Madhuri S. Joshi

A Traitor Identification Technique for Numeric Relational Databases with Distortion Minimization and Collusion Avoidance

An enormous growth in internet usage has resulted into great amounts of digital data to handle. Data sharing has become significant and unavoidable. Data owners want the data to be secured and perennially available. Data protection and any violations thereby become crucial. This work proposes a traitor identification system which securely embeds the fingerprint to provide protection for numeric relational databases. Digital data of numeric nature calls for preservation of usability. It needs to be done so by achieving minimum distortion. The proposed insertion technique with reduced time complexity ensures that the fingerprint inserted in the form of an optimized error leads to minimum distortion. Collusion attack is an integral part of fingerprinting and a provision to mitigate by avoiding the same is suggested. Robustness of the system against several attacks like tuple insertion, tuple deletion etc. is also depicted.

Design and Analysis of Shielded Vertically Stacked Ring Resonator as Complex Permittivity Sensor for Petroleum Oils

Novel design of a shielded vertically stacked ring resonator (VSRR) is presented in this paper. The use of a shielded VSRR with a layer of the low-loss liquid that fills the partial space between the fed patch and the parasitic patch have been investigated. Dependencies of the resonating frequency and input impedance of the shielded VSRR on structure size and material properties of the test liquid layer are discussed. The method, of finding the complex permittivity (CP), particularly of petroleum liquids, is verified using electromagnetic modeling with full wave simulation software ANSYS HFSS-15 and confirmed experimentally. The proposed new design of the resonator will improve the sensitivity of single ring boxed resonator in terms of the quality factor, and in turn, increase the CP measurement sensitivity.

Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization

algorithms (GAs) are useful for solving multimodal problems. It is quite difficult to search the search space of the multimodal problem with large dimensions. There is a challenge to use all the core of the system. The Dual Population GA (DPGA) attempts to explore and exploit search space on the multimodal problems. Parallel GAs (PGAs) are better option to optimize multimodal problems. OpenMP GA is parallel version of GA. The Dual Population GA (DPGA) uses an extra population called reserve population to provide additional diversity to the main population through crossbreeding. DPGA and PGA, both provide niching technique to find optimal solution. Paper presents the experimentation of DPGA, OpenMP GA and SGA. The experimentation results show that the performance of the OpenMP GA is remarkably superior to that of the SGA in terms of execution time and speed up. OpenMP GA gives optimum solution in comparison with OpenMP GA and SGA for same parameter settings. KeywordsAlgorithm (GA), Dual Population GA (DPGA), Serial DPGA, Open Multi Processing (OpenMP), Multimodal Function, Non-linear optimization problems.

Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).

Serial DPGA vs. Parallel Multithreaded DPGA: Threading Aspects

The multiple main populations, reserve populations and subpopulations concepts of a Genetic Algorithms (GAs) offers the advantage of diversity. However, as the population evolves, the GA loses its diversity. As the population converges, it begins to lose its diversity and cannot avoid the local optima problem. This problem is known as Premature Convergence for Parallel GAs (PGA) too. The paper compares the Binary encoded Simple GA (SGA), Binary encoded Serial/ Sequential Dual Population Genetic Algorithm (SDPGA) and Binary encoded Multithreaded Parallel DPGA (MPDPGA) performances for function optimization on multicore system. The Dual Population Genetic Algorithm (DPGA) is an evolutionary algorithm that uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. The experimental results on unimodal and multimodal classes of test problem shows the MPDPGA outperforms over SGA and SDPGA. The performance of MPDPGA with DPGA1 is better in terms of accuracy, number of generations and execution time on multicore system. The performance of MPDPGA with DPGA-ED1 is better for Rosenbrock and Schwefel whereas worse for Ackley and Griewangk.

