Prediction of Breast Cancer using Extremely Randomized Clustering Forests (ERCF) Technique

cancer in breast indeed a significant public health concern in both developed and developing countries female population. It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on Extremely Randomized Clustering Forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for Breast Cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN(Correlation) and k-NN(Euclidean) in this research work (where k-NN refers to k-Nearest Neighbours technique) and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN(Correlation) and k-NN(Euclidean). Hence, ERCF, a randomized technique for pattern classification, is best

Application of correlation pattern recognition technique for neutron– gamma discrimination in the EJ-301 liquid scintillation detector

The ability to distinguish between neutrons and gamma-rays is important in the fast -neutron detection, especially when using the scintillation detector. A dual correlation pattern recognition (DCPR) method that was based on the correlation pattern recognition technique has been developed for classification of neutron/gamma events from a scintillation detector. In this study, an EJ-301 liquid scintillation (EJ301) detector was used to detect neutrons and gamma-rays from the 60Co and 252Cf sources; the EJ301 detector's pulses were digitized by a digital oscilloscope and its pulse-shape discriminant (PSD) parameters were calculated by the correlation pattern recognition(CPR) method with the reference neutron and gamma-ray pulses. The digital charge integration (DCI) method was also used as a reference-method for comparison with DCPR method. The figure-ofmerit (FOM) values which were calculated in the 50 ÷ 1100 keV electron equivalent (keVee) region showed that the DCPR method outperformed the DCI method. The FOMs of 50, 420 and 1000 keVee thresholds of DCPR method are 0.82 , 2.2 and 1.62, which are 1.55, 1.77, and 1.1 times greater than the DCI method, respectively.

A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer

With the rapid growth and utilization of IoT devices around the world, attacks on these devices are also increasing thereby posing a security and privacy issue for industry providers and end-users alike. A common way to detect anomaly behaviour is to analyze the network traffic and categorize the outcome into benign and malignant traffic. With an increase in network traffic and sophistication of attacking techniques daily, there is a need for a state-of-the-art pattern recognition technique that can handle this ever increasing and ever-changing traffic and can also improve over time as attacks become more sophisticated. This research paper proposes a hybrid model for anomaly detection at the IoT fog layer using an ANN as a base model and several binary classifiers (which served as meta-classifiers) connected in series. The proposed model was tested and evaluated on a dataset of ‘x’ observations, demonstrating that such a model is both highly effective and efficient in detecting IoT network traffic anomalies.