Data combination method in Remote Sensing tasks in case of conflicting information sources

Nowadays technologies of UAV-based Remote Sensing are used in different areas, such as: ecological monitoring, agriculture tasks, exploring for minerals, oil and gas, forest monitoring and warfare. Drones provide information more rapidly than piloted aerial vehicles and give images of a very high resolution, sufficiently low cost and high precision.Let’s note, that processing of conflicting information is the most important task in remote sensing. Dempster’s rule of data combination is widely used in solution of different remote sensing tasks, because it can processes incomplete and vague information. However, Dempster’s rule has some disadvantage, it can not deal with highly conflicted data. This rule of data combination yields wrong results, when bodies of evidence highly conflict with each other. That’s why it was proposed a data combination method in UAV-based Remote Sensing. This method has several important advantages: simple calculation and high accuracy. In this paper data combination method based on application of Jaccard coefficient and Dempster’s rule of combination is proposed. The described method can deal with conflicting sources of information. This data combination method based on application of evidence theory and Jaccard coefficient takes into consideration the associative relationship of the evidences and can efficiently handle highly conflicting sources of data (spectral bands).The frequency approach to determine basic probability assignment and formula to determine Jaccard coefficient are described in this paper too. Jaccard coefficient is defined as the size of the intersection divided by the size of the union of the sample sets. Jaccard coefficient measures similarity between finite sets. Some numerical examples of calculation of Jaccard coefficient and basic probability assignments are considered in this work too.This data combination method based on application of Jaccard coefficient and Dempster’s rule of combination can be applied in exploring for minerals, different agricultural, practical and ecological tasks.

Data Combination for Problem-Solving: A Case of an Open Data Exchange Platform

In recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem-solving through data combination have not been fully understood. In big data and interdisciplinary data combinations, large-scale data with many variables are expected to be used, and value is expected to be created by combining data as much as possible. In this study, we conducted three experiments to investigate the characteristics of data, focusing on the relationships between data combinations and variables in each dataset, using empirical data shared by the local government. The results indicate that even datasets that have a few variables are frequently used to propose solutions for problem-solving. Moreover, we found that even if the datasets in the solution do not have common variables, there are some well-established solutions to these problems. The findings of this study shed light on the mechanisms behind data combination for solving problems involving multiple datasets and variables.

Enhancing the Accuracy of Multimodal Biometric Systems

: Biometric acknowledgment frameworks have progressed altogether in the most recent decade and their utilization in explicit applications will increment sooner rather than later. The capacity to direct important correlations and evaluations will be urgent to fruitful organization and expanding biometric selection. Indeed, even the best methodology and unimodal biometric frameworks couldn't completely address the issue of exactness and execution as far as their bogus acknowledge rate (FAR) and bogus oddball rate (FRR). In spite of the fact that multimodal biometric frameworks had the option to moderate a portion of the restrictions experienced in unimodal biometric frameworks, like non-all inclusiveness, uniqueness, non-adequacy, loud sensor information, parody assaults, and execution, the issue of low exactness actually continues. In this paper, we survey research papers zeroed in on the precision improvement in data combination of face and finger impression biometric acknowledgment frameworks, decide the primary highlights of the chose techniques, and afterward call attention to their benefits and inadequacies. We propose a novel methodology in relieving the issue of exactness and execution of data combination of multimodal biometric frameworks. This methodology utilizes multilayer perceptron neural organizations in preparing and testing of the organization while additionally proposing the utilization of the most well-known utilized unique mark in biometric field.