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Objective: Privacy protection in the medical field means the protection of individuals from being associated with undesirable conditions, diagnoses or treatments (Sensitive Attributes). The problem of knowledge discovery from health care data by applying data mining algorithms is inversely related to the privacy of individuals. Due to the tremendous growth of data in a large scale, there is a demand to protect the sensitive data accessible from medical datasets. Methods: This paper considers the problem of building privacy preserving association rule mining algorithm using the notion of TF * IDF derived from the information retrieval domain. The highly sensitive transaction is chosen using the product of Relative Item Frequency and Condensed Frequency. Finally, sensitive fuzzy data is perturbed to hide these refined rules. Results: It has been found that the number of non-sensitive rules lost as a side effect of hiding sensitive rule is 20% less and number of ghost rules is 30% less in proposed work than in previous work using Transactional Impact factor method. The execution time of hiding a rule is 26% lesser on an average in the proposed technique for various values of minimum confidence threshold. It has been observed that the number of modifications to the original dataset after hiding three rules were reduced by 66% in proposed method than in previous work. As the number of modifications to original data is less the chances of generating false association is also reduced. Conclusion: In this paper, a novel method was presented to hide the sensitive rule in quantitative data by decreasing the support of the RHS of the rule. Experimental results demonstrate that the proposed approach is more efficient as it facilitates better rule hiding and minimizes the number of lost rules and ghost rules. Also, this approach makes minimum modifications to the dataset., (Creative Commons Attribution License)

Disease-causing pathogens such as viruses introduce their proteins into the host cells in which they interact with the host's proteins, enabling the virus to replicate inside the host. These interactions between pathogen and host proteins are key to understanding infectious diseases. Often multiple diseases involve phylogenetically related or biologically similar pathogens. Here we present a multitask learning method to jointly model interactions between human proteins and three different but related viruses: Hepatitis C, Ebola virus, and Influenza A. Our multitask matrix completion-based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. We obtain between 7 and 39 percentage points improvement in predictive performance over prior state-of-the-art models. We show how our model's parameters can be interpreted to reveal both general and specific interaction-relevant characteristics of the viruses. Our code is available online.

Genetic algorithms (GA) are often well suited for optimisation problems involving several conflicting objectives. It is more suitable to model the protein structure prediction problem as a multi-objective optimisation problem since the potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms) and experiments have shown that those types of interactions are in conflict, by using the potential energy function, Chemistry at Harvard Macromolecular Mechanics. In this paper, we have modified the immune inspired Pareto archived evolutionary strategy (I-PAES) algorithm and denoted it as MI-PAES. It can effectively exploit some prior knowledge about the hydrophobic interactions, which is one of the most important driving forces in protein folding to make vaccines. The proposed MI-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and the computational time and often results in much better search ability than that of the canonical GA.

This paper presents a theoretical model to predict the electrical and mechanical behavior of Radio Frequency (RF) Microelectro Mechanical System (MEMS) switch. Using the basic force deflection calculation of the fixed-fixed beam, a stand-alone model for calculating the spring constant, critical stress and pull down voltage in static analysis of RF MEMS switch to understand the mechanical operation is obtained. An efficient neural network to analyze the position stabilization of electrostatically actuated beam with force distributed evenly across the section of beam used in MEMS switches has been proposed. The results from the neural model trained by Levenberg-Marquardt back propagation algorithm corresponds closely with those available in literature. [ABSTRACT FROM AUTHOR]

In this paper, a new method of analysis for a singular system-based electronic circuits using the Runge-Kutta (RK)-Butcher algorithm is presented for linear time-invariant and time-varying cases. The discrete solutions obtained using the RK-Butcher algorithms are compared with the exact solutions of the electronic circuit problem and are found to be very accurate. Stability regions for the Single term walsh series method and the RK-Butcher algorithms are presented. Error graphs for inductor currents and capacitor voltages are presented in a graphical form to show the efficiency of this method. This RK-Butcher algorithm can be easily implemented in a digital computer and the solution can be obtained for any length of time. [ABSTRACT FROM AUTHOR]

The parameters governing the arm model of a second-order robot control problem are studied using the Runge–Kutta–Butcher (RK–Butcher) algorithm. The exact solution of the system of equations representing the arm model of a robot are compared with the corresponding discrete solutions (approximate solutions) at different time intervals using the RK–Butcher algorithm. The absolute error between the exact and discrete solutions is also determined. A detailed well-composed comparison is carried out using the results and graphs obtained. [ABSTRACT FROM AUTHOR]

