scholarly journals Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

2019 ◽  
Vol 9 (20) ◽  
pp. 4448 ◽  
Author(s):  
İş ◽  
Tuncer
Keyword(s):  
Nearest Neighbor ◽  
Moving Average ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Asset Values ◽  
Methodological Approaches ◽  
Twitter Users ◽  
Basic Characteristics ◽  
Artificial Neural Network Ann ◽  
Media Manipulation

This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter. Behavioral analyses of Twitter users were performed by using their profile structures and interaction types, and Twitter users were classified according to their effect size values by determining their asset values. User profiles were classified into three different categories, namely popular-active, observer-passive, and spam-bot-malicious by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms. For classification, the study used the basic characteristics of users, such as density, centralization, and diameter, as well as suggested time series such as the simple moving average and cumulative moving average. The highest accuracy was obtained by the K-NN algorithm. The results obtained with K-NN for all classes were higher than the F1-Score values obtained for the other algorithms. According to the results obtained, classification accuracy values were found to reach a maximum of 96.81% and a minimum of 92.33%. Our classification results showed that the proposed method was satisfactory for popular-active, observer-passive, and spam-bot-malicious account separation.

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

2020 ◽  
Author(s):  
Nazrul Anuar Nayan ◽  
Hafifah Ab Hamid ◽  
Mohd Zubir Suboh ◽  
Noraidatulakma Abdullah ◽  
Rosmina Jaafar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

Abstract Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

scholarly journals Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

2020 ◽  
Vol 16 (07) ◽  
pp. 34
Author(s):  
Nayan Nazrul Anuar ◽  
Ab Hamid Hafifah ◽  
Suboh Mohd Zubir ◽  
Abdullah Noraidatulakma ◽  
Jaafar Rosmina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

<p>Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.</p>

scholarly journals Event Driven ECG Processing for Diagnosis of Cardiac Diseases

2018 ◽  
Author(s):  
S. M. Qaisar ◽  
A. Subasi ◽  
W. S. Balakhsher ◽  
D. Zamzami ◽  
G. Alsharbini ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Temporal Variations ◽  
Support Vector ◽  
Cardiac Diseases ◽  
Potential Candidate ◽  
K Nearest Neighbor ◽  
Processing Load ◽  
Event Driven ◽  
Processing Module ◽  
Artificial Neural Network Ann

The aim of this paper is to develop an intelligent event-driven Electrocardiogram (ECG) processing module in order to achieve an efficient solution for diagnosis of the cardiac diseases. The suggested method acquires the signal with an event-driven A/D converter (EDADC). The output of EDADC is passed through the activity selection and interpolation blocks. It allows focusing only on the important signal parts and resampling it uniformly. Later on, the signal is de-noised. The autoregressive (AR) method is used to extract the classifiable features of the de-noised signal. Afterwards, the output is classified by employing different robust classification techniques such as support vector machines (SVMs), K- Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The event-driven feature enables to adapt the system processing load according to the signal temporal variations. This interesting feature of the devised system aptitudes a drastic reduction in its processing activity and therefore in the power consumption as compared to the traditional ones. A comparison of the performance of different classifiers is also made in terms of accuracy. Results show that the proposed system is a potential candidate for an automatic diagnosis of the cardiac diseases.

scholarly journals Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data

2020 ◽  
Vol 9 (2) ◽  
pp. 6-12
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann

Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from there future challenges and concluding remarks can be done.

scholarly journals Development of a regional voice dataset and speaker classification based on machine learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Muhammad Ismail ◽  
Shahzad Memon ◽  
Lachhman Das Dhomeja ◽  
Shahid Munir Shah ◽  
Dostdar Hussain ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Mobile Cellular ◽  
False Identity ◽  
Artificial Neural Network Ann ◽  
Voice Data ◽  
Voice Biometrics ◽  
Mel Frequency Cepstral Coefficient

AbstractAt present, voice biometrics are commonly used for identification and authentication of users through their voice. Voice based services such as mobile banking, access to personal devices, and logging into social networks are the common examples of authenticating users through voice biometrics. In Pakistan, voice-based services are very common in banking and mobile/cellular sector, however, these services do not use voice features to recognize customers. Therefore, the chance to use these services with false identity is always high. It is essential to design a voice-based recognition system to minimize the risk of false identity. In this paper, we developed regional voice datasets for voice biometrics, by collecting voice data in different local accents of Pakistan. Although, there is a global need for voice biometrics especially when voice-based services are common, however, this paper uses Pakistan as a use case to show how to build regional voice dataset for voice biometrics. To build voice dataset, voice samples were recorded from 180 male and female speakers with two languages English and Urdu in form of five regional accents. Mel Frequency Cepstral Coefficient (MFCC) features were extracted from the collected voice samples to train Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF) and K-nearest neighbor (KNN) classifiers. The results indicate that ANN outperformed SVM, RF and KNN by achieving 88.53% and 86.58% recognition accuracy on both datasets respectively.

