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Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machines struggle due to variations in expression. This challenge underscores the necessity for a robust framework capable of matching entities based on their attributes. While previous studies have explored transformation techniques, NLP, and deep learning, they often fall short in accuracy and scalability or overlook internal constraints. Such shortcomings result in costly rework and compromised business decisions. To address these issues, proposed “Deep Semantic Homophonic Synonymy” (DSHS) algorithm, a hybrid approach integrating rule-based, NLP, and deep learning techniques. By employing semantic matching, rule-based preprocessing, NLP enrichment, phonetic analysis, abbreviation variations, and spelling corrections, the DSHS algorithm effectively overcomes entity matching challenges. This solution, comprising six modules including Preprocessing, Transformation, Translation, Word Embedding, Attribute Similarity, and Classification, outperforms existing EM models in terms of accuracy and scalability. This solution is a comprehensive entity matching, offering significant advancements in the field and providing a reliable framework for resolving complex matching requirements.

The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency.

Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

The present age of internet and the rising of business have resulted into many folds of increase in the volume of data which are to be used for various applications on a day to day basis. Therefore, it is an obvious challenge to reduce the dataset and find useful information pertaining to the interest of the organisation. But another challenge lies in hiding sensitive information in order to provide privacy. Thus, attribute reduction and privacy preservation are two major challenges in privacy preserving data mining. In this paper, we propose a sensitive rule hiding model to hide sensitive fuzzy association rules. Proposed model uses rough set on intuitionistic fuzzy approximation spaces with ordering to reduce the dataset dimensionality. We use triangular and trapezoidal membership function to get the fuzzified information system. Finally, decreasing the support of right hand side of the rule is used to hide sensitive fuzzy association rules.

Multimodal image fusion combines information from multiple modalities to generate a composite image containing complementary information. Multimodal image fusion is challenging due to the heterogeneous nature of data, misalignment and nonlinear relationships between input data, or incomplete data during the fusion process. In recent years, several attention mechanisms have been introduced to enhance the performance of deep learning models. However, little literature is available on multimodal image fusion using attention mechanisms. This paper aims to study and analyze the latest deep-learning approaches, including attention mechanisms for multimodal image fusion. As a result of this study, the graphical taxonomy based on the different image modalities, various fusion strategies, fusion levels, and metrics for fusion tasks has been put forth. The focus has been on various Multimodal image fusion frameworks based on deep-learning techniques as their core methodology. This paper also sheds light on the challenges and future research directions in this field, application domains, and benchmark datasets used for multimodal fusion tasks. This paper contributes to the research on Multimodal image fusion and can help researchers select a suitable methodology for their applications.

The rough set philosophy is based on the concept that there is some information associated with each object of the universe. The set of all objects of the universe under consideration for particular discussion is considered as a universal set. So, there is a need to classify objects of the universe based on the indiscernibility relation (equivalence relation) among them. In the view of granular computing, rough set model is researched by single granulation. The granulation in general is carried out based on the equivalence relation defined over a universal set. It has been extended to multi-granular rough set model in which the set approximations are defined by using multiple equivalence relations on the universe simultaneously. But, in many real life scenarios, an information system establishes the relation with different universes. This gave the extension of multi-granulation rough set on single universal set to multi-granulation rough set on two universal sets. In this paper, we define multi-granulation rough set for two universal sets U and V. We study the algebraic properties that are interesting in the theory of multi-granular rough sets. This helps in describing and solving real life problems more accurately.