Vedegenskaper i härskande och behärskade granar vid Remingstorp : Wood properties in dominant and sub-dominant trees of Norway spruce at Remingstorp

Variation in wood properties for Picea abies trees and logs of different dimensions has been studied at two sites in southern Sweden of different site quality class. Trees have been classified as dominant or sub-dominant, according to their height. Log and board grades were classified and strength grade of boards, basic density and annual ring width measured. A similar study made on four northern sites was used as reference material.Sub-dominant trees were of superior quality in comparison to dominant trees, when classified by log and board grades or strength grading. Differences were accentuated for the second log where the sub-dominant trees had superior strength and low amount of boards with coarse branches. The results correspond well to those from the northern region, Jämtland. The classifica¬tion of boards as well as bending strength indicated superior properties on timber from northern sites even though the basic density was similar.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.