluding is not in a position to deal with the general fuzziness along with obscurity of the problems that have various information. The main purpose of this research is to propose a multipolar interval-valued neutrosophic soft set (mPIVNSS) with operations and basic properties. We also develop Hamming distance and Euclidean distance by using mPIVNSS and numerical examples and use the developed distances to introduce similarity measures. By using the developed similarity measures a decision-making approach is presented for mPIVNSS. Finally, we used the developed decision-making approach for medical diagnosis.

Identification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug–drug and nine target–target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug–disease associations, and the cosine scores of drug–drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

Somatic cell count (SCC) can be used as an indicator of subclinical mastitis and its analysis in relation with the milk composition can provide useful information on the existence of some correlations or patterns. Based on milk production data recorded during 5 years (2012-2015, 2017) at the Research and Development Station for Bovine Arad we conducted a statistical analysis aiming to identify correlations between SCC and milk characteristics (protein and fat content, lactose, non-fat solids, milk quantity, pH, casein) and to find potential profiles of SCC evolution. The correlation analysis was based on 226 lactating cows for which at least 20 measurements were available. Both classical correlation coefficient (i.e. Pearson) and correlation coefficient for repeated measurements (i.e. Bland-Altman) have been computed. In both cases, a moderate negative correlation between SCC and the lactose level has been identified while no significant correlation between SCC and the other milk characteristics has been detected. However, a more accurate description of the relation between SCC and lactose was obtained using a linear mixed model. Aiming to analyse SCC profiles, an additional attribute has been added to the data based on the following encoding rule: the attribute has value 0 if SCC is smaller than 2x105 cells/ml, 1 if it is larger than 2x105 cells/mL and 2 if the value is missing. In this way, data vectors containing 13 values per year have been constructed for 175 cows and a dissimilarity matrix has been constructed as a first step for cluster analysis. Overall, the results have shown that lactose and SCC were negatively correlated.

The generalized trapezoidal fuzzy numbers and generalized trapezoidal interval-valued fuzzy numbers cannot handle the complex situation, although individually they are having their own worth in the particular environment. So, we combined both the generalized trapezoidal fuzzy numbers and generalized trapezoidal interval-valued fuzzy numbers and define a hybrid structure named as generalized trapezoidal cubic numbers (GTCNs) which can capture the complex situation in a better way. For utilizing generalized trapezoidal cubic numbers (GTCNs) in real-life problems, we discuss their basic arithmetic operations. Then, we proposed different types of similarity measures between the generalized trapezoidal cubic numbers (GTCNs) so that one can easily handle problems where similarities often exits. We discuss Chen's similarity measure, Hsieh and Chen's similarity measures, Lee's similarity measures, Chen and Chen's similarity measures, Yong et al. similarity measures, Wei and Chen's similarity measures, Xu et al.'s similarity measures, Hejazi et al.'s similarity measures, Patra and Mondal's similarity measure, Khorshidi and Nikfalazar's similarity measures and Chutia's similarity measures. Finally, we discuss the application of generalized trapezoidal cubic numbers (GTCNs) in risk analysis using proposed similarity measures. [ABSTRACT FROM AUTHOR]

N-valued neutrosophic trapezoidal numbers (NVNT-numbers) are a special neutrosophic multi-sets on real number set based on neutrosophic sets. In the NVNT-numbers the occurrences are more than one with the possibility of the same or the different truth-membership function, indeterminacy-membership function and falsity-membership functions. In this paper, NVNT-numbers based on multi-criteria decision-making problems in which the ratings of alternatives are expressed with NVNT-numbers are defined. Firstly, some operational laws of NVNT-numbers by using t-norm and t-conorm are introduced. Then, some aggregation operators of NVNT-numbers including NVNT-numbers weighted geometric operator and NVNT-numbers weighted arithmetic operators are given. Also, a TOPSIS method for the ranking order of alternatives with NVNT-numbers is given according to the similarity of alternative with respect to the positive and negative ideal solution. Moreover, an illustrative example of multi-criteria decision-making in which the ratings of alternatives are expressed with NVNT-numbers is given to verify the developed TOPSIS method and to demonstrate its practicality and effectiveness. Finally, a comparative analysis is presented with illustrative example. [ABSTRACT FROM AUTHOR]

