Faculty Publications
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Publications by NITK Faculty
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Item A multiobjective phenomic algorithm for inference of gene networks(2012) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.Reconstruction of gene networks has become an important activity in Systems Biology. The potential for better methods of drug discovery and of disease diagnosis hinge upon our understanding of the interaction networks between the genes. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However, all these methods are based on processing of genotypic information. We have presented an evolutionary algorithm for reconstructing gene networks from expression data using phenotypic interactions, thereby avoiding the need for an explicit objective function. Specifically, we have also extended the basic phenomic algorithm to perform multiobjective optimization for gene network reconstruction. We have applied this novel algorithm to the yeast sporulation dataset and validated it by comparing the results to the links found between genes of the yeast genome at the SGD database. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.Item A phenomic algorithm for inference of gene networks using S-systems and memetic search(2012) D'Souza G, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.In recent years, evolutionary methods have seen unprecedented success in elucidation of gene networks, especially from microarray data. We have implemented the Phenomic Algorithm which is an evolutionary method for inference of gene networks based on population dynamics. We have used S-systems to model gene interactions and applied memetic search to fine tune the parameters of the inferred networks. We have tested the novel algorithm on artificial gene expression datasets obtained from simulated gene networks. We have also compared the results to those obtained from two other similar algorithms. Results showed that the new method, which we call as Phenomic Algorithm with Memetic Search (PAMS), is an effective method for inference of gene networks. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.Item A differential evolution based neural network approach to nonlinear system identification(2011) Subudhi, B.; Jena, D.This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input-multi-output system (TRMS) to verify the identification performance. © 2010 Elsevier B.V. All rights reserved.Item Vibrational spectra of Ruthenium Carbide structures yielded by the structure search employing evolutionary algorithm(Elsevier Ltd, 2015) Harikrishnan, G.; Ajith, K.M.; Chandra, S.; Valsakumar, M.C.Out of the three dynamically stable structures of Ruthenium Carbides yielded by the exhaustive structure search employing evolutionary algorithm, Born effective charges are computed for the semiconducting RuC in Zinc blende structure using density functional perturbation theory. Using the phonon frequencies and the Born effective charge tensors of Ru and C in this structure, infrared spectrum is generated for this system. Computations of these dynamical quantities and IR spectra from first principles can be helpful in the unambiguous determination of the stoichiometry and structure by comparison of the experimental measurements with the computational predictions. The positive formation energies of the three systems show that high pressure and possibly high temperature may be necessary for their synthesis. Formation energies of these systems at different pressures are computed. One of the structurally stable systems, Ru3C with hexagonal structure (P6¯m2), has negative formation energy at 200 GPa. The system reported from the first synthesis of Ruthenium Carbide also has the same symmetry, though it has a different stoichiometry. © 2015 Elsevier Ltd. All rights reserved.Item Modelling and multi-objective optimisation of squeeze casting process using regression analysis and genetic algorithm(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.In the present work, an attempt has been made using statistical tools to develop a non-linear regression model and to identify the significant contribution of squeeze cast process parameters on surface roughness, hardness and tensile strength. Microstructure examination performed on the squeeze cast samples has revealed that a maximum of 100 MPa pressure is good enough to eliminate all possible casting defects. Accuracy of the developed models has been tested with the help of ten test cases. It is important to note that the developed models predict responses with a reasonably good accuracy and the developed mathematical input–output relationship helps the foundry-man to make better predictions. The present work comprises four objectives, which are conflicting in nature. Hence, mathematical formulation is used to convert four objective functions into a single objective function. The popular evolutionary algorithm, that is genetic algorithm has been utilised to determine the optimal process parameters. © 2015 Engineers Australia.Item An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions(Elsevier Ltd, 2016) Suresh, S.; Lal, S.Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for lévy flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna?s method forlévy flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications. © 2016 Elsevier Ltd. All rights reserved.Item Evolutionary algorithm based structure search and first-principles study of B12C3polytypes(Elsevier Ltd, 2017) Harikrishnan, H.; Ajith, K.M.; Chandra, S.; Mundachali Cheruvalath, V.The structure search based on evolutionary algorithm has yielded six unique Boron Carbide structures in B12C3stoichiometry, three of them with negative formation energies. Their formation energies lie within a band of 166 meV/atom, so they can be formed together in any optimal high temperature synthesis of B12C3and they are thermodynamically stable at temperatures up to 660 K. This work is the first independent confirmation using structure search that B11Cp(CBC) is the ground state structure of B12C3stoichiometry. New structures like the 14-atom-cage and the supercell (B11Cp)(B10Cpe 2)(CBC)(CBB) have also emerged in the structure search. Five structures have base-centered monoclinic symmetry and the supercell has triclinic symmetry, implying that the determination of monoclinic symmetry in B12C3by experimental measurements is an option for further inquiry. The mechanical stability of these systems are established through the analysis of their elastic constants and their dynamical stability from the phonon data. The high value of Bulk modulus (?250 GPa) indicates their high hardness and the B/G value confirms their brittle nature. The electronic structure shows that they are semiconductors with a significant reduction in the band gap when the structure does not contain the CBC chain. The curve fitting of the cumulative IR spectrum against the experimental spectrum implies that the presence of B11Cp(CBC) in the ground state composition could mostly be through structures of larger unit cells. The hardness values of these systems estimated by using the semi-empirical model based on bond strength are in excellent agreement with the experimental values. For the four structures with chain the hardness values are close to the superhard regime (>40 GPa). © 2016 Elsevier B.V.Item A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment(Elsevier Ltd, 2017) Simu, S.; Lal, S.In this paper, a study and performance comparison of various evolutionary and non-evolutionary segmentation techniques on digital hand radiographs for bone age assessment is presented. The segmented hand bones are of vital importance in process of automated bone age assessment (ABAA). Bone age assessment is a technique of checking the skeletal development and detecting growth disorder in a person. However, it is very difficult to segment out the bone from the soft tissue. The problem arises from overlapping pixel intensities between bone region and soft tissue region and also between soft tissue region and background. Thus there is a requirement for a robust segmentation technique for hand bone segmentation. Taking this into consideration we make a comparison between non-evolutionary and evolutionary segmentation algorithms implemented on hand radiographs to recognize bone borders and shapes. The simulation and experimental results demonstrate that multiplicative intrinsic component optimization (MICO) algorithm provides better results as compared to other existing evolutionary and non-evolutionary algorithms. © 2016 Elsevier LtdItem Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images(Elsevier Ltd, 2017) Suresh, S.; Lal, S.This paper proposes an improved variant of Darwinian Particle Swarm Optimization algorithm based on chaotic functions. Most of the evolutionary algorithms faces the problem of getting trapped in local optima in its search for global optimum solutions. This is highly influenced by the use of random sequences by different operators in these algorithms along their run. The proposed algorithm replaces random sequences by chaotic sequences mitigating the problem of premature convergence. Experiments were conducted to investigate the efficiency of 10 defined chaotic maps and the best one was chosen. Performance of the proposed Chaotic Darwinian Particle Swarm Optimization (CDPSO) algorithm is compared with chaotic variants of optimization algorithms like Cuckoo Search, Harmony Search, Differential Evolution and Particle Swarm Optimization exploiting the chosen optimal chaotic map. Various histogram thresholding measures like minimum cross entropy and Tsallis entropy were used as objective functions and implemented for satellite image segmentation scenario. The experimental results are validated qualitatively and quantitatively by evaluating the mean, standard deviation of the fitness values, PSNR, MSE, SSIM and the total time required for the execution of each optimization algorithm. © 2017 Elsevier B.V.Item Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM(Elsevier Ltd, 2017) Satapathi, G.S.; Srihari, P.This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs. © 2017 Elsevier Ltd