Adaptive Feature Selection for Ultrasound Image Processing and its Application to Liver Cirrhosis Detection

2013 ◽  
Vol 411-414 ◽  
pp. 1372-1376
Author(s):  
Wei Tin Lin ◽  
Shyi Chyi Cheng ◽  
Chih Lang Lin ◽  
Chen Kuei Yang
Keyword(s):  
Clustering Algorithm ◽  
Ultrasound Image ◽  
Image Features ◽  
Ultrasound Images ◽  
Analytic Hierarchy ◽  
Low Level ◽  
Adaptive Feature Selection ◽  
High Level ◽  
Objects Detection ◽  
Hierarchy Process

An approach to improve the regions of interesting (ROIs) selection accuracy automatically for medical images is proposed. The aim of the study is to select the most interesting regions of image features that good for diffuse objects detection or classification. We use the AHP (Analytic Hierarchy Process) to obtain physicians high-level diagnosis vectors and are clustered using the well-known K-Means clustering algorithm. The system also automatically extracts low-level image features for improving to detect liver diseases from ultrasound images. The weights of low-level features are adaptively updated according the feature variances in the class. Finally, the high-level diagnosis decision is made based on the high-level diagnosis vectors for the top K near neighbors from the medical experts classified database. Experimental results show the effectiveness of the system.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Clustering Mashups by Integrating Structural and Semantic Similarities Using Fuzzy AHP

2021 ◽  
Vol 18 (1) ◽  
pp. 34-57
Author(s):  
Weifeng Pan ◽  
Xinxin Xu ◽  
Hua Ming ◽  
Carl K. Chang
Keyword(s):  
Semantic Similarity ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Fuzzy Ahp ◽  
Real Data ◽  
Structural Similarity ◽  
Analytic Hierarchy ◽  
Data Set ◽  
Novel Approach ◽  
Hierarchy Process

Mashup technology has become a promising way to develop and deliver applications on the web. Automatically organizing Mashups into functionally similar clusters helps improve the performance of Mashup discovery. Although there are many approaches aiming to cluster Mashups, they solely focus on utilizing semantic similarities to guide the Mashup clustering process and are unable to utilize both the structural and semantic information in Mashup profiles. In this paper, a novel approach to cluster Mashups into groups is proposed, which integrates structural similarity and semantic similarity using fuzzy AHP (fuzzy analytic hierarchy process). The structural similarity is computed from usage histories between Mashups and Web APIs using SimRank algorithm. The semantic similarity is computed from the descriptions and tags of Mashups using LDA (latent dirichlet allocation). A clustering algorithm based on the genetic algorithm is employed to cluster Mashups. Comprehensive experiments are performed on a real data set collected from ProgrammableWeb. The results show the effectiveness of the approach when compared with two kinds of conventional approaches.

Modeling and Simulation of Partnership Network for ?an Intelligent Supply Chain

2011 ◽  
pp. 202-225
Author(s):  
Fouzia Ounnar ◽  
Patrick Pujo ◽  
Selma Limam Mansar
Keyword(s):  
Distributed Simulation ◽  
Evaluation Criteria ◽  
Multicriteria Analysis ◽  
High Level Architecture ◽  
Analytic Hierarchy ◽  
Logistics Networks ◽  
Best Response ◽  
Partnership Network ◽  
High Level ◽  
Hierarchy Process

Contrary to actual logistics networks in which chains are frozen, in the proposed partnership network, a dynamic chain is only built each time an order is requested; nothing is planned ahead of time. An isoarchic control model based on the holonic paradigm is proposed. The control of the partnership network can be seen through a simultaneous analysis of the holon views. The proposed control is based on a multicriteria analysis method by complete aggregation (Analytic Hierarchy Process (AHP)). The assignment of orders is based on the search for the best response to a Call For Proposals submitted by a customer. The solution that appears to be the most efficient in terms of the evaluation criteria will be adopted. For validation purposes, a simulation of the proposed approach was implemented using a distributed simulation environment HLA (High Level Architecture). A set of realistic tests were used to evaluate the proposed approach.

scholarly journals Evaluating the Performance of the Government Venture Capital Guiding Fund Using the Intuitionistic Fuzzy Analytic Hierarchy Process

