Summary and Semi-average Similarity Criteria for Individual Clusters

2013 ◽  
pp. 101-126
Author(s):  
Boris Mirkin
Keyword(s):  
Similarity Criteria ◽  
Average Similarity

Mobile-Friendly Dashboards to Display Associations Between Cited-by and Similar Journals on Mobile Health Research: A Bibliometric Study (Preprint)

2018 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Hsien-Yi Wang ◽  
Yang Shao ◽  
Willy Chou
Keyword(s):  
Social Network ◽  
Mobile Health ◽  
Health Research ◽  
Similarity Criteria ◽  
Network Density ◽  
Deep Insight ◽  
Density Indices ◽  
The Subject ◽  
The Given ◽  
Insight Into

BACKGROUND Researchers often spend a great deal of time and effort retrieving related journals for their studies and submissions. Authors often designate one article and then retrieve other articles that are related to the given one using PubMed’s service for finding cited-by or similar articles. However, to date, none present the association between cited-by and similar journals related to a given journal. Authors need one effective and efficient way to find related journals on the topic of mobile health research. OBJECTIVE This study aims (1) to show the related journals for a given journal by both cited-by and similarity criteria; (2) to present the association between cited-by and similarity journals related to a given journal; (3) to inspect the patterns of network density indices among clusters classified by social network analysis (SNA); (4) to investigate the feature of Kendall's coefficient(W) of concordance. METHODS We obtained 676 abstracts since 2013 from Medline based on the keywords of ("JMIR mHealth and uHealth"[Journal]) on June 30, 2018, and plotted the clusters of related journals on Google Maps by using MS Excel modules. The features of network density indices were examined. The Kendall coefficient (W) was used to assess the concordance of clusters across indices. RESULTS This study found that (1) the journals related to JMIR mHealth and uHealth are easily presented on dashboards; (2) a mild association(=0.14) exists between cited-by and similar journals related to JMIR mHealth and uHealth; (3) the median Impact Factor were 3.37 and 2.183 based on the representatives of top ten clusters grouped by the cited-by and similar journals, respectively; (4) all Kendall’s coefficients(i.e., 0.82, 0.89, 0.92, and 0.75) for the four sets of density centrality have a statistically significant concordance (p < 0.05). CONCLUSIONS SNA provides deep insight into the relationships of related journals to a given journal. The results of this research can provide readers with a knowledge and concept diagram to use with future submissions to a given journal in the subject category of Mobile Health Research. CLINICALTRIAL Not available

scholarly journals A hybrid recommender system based-on link prediction for movie baskets analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Randomized Algorithm ◽  
Similarity Criterion ◽  
Absolute Error ◽  
Similarity Criteria ◽  
Online Systems ◽  
Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

Thermo-mechanical coupling particle simulation of three-dimensional large-scale non-isothermal problems

2017 ◽  
Vol 34 (5) ◽  
pp. 1551-1571 ◽  
Author(s):  
Ming Xia
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Simulation Method ◽  
Particle Simulation ◽  
Similarity Criteria ◽  
Particle Model ◽  
Length Scale ◽  
Content Type ◽  
Mechanical Coupling ◽  
Particle Simulation Method

Purpose The main purpose of this paper is to present a comprehensive upscale theory of the thermo-mechanical coupling particle simulation for three-dimensional (3D) large-scale non-isothermal problems, so that a small 3D length-scale particle model can exactly reproduce the same mechanical and thermal results with that of a large 3D length-scale one. Design/methodology/approach The objective is achieved by following the scaling methodology proposed by Feng and Owen (2014). Findings After four basic physical quantities and their similarity-ratios are chosen, the derived quantities and its similarity-ratios can be derived from its dimensions. As the proposed comprehensive 3D upscale theory contains five similarity criteria, it reveals the intrinsic relationship between the particle-simulation solution obtained from a small 3D length-scale (e.g. a laboratory length-scale) model and that obtained from a large 3D length-scale (e.g. a geological length-scale) one. The scale invariance of the 3D interaction law in the thermo-mechanical coupled particle model is examined. The proposed 3D upscale theory is tested through two typical examples. Finally, a practical application example of 3D transient heat flow in a solid with constant heat flux is given to illustrate the performance of the proposed 3D upscale theory in the thermo-mechanical coupling particle simulation of 3D large-scale non-isothermal problems. Both the benchmark tests and application example are provided to demonstrate the correctness and usefulness of the proposed 3D upscale theory for simulating 3D non-isothermal problems using the particle simulation method. Originality/value The paper provides some important theoretical guidance to modeling 3D large-scale non-isothermal problems at both the engineering length-scale (i.e. the meter-scale) and the geological length-scale (i.e. the kilometer-scale) using the particle simulation method directly.