The problem of optimal control of time-varying linear singular systems with quadratic performance index has been studied using the Runge–Kutta–Butcher algorithm. The results obtained using the Runge–Kutta (RK) method based on the arithmetic mean (RKAM) and the RK–Butcher algorithms are compared with the exact solutions of the time-varying optimal control of linear singular systems. It is observed that the result obtained using the RK–Butcher algorithm is closer to the true solution of the problem. Stability regions for the RKAM algorithm, the single-term Walsh series method and the RK–Butcher algorithms are presented. Error graphs for the simulated results and exact solutions are presented in graphical form to highlight the efficiency of the RK–Butcher algorithm. This algorithm can easily be implemented using a digital computer. An additional advantage of this method is that the solution can be obtained for any length of time for this type of optimal control of time-varying linear singular systems. [ABSTRACT FROM AUTHOR]

This article describes a compact numerical algorithm based on the generalized differential quadrature (GDQ) method for the numerical analysis of natural convection in a differentially heated square cavity. The velocity–vorticity form of the Navier–Stokes equations and energy equation are used to represent the mass, momentum, and energy conservations of the fluid medium in the cavity. The GDQ form of the governing equations and the vorticity definition at the boundaries are solved by a coupled solution algorithm using a global matrix scheme for all the field variables. The vorticity values at the boundary are correctly imposed using the GDQ method, which approximates a given space derivative with higher-order accuracy compared to the existing schemes based on Taylor's series expansion. This has assured a divergence-free solution for the flow field by satisfying the continuity constraint, though the pressure term is not used directly in the present formulation. The proposed algorithm is validated for a lid-driven cavity flow for Reynolds number Re = 100, 400, and 1,000, and the predicted velocity profiles are in excellent agreement with the benchmark solutions. The algorithm is then used to compute the average Nusselt number and flow parameters for natural convection in a square cavity for Rayleigh number Ra = 10 3 , 10 4 , 10 5 , and 10 6 . These results are in better agreement with the benchmark solutions than the results obtained by other numerical schemes, which used much finer grids compared to the present scheme. [ABSTRACT FROM AUTHOR]

This paper describes a finite element solution algorithm for the numerical solution of large size three-dimensional flow problems on a personal computer. To demonstrate the algorithm, the Stokes equations in velocity–vorticity form are solved for a lid-driven cubical cavity problem. The Galerkin's weighted residual form of the governing equations is evaluated for all the elements of the computational domain and kept as element-matrices and element-vectors. This results in a set of simultaneous equations corresponding to the global nodes of each element. Those elements that contain the boundary nodes are modified to incorporate the Dirichlet boundary conditions. A conjugate gradient iterative scheme is employed to solve the simultaneous equations in element form to get the solution at the global nodes. The matrix–vector products used in the conjugate gradient iterative solver are performed in element level, assembling only the element-level vectors to form the global vectors. Since the element-level computation has eluded the formation of global matrices, the numerical solution of three-dimensional Stokes equations using a mesh of size as high as 513 could be achieved on a personal computer. The algorithm is validated by comparing the results for a three-dimensional transient diffusion problem and Stokes flow in a lid-driven cubical cavity. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this article, the Runge-Kutta (RK)-Butcher algorithm is used to study the observer design of singular systems (transistor circuits) for time-invariant and time-varying cases. The obtained discrete solutions using the RK-Butcher algorithms are compared with STWS-I, STWS-II methods and with the exact solutions of the transistor circuit problems and are found to be very accurate. Stability analysis for the RK-Butcher algorithm is presented. Error graphs for time-invariant and time-varying cases are presented in a graphical form to show the efficiency of this method. This RK-Butcher algorithm can be easily implemented in a digital computer and the solution can be obtained for any length of time for this observer design of transistor circuit problem. ‡ On leave from NIT, Tiruchirappalli, India. [ABSTRACT FROM AUTHOR]

In this article, the Runge-Kutta (RK)-Butcher algorithm is used to study the optimal control of linear singular systems with quadratic performance cost. The obtained discrete solutions using the RK-Butcher algorithms are compared with the exact solutions of the optimal control problem and are found to be very accurate. Stability analysis for the RK-Butcher algorithm is presented. Error graphs for discrete and exact solutions are presented in a graphical form to show the efficiency of this method. This RK-Butcher algorithm can be easily implemented in a digital computer and the solution can be obtained for any length of time. [ABSTRACT FROM AUTHOR]