scholarly journals Android APK Identification using Non Neural Network and Neural Network Classifier

2021 ◽  
Vol 5 (2) ◽  
pp. 149-157
Author(s):  
Djarot Hindarto ◽  
Handri Santoso
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Mobile Applications ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbor ◽  
Bayes Methods ◽  
K Nearest Neighbor Algorithm ◽  
Artificial Neural Network Ann

Currently adoption of mobile phones and mobile applications based  on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while  the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of  91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of  99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.

Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

2020 ◽  
Vol 11 (4) ◽  
pp. 21-40
Author(s):  
Lazhar Khriji ◽  
Ahmed Chiheb Ammari ◽  
Medhat Awadalla
Keyword(s):  
Nearest Neighbor ◽  
Vision System ◽  
Texture Feature ◽  
Automatic Classification ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Accuracy Performance

This paper proposes a hardware/software (HW/SW) co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. Three artificial intelligence (AI) techniques are used for qualitative comparisons: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The accuracy performance of all AI classifiers is characterized for multiple color, shape, size, and texture feature combinations and for different critical parameter settings of the classifiers. In total, 600 date samples (100 dates/variety) are selected and imaged each sample individually. The system starts with preprocessing and segmentation of the colored input images. A total of 19 features are extracted from each image for use in classification models. The ANN classifier is shown to outperform all other classifiers. 97.26% highest classification accuracy is achieved using a combination of 15 color and shape-size features.

Human Computer Interface Using Electroencephalography for Driver Behavior Classification

2019 ◽  
Author(s):  
Vamsi K. Manchala ◽  
Alvaro V. Clara ◽  
Susheelkumar C. Subramanian ◽  
Sangram Redkar ◽  
Thomas Sugar
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Affective States ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

Abstract It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.

scholarly journals Predicting heart failure using a wrapper-based feature selection

2021 ◽  
Vol 21 (3) ◽  
pp. 1530
Author(s):  
Minh Tuan Le ◽  
Minh Thanh Vo ◽  
Nhat Tan Pham ◽  
Son V.T Dao
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Medical Practitioners ◽  
Machine Learning Model ◽  
Heart Contraction ◽  
Artificial Neural Network Ann

In the current health system, it is very difficult for medical practitioners/physicians to diagnose the effectiveness of heart contraction. In this research, we proposed a machine learning model to predict heart contraction using an artificial neural network (ANN). We also proposed a novel wrapper-based feature selection utilizing a grey wolf optimization (GWO) to reduce the number of required input attributes. In this work, we compared the results achieved using our method and several conventional machine learning algorithms approaches such as support vector machine, decision tree, K-nearest neighbor, naïve bayes, random forest, and logistic regression. Computational results show not only that much fewer features are needed, but also higher prediction accuracy can be achieved around 87%. This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

Research on Performance of the Classifying Models Based on Chinese, Pakistani, and Other Genres

2021 ◽  
Vol 11 (4) ◽  
pp. 61-79
Author(s):  
Ejaz ud Din ◽  
Long Hua ◽  
Zhongyu Lu
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Support Vector ◽  
The Internet ◽  
Pop Music ◽  
Music Recommendation ◽  
Audio Classification ◽  
K Nearest Neighbor ◽  
Chinese Music ◽  
Artificial Neural Network Ann

In recent years, with the increase in the amount of audio on the internet, the demand for audio classification is increasing. This paper focuses on finding the performance of the classifiers, uses Python for the simulation part, compares the performance, and finds the best classifier. Two experiments are performed for this paper; for the first part of the experiment, Pakistan and Chinese music samples are considered, and classifiers are used to classify these music samples. It is found that the artificial neural network (ANN) has lowest accuracy of 81.4%; additionally, support vector machine (SVM), k-nearest neighbor (KNN), and convolutional (CNN) accuracies remain between 82% to 86% based on the dataset. Random forest model has the highest accuracy of 94.3%. It is considered to be the best classifier. For the second part of the experiment, other genres such as classical, country, and pop music were added to the previous dataset. After adding these genres, performance of the classifying models varies slightly; it fluctuates between 75% to 84%. These results can be used for music recommendation applications.

Sign in / Sign up

Export Citation Format

Share Document