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Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual explanation images (VEIs) by using the feature maps of the distorted image and the corresponding PRI, iv) measuring the similarity between the two VEIs using an image similarity metric, and v) employing a non-linear mapping function for quality score alignment. In our experiments, we evaluated the efficacy of the proposed method across various forms of distortion using four benchmark datasets (LIVE, SIQAD, CSIQ, and TID2013). The proposed approach demonstrates parity with the latest methods, as evidenced by comparisons with both hand-crafted NR-IQA and deep learning-based approaches. [ABSTRACT FROM AUTHOR]

In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the data sets are related and comparable. Measures of similarity give a numerical value that reveals the strength of associations between sets or sets of variables. In this paper, we initiate a new concept of generalized interval-valued intuitionistic fuzzy soft expert sets and their fundamental operations. This new concept is more flexible than existing concepts based on their algebraic definition. Unlike fuzzy sets, the concept of generalized interval-valued intuitionistic fuzzy soft expert sets is characterized by a degree of membership and degree of non-membership along with fuzzy set theory. The proposed methodology is validated through an empirical application in medical diagnosis, where (GIVIFSESs) are employed to model the uncertainty and imprecision inherent in expert assessments. The selected similarity measures are then applied to quantify the degree of resemblance between different medical cases, facilitating a more informed decision-making process. We introduce several types of similarity measures on generalized interval-valued intuitionistic fuzzy soft expert sets. We also discuss a similarity measure of Type-I, Type-II, and Type-III for two (GIVIFSESs) and its application in medical diagnosis problems. [ABSTRACT FROM AUTHOR]

Concerning decision support in specific disease areas, making faster and more accurate medical diagnoses has become more challenging. Due to indeterminate, inconsistent, and huge patient data, judging a specific disease may be challenging. Similarity is an effective method to enhance healthcare systems by simplifying medical diagnostic generation compared with illness profiles. This paper proposes a new medical diagnosis algorithm that uses interval type-2 fuzzy similarity measures (IT-2FSMs). This algorithm calculates the degree of similarity between varieties of components of patient data and establishes methods of clustering patients based on close distances between some of their features. The proposed approach will be evaluated on UCI Machine Learning Repository medical datasets. Comparative studies will be done between different IT-2 FSMs (Cherif, Jaccard, Bustince, Mitchell, Gorzalczany, Zeng and Li) to demonstrate the capability of IT-2FSMs to make quick medical diagnoses, even with different noise levels. The proposed IT-2FSMs are utilized as a clustering method and compared against existing clustering algorithms such as type-2 fuzzy c-means, cluster forest, bagged clustering, evidence accumulation, and random projection. The IT-2FSMs demonstrate a pertinent classification accuracy comparable to the other algorithms, as assessed by the clustering quality parameter R. [ABSTRACT FROM AUTHOR]

Spectrum-based fault localization (SBFL) is one of the most popular fault localization techniques that uses coverage information and test results to calculate a suspicious score for every program statement. The effectiveness of SBFL suffers from the occurrences of coincidental correctness, which occurs when a fault is executed but no failure is detected. Identifying coincidental correct (CC) test cases can be modeled as a classification problem. Except in exceptional cases, proven identification of CC tests is not possible, so instead of using 0/1 results, we propose a similarity-based approach to identify CC test cases. A strategy is suggested to manipulate CC test cases for SBFL. In the first step, a low-cost computational method is proposed to identify CC test cases based on the similarity of the passed executions to the failed ones. Then, we proposed new similarity measures based on the original ones (such as Jaccard similarity and Euclidean distance) and presented a method to identify proven CC. Finally, a weighted CC test case manipulation strategy is proposed to mitigate the negative impact of CC test cases in SBFL. We evaluated the proposed method by conducting extensive experiments on 443 faulty versions of 13 popular subject programs, containing artificial and real faults. The results show that the proposed method can improve the accuracy of SBFL techniques with a very low computational cost. [ABSTRACT FROM AUTHOR]

