scholarly journals Interrogation of Sentiment Perusing with Hash Counting Vectorizer and Term Inverse Frequency Transformer using Machine Learning Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 3895-3901
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Customer Satisfaction ◽  
Current Trend ◽  
Random Forest Classifier ◽  
Gradient Boosting ◽  
Svm Classifier ◽  
Bayes Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

With the fast growing technology, the business is moving towards increasing their profit by interpreting the customer satisfaction. The customer satisfaction can be analyzed in many ways. It is the responsibility of the business to analyze the customer satisfaction in order to improve their turnover and profit. With the current trend, the customers are giving their feedback through mobile and internet. With this overview, this paper attempts to analyze the sentiment of the customer feedback for the movie. The sentiment Analysis on movie Review dataset from the KAGGLE Machine learning repository is used for implementation. The type of sentiment classes is predicted through the following ways. Firstly, the sentiment count for each class is displayed and the top feature words for each sentiment class are also extracted from the dataset. Secondly, the dataset is sampled with counting vectorizer and then fitted with the classifiers like Logistic Regression Classifier, Linear SVM Classifier, Multinomial Naives Bayes Classifier, Gradient Boosting Classifer, Guassian Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier and and Extra Tree Classifier. Thirdly, the dataset is sampled with Hashing vectorizer and then fitted with the above specified classifiers. Fourth, the dataset is sampled with TFIFD vectorizer and then fitted with the above specified classifiers. Fifth, the dataset is sampled with TFIFD Transformer and then fitted with the above specified classifiers. Sixth, the Performance analysis of classifiers is performed by analyzing the metrics like Precision, Recall, Fscore and Accuracy. The implementation is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that the analysis of sentiment done by the random forest classifier is found to be more effective with the Accuracy of 89% for Counting vectorizer and TFIFD transformer, Accuracy of 87% for Hashing vectorizer and Accuracy of 88% for TFIFD vectorizer.

scholarly journals Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 557-561
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Gradient Boosting ◽  
Svm Classifier ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..

scholarly journals Predicting Fitness Centre Dropout

2021 ◽  
Vol 18 (19) ◽  
pp. 10465
Author(s):  
Pedro Sobreiro ◽  
Pedro Guedes-Carvalho ◽  
Abel Santos ◽  
Paulo Pinheiro ◽  
Celina Gonçalves
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Length Of Stay ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Registration Data ◽  
Relevant Variables ◽  
Overall Performance ◽  
Number Of Visits

The phenomenon of dropout is often found among customers of sports services. In this study we intend to evaluate the performance of machine learning algorithms in predicting dropout using available data about their historic use of facilities. The data relating to a sample of 5209 members was taken from a Portuguese fitness centre and included the variables registration data, payments and frequency, age, sex, non-attendance days, amount billed, average weekly visits, total number of visits, visits hired per week, number of registration renewals, number of members referrals, total monthly registrations, and total member enrolment time, which may be indicative of members’ commitment. Whilst the Gradient Boosting Classifier had the best performance in predicting dropout (sensitivity = 0.986), the Random Forest Classifier was the best at predicting non-dropout (specificity = 0.790); the overall performance of the Gradient Boosting Classifier was superior to the Random Forest Classifier (accuracy 0.955 against 0.920). The most relevant variables predicting dropout were “non-attendance days”, “total length of stay”, and “total amount billed”. The use of decision trees provides information that can be readily acted upon to identify member profiles of those at risk of dropout, giving also guidelines for measures and policies to reduce it.

scholarly journals Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 1477-1483
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Naive Bayes ◽  
Gradient Boosting ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Feature Importance

With the fast moving technological advancement, the internet usage has been increased rapidly in all the fields. The money transactions for all the applications like online shopping, banking transactions, bill settlement in any industries, online ticket booking for travel and hotels, Fees payment for educational organization, Payment for treatment to hospitals, Payment for super market and variety of applications are using online credit card transactions. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the institution. With this background, this paper focuses on predicting the fraudulent credit card transaction. The Credit Card Transaction dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of fraudulent credit card transaction is achieved in four ways. Firstly, the relationship between the variables of the dataset is identified and represented by the graphical notations. Secondly, the feature importance of the dataset is identified using Random Forest, Ada boost, Logistic Regression, Decision Tree, Extra Tree, Gradient Boosting and Naive Bayes classifiers. Thirdly, the extracted feature importance if the credit card transaction dataset is fitted to Random Forest classifier, Ada boost classifier, Logistic Regression classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Naive Bayes classifier. Fourth, the Performance Analysis is done by analyzing the performance metrics like Accuracy, FScore, AUC Score, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Results shows that the Decision Tree classifier have achieved the effective prediction with the precision of 1.0, recall of 1.0, FScore of 1.0 , AUC Score of 89.09 and Accuracy of 99.92%.

scholarly journals Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

2021 ◽  
pp. 29-37
Author(s):  
Premkumar Borugadda ◽  
R. Lakshmi ◽  
Surla Govindu
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Performance Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cotton Leaf ◽  
Decision Tree Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier ◽  
Fully Connected

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.  

scholarly journals A Study of Machine Learning Algorithms for DDoS Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 174-178
Author(s):  
Sheikh Shehzad Ahmed
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Random Forest Classifier ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Ddos Attacks ◽  
Decision Tree Classifier ◽  
Tree Classifier