2020 ◽  
Vol 12 (17) ◽  
pp. 6908 ◽  
Author(s):  
Jianjun Xu ◽  
Lijie Yu ◽  
Rakesh Gupta
Keyword(s):  
Venture Capital ◽  
Analytic Hierarchy Process ◽  
Analytical Framework ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analysis Model ◽  
Analytic Hierarchy ◽  
Intuitionistic Fuzzy ◽  
The Government ◽  
High Level ◽  
Hierarchy Process

The performance evaluation of the government venture capital guiding fund (GVCGF) has come into focus in the field of venture capital. Most of the existing studies, such as whether the GVCGF has guided social capital to start-up enterprises and has played its due role in the process of enterprise growth and innovation, are all based on relevant work under the framework of econometric analysis. Unlike in these existing studies, we construct the performance analysis model of the GVCGF from four dimensions, including the standardization development of the guidance fund, the risk control ability, and the leverage and the support effects under the framework of a multi-attribute decision-making analysis. Taking a GVCGF project in Ningbo City, China, as an example, we comprehensively evaluate the development performance of the GVCGF using the intuitionistic fuzzy analytic hierarchy process (IFAHP). The results show that the development performance of the GVCGF is at a “relatively high” level. Compared with the traditional analytic hierarchy process (AHP), the IFAHP effectively avoids the false, enlarged influence caused by data subjectivity and evaluation uncertainty. This study provides a feasible analytical framework for the application of the IFAHP in other project performance evaluations.

A Critical Assessment of the Sustainability Performance of a Typical Mega City in China - A Case Study of Jinan

2013 ◽  
Vol 734-737 ◽  
pp. 1565-1569
Author(s):  
Ming Xing Sun ◽  
Yu Tao Wang ◽  
Shu Ping Zhang ◽  
Ren Qing Wang
Keyword(s):  
Sustainable Development ◽  
Indicator System ◽  
Pension Insurance ◽  
Analytic Hierarchy ◽  
Development Level ◽  
Jinan City ◽  
High Level ◽  
Hierarchy Process ◽  
Very High

This paper, based on the strong sustainable principles, is derived from Jinan Citys statistics yearbook and statistical bulletin materials. The author constructed the environmental indicator system and socioeconomic indicator system respectively with the help of the analytic hierarchy process (AHP), and calculated Jinan Citys sustainability indices for 2003-2010. The results show that the environmental sustainable development level in Jinan city was average in 2003-2005 and 2007; however, in 2006, 2008-2010 it achieved a high level. The socioeconomic sustainable development level in Jinan City was high in 2003-2007, and was very high in 2008-2010. The results of the analyses suggest that Jinan City should improve its air quality and strengthen its environmental investment. It is also urgent that Jinan improve citizens livelihood, especially for housing and pension insurance projects.

scholarly journals PENERAPAN DATA MINING DALAM MENGELOMPOKAN PRODUKSI JAGUNG MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Nanda Erlangga ◽  
Solikhun Solikhun ◽  
Irawan Irawan
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Domestic Market ◽  
Corn Production ◽  
Low Level ◽  
High Level ◽  
Level Cluster ◽  
Central Statistics

Corn needs are currently experiencing a fairly rapid development can be seen in terms of the domestic market, here researchers want to increase the productivity and quality of corn production. The data that will be used is the data from the Central Statistics Agency. The method in this study is the K-means clustering algorithm and the application used is Rapidminer which will be grouped into 2 clustering, namely high and low. The results of this study are 2 high level cluster provinces, 32 low level cluster provincesKeywords: Corn, Data mining, K-means Clustering c

scholarly journals Image Searching System

2021 ◽  
Vol 6 (2) ◽  
pp. 161-167
Author(s):  
Eduard Yakubchykt ◽  
◽  
Iryna Yurchak
Keyword(s):  
Semantic Content ◽  
Image Features ◽  
Multimedia Databases ◽  
Semantic Level ◽  
Low Level ◽  
Essential Properties ◽  
Calculation Algorithms ◽  
Similar Images ◽  
Definition Of ◽  
High Level

Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.

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