A New Foreground-Background based Method for Behavior-Oriented Social Media Image Classification

2021 ◽  
Vol 17 (4) ◽  
pp. 1-25
Author(s):  
Lokesh Nandanwar ◽  
Palaiahnakote Shivakumara ◽  
Divya Krishnani ◽  
Raghavendra Ramachandra ◽  
Tong Lu ◽  
...  
Keyword(s):  
Social Media ◽  
Nearest Neighbor ◽  
Abnormal Behavior ◽  
Background Information ◽  
Svm Classifier ◽  
Color Spaces ◽  
Classification Rate ◽  
Media Image ◽  
Fusion Operation

Due to various applications, research on personal traits using information on social media has become an important area. In this paper, a new method for the classification of behavior-oriented social images uploaded on various social media platforms is presented. The proposed method introduces a multimodality concept using skin of different parts of human body and background information, such as indoor and outdoor environments. For each image, the proposed method detects skin candidate components based on R, G, B color spaces and entropy features. The iterative mutual nearest neighbor approach is proposed to detect accurate skin candidate components, which result in foreground components. Next, the proposed method detects the remaining part (other than skin components) as background components based on structure tensor of R, G, B color spaces, and Maximally Stable Extremal Regions (MSER ) concept in the wavelet domain. We then explore Hanman Transform for extracting context features from foreground and background components through clustering and fusion operation. These features are then fed to an SVM classifier for the classification of behavior-oriented images. Comprehensive experiments on 10-class datasets of Normal Behavior-Oriented Social media Image (NBSI) and Abnormal Behavior-Oriented Social media Image (ABSI) show that the proposed method is effective and outperforms the existing methods in terms of average classification rate. Also, the results on the benchmark dataset of five classes of personality traits and two classes of emotions of different facial expressions (FERPlus dataset) demonstrated the robustness of the proposed method over the existing methods.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

scholarly journals Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Xuan Wei ◽  
Jin-Cheng He ◽  
Da-Peng Ye ◽  
Deng-Fei Jie
Keyword(s):  
Multispectral Imaging ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Low Cost ◽  
Imaging Systems ◽  
Correct Classification Rate ◽  
Discriminate Analysis ◽  
Classification Rate

Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of (T670+T760-T640)/(T670+T760+T640) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index ((T670+T760-T640)/(T670+T760+T640)). Different indexes were used as the input of linear discriminate analysis (LDA) and of k-nearest neighbor (k-NN) algorithm to identify the maturity, and it was found that k-NN with (T670+T760-T640)/(T670+T760+T640) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

Sentiment Analysis System for Classification of Patient-Generated Health Reviews Using Rough Set Theory and Machine Learning Technique

2020 ◽  
Vol 10 (10) ◽  
pp. 2361-2368
Author(s):  
Alaa Omran Almagrabi ◽  
Shakeel Ahmad
Keyword(s):  
Social Media ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis System ◽  
Theory Technique

Advancements in social media domain have led to a prominent progress in the number of online communities. Sites, such as Twitter and Facebook, provide an avenue for the unrestricted generation, communication, and distribution of messages as well as information. In this work, we propose a sentiment classification system from patient-generated content posted by users on medical forums and social media sites. The rough set theory is a numerical rule-based technique employed for categorizing and examining doubtful, partial or indistinct data. The emphasis of this study is on the employment of the rough set theory technique for sentiment classification of patient-generated health reviews. We investigated four rough set theory-based algorithms, namely: Genetic, Learning from Examples Module version 2 (LEM2), Exhaustive and Covering, to generate rules for sentiment classification of patient-generated health reviews text. The Rough Set Exploration System (RSES 2.0) software is utilized to conduct experiments. Additionally, we applied SVM classifier to classify emotions. The experimental results show that the Genetic algorithm outperforms the comparing algorithms with an accuracy of 84.2% and Support Vector Machine outperforms other classifiers with an accuracy of 80.5%.

SUPPORT VECTOR MACHINE BASED AUTOMATIC CLASSIFICATION OF HUMAN BRAIN USING MR IMAGE FEATURES

2006 ◽  
Vol 06 (03) ◽  
pp. 357-370 ◽  
Author(s):  
HENRY SELVARAJ ◽  
S. THAMARAI SELVI ◽  
D. SELVATHI ◽  
R. RAMKUMAR
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Volume Data ◽  
K Nearest Neighbor ◽  
Incorrect Diagnosis ◽  
Quantitative Indication

This paper proposes an intelligent classification technique to identify two categories of MRI volume data as normal and abnormal. The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to incorrect diagnosis when a large number of MRIs are analyzed. In this work, the textural features are extracted from the MR data of patients and these features are used to classify a patient as belonging to normal (healthy brain) or abnormal (tumor brain). The categorization is obtained using various classifiers such as support vector machine (SVM), radial basis function, multilayer perceptron and k-nearest neighbor. The performance of these classifiers are analyzed and a quantitative indication of how better the SVM performance is when compared with other classifiers is presented. In intelligent computer aided health care system, the proposed classification system using SVM classifier can be used to assist the physician for accurate diagnosis.

SENTIMENT CLASSIFICATION OF THE LOCAL VISITORS' SOCIAL MEDIA REVIEWS

2018 ◽  
Vol 4 (26) ◽  
pp. 5534-5538
Author(s):  
Semra AKTAŞ POLAT
Keyword(s):  
Social Media ◽  
Sentiment Classification

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

2021 ◽  
pp. 1-15
Author(s):  
Phawis Thammasorn ◽  
Wanpracha A. Chaovalitwongse ◽  
Daniel S. Hippe ◽  
Landon S. Wootton ◽  
Eric C. Ford ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Nearest Neighbor ◽  
Small Sample ◽  
Imaging Data ◽  
Medical Imaging Data

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals A Novel Co-Training-Based Approach for the Classification of Mental Illnesses Using Social Media Posts

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166165-166172
Author(s):  
Subhan Tariq ◽  
Nadeem Akhtar ◽  
Humaira Afzal ◽  
Shahzad Khalid ◽  
Muhammad Rafiq Mufti ◽  
...  
Keyword(s):  
Social Media ◽  
Mental Illnesses
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