Around the world cervical cancer or malignancy is the main motivation of cancer or tumor death in ladies. It impacts the cervix in the female regenerative framework which prompts death. The decision tree machine learning approach recognizes the phases of cervical disease. Decision tree arrange the phases of the cervical tumor in progressive basic leadership framework approach which manage the o...

We explore the potential of applying machine learning techniques to the management of patient flow in hospitals. For this project, we have obtained the Weka machine learning library and three years of historical ward occupancy data from Rockyview Hospital. We use Weka’s classifier algorithms and the Rockyview data to build a model of patient flow through each ward. Using Weka, we then attempt t...

Knowledge exploration from the large set of data, generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how t...

Heart disease is a major cause of morbidity and mortality in the modern society. Almost 60% of the world population fall victim to the heart disease. Although significant progress has been made in the diagnosis and treatment of coronary heart disease, further investigation is still needed. Data mining, as a solution to extract hidden pattern from the clinical dataset are applied to a database i...

We present ML4PG — a machine learning extension for Proof General. It allows users to gather proof statistics related to shapes of goals, sequences of applied tactics, and proof tree structures from the libraries of interactive higher-order proofs written in Coq and SSReflect. The gathered data is clustered using the state-of-the-art machine learning algorithms available in MATLAB and Weka. ML4...

CEKA is a software package for developers and researchers to mine the wisdom of crowds. It makes the entire knowledge discovery procedure much easier, including analyzing qualities of workers, simulating labeling behaviors, inferring true class labels of instances, filtering and correcting mislabeled instances (noise), building learning models and evaluating them. It integrates a set of state-o...

The performance of Support Vector Machines, as many other machine learning algorithms, is very sensitive to parameter tuning, mainly in real world problems. In this paper, two well known and widely used SVM implementations, Weka SMO and LIBSVM, were compared using Simulated Annealing as a parameter tuner. This approach increased significantly the classification accuracy over the Weka SMO and LI...

We present context-sensitive Multiple Task Learning, or csMTL as a method of inductive transfer embedded in the well known WEKA machine learning suite. csMTL uses a single output neural network and additional contextual inputs for learning multiple tasks. Inductive transfer occurs from secondary tasks to the model for the primary task so as to improve its predictive performance. The WEKA multi-...

Geographic data preprocessing is the most effort and time consuming step in spatial data mining. In order to facilitate geographic data preprocessing and increase the practice of spatial data mining, this paper presents Weka-GDPM, an interoperable module that supports automatic geographic data preprocessing for spatial data mining. GDPM is implemented into Weka, which is a free and open source ...