An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

Day By Day

Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.

Sentiment Analysis Using Hybrid Approach

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39202 ◽

2021 ◽

Vol 9 (12) ◽

pp. 282-285

Author(s):

Ganesh K. Shinde

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Learning Algorithm ◽

Hybrid Approach ◽

Online Review ◽

Second Step ◽

Learning Approach ◽

Machine Learning Classifiers ◽

Mining Methods

Abstract: Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.

New Heat Flux Model for Antarctica with a Machine Learning Approach

10.5194/egusphere-egu2020-7014 ◽

2020 ◽

Author(s):

Mareen Lösing ◽

Jörg Ebbing ◽

Wolfgang Szwillus

Keyword(s):

Machine Learning ◽

Heat Flux ◽

Learning Algorithm ◽

Learning Approach ◽

Data Sets ◽

Tree Model ◽

Geothermal Heat ◽

Boosted Regression Tree ◽

Radiogenic Heat ◽

Improving the understanding of geothermal heat flux in Antarctica is crucial for ice-sheet modelling and glacial isostatic adjustment. It affects the ice rheology and can lead to basal melting, thereby promoting ice flow. Direct measurements are sparse and models inferred from e.g. magnetic or seismological data differ immensely. By Bayesian inversion, we evaluated the uncertainties of some of these models and studied the interdependencies of the thermal parameters. In contrast to previous studies, our method allows the parameters to vary laterally, which leads to a heterogeneous West- and a slightly more homogeneous East Antarctica with overall lower surface heat flux. The Curie isotherm depth and radiogenic heat production have the strongest impact on our results but both parameters have a high uncertainty.To overcome such shortcomings, we adopt a machine learning approach, more specifically a Gradient Boosted Regression Tree model, in order to find an optimal predictor for locations with sparse measurements. However, this approach largely relies on global data sets, which are notoriously unreliable in Antarctica. Therefore, validity and quality of the data sets is reviewed and discussed. Using regional and more detailed data sets of Antarctica&#8217;s Gondwana neighbors might improve the predictions due to their similar tectonic history. The performance of the machine learning algorithm can then be examined by comparing the predictions to the existing measurements. From our study, we expect to get new insights in the geothermal structure of Antarctica, which will help with future studies on the coupling of Solid Earth and Cryosphere.

Predicting laboratory earthquakes using machine learning

10.5194/egusphere-egu21-553 ◽

2021 ◽

Author(s):

Silke van Klaveren ◽

Ivan Vasconcelos ◽

Andre Niemeijer

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Acoustic Emissions ◽

Glass Beads ◽

Learning Approach ◽

Data Set ◽

Radiated Energy ◽

The Difference

The successful prediction of earthquakes is one of the holy grails in Earth Sciences. Traditional predictions use statistical information on recurrence intervals, but those predictions are not accurate enough. In a recent paper, a machine learning approach was proposed and applied to data of laboratory earthquakes. The machine learning algorithm utilizes continuous measurements of radiated energy through acoustic emissions and the authors were able to successfully predict the timing of laboratory earthquakes. Here, we reproduced their model which was applied to a gouge layer of glass beads and applied it to a data set obtained using a gouge layer of salt. In this salt experiment different load point velocities were set, leading to variable recurrence times. The machine learning technique we use is called random forest and uses the acoustic emissions during the interseismic period. The random forest model succeeds in making a relatively reliable prediction for both materials, also long before the earthquake. Apparently there is information in the data on the timing of the next earthquake throughout the experiment. For glass beads energy is gradually and increasingly released whereas for salt energy is only released during precursor activity, therefore the important features used in the prediction are different. We interpret the difference in results to be due to the different micromechanics of slip. The research shows that a machine learning approach can reveal the presence of information in the data on the timing of unstable slip events (earthquakes). Further research is needed to identify the responsible micromechanical processes which might be then be used to extrapolate to natural conditions.

SMO-RF:A machine learning approach by random forest for predicting class imbalancing followed by SMOTE

Materials Today Proceedings ◽

10.1016/j.matpr.2020.12.891 ◽

2021 ◽

Author(s):

Ankur Goyal ◽

Likhita Rathore ◽

Avinash Sharma

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Approach ◽

A machine learning approach using random forest and LASSO to predict wine quality

International Journal of Sustainable Agricultural Management and Informatics ◽

10.1504/ijsami.2021.10040429 ◽

2021 ◽

Vol 7 (3) ◽

pp. 1

Author(s):

Dimitris Ioannidis ◽

Ioannis Athanasiadis

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Approach ◽

Wine Quality ◽

A Machine Learning Approach to Coreference Resolution of Noun Phrases

Computational Linguistics ◽

10.1162/089120101753342653 ◽

2001 ◽

Vol 27 (4) ◽

pp. 521-544 ◽

Cited By ~ 287

Author(s):

Wee Meng Soon ◽

Hwee Tou Ng ◽

Daniel Chung Yong Lim

Keyword(s):

Machine Learning ◽

Noun Phrase ◽

State Of The Art ◽

Noun Phrases ◽

Learning Approach ◽

Data Sets ◽

Coreference Resolution ◽

In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of “organization,” “person,” or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.

Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

10.32920/ryerson.14657556 ◽

2021 ◽

Author(s):

Merlin James Rukshan Dennis

Keyword(s):

Machine Learning ◽

Random Forest ◽

Statistical Approach ◽

Denial Of Service ◽

Attack Detection ◽

Learning Approach ◽

Ddos Attack ◽

Ddos Detection ◽

Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

Telugu News Data Classification Using Machine Learning Approach

10.4018/978-1-7998-7685-4.ch014 ◽

2022 ◽

pp. 181-194

Author(s):

Bala Krishna Priya G. ◽

Jabeen Sultana ◽

Usha Rani M.

Keyword(s):

Machine Learning ◽

Social Media ◽

Research Work ◽

Learning Approach ◽

Fake News ◽

Learning Models ◽

Machine Learning Classifiers ◽

Proposed Model ◽

Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

DYNA ◽

10.15446/dyna.v87n212.80202 ◽

2020 ◽

Vol 87 (212) ◽

pp. 63-72

Author(s):

Jorge Iván Pérez Rave ◽

Favián González Echavarría ◽

Juan Carlos Correa Morales

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Approach ◽

Predictive Capability ◽

Predictive Capacity ◽

Machine Learning Model ◽

Property Price ◽

Object Of Study ◽

Online Pricing

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.