Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree

Application of Artificial Intelligence-based predictive methods in Ionic liquid studies: A review

Article history: Received: October 18, 2018 Received in revised format: December 20, 2018 Accepted: January 8, 2019 Available online: January 8, 2019 Due to the increasing technological advances in all fields, a considerable amount of data has been collected to be processed for different purposes. Data mining is the process of determining and analyzing hidden information from different perspectives to obtain useful knowledge. Data mining can have many various applications, one of them is in medical diagnosis. Today, many diseases are regarded as dangerous and deadly. Heart disease, breast cancer, and diabetes are among the most dangerous ones. This paper investigates 168 articles associated with the implementation of data mining for diagnosing such diseases. The study concentrates on 85 selected papers which have received more attention between 1997 and 2018. All algorithms, data mining models, and evaluation methods are thoroughly reviewed with special consideration. The study attempts to determine the most efficient data mining methods used for medical diagnosing purposes. Also, one of the other significant results of this study is the detection of research gaps in the application of data mining in health care. © 2019 by the authors; licensee Growing Science, Canada.

http://m.growingscience.com/ijds/Vol3/ijdns_2019_12.pdf

Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Natural language processing for smart construction: Current status and future directions

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naive Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

/pdf/a-comparative-study-on-decision-tree-and-random-forest-using-4lw0x6o69w.pdf

A Comparative Study on Decision Tree and Random Forest Using R Tool

Identifying the potential users matters a lot for the growth of any company. This can be estimated well in advance based on several factors like the user’s opinion regarding a product before its sale is announced. Considering this, an application will be developed which will be easy to use, accurate intention prediction Website.The website is built using HTML, CSS, Javascript and Django. A text analytical model is built using an ensemble model, made by collecting tweets from users. The model will find out potential buyers who have tweeted about the product but are yet to purchase. The model would estimate the most likelihood of a customer’s purchase. Various Parameters like Accuracy, Precision, Recall and F1 score are calculated for getting the best result. Presently there are not any notable applications available that use multiple algorithms and are used for purchase intention prediction with this sophisticated architecture, which makes the application unique.

Prajwala T R

Papers

A Comparative Study on Decision Tree and Random Forest Using R Tool

Customer Purchase Intention Prediction Using Text Analytical Models