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A Multi-Level Threshold Method for Edge Detection and Segmentation Based on Entropy

Computers, Materials & Continua 2020年第4期63卷 1-16页

作者： Mohamed A.El-Sayed abdelmgeid a.ali Mohamed E.Hussien Hameda A.Sennary Department of Mathematics Faculty of ScienceFayoum UniversityFayoumEgypt.CurrentlyComputer Science DepartmentTaif UniversityTaifSaudi Arabia Computer Science Department Faculty of Computers and Information Minia UniversityMiniaEgypt Department of Mathematics Faculty of ScienceAswan UniversityAswanEgypt

The essential tool in image processing,computer vision and machine vision is edge detection,especially in the fields of feature extraction and feature *** is a basic area in information *** entropy,in image processing field has a role associated with image *** an initial step in image processing,the entropy is always used the image’s segmentation to determine the regions of image which is used to separate the background and objects in *** segmentation known as the process which divides the image into multiple regions or sets of *** applications have been development to enhance the image *** paper utilizes the Shannon entropy to achieve edge detection process and segmentation of the *** introduces a new method of edge detection for 2-D histogram and Shannon entropy based on multilevel *** method utilizes the gray value and the average gray value of the pixels to achieve the two dimensional *** current method has apriority in comparison to some upper classical *** experimental results exhibited that the proposed method could capture a moderate quality and execution time better than other comparative methods,particularly in the largest images *** proposed method offers good results when applied with images of different sizes from the civilization of ancient Egyptians.

关键词： Multi-level threshold edge detection 2D histogram entropy

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COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

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Computers, Materials & Continua 2022年第2期70卷 2507-2524页

作者： Ahmed F.Ibrahim M.Hassaballah abdelmgeid a.ali Yunyoung Nam Ibrahim A.Ibrahim Department of Computer Science Faculty of Computer ScienceNahda UniversityBanisuefEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Faculty of Computers and Information Minia UniversityAlMiniaEgypt Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

关键词： COVID19 COVID data sentiment analysis hierarchical clustering sentiment tree

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