The use of Neural Networks in real estate appraisal has been recently subject of renewed interest by the scientific community. Generally, their effective use requires the availability of a large database, otherwise facing the real risk, even with an excellent performance on the «training set», of obtaining unsatisfactory generalisation properties (the so called over fitting effect). The well-known multiple regression models (MRAs), on the other side, require fewer parameters for their optimisation but are unable to capture complex nonlinear relationships. Since large databases are usually difficult to find in the real estate market, MRA models often provide better results than Artificial Neural Networks (ANNs). Furthermore, the latter require considerable effort to be effectively trained, both in finding the best structure and in estimating the characterising parameters. The optimisation process that leads to an efficient neural network requires a long job as well as considerable computational capabilities. This contribution, after outlining the state of the art in the use of ANNs and confirming that the scarcity of real estate market data often turned out to be a serious obstacle in their concrete application, proposed an innovative algorithm for selecting the data used in the training process. Such an algorithm seems to be able to improve predictive performance: networks that seek to take full advantage of the information available for learning seem to have better abilities in generalising the behaviour of the underlying phenomenon than those that are trained with completely randomly selected data, as usually done in practice.

Abstract With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models.

Genetic similarity determines the extent to which two genotypes share common genetic material. It can be measured in various ways, such as by comparing DNA sequences, proteins, or other genetic markers. The significance of genetic similarity is multifaceted and encompasses various fields, including evolutionary biology, medicine, forensic science, animal and plant breeding, and anthropology. Genetic similarity is an important concept with wide application across different scientific disciplines. The research material included 21 rapeseed genotypes (ten interspecific Brassicaceae hybrids of F2 generation and 11 of their parental forms) and 146 alleles obtained using 21 ISSR molecular markers. In the presented study, six measures for calculating genetic similarity were compared: Euclidean, Jaccard, Kulczyński, Sokal and Michener, Nei, and Rogers. Genetic similarity values were estimated between all pairs of examined genotypes using the six measures proposed above. For each genetic similarity measure, the average, minimum, maximum values, and coefficient of variation were calculated. Correlation coefficients between the genetic similarity values obtained from each measure were determined. The obtained genetic similarity coefficients were used for the hierarchical clustering of objects using the unweighted pair group method with an arithmetic mean. A multiple regression model was written for each method, where the independent variables were the remaining methods. For each model, the coefficient of multiple determination was calculated. Genetic similarity values ranged from 0.486 to 0.993 (for the Euclidean method), from 0.157 to 0.986 (for the Jaccard method), from 0.275 to 0.993 (for the Kulczyński method), from 0.272 to 0.993 (for the Nei method), from 0.801 to 1.000 (for the Rogers method) and from 0.486 to 0.993 (for the Sokal and Michener method). The results indicate that the research material was divided into two identical groups using any of the proposed methods despite differences in the values of genetic similarity coefficients. Two of the presented measures of genetic similarity (the Sokal and Michener method and the Euclidean method) were the same. [ABSTRACT FROM AUTHOR]

The effectiveness of learning models is significantly influenced by the accessibility and sufficiency of training data. To address the challenge of dataset adequacy, researchers have extensively explored data augmentation (DA) as a promising strategy. DA involves generating new data instances through applied transformations to existing data, thereby augmenting the dataset in terms of both size and variability. This method has demonstrated success in enhancing model performance and accuracy across a spectrum of natural language processing (NLP) tasks. Despite its proven efficacy, there has been limited exploration of DA specifically for the Arabic language, with existing studies relying predominantly on conventional methods such as paraphrasing or techniques based on introducing noise. In this study, we introduce an innovative approach to Arabic DA, tailored for the Moroccan dialect. We leverage the advanced modeling technique AraGPT-2 for the augmentation operation. The generated sentences are assessed based on context, semantics, diversity, and novelty, utilizing metrics such as Euclidean distance, cosine similarity, Jaccard index, and BLEU score. Subsequently, we employ the Multi-dialect-Arabic-BERT transformer for the Named Entity Recognition (NER) task, evaluating its performance on the augmented Moroccan dialect dataset. The experiments were conducted on the DarNER-corpus, representing the Moroccan dialect. The outcomes illustrate that our proposed methodology significantly enhances the NER task for the Moroccan dialect, showcasing a noteworthy increase in F1-score by 2.89 points. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a distance measure on single-valued neutrosophic sets by sine function which is a generalization of intuitionistic fuzzy sine distance measure. The axiom of metric on single-valued neutrosophic sets is verified and shows that the difference of distance measure from unity is a similarity measure. A new methodology for multi-attribute decision-making problems (MADM) is developed for the most common decision by the smallest measure value of the proposed single-valued neutrosophic distance measure. We further apply this distance measure to a multi-attribute decision-making problem (MADM) for student career determination in a neutrosophic environment to find the best career for suitable students. Finally, the comparison is made between the proposed distance measure and the other distance measures for the final decision chosen from the most common decisions of them. [ABSTRACT FROM AUTHOR]