The Internet is used practically everywhere in today's digital environment. With the increased use of the Internet comes an increase in the number of threats. DDoS attacks are one of the most popular types of cyber-attacks nowadays. With the fast advancement of technology, the harm caused by DDoS attacks has grown increasingly severe. Because DDoS attacks may readily modify the ports/protocols utilized or how they function, the basic features of these attacks must be examined. Machine learning approaches have also been used extensively in intrusion detection research. Still, it is unclear what features are applicable and which approach would be better suited for detection. With this in mind, the research presents a machine learning-based DDoS attack detection approach. To train the attack detection model, we employ four Machine Learning algorithms: Decision Tree classifier (ID3), k-Nearest Neighbors (k-NN), Logistic Regression, and Random Forest classifier. The results of our experiments show that the Random Forest classifier is more accurate in recognizing attacks.

scholarly journals Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

2021 ◽  
Vol 49 (1) ◽  
pp. 225-232
Author(s):  
Dušan Radivojević ◽  
Nikola Mirkov ◽  
Slobodan Maletić
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Series Data ◽  
Test Group ◽  
Wearable Devices ◽  
Random Forest Classifier ◽  
Series Data ◽  
Machine Learning Classification ◽  
Improving Accuracy

This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms. The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset. Training accuracy reaches approximately 95% and 100% for Deep Learning and Random Forest model respectively. Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. The influence of data sample precision to the accuracy of the models is examined since the input data could be given from various wearable devices.

scholarly journals Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

2021 ◽  
Author(s):  
Jordi Pascual-Fontanilles ◽  
Aida Valls ◽  
Antonio Moreno ◽  
Pedro Romero-Aroca
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Diabetic Patients ◽  
Similar Data ◽  
Machine Learning Classification ◽  
Inherent Ambiguity ◽  
New Iterative Method

Random Forests are well-known Machine Learning classification mechanisms based on a collection of decision trees. In the last years, they have been applied to assess the risk of diabetic patients to develop Diabetic Retinopathy. The results have been good, despite the unbalance of data between classes and the inherent ambiguity of the problem (patients with similar data may belong to different classes). In this work we propose a new iterative method to update the set of trees in the Random Forest by considering trees generated from the data of the new patients that are visited in the medical centre. With this method, it has been possible to improve the results obtained with standard Random Forests.

scholarly journals Machine learning classification of Kuiper belt populations

2020 ◽  
Vol 497 (2) ◽  
pp. 1391-1403
Author(s):  
Rachel A Smullen ◽  
Kathryn Volk
Keyword(s):  
Machine Learning ◽  
Kuiper Belt ◽  
Giant Planet ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Gradient Boosting ◽  
Class Membership ◽  
Machine Learning Classification ◽  
Tree Classifier

ABSTRACT In the outer Solar system, the Kuiper belt contains dynamical subpopulations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration. The subdivision of observed Kuiper belt objects (KBOs) into different dynamical classes is based on their current orbital evolution in numerical integrations of their orbits. Here, we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification. Using a Gradient Boosting Classifier, a type of machine learning regression tree classifier trained on features derived from short numerical simulations, we sort observed KBOs into four broad, dynamically distinct populations – classical, resonant, detached, and scattering – with a >97 per cent accuracy for the testing set of 542 securely classified KBOs. Over 80 per cent of these objects have a >3σ probability of class membership, indicating that the machine learning method is classifying based on the fundamental dynamical features of each population. We also demonstrate how, by using computational savings over traditional methods, we can quickly derive a distribution of class membership by examining an ensemble of object clones drawn from the observational errors. We find two major reasons for misclassification: inherent ambiguity in the orbit of the object – for instance, an object that is on the edge of resonance – and a lack of representative examples in the training set. This work provides a promising avenue to explore for fast and accurate classification of the thousands of new KBOs expected to be found by surveys in the coming decade.

scholarly journals Fake News Data Exploration and Analytics

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2326
Author(s):  
Mazhar Javed Awan ◽  
Awais Yasin ◽  
Haitham Nobanee ◽  
Ahmed Abid Ali ◽  
Zain Shahzad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Random Forest Classifier ◽  
The Internet ◽  
Fake News ◽  
Learning Models ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Machine Learning Models

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

scholarly journals Stratification of Spam and Ham Short Message Service using Machine Learning Hash Vectorization

2020 ◽  
Vol 8 (5) ◽  
pp. 472-476
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Svm Classifier ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Tree Classifier ◽  
Number Of Customers

With the increase in the usage of mobile technology, the rate of information is duplicated as a huge volume. Due to the volume duplication of message, the identification of spam messages leads to challenging task. The growth of mobile usage leads to instant communication only through messages. This drastically leads to hackers and unauthorized users to the spread and misuse of sending spam messages. The identification of spam messages is a research oriented problem for the mobile service providers in order to raise the number of customers and to retain them. With this overview, this paper focuses on identifying and prediction of spam and ham messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The identification of spam and ham messages is done in the following ways. Firstly, the levels of spread of target variable namely spam or ham is identified and they are depicted as a graph. Secondly, the essential tokens that are responsible for the spam and ham messages are identified and they are found by using the hashing Vectorizer and it is portrayed in the form of spam and Ham messages word cloud. Thirdly, the hash vectorized SMS Spam Message detection dataset is fitted to various classifiers like Ada Boost Classifier, Extra Tree classifier, KNN classifier, Random Forest classifier, Linear SVM classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. The evaluation of the classifier models are done by analyzing the Performance analysis metrics like Accuracy, Recall, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Linear Support Vector Machine classifier have achieved the effective performance indicators with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.71%.

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