scholarly journals Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

2021 ◽  
Author(s):  
Dayal Kumar Behera ◽  
Madhabananda Das ◽  
Subhra Swetanisha ◽  
Janmenjoy Nayak ◽  
S. Vimal ◽  
...  
Keyword(s):  
Link Prediction ◽  
Classification Model ◽  
Multiple Graph

scholarly journals Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 214
Author(s):  
Pokpong Songmuang ◽  
Chainarong Sirisup ◽  
Aroonwan Suebsriwichai
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Link Prediction ◽  
Classification Model ◽  
Density Level ◽  
Missing Link ◽  
Common Features ◽  
The Common

The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.

A glance on dystonias, how to recognize and handle them

2019 ◽  
pp. 22-29
Author(s):  
F. N. Mercan ◽  
E. Bayram ◽  
M. C. Akbostanci
Keyword(s):  
Differential Diagnosis ◽  
Movement Disorder ◽  
Age Of Onset ◽  
Clinical Manifestations ◽  
Body Part ◽  
Classification Model ◽  
Treatment Choices ◽  
Muscle Groups

Dystonia refers to an involuntary, repetitive, sustained, painful and twisting movements of the affected body part. This movement disorder was first described in 1911 by Hermain Oppenheim, and many studies have been conducted to understand the mechanism, the diagnosis and the treatment of dystonia ever since. However, there are still many unexplained aspects of this phenomenon. Dystonia is diagnosed by clinical manifestations, and various classifications are recommended for the diagnosis and the treatment. Anatomic classification, which is based on the muscle groups involved, is the most helpful classification model to plan the course of the treatment. Dystonias can also be classified based on the age of onset and the cause. These dystonic syndromes can be present without an identified etiology or they can be clinical manifestations of a neurodegenerative or neurometabolic disease. In this review we summarized the differential diagnosis, definition, classifications, possible mechanisms and treatment choices of dystonia.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Aulia Dwi Oktavia ◽  
Aam Alamudi ◽  
Budi Susetyo
Keyword(s):  
College Graduates ◽  
Influential Factors ◽  
Classification Model ◽  
Statistical Software ◽  
Waiting Period ◽  
Level Of Education ◽  
Economic Problems ◽  
Factors Influencing

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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