Summary: A generic variation of hierarchical clustering (HC) that builds clusters from the bottom up is called agglomerative hierarchical clustering (AHC). The extension of AHC techniques using similarity criteria is the main topic of this research. Based on this, we create an AHC method that accomplishes clustering through ensemble approaches and combines the clustering of clusters with an original similarity measure. Three steps make up the proposed method's primary section. The first phase combines several individual AHC techniques to identify links between samples and create preliminary clusters. A heuristic similarity measure based on the developed clusters is used to determine how similar the samples are. The initial clusters produced using various techniques are all re‐clustered to create superclusters in the second step. The third phase involves creating the final clusters by assigning each sample to a supercluster with the greatest similarity after the clusters have been formed. Based on several benchmark datasets from the UCI machine learning repository, extensive experimental research has been done to assess the performance of the suggested approach. The outcomes unequivocally demonstrate that the suggested AHC‐based paradigm outperforms cutting‐edge techniques. [ABSTRACT FROM AUTHOR]

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we introduce a distance measure on single-valued neutrosophic sets by sine function which is a generalization of intuitionistic fuzzy sine distance measure. The axiom of metric on single-valued neutrosophic sets is verified and shows that the difference of distance measure from unity is a similarity measure. A new methodology for multi-attribute decision-making problems (MADM) is developed for the most common decision by the smallest measure value of the proposed single-valued neutrosophic distance measure. We further apply this distance measure to a multi-attribute decision-making problem (MADM) for student career determination in a neutrosophic environment to find the best career for suitable students. Finally, the comparison is made between the proposed distance measure and the other distance measures for the final decision chosen from the most common decisions of them.

With the development of mobile multimedia devices, mobile multimedia based mobile health healthcare has become a popular style. However, multimedia mobile health healthcare is not an easy task as it involves human decision making which is imprecise, vague and uncertain. To handle the uncertainty of information, picture fuzzy sets (PFSs) is a good choice. As one of the important topics in PFSs, the similarity measure between PFS has been assessed by several studies within the literature. Most existing measures of PFSs, however, cannot completely meet the properties of similarity measures in some special cases. In this paper, on the basic of the transformed right-angled triangular-based pyramids, a novel similarity measure is developed to handle pattern recognition problems. First, we originally present the conversion techniques between picture fuzzy numbers (PFNs) and their corresponding right-angled triangular-based pyramids fuzzy numbers. Then, we establish a new similarity measure for PFNs on the basic of the barycenter points of the corresponding right-angled triangular-based pyramids transformed from PFNs. Moreover, a comparison of different algorithms as regards PFSs and Neutrosophic sets (NSs) is performed to test performance of the established similarity measure within picture fuzzy environment. Finally, a new proposed TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is used to identifying the ranking order of the determinants of adoption of mobile multimedia healthcare services for the aged. The findings indicate that the established algorithm can fully fulfill the conditions of similarity measure, produce more reasonable and creditable results, and perform well in complex situations. [ABSTRACT FROM AUTHOR]

Conservation of lichens is impeded by knowledge gaps on their distribution and habitat requirements. This is a significant issue in vast, isolated and harsh environment regions such as subarctic regions, where lichen communities remain under documented. Lichen biodiversity assessments provide useful insights to better understand the functions supported by these ecologically important and sensitive species. This study aims i) to describe the conservation status of lichen communities and their α- and β-diversity components across a subarctic region in Northern Quebec (190 km2), ii) to describe their functional traits across the habitat types found in this region, and iii) to identify habitat types constituting lichen biodiversity hotspots. Lichens were sampled in 45 plots in habitats ranging from coniferous and deciduous forests to bogs, fens and rocky outcrops. A total of 115 species of largely macrolichens, calicioid lichens and allied fungi were identified, of which 18% currently have ranks of conservation concern at the provincial level, and 38% are newly reported for the region (∼124,000 km2 around our study area). Richness across plots averaged 36 ± 9 species and plots richer in microhabitats often harbored more species (R2 = 0.22). Differences in species composition were identified among plots and habitat types through NMDS and perMANOVA analyses (R2 = 0.35; p < 0.001), both being supported by differences in microhabitat composition (Mantel r = 0.22 and perMANOVA R2 = 0.29, respectively; p < 0.001). Thirty-five species showed significant preferences for a habitat based on the Pearson's phi coefficient of association. Our analysis on functional traits revealed significant patterns of association with habitat types, including i) saxicolous species with rocky outcrops, ii) epiphytic/epixylic species and vegetative reproduction traits (soredia, isidia) with coniferous stands, and iii) species belonging to the Parmeliaceae family with fens. Rocky outcrops represented the main lichen biodiversity hotspots in the region, while other habitat types were also important for maintaining total and complementary biodiversity. We are confident that our methodological approach including systematic sampling, full habitat variability representation, and the evaluation of complementary biodiversity components, can be effectively applied to fill gaps on lichen biodiversity in other underexplored regions across the globe. [ABSTRACT FROM AUTHOR]

