Accepted Papers

  • Ensemble Classifier Approach in Breast Cancer Detection and Malignancy Grading - A Review
    Deepti Ameta, Banasthali University, India

    The diagnosed cases of Breast cancer is increasing annually and unfortunately getting converted into a high mortality rate. Cancer, at the early stages, is hard to detect because the malicious cells show similar properties (density) as shown by the non-malicious cells. The mortality ratio could have been minimized if the breast cancer could have been detected in its early stages. But we have not been able to achieve a fully automatic system which does not just detect the breast cancer but also can detect the stage of it. Estimation of malignancy grading is important in diagnosing the degree of growth of malicious cells as well as in selecting a proper therapy for the patient. Therefore, a complete and efficient clinical decision support system is proposed which is capable of achieving breast cancer malignancy grading scheme very efficiently. The system is based on Image processing and machine learning domains. Classification Imbalance problem, a machine learning problem, occurs when instances of one class is much higher than the instances of the other class resulting in an inefficient classification of samples and hence a bad decision support system. Therefore EUSBoost, ensemble based classifier is proposed which is efficient and is able to outperform other classifiers as it takes the benefits of both- boosting algorithm with Random Undersampling techniques. Also comparison of EUSBoost with other techniques is shown in the paper.

  • An Anonymous Mutual Authentication Scheme for Healthcare RFID Systems
    Sarah A.Moniemy, Sanaa Tahay and Haithem S.Hamzay, Cairo University, Egypt

    Radio Frequency Identification (RFID) is widely deployed nowadays in many applications, such as toll road collection, access control, asset tracking, and supply chain, in order to identify, track, and locate mobile objects with high accuracy. However, the RFID tags are vulnerable to anonymity and location privacy threats due to the ID-query replies transmitted from those tags. Accordingly, in this paper and based on keyed hash functions, we propose an anonymous mutual authentication scheme, which achieves anonymity, unlinkability, untraceability, and location privacy for RFID tags. Security analysis shows that our proposed scheme thwarts illicit tracking, replay, relay, Dos, and backward secrecy attacks. Additionally, our scheme can be implemented in healthcare RFID systems because it employs lightweight security operations, and it achieves up to 21% and 84% less communication and communication overheads, respectively, compared to those in current anonymous authentication schemes.

  • Analysis of Social Data Opinion Through Public User Raw Information
    Shilna K.S and T. Sivakumar, Maharaja Institute of Technology, India

    In this paper, we move one step further to interpret sentiment variations. To the best of our knowledge, our study is the proposed work that tries to analyze and interpret the public sentiment variations in micro blogging services. Two novel generative models are developed to solve the reason mining problem. The two proposed models can be applied to other tasks such as finding topic differences between two sets of documents. We propose a sentimental data analysis model using Neural Networks. Both positive and negative feed backs will be calculated here. These foreground topics can give potential interpretations of the sentiment variations. To further enhance the readability of the mined reasons, we select the most representative tweets for foreground topics and develop another generative model called Reason Candidate and Background LDA (RCB-LDA) to rank them with respect to their popularity within the variation period.