In this work, we generalize the notion of restricted equivalence function for type-2 fuzzy sets, leading to the notion of extended restricted equivalence functions. We also study how under suitable conditions, these new functions recover the standard axioms for restricted equivalence functions in the real setting. Extended restricted equivalence functions allow us to compare any two general type-2 fuzzy sets and to generate a similarity measure for type-2 fuzzy sets. The result of this similarity is a fuzzy set on the same referential set (i.e., domain) as the considered type-2 fuzzy set. The latter is crucial for applications such as explainable AI and decision-making, as it enables an intuitive interpretation of the similarity within the domain-specific context of the fuzzy sets. We show how this measure can be used to compare type-2 fuzzy sets with different membership functions in such a way that the uncertainty linked to type-2 fuzzy sets is not lost. This is achieved by generating a fuzzy set rather than a single numerical value. Furthermore, we also show how to obtain a numerical value for discrete referential sets. [ABSTRACT FROM AUTHOR]

There are various models, methodologies and algorithms that can be used today for document classification, information retrieval and other text mining applications and systems. One of them is the vector space–based models, where distance metrics or similarity measures lie at the core of such models. Vector space–based model is one of the fast and simple alternatives for the processing of textual data; however, its accuracy, precision and reliability still need significant improvements. In this study, a new similarity measure is proposed, which can be effectively used for vector space models and related algorithms such as k -nearest neighbours (k -NN) and Rocchio as well as some clustering algorithms such as K -means. The proposed similarity measure is tested with some universal benchmark data sets in Turkish and English, and the results are compared with some other standard metrics such as Euclidean distance, Manhattan distance, Chebyshev distance, Canberra distance, Bray–Curtis dissimilarity, Pearson correlation coefficient and Cosine similarity. Some successful and promising results have been obtained, which show that this proposed similarity measure could be alternatively used within all suitable algorithms and models for information retrieval, document clustering and text classification. [ABSTRACT FROM AUTHOR]

Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time. [ABSTRACT FROM AUTHOR]

The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases. [ABSTRACT FROM AUTHOR]

IFS is the further extension of ordinary fuzzy set, widely used in distinct applications to tackle uncertainty and fuzzy problems. Generally, similarity/distance measures are significant technique to discriminate between two sets and further they can applied to the problems of pattern recognition and decision-making. Though various similarity measures have already been suggested, still a lot of scope is there because some of them could not satisfy the properties of similarity measures and provide contradictory results. In this paper, we suggested new similarity measures having ability to contrast intuitionistic fuzzy (IF) sets. Furthermore, we have also analyzed their properties to validate the existence of proposed measure. After that, we provided some experimental analysis to verify the effectiveness of proposed measures which includes numerical experiment pattern recognition and clustering analysis. In experimental analysis, firstly we included numerical experiment by taking different cases to study the performance of proposed measures. Then, we dealt with the problems of pattern recognition and incorporated a performance index in terms of "Degree of Confidence" (DOC). Furthermore, we introduced a IF-MST clustering algorithm to deal with IFSs using the notion of MST ("Maximum Spanning Tree"). Additionally, we have modified similarity measure into knowledge measure and contrasted its performance with others knowledge measures to show the superiority of proposed measure. [ABSTRACT FROM AUTHOR]

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ( N > 150 ) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge. [ABSTRACT FROM AUTHOR]

Computing trajectory similarity is a fundamental operation in movement analytics, required in search, clustering, and classification of trajectories, for example. Yet the range of different but interrelated trajectory similarity measures can be bewildering for researchers and practitioners alike. This paper describes a systematic comparison and methodical exploration of trajectory similarity measures. Specifically, this paper compares five of the most important and commonly used similarity measures: dynamic time warping (DTW), edit distance (EDR), longest common subsequence (LCSS), discrete Fréchet distance (DFD), and Fréchet distance (FD). The paper begins with a thorough conceptual and theoretical comparison. This comparison highlights the similarities and differences between measures in connection with six different characteristics, including their handling of a relative versus absolute time and space, tolerance to outliers, and computational efficiency. The paper further reports on an empirical evaluation of similarity in trajectories with contrasting properties: data about constrained bus movements in a transportation network, and the unconstrained movements of wading birds in a coastal environment. A set of four experiments: a. creates a measurement baseline by comparing similarity measures to a single trajectory subjected to various transformations; b. explores the behavior of similarity measures on network-constrained bus trajectories, grouped based on spatial and on temporal similarity; c. assesses similarity with respect to known behavioral annotations (flight and foraging of oystercatchers); and d. compares bird and bus activity to examine whether they are distinguishable based solely on their movement patterns. The results show that in all instances both the absolute value and the ordering of similarity may be sensitive to the choice of measure. In general, all measures were more able to distinguish spatial differences in trajectories than temporal differences. The paper concludes with a high-level summary of advice and recommendations for selecting and using trajectory similarity measures in practice, with conclusions spanning our three complementary perspectives: conceptual, theoretical, and empirical.