A new method of aggregation and ranking on an ordinal scale is proposed based on the method of evidential ranking previously developed by the author, but using the tools of threshold aggregation of bodies of evidence. This method has better robustness and stability compared to the threshold-free method. The method allows you to take into account focal elements that are significant and insignificant for aggregation with different weights, which improves its sensitivity. The application of the developed method to aggregating information from reviewers and ranking reports/articles in a conference management system such as EasyChair is considered. This method can be used to rank alternatives presented as a set of bodies of evidence obtained from different sources of information. [ABSTRACT FROM AUTHOR]

Every day, the globe becomes more contemporary and industrialized. As a result, the number of vehicles and engines is growing. However, the energy sources utilized in these engines are scarce and dwindling over time. This circumstance prompts the search for alternate fuel. As civilization develops, transportation becomes a need for daily living. The largest issue is the diminishing supply of fossil fuels and the expanding population. As a result, everyone needs alternate energy sources for their automobiles. Therefore, in this investigation, we identify the best substitute for petrol. We offer the similarity measure(SM) for a hybrid structure of a Linear Diophantine Multi-Fuzzy Soft Set(LDMFSS) with the goal of determining this issue. Because the range of grade values has been expanded, decision-makers now have greater freedom in selecting their grade. An exemplary case study is illustrated that shows the appropriateness of our recommended approach. A comparative analysis is provided to show the outcomes of the proposed method are more achievable and beneficial than those of the existing methodologies. Additionally, its applicability and attainability are evaluated by comparing its structure to those of the already used procedures. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the impact of normalization techniques on the ranking of alternatives obtained using the combined compromise solution (CoCoSo) method. Similarity measures known from the literature and a new measure called the TOPSIS similarity measure (TOPSIS-SM) are used to assess the resulting rankings. This new measure is based on the TOPSIS algorithm, where the arithmetic mean of the considered rankings is taken as the ideal solution. In contrast, the antiideal solution is divided into a minimum and a maximum solution, which exhibit maximum separation from the ideal solution. The results obtained by this new method are different from those obtained using other similarity measures known from the literature. [ABSTRACT FROM AUTHOR]

Fermatean fuzzy sets (FFSs) constitute a formidable tool for tackling uncertainty in information, thereby enjoying widespread adoption across multiple domains. Similarity measures play a crucial role in determining the similarity between different FFSs. However, existing similarity measures for FFSs sometimes encounter issues that result in unreasonable outcomes when differentiating between FFSs. Therefore, efficiently quantifying the similarity between FFSs has emerged as a pressing issue demanding immediate resolution. In this paper, we introduce twenty-four novel similarity measures based on elementary function. Some properties of these similarity measures and weighted similarity measures are verified. Furthermore, we implement the proposed similarity measures in medical pattern recognition and multi-criteria decision-making to validate their feasibility in practical applications. Compared with some existing measures for FFSs, the outcomes conclusively demonstrate that the introduced similarity measures lead to significantly efficient outcomes.

