Disease prediction through gene is a challenging task. Researchers have proposed algorithms to identify disease from genes. Traditional algorithms prioritize through annotation and combines the structures in biological process or molecular functions and compared with annotations of known disease genes for classification. Pediatric Cardiomyopathy is a disease due to disorder in heart muscle and identification at early stage is a challenging problem. In this paper, the above problem solves through Window Based Correlation (WBC). In WBC, Global data is reduced to spatial data using block reduction technique. After Data reduction, strong relationship analysis between the genes is identified through RMSE values between the genes. This RMSE values helps to detect the pediatric cardiomyopathy at early stage using Window based correlation method. From the results, ablation study proves an accuracy of prediction is about 85%.
Mobile ad hoc networks (MANET) have become one of the hottest research areas in computer science, including in military and civilian applications. Such applications have formed a variety of security threats, particularly in unattended environments. An Intrusion detection system (IDS) must be in place to ensure the security and reliability of MANET services. These IDS must be compatible with the characteristics of MANETs and competent in discovering the biggest number of potential security threats. In this work, a specialized dataset for MANET is implemented to identify and classify three types of Denial of Service (DoS) attacks: Blackhole, Grayhole and Flooding Attack. This work utilized a cluster-based routing algorithm (CBRA) in MANET.A simulation to gather data, then processed to create eight attributes for creating a specialized dataset using Java. Mamdani fuzzy-based inference system (MFIS) is used to create dataset labelling. Furthermore, an ensemble classification technique is trained on the dataset to discover and classify three types of attacks. The proposed ensemble classification has six base classifiers, namely, C4.5, Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM). The experimental results demonstrate that MFIS with the Ensemble classification technique enables an enhancing security in MANET’s by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET.
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