Enacting Segmentation Algorithms for Classifying Fish Species

Bhatia, Madhulika; Pandey, Madhulika; Kumar, Neeraj; Hooda, Madhurima; Akriti,

doi:10.1007/978-981-10-3818-1_5

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2019

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Cited by 2 publications

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“…Where and are the minimum value in x and maximum value in given its range. is current value at which value is to be normalize [12][13][14][15].…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

User Preference Based Mobile Application Recommendation

2019

IJITEE

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Application recommendation is one of the larger scales and sophisticated recommendation system currently exists. In this research work, we devised an approach which will deal with suggesting application based on users click on a particular application. The approach described in this paper is efficient and with less memory requirement than other traditional methods. This paper also includes the details about the implementation of the approach with User Interface. The paper also provides the details that how it can be implemented on a large scale. The approach is implemented in a mobile-based platform with react native support. The main objective of this paper is to describe an approach, which will be efficient and completely based on users data. The main objective of an Application Recommendation to recommend applications to increase user experience and recommend application based on their needs. Companies like Google, Apple, Samsung etc. are implementing it also.

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“…Where and are the minimum value in x and maximum value in given its range. is current value at which value is to be normalize [12][13][14][15].…”

Section: Mathematical Preliminariesmentioning

confidence: 99%