In fuzzy set theory, the similarity measure is a significant device that measures the degree of correlation between two objects. An extension to intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PFS) have been widely employed in numerous disciplines. It is critical to investigate the similarity measure of PFS. The study proposes the trigonometric function to suggest new similarity measures of PFS to handle the uncertainty that the existing similarity measures are unable to differentiate. Firstly, axiomatic descriptions of similarity measures for the proposed measures are proved. Then, an example is used to validate the proposed measures. Application to pattern recognition and medical diagnosis is also discussed in real-life scenarios. The validity of the suggested similarity measures is proved by comparing the results to the effectiveness of current equivalent similarity measures. Finally, a comparative study of these real-life examples reveals that the novel similarity measures are more flexible and dependable than the current similarity measures in dealing with various real application difficulties.

Despite the significant advancements in fuzzy set theory, existing similarity measures for complex picture fuzzy sets (CPFSs) often result in impractical results in real-world scenarios. This presents a critical gap in accurately modeling and analyzing CPFSs, particularly in applications like pattern recognition and medical diagnosis. The present work addresses this problem by introducing various novel similarity measures for CPFSs, accompanied by rigorous axiomatic validation and a thorough discussion of their properties. Different sets of CPFSs have been empirically evaluated using both existing and proposed similarity measures, demonstrating the practical applicability and superiority of the latter. Based on the principle of maximum similarity, a comprehensive methodology involving these proposed measures has been illustrated, along with their implementation in solving different problems in pattern recognition and medical diagnosis. Additionally, a comparative analysis has been conducted to provide better clarity and understanding of the effectiveness of these measures. The results indicate that the proposed similarity measures offer significant advantages and improved accuracy for pattern recognition and medical diagnosis problems.

Three-dimensional voxel models are widely applied in various fields such as 3D imaging, industrial design, and medical imaging. The advancement of 3D modeling techniques and measurement devices has made the generation of three-dimensional models more convenient. The exponential increase in the number of 3D models presents a significant challenge for model retrieval. Currently, these models are numerous and typically represented as point clouds or meshes, resulting in sparse data and high feature dimensions within the retrieval database. Traditional methods for 3D model retrieval suffer from high computational complexity and slow retrieval speeds. To address this issue, this paper combines spatial-filling curves with octree structures and proposes a novel approach for representing three-dimensional voxel model sequence data features, along with a similarity measurement method based on symbolic operators. This approach enables efficient similarity calculations and rapid dimensionality reduction for the three-dimensional model database, facilitating efficient similarity calculations and expedited retrieval. [ABSTRACT FROM AUTHOR]

Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making. [ABSTRACT FROM AUTHOR]

Possibility neutrosophic hypersoft set is the generalisation of possibility neutrosophic soft set, and it is the blend of possibility neutrosophic set and hypersoft set. It tackles the insufficiencies of possibility intuitionistic fuzzy set and soft set for the entitlement of degree of indeterminacy and multi-argument approximate function, respectively. This kind of function maps the sub-parametric tuples to the power set of universe. It emphasises the partitioning of each attribute into its attribute-valued set that is missing in the existing fuzzy soft set-like structures. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. This research aims to initiate a novel structure that hybridises the possibility neutrosophic set with the hypersoft set for dealing with uncertainties efficiently. Therefore, the possibility neutrosophic hypersoft set is developed by employing theoretic, axiomatic, graphical and algorithmic approaches. After conceptual characterisation of its essential elementary notions, decision-support systems are presented based on AND and OR operations with the proposal of algorithms to assist the decision-making process. Similarity measures between possibility neutrosophic hypersoft sets are elaborated with the support of daily life applications. The comparison of the proposed work is discussed with the existing relevant models under certain evaluating features. [ABSTRACT FROM AUTHOR]

In this paper we present Spatial-KWD, a free open-source tool for efficient computation of the Kantorovich-Wasserstein Distance (KWD), also known as Earth Mover Distance, between pairs of binned spatial distributions (histograms) of a non-negative variable. KWD can be used in spatial statistics as a measure of (dis)similarity between spatial distributions of physical or social quantities. KWD represents the minimum total cost of moving the "mass" from one distribution to the other when the "cost" of moving a unit of mass is proportional to the euclidean distance between the source and destination bins. As such, KWD captures the degree of "horizontal displacement" between the two input distributions. Despite its mathematical properties and intuitive physical interpretation, KWD has found little application in spatial statistics until now, mainly due to the high computational complexity of previous implementations that did not allow its application to large problem instances of practical interest. Building upon recent advances in Optimal Transport theory, the Spatial-KWD library allows to compute KWD values for very large instances with hundreds of thousands or even millions of bins. Furthermore, the tool offers a rich set of options and features to enable the flexible use of KWD in diverse practical applications. [ABSTRACT FROM AUTHOR]