The concept of picture fuzzy sets (PFS) is a generalization of ordinary fuzzy sets and intuitionistic fuzzy sets, which is characterized by positive membership, neutral membership, and negative membership functions. Keeping in mind the importance of similarity measures and applications in data mining, medical diagnosis, decision making, and pattern recognition, several studies have been proposed in the literature. Some of those, however, cannot satisfy the axioms of similarity and provide counter-intuitive cases. In this paper, we propose new similarity measures for PFSs based on two parameters t and p, where t identifies the level of uncertainty and p is the Lp norm. The properties of the bi-parametric similarity and distance measures are discussed. We provide some counterexamples for existing similarity measures in the literature and show how our proposed similarity measure is important and applicable to the pattern recognition problems. In the end, we provide an application of a proposed similarity measure for medical diagnosis.

This paper introduces and applies a novel methodology that integrates watershed modeling with the traditional regional flood frequency analysis. The methodology introduces a novel site-similarity measure that relies on hydrologic simulations and accounts for the effect of land depressions on streamflow generation. The new measure is tested along with other traditional measures for regional flood frequency analysis in the Canadian prairies. The case study is chosen carefully to critically test the new methodology. An application of 30 combinations of the new and traditional site similarity measures is assessed for pooling 109 sites. The homogeneity of the clustered groups is evaluated, and different probability distributions are applied to describe at-site and regional annual maximum flows. The results present enhanced groups' homogeneity when the new measure is employed due to a better representation of the hydrologic similarity between the pooled sites. Furthermore, the regionally estimated quantiles are found susceptible to the chosen site similarity measures in the pooling process, which highlights the importance of considering the proposed measure that describes a key hydrologic aspect when land depressions exist. [ABSTRACT FROM AUTHOR]

In this paper, we explore the concept of local contrast of a fuzzy relation, which can be perceived as a measure for distinguishing the degrees of membership of elements within a defined region of an image. We introduce four distinct methods for constructing fuzzy local contrast: one uses a similarity measure, the second relies on the aggregation of similarity, the third is based on the aggregation of restricted equivalence, and the fourth utilizes the notion of equivalence. We further divide the constructions using similarity measures into two categories based on the two known definitions of similarity: distance-based similarity and aggregation function-based similarity. These construction methods also incorporate fuzzy implications and negations. Aggregation functions, which can be manipulated to enhance the effectiveness of the constructed fuzzy local contrast, play a significant role in most of our proposed constructions. For each construction method, several examples of fuzzy local contrasts are provided. The usefulness of the new fuzzy local contrasts is examined by applying them in image processing for salient region detection. • Aggregation functions used in a few construction methods of a fuzzy local contrast. • Numerous examples of fuzzy local contrasts provided. • Effectivness of aggregation functions examined in fuzzy local contrast in image processing for salient region detection. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices), proposed by Enginoğlu and Arslan in 2020, are worth utilizing in data classification in supervised learning due to coming into prominence with their ability to model decision-making problems. This study aims to define the concepts metrics, quasi-, semi-, and pseudo-metrics and similarities, quasi-, semi-, and pseudo-similarities over ifpifs-matrices; develop a new classifier by using them; and apply it to data classification. To this end, it develops a new classifier, i.e., Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Classifier (IFPIFSC), based on six pseudo-similarities proposed herein. Moreover, this study performs IFPIFSC’s simulations using 20 datasets provided in the UCI Machine Learning Repository and obtains its performance results via five performance metrics, accuracy (Acc), precision (Pre), recall (Rec), macro F-score (MacF), and micro F-score (MicF). It also compares the aforementioned results with those of 10 well-known fuzzy-based classifiers and 5 non-fuzzy-based classifiers. As a result, the mean Acc, Pre, Rec, MacF, and MicF results of IFPIFSC, in comparison with fuzzy-based classifiers, are 94.45%, 88.21%, 86.11%, 87.98%, and 89.62%, the best scores, respectively, and with non-fuzzy-based classifiers, are 94.34%, 88.02%, 85.86%, 87.65%, and 89.44%, the best scores, respectively. Later, this study conducts the statistical evaluations of the performance results using a non-parametric test (Friedman) and a post hoc test (Nemenyi). The critical diagrams of the Nemenyi test manifest the performance differences between the average rankings of IFPIFSC and 10 of the 15 are greater than the critical distance (4.0798). Consequently, IFPIFSC is a convenient method for data classification. Finally, to present opportunities for further research, this study discusses the applications of ifpifs-matrices for machine learning and how to improve IFPIFSC.

