In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, many social media networks collect the unstructured The conversion of into structured or meaningful is a very tedious task. convert this paper, authors first research frequentl...

Background: Multiple Sclerosis (MS) is one of the most debilitating disease among young adults. Understanding the disability score (Expanded Disability Status Scale (EDSS)) of these patients is helpful in choosing their treatment process. Calculating EDSS takes a lot of time for Neurologists, so having a way to estimate EDSS can be helpful. This study aimed to estimate the EDSS score of MS pati...

Machine learning (ML) was used to develop classification models predict individual tumor patients’ outcomes. Binary defined whether the malignant or benign. This paper presents a comparative analysis of machine algorithms for breast cancer prediction. study dataset obtained from National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep methods were includ...

Abstract The scientific community has widely accepted the use of machine learning techniques to tackle complex engineering problems. Among most intriguing problems is finding correlation between alloy steel properties and cyclic fatigue crack growth rate. Employing machine‐learning models can provide more robust accurate predictive address such challenges. This paper presents application four m...

Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Clas...

In early 2000’s, Rivest [Riv00,MR02] and Micali [MR02] introduced the notion of transitive signature, which allows a third party to generate a valid signature for a composed edge (vi, vk), from the signatures for two edges (vi, vj) and (vj , vk), and using the public key only. Since then, a number of works, including [MR02,BN02,Hoh03,SFSM05,BN05], have been devoted on transitive signatures. Mos...

For the well-known concept of decision trees as it is used for inductive inference we study the natural concept of equivalence: two decision trees are equivalent if and only if they represent the same hypothesis. We present a simple e cient algorithm to establish whether two decision trees are equivalent or not. The complexity of this algorithm is bounded by the product of the sizes of both dec...

Structural identification using dynamical parameters (such as the natural vibration frequencies and mode shapes) is an important issue, especially in bridges or high-rise buildings. However, incorrect decisions could happen on Health Monitoring (SHM) strategy System Identification (SSI) analysis that makes sometimes expensive time-consuming process useless due to large uncertainty of resulting ...

Prediction of carbon content in fly ash is a valuable index in both designing and optimizing coal-fired boilers. In this study, methods of multiple regression (MR), decision trees (DT), and support vector regression (SVR) were used for prediction of unburned carbon content of fly ash in a 300-MW power plant with a tangentially fired furnace using a low-NOx concentric firing system. Twenty-one b...

We have developed a decision-theoretic planner based upon the Graphplan planning algorithm, DT-Graphplan. DT-Graphplan reasons about probabilities, costs, and rewards at a propositional level, reconstructing limited state information. We are applying the planner to our robot task architecture to function on a miniature golf domain. By incorporating decision theory into planning, we seek to redu...