scholarly journals Twitter Bots Detection Using Machine Learning Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 1536-1541
Author(s):  
Deekshith S G
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Identity Theft ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Critical Problem ◽  
Learning Techniques ◽  
The Social ◽  
Crucial Part

The social network, a crucial part of our life is plagued by online impersonation and fake accounts. Fake profiles are mostly used by the intruders to carry out malicious activities such as harming person , identity theft and privacy intrusion in Online Social Network(OSN). Hence identifying an account is genuine or fake is one of the critical problem in OSN .In this paper we proposed many classification algorithm like Support Vector Machine algorithm ,KNN, and Random Forest algorithm. It also studies the comparison of classification methods on Spam User dataset which is used to select the best.

scholarly journals A fusion fuzzy model for detecting phony accounts in social networks

2018 ◽  
Vol 7 (4) ◽  
pp. 2738
Author(s):  
P. Srinivas Rao ◽  
Jayadev Gyani ◽  
G. Narsimha
Keyword(s):  
Social Network ◽  
Fuzzy Model ◽  
Machine Learning Techniques ◽  
Personal Attributes ◽  
Data Sets ◽  
Learning Techniques ◽  
The Social ◽  
Using Data ◽  
Removal Technique ◽  
Normal Rank

In online social network’s phony account detection is one of the major task among the ability of genuine user from forged user account. The fundamental objective of detection of phony account framework is to detect fake account and removal technique in Social network user sites. This work concentrates on detection of phony account in which it depends on normal basis framework, transformative Algorithms and fuzzy technique. Initially, the most essential attributes including personal attributes, comparability techniques and various real user review, tweets, or comments are extricated. A direct blend of these attributes demonstrates the significance of each reviews tweets comments etc. To compute closeness measure, a consolidated strategy in view of artificial honey bee state Algorithm and fuzzy technique are utilized. Second approach is proposed to alter the best weights of the normal user attributes utilizing the social network activities/transaction and inherited Algorithm. Finally, a normal rank rationale framework is utilized to calculate the final scoring of normal user activities. The decision making of proposed approach to find phony account are variation with existing techniques user behavioral analysis using data sets and machine learning techniques such as crowdflower_sample and genuine_accounts_sample dataset of facebook and Twitter. The outcomes demonstrate that proposed strategy overcomes the previously mentioned strategies. 

scholarly journals Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

2019 ◽  
Vol 4 (1) ◽  
pp. 43
Author(s):  
Nfn Nofriani
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Social Assistance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
K Nearest Neighbor ◽  
Learning Techniques

Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best.

scholarly journals Kinematic-Based Classification of Social Gestures and Grasping by Humans and Machine Learning Techniques

2021 ◽  
Vol 8 ◽  
Author(s):  
Paul Hemeren ◽  
Peter Veto ◽  
Serge Thill ◽  
Cai Li ◽  
Jiong Sun
Keyword(s):  
Machine Learning ◽  
Strong Association ◽  
Critical Role ◽  
Locality Sensitive Hashing ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Social Quality ◽  
Learning Techniques ◽  
The Social

The affective motion of humans conveys messages that other humans perceive and understand without conventional linguistic processing. This ability to classify human movement into meaningful gestures or segments plays also a critical role in creating social interaction between humans and robots. In the research presented here, grasping and social gesture recognition by humans and four machine learning techniques (k-Nearest Neighbor, Locality-Sensitive Hashing Forest, Random Forest and Support Vector Machine) is assessed by using human classification data as a reference for evaluating the classification performance of machine learning techniques for thirty hand/arm gestures. The gestures are rated according to the extent of grasping motion on one task and the extent to which the same gestures are perceived as social according to another task. The results indicate that humans clearly rate differently according to the two different tasks. The machine learning techniques provide a similar classification of the actions according to grasping kinematics and social quality. Furthermore, there is a strong association between gesture kinematics and judgments of grasping and the social quality of the hand/arm gestures. Our results support previous research on intention-from-movement understanding that demonstrates the reliance on kinematic information for perceiving the social aspects and intentions in different grasping actions as well as communicative point-light actions.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals Big Data: New Tricks for Econometrics

2014 ◽  
Vol 28 (2) ◽  
pp. 3-28 ◽  
Author(s):  
Hal R. Varian
Keyword(s):  
Big Data ◽  
Linear Models ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Model Complex ◽  
Vector Machines ◽  
Sheer Size ◽  
Complex Relationships

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

Sign in / Sign up

Export Citation Format

Share Document