The theory and applications of wavelets have undoubtedly dominated the journals in all mathematical, engineering and related fields throughout the last decade. A variety of powerful and sophisticated wavelet-based schemes for image compression have been developed and implemented. Nevertheless these traditional approaches secure some severe limitations. Wavelets for example fail to capture regul...

This paper advances the methodology of hybrid classification architectures for face and hand gesture recognition tasks and shows their feasibility through experimental studies using the FERET data base and gesture images. The hybrid architecture, consisting of an ensemble of connectionist networks radial basis functions (RBF) and inductive decision trees (DT), combines the merits of 'holistic' ...

Rainfall prediction is one of the most challenging task faced by researchers over years. Many machine learning and AI based algorithms have been implemented on different datasets for better purposes, but there not a single solution which perfectly predicts rainfall. Accurate still remains question to researchers. We offer learning-based comparison evaluation rainfall models Kashmir province. Bo...

BACKGROUND Destination therapy left ventricular assist devices (DT LVADs) are one of the most invasive medical interventions for end-stage illness. How patients decide whether or not to proceed with device implantation is unknown. We aimed to understand the decision-making processes of patients who either accept or decline DT LVADs. METHODS AND RESULTS Between October 2012 and September 2013,...

The objective in the Scenic Beauty Estimation (SBE) problem is to develop an automatic classification algorithm that matches human subjective ratings. Algorithms such as Principal Components Analysis (PCA) and Decision Trees (DT) have been applied to this problem with limited success, motivating our search for a better classifier. Since this is obviously a nonlinear classification problem, we a...

Decision trees and their ensembles are very popular models of supervised machine learning. In this paper we merge the ideas underlying decision trees, FCA by proposing a new learning model which can be constructed in polynomial time is applicable for both classification regression problems. Specifically, first propose polynomial-time algorithm constructing part concept lattice that based on tre...

Conventional soil classification methods are expensive and demand extensive field laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s behavioral types. study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector (SVM), decision trees (DT), to classify from 2...

In artificial intelligence, text identification and analysis that are based on images play a vital role in the retrieving process. Automatic recognition system development is difficult task machine learning, but case of cursive languages, it poses big challenge to research community due slight changes character’s shapes unavailability standard dataset. While this becomes more challenging Pashto...

conclusions: absence of the c allele was significantly associated with failure of response. the presence of c allele was associated with a favorable outcome. afp is a strong baseline predictor of hcv treatment response. a decision tree model is useful for predicting the probability of response to therapy. patients and methods: the study included 263 chronic hcv egyptian patients receiving peg-i...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...