http://ijeais.org/wp-content/uploads/2019/02/IJAISR190201.pdf

Developing Artificial Neural Network for Predicting Mobile Phone Price Range

Heart disease is increasing rapidly due to number of reasons. If we predict cardiac arrest (dangerous conditions of heart) in the early stages, it will be very helpful to cured this disease. Although doctors and health centres collect data daily, but mostly are not using machine learning and pattern matching techniques to extract the knowledge that can be very useful in prediction. Bioinformatics is the real world application of machine learning to extract patterns from the datasets using several data mining techniques. In this research paper, data and attributes are taken from the UCI repository. Attribute extraction is very effective in mining information for the prediction. By utilizing this, various patterns can be derived to predict the heart disease earlier. In this paper, we enlighten the number of techniques in Artificial Neural Network (ANN). The accuracy is calculated and visualized such as ANN gives 94.7% but with Principle Component Analysis (PCA) accuracy rate improve to 97.7%.

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Prediction of Heart Disease using Artificial Neural Network

Terms that occur too frequently or rarely in various texts are not useful for text classification. Pruning can be used to remove such irrelevant terms reducing the dimensionality of the feature space and, thus making feature selection more efficient and effective. Normally, pruning is achieved by manually setting threshold values. However, incorrect threshold values can result in the loss of many useful terms or retention of irrelevant ones. Existing feature ranking metrics can assign higher ranks to these irrelevant terms, thus degrading the performance of a text classifier. In this paper, we propose a new feature ranking metric, which can select the most useful terms in the presence of these too frequently and rarely occurring terms, thus eliminating the need for pruning these terms. To investigate the usefulness of the proposed metric, we compare it against seven well-known feature selection metrics on five data sets namely Reuters-21578 (re0, re1, r8) and WebACE (k1a, k1b) using multinomial naive Bayes and support vector machines classifiers. Our results based on a paired t-test show that the performance of our metric is statistically significant than that of the other seven metrics.

A new feature selection metric for text classification: eliminating the need for a separate pruning stage

Price prediction is one of the examples related to forecasting tasks and is a project based on data science Price prediction analyzes data and predicts the cost of new products The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena The experimental results show 883% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques

Multimodal Price Prediction

Valorization is one of the most heated discussions in the business community, and commodities valorization is one subset in this task. Features of a product is an essential characteristic in valorization and features are categorized into two classes: graphical and non-graphical. Nowadays, the value of products is measured by price. The goal of this research is to achieve an arrangement to predict the price of a product based on specifications of that. We propose five deep learning models to predict the price range of a product, one unimodal and four multimodal systems. The multimodal methods predict based on the image and non-graphical specification of product. As a platform to evaluate the methods, a cellphones dataset has been gathered from GSMArena. In proposed methods, convolutional neural network is an infrastructure. The experimental results show 88.3% F1-score in the best method.

Multimodal price prediction

To predict “If the mobile with given features will be Economical or Expensive” is the main motive of this research work. Real Dataset is collected from website www.GSMArena.com . Different feature selection algorithms are used to identify and remove less important and redundant features and have minimum computational complexity. Different classifiers are used to achieve as higher accuracy as possible. Results are compared in terms of highest accuracy achieved and minimum features selected. Conclusion is made on the base of best feature selection algorithm and best classifier for the given dataset. This work can be used in any type of marketing and business to find optimal product(with minimum cost and maximum features). Future work is suggested to extend this research and find more sophisticated solution to the given problem and more accurate tool for price estimation. General Terms Machine Learning

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Muhammad Asim

Papers

Mobile Price Class prediction using Machine Learning Techniques