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. • Novel discipline distance measurement approach based on co-occurrences. • Collaborative data for quantification of research discipline distance. • Comprehensive analysis and validation of distance measures. • Visualization of discipline profiles associated with a research information system. [ABSTRACT FROM AUTHOR]

We discuss some aspects of similarity measures in the context of Atanassov's intuitionistic fuzzy sets (IFSs, for short). IFSs, proposed in 1983, are a relatively new tool for the modeling and simulation and, because of their construction, present us with new challenges as far the similarity measures are concerned. Specifically, we claim that the distances alone are not a proper measure of similarity for the IFSs. We stress the role of a lack of knowledge concerning elements (options, decisions, etc.) and point out the role of the opposing (complementing) elements. We also pay attention to the fact that it is not justified to talk about similarity when one has not enough knowledge about the compared objects/elements. Some novel measures of similarity are presented. [ABSTRACT FROM AUTHOR]

The comparison of documents—such as articles or patents search, bibliography recommendations systems, visualization of document collections, etc.—has a wide range of applications in several fields. One of the key tasks that such problems have in common is the evaluation of a similarity metric. Many such metrics have been proposed in the literature. Lately, deep learning techniques have gained a lot of popularity. However, it is difficult to analyze how those metrics perform against each other. In this paper, we present a systematic empirical evaluation of several of the most popular similarity metrics when applied to research articles. We analyze the results of those metrics in two ways, with a synthetic test that uses scientific papers and Ph.D. theses, and in a real-world scenario where we evaluate their ability to cluster papers from different areas of research. [ABSTRACT FROM AUTHOR]

In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts (sentences), specialized document types (legal case texts), and, above all, the unique definition of document relevance provided in detailed annotation guidelines. To support our experiments we assembled a data set comprising 42 queries (26,959 sentences) which we plan to release to the public in the near future in order to support further research. Most importantly, we investigate the feasibility of developing a system that responds to a query with a list of sentences that mention the term in a way that is useful for understanding and elaborating its meaning. This is accomplished by a systematic assessment of different features that model the sentences' usefulness for interpretation. We combine features into a compound measure that accounts for multiple aspects. The definition of the task, the assembly of the data set, and the detailed task analysis provide a solid foundation for employing a learning-to-rank approach. [ABSTRACT FROM AUTHOR]

A similarity measure is used to tackle many issues that include indistinct and blurred information, excluding is not able to deal with the general fuzziness and obscurity of the problems that have various information. The neutrosophic hypersoft set is the most generalized and advanced extension of neutrosophic sets, which deals with the multi sub-attributes of the considered parameters. In this paper, we study some basic concepts which are helpful to build the structure of the article, such as soft set, neutrosophic soft set, hypersoft set, and neutrosophic hypersoft set, etc. The main objective of the present research is to develop a cosine similarity measure and set-theoretic similarity measure for an NHSS with their necessary properties. A decision-making approach has been established by using cosine and set-theoretic similarity measures. Furthermore, we used to develop a technique to solve multi-criteria decision-making problems. Finally, the advantages, effectiveness, flexibility, and comparative analysis of the algorithms are given with prevailing methods. [ABSTRACT FROM AUTHOR]

