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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

Software defined network is an emerging network architecture that separates the traditional integrated control logic and data forwarding functionality into different planes, namely the control plane and data forwarding plane. The data plane does an end-to-end data delivery. And the control plane does the actual network traffic forwarding and routing between different network segments. In software defined network the networking infrastructure layer is where the entire networking device, such as switches and routers are connected with the separate controller layer with the help of standard called OpenFlow protocol. The OpenFlow is a standard protocol that allows different vendor devices like juniper, cisco and huawei switches to be connected to the controller. The centralization of the software defined network (SDN) controller makes the network more flexible, manageable and dynamic, such as provisioning of bandwidth, dynamic scale out and scale in compared to the traditional communication network, however, the centralized SDN controller is more vulnerable to security risks such as DDOS and flow rule poisoning attack. In this paper, we will explore the architectures, the principles of software defined network and security risks associated with the centralized SDN controller and possible ways to mitigate these risks.