A Fingerprinting Technique for Numeric Relational Databases with Distortion Minimization

With the ever-increasing usage of internet, the availability of digital data is in tremendous demand. In this context, it is essential to protect the ownership of the data and to be able to find the guilty user. In this paper, a fingerprinting scheme is proposed to provide protection for Numeric Relational Database (RDB), which focuses on challenges like: 1. Minimum distortion in Numeric database, 2. Usability preservation, 3. Non-violation of the requirement of blind decoding. When the digital data in concern is numeric in nature the usability of data needs to be keenly preserved, this is made possible by achieving minimum distortion.

Performance Enhancement for Detection of Myocardial Infarction from Multilead ECG

Computer-aided diagnosis have emerged as additional help to the medical domain. Over the years ECG signal being simple, cheap, and noninvasive, is explored for the diagnosis of heart diseases. Multilead simultaneously acquired ECG improves the accuracy in diagnosis of heart diseases. The paper focuses on diagnosing Myocardial Infarction from multilead ECG using Multilayer Perceptron Model. In the present work, the proposed feature vector used for the classification includes QRS point score as one of the feature along with the other morphological features. The study is an attempt to discuss the utility of point score as a feature in the feature vector for classification of Myocardial Infarction disease from ECG signal to enhance the performance of classification. The results show significant improvement when the point score is used in the feature vector. The model is evaluated with 34 ECG signals of normal subjects and 33 ECG signals of MI patients from PTB database, collected from physionet. The classification accuracy is above 95% including point score feature and the same is less than 85% excluding the point score in all the leads. The inclusion of point score as a feature for diagnosing Myocardial infarction results in better accuracy.

Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisations

The genetic algorithms GAs metaheuristic deals with large scale combinatorial optimisation problems. It is biologically inspired by the method, based on the principle of survival of the fittest. In GAs, the concept of multiple populations offers an advantage of diversity. However, as the population evolves, the GA loses its diversity and sometimes it cannot avoid the local optima problem also known as premature convergence. The dual population genetic algorithm DPGA uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Crossbreeding solves the problem of premature convergence and helps to converge early. This paper is the empirical study of the Binary encoded parallel DPGA PDPGA. It is compared with metaheuristics given in literature based on reliability, efficacy and efficiency. The performance of PDPGA is competitive over other nature-inspired optimisation methods like genetic algorithm GA, particle swarm optimisation PSO, differential evolution DE, ANTS, bee colony, grenade explosion method GEM and bee colony optimisation BCO, but not better than artificial bee colony ABC and teaching-learning-based optimisation TLBO.

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

Maintaining population diversity is a challenge for the success of genetic algorithm. A numerous approaches have been proposed by researchers for adding diversity to the population. Dual-population genetic algorithm (DPGA) is one of them which is an effective optimization algorithm and provides diversity to the main population. Problems in GA such as premature convergence and population diversity is well addressed by DPGA. The aim of writing this review paper is to study how DPGA has been evolved. DPGA is inherently parallelizable, and hence, it can be port to parallel programming architecture for large-scale or large-dimension problems.

Dynamic causal modelling for schizophrenia

Schizophrenia is a complex psychiatric disorder which leads to local abnormalities in brain activity. Functional Magnetic Resonance Imaging (fMRI) technology enables medical doctors to observe brain activity patterns that represent the execution of subject tasks, both physical and mental. In general, each subject exhibits his own activation pattern for a given task, whose intensity is affected by the physiology of the subjects brain, the usage of medications, and the parameters of the scanner used for image acquisition. Since it is possible to co-register the resulting activation map to a standard brain, all activation patterns from the different individuals can be analyzed in terms of consistency on the brain sections or brain coordinates where the activation is observed. The dynamic Causal Model using Bayesian networks (DBNs) extracts causal relationships from functional magnetic resonance imaging (fMRI) data applying HITON-PC, a local causal algorithm. Based on these relationships, a dynamic causal model is to be build that is used to classify patient data as belonging to healthy or ill subjects. Causal Explorer is a Matlab library of computational causal discovery and variable selection algorithms.