Pharmaceutical logistics are primarily concerned with handling transportation and supply chain management of numerous complex goods most of which need particular requirements for their logistical care. To find the high level of specialization, suppliers of pharmaceutical logistics must be selected under a mathematical model that can treat vague and uncertain real-life circumstances. The notion of bipolarity is a key factor to address such uncertainties. A bipolar fuzzy soft set (BFSS) is a strong mathematical tool to cope with uncertainty and unreliability in various real-life problems including logistics and supply chain management. In this paper, we introduce new similarity measures (SMs) based on certain properties of bipolar fuzzy soft sets (BFSSs). The proposed SMs are the extensions of Frobenius inner product, cosine similarity measure, and weighted similarity measure for BFSSs. The proposed SMs are also illustrated with respective numerical examples. An innovative multi-attribute decision-making algorithm (MADM) and its flow chart are being developed for pharmaceutical logistics and supply chain management in COVID-19. Furthermore, the application of the suggested MADM method is presented for the selection of the best pharmaceutical logistic company and a comparative analysis of the suggested SMs with some of the existing SMs is also demonstrated. [ABSTRACT FROM AUTHOR]

With the tremendous booming of uploading and sharing images over the web, there is an es)sential demand for illustrating the content of such images. Title and description provide a po)etic explanation for the images while tags tend to be more appropriate in terms of determining the content. This refers to the keywords obtained from tags which facilitate the process of fig)uring out the exact content. Several methods have been implemented toward enhancing the tag suggestion. However, there are cases where the suggestion misleads the search. Such cases are targeting a domain-specific for instance, searching for an image of the house using sign lan)guage, most of the obtained results will be full of house images. This is due to overlapping between two domains of specific which are Houses and Sign language. Therefore, this study proposed a domain-specific algorithm for Arabic tag suggestions. The data is a set of images of objects customized for sign language which have been collected from an institution for the disabled. Hence, using a game-based approach those images will be tagged. Consequently, those tags will undergo preprocessing including translation, normalization, and tokenization. After that, a domain-specific algorithm will be carried out using three similarity measures which are Cosine, Dice, and Jaccard. The evaluation has been performed using the common information retrieval metrics Precision, Recall, and F-measure. [ABSTRACT FROM AUTHOR]

In the past few decades recommender system has reshaped the way of information filtering between websites and the users. It helps in identifying user interest and generates product suggestions for the active users. This paper presents an enlightening analysis of various recommender system such as content-based, collaborative-based and hybrid recommendation techniques along with few optimization models that has been applied to improvise the parameters being considered by the aforementioned techniques. We explored 125 articles published from 1992 to 2019 in order to discuss the problems associated with the existing models. Various advantages and disadvantages of each recommendation model including the input methods has been elaborated. Critical review on research problems based on the explored techniques and future directions has also been covered. [ABSTRACT FROM AUTHOR]

The primary focus of this manuscript comprises three sections. Initially, we discuss the notion of an interval-valued intuitionistic neutrosophic soft set. We impose an intuitionistic condition between the membership grades of truth and falsity such that their supremum sum does not exceed unity. Similarly, for indeterminacy, the membership grade is in interval from the closed interval [0, 1]. Hence in this case, the supremum sum of membership grades of truth, indeterminacy, and falsity does not exceed two. We present the notion of necessity, possibility, concentration, and dilation operators and establish some of its properties. Second, we define the similarity measure between two interval-valued intuitionistic neutrosophic soft sets. Also, we discuss its superiority by comparing it with existing methods. Finally, we develop an algorithm and illustrate with an example of diagnosing psychiatric disorders. Even though the similarity measure plays a vital role in diagnosing psychiatric disorders, existing methods deal hardly in diagnosing psychiatric disorders. By nature, most of the psychiatric disorder behaviors are ambivalence. Hence, it is vital to capture the membership grades by using interval-valued intuitionistic neutrosophic soft set. In this manuscript, we provide a solution in diagnosing psychiatric disorders, and the proposed similarity measure is valuable and compatible in diagnosing psychiatric disorders in any neutrosophic environment.

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.).

Today’s industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.