Machine Intelligence Research Labs MIR Labs

Washington, Washington, United States

Machine Intelligence Research Labs MIR Labs

Washington, Washington, United States
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Shrivastava L.,Madhav Institute of Technology and Science | Bhadauria S.S.,Madhav Institute of Technology and Science | Tomar G.S.,Machine Intelligence Research Labs MIR Labs
Proceedings - 2011 International Conference on Communication Systems and Network Technologies, CSNT 2011 | Year: 2011

The performance of the routing protocols in mobile Ad hoc networks degrades with increasing traffic load. Many routing protocols for such networks have been proposed so far. Amongst the most popular ones are Ad hoc On-demand Distance Vector (AODV), Destination-Sequenced Distance-Vector Routing protocol (DSDV), Dynamic Source Routing Protocol (DSR). In this paper we present our observations regarding the performance comparison of the above protocols for varying traffic load in mobile ad hoc networks (MANETs). We perform extensive simulations, using NS-2 simulator. Our studies have shown that reactive protocols perform better than proactive protocols. © 2011 IEEE.

Huang H.-C.,National University of Kaohsiung | Chen Y.-H.,Far East University of Taiwan | Abraham A.,Machine Intelligence Research Labs MIR Labs
Journal of Information Hiding and Multimedia Signal Processing | Year: 2010

Digital watermarking has been a popular topic in both scientific research and applications in the last decade. Based on previous experiences for the development of watermarking algorithm in literature, there are lots of requirements that need to be considered based on the purpose for applications. Therefore, how to assess the effec- tiveness of the algorithm should be based on the preset requirements. Here we use a recent optimization technique, named bacterial foraging, to search for the tradeoff among requirements, and we employ the concept in fuzzy theory to design an effective fitness function with the pre-determined requirements. Unlike conventional scheme to fix the components in the fitness function, by using the fuzzy concept in conjunction with swarm intelligence, better results could be obtained. Simulation results demonstrate the advan- tages of the proposed algorithm over existing ones in the literature. © 2010.

Ojha V.K.,VSB - Technical University of Ostrava | Abraham A.,Machine Intelligence Research Labs MIR Labs | Snasel V.,VSB - Technical University of Ostrava
Engineering Applications of Artificial Intelligence | Year: 2017

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era. © 2017 Elsevier Ltd

Panda M.,Gandhi Institute Of Engg And Technology | Abraham A.,Machine Intelligence Research Labs MIR Labs | Patra M.R.,Berhampur University
2010 6th International Conference on Information Assurance and Security, IAS 2010 | Year: 2010

This paper applies discriminative multinomial Naive Bayes with various filtering analysis in order to build a network intrusion detection system. For our experimental analysis, we used the new NSL-KDD dataset, which is considered as a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. We perform 2 class classifications with 10-fold cross validation for building our proposed model. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network intrusion detection system. © 2010 IEEE.

Ali M.,Indian Institute of Technology Roorkee | Pant M.,Indian Institute of Technology Roorkee | Abraham A.,Machine Intelligence Research Labs MIR Labs
International Journal of Bio-Inspired Computation | Year: 2011

Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence. This paper proposes a modified variant of DE algorithm called improved differential evolution (IDE). It works in three phases: decentralisation, evolution and centralisation of the population. Initially, the individuals of the population are partitioned into several groups of subpopulations (decentralisation phase) through a process of shuffling. Each subpopulation is allowed to evolve independently from each other with the help of DE (evolution phase). Periodically, the subpopulations are merged together (centralisation phase) and again new subpopulations are reassigned to different groups. These three phases helps in searching all the potential regions of the search domain effectively, thereby, maintaining the diversity. The promising nature of IDE is demonstrated on a testbed of 16 benchmark problems having box constraints. Comparison of numerical results shows that IDE is either better or at par with other contemporary algorithms. Copyright © 2011 Inderscience Enterprises Ltd.

Izakian H.,University of Isfahan | Izakian H.,Machine Intelligence Research Labs MIR Labs | Ladani B.T.,University of Isfahan | Abraham A.,Machine Intelligence Research Labs MIR Labs | Snasel V.,VSB - Technical University of Ostrava
International Journal of Innovative Computing, Information and Control | Year: 2010

Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature of grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be looked for. This paper represents a discrete Particle Swarm Optimization (DPSO) approach for grid job scheduling. PSO is a population based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or nearoptimal solutions. In this paper, the scheduler aims at minimizing makespan and flowtime simultaneously in grid environment. Experimental studies illustrate that the proposed method is more efficient and surpasses those of reported metaheuristic algorithms for this problem. © 2010 ICIC INTERNATIONAL.

Puranik P.,G.H. Raisoni College of Engineering | Bajaj P.,G.H. Raisoni College of Engineering | Abraham A.,Machine Intelligence Research Labs MIR Labs | Palsodkar P.,G.H. Raisoni College of Engineering | Deshmukh A.,G.H. Raisoni College of Engineering
Journal of Information Hiding and Multimedia Signal Processing | Year: 2011

In computer vision, image processing is any form of signal processing for which the input is an image, such as photographs or frames of videos. The output of image processing can be either an image or a set of characteristics or parameters related to image.The color vision systems require a first step of classifying pixels in a given image into a discrete set of color classes. The aim is to produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Fuzzy sets are defined on the H, S and L components of the HSL color. Particle swarm optimization algorithm is a recent metaheuristic that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. During the search process, a population member tries to maximize a fitness criterion, which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. In Comprehensive learning, particle Swarm optimization specific weight is assigned to each color for obtaining high classification rate. © 2011 ISSN 2073-4212.

Rajasekhar A.,National Institute of Technology Warangal | Abraham A.,VSB - Technical University of Ostrava | Abraham A.,Machine Intelligence Research Labs Mir Labs | Pant M.,Indian Institute of Technology Roorkee
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics | Year: 2011

This paper proposes an improved version of Artificial Bee Colony (ABC) algorithm with mutation based on Levy Probability Distributions. The Levy distribution has a peculiar property of generating an offspring farther away from its parent which depends on internal parameter α compared to that of Gaussian mutations, this property enables in finding out most optimal solutions to the problems than that of conventional methods. The proposed algorithm is tested on 7 standard benchmark functions and on a set of non-traditional problems suggested in the special session of CEC'2008. Analysis and comparison of results with other state of art optimization algorithms like GA and PSO, shows the superiority of improved mutation, especially on high dimensional problems. This paper finally investigates the performance of proposed algorithm on the frequency-modulated sound wave synthesis problem, a real world problem in the field on communication engineering © 2011 IEEE.

Ojha V.K.,VSB - Technical University of Ostrava | Abraham A.,Machine Intelligence Research Labs MIR Labs | Snasel V.,VSB - Technical University of Ostrava
Applied Soft Computing Journal | Year: 2016

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A . diversity index measure along with . approximation error and . complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population. © 2016 Elsevier B.V.

Madureira A.,Polytechnic Institute of Porto | Pereira I.,Polytechnic Institute of Porto | Pereira P.,Polytechnic Institute of Porto | Abraham A.,Machine Intelligence Research Labs MIR Labs
Neurocomputing | Year: 2014

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility.This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization. © 2013 Elsevier B.V.

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