The Aichi biodiversity targets were established by the UN convention of biological Diversity and consist of 20 specific targets to address and mitigate biodiversity loss across the globe. We determine how well OECD countries are achieving the Aichi targets. We use the Sustainable Development Goals to make the determination. The Biodiversity and Habitat issue category assesses countries’ actions toward retaining natural ecosystems and protecting the full range of biodiversity within their borders. It consists of seven indicators: terrestorial biome protection (weighted for national and global rarity of biomes), marine protected areas, Protected Areas Representativeness Index, Species Habitat Index, Species Protection Index, and Biodiversiy Index, [4]. We determine the similarity between the rankings determined by the weighted average values and the Environmental Performance Index (EPI) scores. [ABSTRACT FROM AUTHOR]

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Given the extent of using deep convolutional neural networks to model the mechanism of object recognition, it becomes important to analyse the evidence of their similarity and the explanatory potential of these models. I focus on one frequent method of their comparison—representational similarity analysis, and I argue, first, that it underdetermines these models as how-actually mechanistic explanations. This happens because different similarity measures in this framework pick out different mechanisms across DCNNs and the brain in order to correspond them, and there is no arbitration between them in terms of relevance for object recognition. Second, the reason similarity measures are underdetermining to a large degree stems from the highly idealised nature of these models, which undermines their status as how-possibly mechanistic explanatory models of object recognition as well. Thus, building models with more theoretical consideration and choosing relevant similarity measures may bring us closer to the goal of mechanistic explanation. [ABSTRACT FROM AUTHOR]

The issue of decision-making (DM) is intricate due to the environment's ambiguous, imprecise, and uncertain nature, particularly when multiple attributes are involved and further subdivided. To address such complex problems, the concept of the hypersoft set has been employed. This article shows how to combine the multi-polar interval-valued neutrosophic set with the hypersoft set. This can help you solve DM problems that have more than one attribute. Additionally, we define similarity measures for multipolar interval-valued neutrosophic hypersoft sets (mPIVNHSs). We discuss the proposed extensions' definitions and mathematical operations and present an algorithm to solve DM problems related to everyday life using the proposed operators under mPIVNHSs. A machine learning method called K-Nearest Neighbor (KNN) is also employed to calculate ranking in the selection of sites for a new store. By utilizing the potential of data point similarity, K-Nearest Neighbors (KNN) and similarity measurements have practical value in daily life. KNN mimics the human propensity to draw from related experiences, resulting in applications like customized suggestions. Parallel to this, similarity metrics statistically evaluate resemblance to support tasks like personalized advice across fields like biology and social networks. Finally, we conclude the present study by comparing it with the existing studies, and future directions are also given.

The Aichi biodiversity targets were established by the UN convention of biological Diversity and consist of 20 specific targets to address and mitigate biodiversity loss across the globe. We determine how well OECD countries are achieving the Aichi targets. We use the Sustainable Development Goals to make the determination. The Biodiversity and Habitat issue category assesses countries’ actions toward retaining natural ecosystems and protecting the full range of biodiversity within their borders. It consists of seven indicators: terrestorial biome protection (weighted for national and global rarity of biomes), marine protected areas, Protected Areas Representativeness Index, Species Habitat Index, Species Protection Index, and Biodiversiy Index, [4]. We determine the similarity between the rankings determined by the weighted average values and the Environmental Performance Index (EPI) scores.

In the realm of higher education management, data mining has emerged as a potent tool for elevating the quality of instruction and administrative operations. This study presents the development of a curriculum validation analysis model utilizing association rule clustering. We collected and processed data from design students at a university, addressing missing values and eliminating outliers. Through data mining, we explored the relationships between students’ coursework, skill acquisition, and the demands of potential employers in society. This analysis underpinned a series of reforms in higher education curricula. To assess the impact of these curricular changes, two instructors conducted both process and outcome evaluations of the revised programs. The evaluations revealed that five student groups achieved scores above 80, one group scored between 70 and 80, and three groups scored between 60 and 70. All student groups scored above 60, demonstrating their competence in fundamental job-related tasks and validating their readiness for professional employment. This research offers valuable insights for the revision of professional training programs and curriculum reform in higher education.