IRIS Feature Extraction and Classification using FPGA

Patil, Babasaheb G.; Mane, Nikhil Niwas; Subbaraman, Shaila

doi:10.11591/ijece.v2i2.158

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…The query relevance value will be compared with the sentence similarity value for summary extraction. The vector space model measures the similarity between a document and a query [33]. In this model, queries and documents are considered vectors in an n-dimensional space, where n is the sum of all the terms in the lexicon.…”

Section: 𝑊𝑑 𝑡 = 𝑡𝑓𝑑 𝑡 * 𝐼𝐷𝐹𝑡mentioning

confidence: 99%

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Gunawan,

Fitria,

Setiawan

et al. 2023

Knowl. Eng. Data Sci.

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Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.

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Section: 𝑊𝑑 𝑡 = 𝑡𝑓𝑑 𝑡 * 𝐼𝐷𝐹𝑡mentioning

confidence: 99%

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Gunawan,

Fitria,

Setiawan

et al. 2023

Knowl. Eng. Data Sci.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the object image segmentation process is done to obtain a binary image of the object image by using the concept of morphology consists of thresholding, edge detection, and opening. A process of feature extraction is followed after the process of image segmentation [17]. Segmentation is a very important step in object recognition.…”

Section: Preprocessingmentioning

confidence: 99%

Leaf Morphological Feature Extraction of Digital Image Anthocephalus Cadamba

2016

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“…In fuzzy logic, the i value = 0. Neutrosophic approach to segmentation based on watershed method has been carried out [3]. Steps in the neutrosophic approach to segmentation based on watershed method is described as follows:…”

Section: Neutrosophic Approach To Segmentationmentioning

confidence: 99%

“…A process of feature extraction is followed after the process of image segmentation [3].Segmentation is a very important step in object recognition. There are various segmentation methods that can be used.…”

Section: Introductionmentioning

confidence: 99%