B. K. Tripathy

As we approach Web 4.0, which relates to the uprising of internet of things (IoT)-enabled smart manufacturing devices that serve as data generator devices for companies, the...

Normally, big data refers to large and complex data sets that are derived from various sources and require new mechanisms for processing as existing mechanisms cannot handle such...

Recognition of reliability of people entering into a system has been a vital downside in numerous business concerns. Biometrics, which provides recognition of personnel using...

There is a strong consensus that quantum computers are likely to solve previously intractable problems, especially in data science and AI. Nowadays, processing power of machine...

This chapter is mainly an advanced version of the previous version of the chapter named “An Insight to Deep Learning Architectures” in the encyclopedia. This chapter mainly...

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning...

Soft set is one of the latest mathematical models to handle uncertainty. In a soft set, every element of its parameter set is associated with a subset of the universe of...

In recent years, most of the applications in game theory have been developed based on the theory of fuzzy sets. But the inadequacy of the parameterization tool in fuzzy set...

Rough set theory partitions a universe using single-layered granulation. The equivalence classes induced by rough sets are based on discretized values. Considering the fact that...

Information retrieval can be visualized as the extraction of the desired information from the flooded resources that spread over world wide web. Image retrieval is the...

This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter...

Integration of Computer Science with Bio Science has led to new field Computational Biology which created an opportunity in speeding up the process of analyzing the Bio-data. DNA...

One of the extensions of the basic rough set model introduced by Pawlak in 1982 is the notion of rough sets on fuzzy approximation spaces. It is based upon a fuzzy proximity...

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate...

The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to...

Rough Set Theory partitions a universe using single layered granulation. The equivalence classes induced by rough sets are based on discretised values. Considering the fact that...

Information retrieval can be visualized as the extraction of the desired information from the flooded resources that spread over World Wide Web. Image retrievals are the...

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many...

Technology has become profoundly integrated into modern society; however, this increases the risk of vulnerabilities, such as hacking and other system errors, along with other...

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the...

Nature-inspired algorithms are still at a very early stage with a relatively short history, comparing with many traditional, well-established methods. Metaheuristics, in their...

In the new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological...

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the...

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning...

Modern epidemiological studies involve understanding individual and social level inferences and their role in the transmission and distribution of disease instances. The...

Data clustering has been an integral and important part of data mining. It has wide applications in database anonymization, decision making, image processing and pattern...

Soft sets were introduced by Molodtsov to handle uncertainty based problems. In 2003, Maji et al. extended the soft set approach to decision making. Decision making plays an...

Epidemiological studies are largely purposed to provide outcomes that may be used for interventions and development programs. In recent years, geo-referencing of epidemiological...

With the increasing volume of data, developing techniques to handle it has become the need of the hour. One such efficient technique is clustering. Data clustering is under...

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level...

Epidemiological studies are carried out to understand the pattern and transmission of disease instances. Some prominent dimensions considered for analysis are cohort studies...

Although multiple occurrences of elements are immaterial in sets, in real life situations repetition of elements is useful. So, the notion of multisets (also called as bags) was...

Uncertainty is an inherent characteristic of modern day databases. In order to handle such databases with uncertainty, several new models have been introduced in the literature....

Several tasks under human activities need to be performed in a sequence of navigation and manipulation of objects. In several applications of human activities like robotics...

With tremendous development in the field of science and technology, there is vast amount of data which are used in analytics for decision making. Considering its spatial...

Modern epidemiological studies involve understanding individual and social level inferences and their role in the transmission and distribution of disease instances. The...

Modeling intelligent system in the field of medical diagnosis is still a challenging work. Intelligent systems in medical diagnosis can be utilized as a supporting tool to the...

This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter...

Cloud computing represents a paradigm shift and it can be applied to a wide range of areas, including e-commerce, health, education, communities, etc which are emerging as the...

The data used in the real world applications are uncertain and vague. Several models to handle such data efficiently have been put forth so far. It has been found that the...

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level...

Integration of Computer Science with Bio Science has led to new field Computational Biology which created an opportunity in speeding up the process of analyzing the Bio-data. DNA...

As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans...

Technological advancements have extracted a vast amount of useful knowledge and information for applications and services. These developments have evoked intelligent solutions...

Several models have been introduced to capture impreciseness in data. Fuzzy sets introduced by Zadeh and Rough sets introduced by Pawlak are two of the most popular such models....

The concept of list is very important in functional programming and data structures in computer science. The classical definition of lists was redefined by Jena, Tripathy, and...

Granular Computing has emerged as a framework in which information granules are represented and manipulated by intelligent systems. Granular Computing forms a unified conceptual...

Image Segmentation is the process of dividing an image into semantically relevant regions. The problem is still an active area due to wide applications in object detection and...

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently...

Publication of Data owned by various organizations for scientific research has the danger of sensitive information of respondents being disclosed. The policy of removal or...