The Relation Among Obesity and Sugar Consumption: A Machine Learning Approach

We have collected two data sets. First data set consisted of 45 thousand data and second one 43. One data set consisted of food information , like calorie count, sugar in per 100 gram, fat in per 100 gram and so on. Second data set consisted of Obesity rate among USA people from age 0 to 80. We wanted to show a relation with sugar intake and obesity rate. Last of all our experiment found that ther's a significance evidence that there's a link between obesity and sugar intake . We used the machine learning approach for our experimental analysis.

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.

A hierarchical approach to mood classification in blogs

Natural Language Engineering ◽

10.1017/s1351324911000118 ◽

2011 ◽

Vol 18 (1) ◽

pp. 61-81 ◽

Cited By ~ 11

Author(s):

FAZEL KESHTKAR ◽

DIANA INKPEN

Keyword(s):

Machine Learning ◽

Error Analysis ◽

Learning Approach ◽

Hierarchical Approach ◽

Data Set ◽

Novel Approach ◽

Sentiment Orientation ◽

Mood Classification

AbstractIn this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

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

10.36227/techrxiv.14867175 ◽

2021 ◽

Author(s):

Diti Roy ◽

Md. Ashiq Mahmood ◽

Tamal Joyti Roy

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Learning Algorithm ◽

Modern Technology ◽

Learning Approach ◽

Data Sets ◽

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.

Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

Applied Sciences ◽

10.3390/app112411710 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11710

Author(s):

Matteo Miani ◽

Matteo Dunnhofer ◽

Fabio Rondinella ◽

Evangelos Manthos ◽

Jan Valentin ◽

...

Keyword(s):

Machine Learning ◽

Bayesian Optimization ◽

Automatic Identification ◽

Learning Approach ◽

Ann Model ◽

Data Set ◽

Bituminous Mixtures ◽

Novel Approach ◽

The Neural Network ◽

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach

10.35940/ijitee.i9364.0710921 ◽

2021 ◽

Vol 10 (9) ◽

pp. 67-72

Author(s):

Talasila Bhanuteja ◽

◽

Kilaru Venkata Narendra Kumar ◽

Kolli Sai Poornachand ◽

Chennupati Ashish ◽

...

Keyword(s):

Machine Learning ◽

Well Being ◽

Local Area ◽

Learning Approach ◽

Disease Prediction ◽

Decision Tree Classifier ◽

Data Set ◽

Tree Classifier ◽

Disease Forecast

The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and LightGBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.

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.

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

10.36227/techrxiv.14867175.v1 ◽

2021 ◽

Author(s):

Diti Roy ◽

Md. Ashiq Mahmood ◽

Tamal Joyti Roy

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Learning Algorithm ◽

Modern Technology ◽

Learning Approach ◽

Data Sets ◽

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.

Handwritten digits recognition with decision tree classification: a machine learning approach

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i5.pp4446-4451 ◽

2019 ◽

Vol 9 (5) ◽

pp. 4446

Author(s):

Tsehay Admassu Assegie ◽

Pramod Sekharan Nair

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Classification Model ◽

Learning Approach ◽

Data Sets ◽

The Past ◽

Decision Tree Classification ◽

Future Data ◽

Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.