A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation o...

We describe an experimental study of Option Decision Trees with majority votes. Option Decision Trees generalize regular decision trees by allowing option nodes in addition to decision nodes; such nodes allow for several possible tests to be conducted instead of the commonly used single test. Our goal was to explore when option nodes are most useful and to control the growth of the trees so tha...

The machine does not isolate man from the great problems of nature but plunges him more deeply into them. —Antoine de Saint-Exupery Older patients with chronic progressive illness are increasingly facing difficult decisions around potentially life-prolonging technologies. A stark example is destination therapy left ventricular assist devices (DT LVADs), currently offered as a long-term permanen...

This paper presents a new approach for fault detection and classification in transmission line using Decision Tree (DT). The DT based fault detection algorithm uses 1/4th cycle data of fault currents from fault inception, to generate the optimal decision tree for fault detection. Similarly, the DT based classification algorithm takes half cycle data from fault inception of three phase currents ...

We describe an experimental study of Option Decision Trees with majority votes. Option Decision Trees generalize regular decision trees by allowing option nodes in addition to decision nodes; such nodes allow for several possible tests to be conducted instead of the commonly used single test. Our goal was to explore when option nodes are most useful and to control the growth of the trees so tha...

+ degT (N(vj))], where dT (vi, vj) is the distance between the vertices vi, vj ∈ V (T ), is equal to the length of the shortest path starting at vi and ending at vj and degT (N(v)) is the cardinality of degT (u), where uv ∈ E(T ). In this paper, trees with minimum and maximum A are characterized and the expressions for the Ashwini index are obtained for detour saturated trees T3(n), T4(n) as we...

Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. Howevel; learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficie...

DT Tutor (DT), an ITS that uses decision theory to select tutorial actions, was compared with both a Fixed-Policy Tutor (FT) and a Random Tutor (RT). The tutors were identical except for the method they used to select tutorial actions: FT employed a common fixed policy while RT selected randomly from relevant actions. This was the first comparison of a decision-theoretic tutor with a non-trivia...

In order for Parkinson’s disease (PD) treatment and examination to be logical, a key requirement is that estimates of stage severity are quantitative, reliable, repeatable. The PD research in the past 50 years has been overwhelmed by subjective emotional evaluation human’s understanding characteristics during clinical visits. data set contains 23 features 197 instances, which 8 patients sound p...

Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DTRNN model which uses dependency trees to embed sentences into a vector space in order to retr...