Experimental investigation of bubble formation and motion mechanism near the moonpool

2021 ◽  
pp. 147509022110449
Author(s):  
Xiongliang Yao ◽  
Xianghong Huang ◽  
Zeyu Shi ◽  
Wei Xiao ◽  
Kainan Huang
Keyword(s):  
High Speed ◽  
Bubble Formation ◽  
Similarity Criteria ◽  
Dominant Factor ◽  
Acoustic Detection ◽  
Prototype Test ◽  
Tracer Particles ◽  
Research Ship ◽  
Image Velocimetry ◽  
Ventilation Method

When a research ship sails at a high speed, there is relative motion between the ship and fluid. The ship is slammed by the fluid. To reduce the direct impact of the fluid, sonar is installed in the moonpool, and acoustic detection equipment is installed along the research ship bottom behind the moonpool. However, during high-speed sailing, a large number of bubbles form in the moonpool. Some bubbles escape from the moonpool and flow backward along the bottom of the ship. When a large number of bubbles are around the sonar and acoustic detection equipment, the equipment malfunctions. However, there have been few studies on bubble formation in the moonpool with sonar and distribution along the ship bottom behind the moonpool. Therefore, a related model was developed and prototype tests were carried out in this study. The appropriate similarity criteria were selected and verified to ensure the reliability of the experiment. Considering the influences of speed, sonar, moonpool shape, and draft, the reason and mechanism of bubble formation in a sonar moonpool were studied. An artificial ventilation method was used to simulate a real navigation environment. Because the bubbles are in a bright state under laser irradiation, the bubbles can be used as tracer particles. A high-speed camera captured illuminated bubbles. The distribution mechanism of bubbles along the ship bottom behind the moonpool was investigated using particle image velocimetry under the influence of the moonpool shape and sailing speed. The model experimental results agreed well with those of the prototype test. The air sucked into the water was the dominant factor in bubble formation in the moonpool. The bubbles were distributed in a W shape under the ship bottom.

scholarly journals Parametric diagnostics of the condition of a dual-flow turbojet engine using neural network simulation of the operating process

2018 ◽  
Vol 224 ◽  
pp. 02057
Author(s):  
Anas S. Gishvarov ◽  
Julien Celestin Raherinjatovo
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Combustion Chamber ◽  
Early Stage ◽  
Low Pressure ◽  
Similarity Criteria ◽  
Ann Model ◽  
Turbojet Engine ◽  
Operating Process ◽  
Pressure Compressor

The article presents a method of parametric diagnostics of the condition of a dual-flow turbojet engine (DFTE). The method is based on the identification (determination) of the condition of the DFTE components (the compressor, combustion chamber, turbine) with application of a mathematical model of the operating process which is presented as an artificial neural network (ANN) model. This model describes the relation between the monitored parameters of the DFTE (the air temperatures (Tlpc*, Thpc*) beyond the low pressure compressor (LPC) and the high pressure compressor (HPC), the pressure beyond the LPC (Plpc), the fuel consumption rate (Gf), the gas temperatures (Thpt*, Tlpt*) beyond the high pressure turbine (HPT) and the low pressure turbine (LPT)) and the parameters of the condition of its components (the efficiencies of the LPC and the HPC (ηlpc*, ηhpc*), the stagnation pressure recovery factor in the combustion chamber (σcc), the efficiencies of the HPT and the LPT (ηhpt*, ηlpt*)). The parameters of the condition of the engine components (ηlpc*, ηhpc*, σcc, ηhpt*, ηlpt*) are the similarity criteria (integral criteria) which enable to identify the condition of the DFTE components to a high degree of reliability. Such analysis enables to detect defects at an early stage, even if the values of the monitored parameters (Тlpc*, Тhpc*, Plpc, Gf, Тhpt*, Тlpt*) are within the permissible limits. We provide the sequence for development of the ANN model and the results of its performance study during the parametric diagnostics of the condition of the DFTE.

scholarly journals Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
R. J. Hemalatha ◽  
V. Vijaybaskar ◽  
T. R. Thamizhvani
Keyword(s):  
Active Contour ◽  
Performance Metrics ◽  
Ultrasound Image ◽  
Medical Image Segmentation ◽  
Similarity Criteria ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Region Detection

Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

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