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Summary: This book constitutes the refereed proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015, held in Hyderabad, India, in December 2015. The 48 full papers and 8 short papers presented together with 4 doctoral consortium papers were carefully reviewed and selected from 185 submissions. The papers cover a wide range of topics including information retrieval, machine learning, pattern recognition, knowledge discovery, classification, clustering, image processing, network security, speech processing, natural language processing, language, cognition and computation, fuzzy sets, and business intelligence.

In this paper, a recently developed fiber tracking algorithm to be used with diffusion tensor (DT) fields acquired via magnetic resonance imaging (MRI) is improved and applied to real brain DT-MR images. The method performs satisfactorily in regions where branching and crossing fibers exist and offers the capability of reporting a probability value for the computed tracts. This certainty figure takes into account both the anisotropy and the information provided by all the eigenvectors and eigenvalues of the diffusion matrix at each voxel. In previous papers of the authors, a simpler algorithm was applied only to elementary synthetic DT-MR images. As now presented, this algorithm is now adequately used with more intricate synthetic images and is applied to real white matter DT-MR images with successful results. A novel neural network is used to estimate the main parameters of the algorithm. Numerical experiments show a performance gain over previous approaches, specially with respect to convergence and computational load. The tracking of white matter fibers in the human brain will improve the diagnosis and treatment of many neuronal diseases. [ABSTRACT FROM AUTHOR]

Moving object detection is very important for video surveillance. In this paper, we present a new real time motion detection algorithm that is based on the integration of accumulative optical flow and double background filtering method (long-term background and short-term background) to achieve better performance. The accumulative optical flow method is used to obtain and keep a stable background image to cope with variations on environmental changing conditions and the double background filtering method is used to eliminate the background information and separate the moving object from it. The biggest advantage of this algorithm is that it does not need to learn the background model from hundreds of images and can handle quick image variations without prior knowledge about the object size and shape. The algorithm has high capability of anti-interference and preserves high accurate rate detection at the same time. The effectiveness of the proposed algorithm for motion detection is demonstrated in a simulation environment and the evaluation results are reported in this paper. [ABSTRACT FROM AUTHOR]

Multi-spinner is used in Photoresist process of semiconductor manufacturing. Photoresist process is the process of wafer deposal including wafer cleaning, surface treatment, Photoresist spread and Soft Baking. Though each successful process of this Multi-spinner equipment works by moving wafer, if optical scheduling applied to improve transfer path, it increases the volume of produced semiconductors. The main concern of paper is to optimize scheduling method using artificial neural network to solve scheduling problem of each processes of Multi-spinner. The effectiveness of optical scheduling is proved by computer simulation. [ABSTRACT FROM AUTHOR]

Group Decision Making (GDM) is an important human activity and it has many practical applications in society, economy, management and engineering, etc. Researchers are faced with new challenges in research on theory and methods of GDM with the rapid advent of internet and information technology. One of the challenges in collaborative work is social decision making in a computer mediated environment. Social trust models like recommender system, Bayesian trust for pervasive computing, are becoming invaluable part of distributed systems, where uncertainty prevails. In this paper we have proposed a Trust based collaborative decision making algorithm for distributed environment in which a group of agents collaborate for decision making. [ABSTRACT FROM AUTHOR]

Our work explores the use of several text categorization techniques for classification of manufacturing quality defect and service shop data sets into fixed categories. Although our work was in the area of manufacturing quality the technique is applicable to free form, short text summaries of data that may be stored in a database, file, or document. We refer to these types of text as "technical text passages". These summaries may not follow standard grammar conventions; they commonly contain abbreviations, technical phrases, misspelled words and industry specific acronyms. Typical types of text to be classified include aircraft engine repair shop findings, industrial manufacturing quality problems and corrective actions, and standardization of attributes in a bill-of-materials. In this paper, we will present our results in using machine learning and rule based algorithms to categorize text. Our results show that the rules based approach is as good as several machine learning approaches. For example, using Support Vector Machine algorithms we were able to achieve 82% accuracy on validation set, using 1,645 training samples and 823 validation samples. Each category had 50 or more samples. Using rule-based approach we were able to achieve 80% accuracy. [ABSTRACT FROM AUTHOR]

The build up of water ice on aircraft flight surfaces poses a significant safety risk. As a result, much effort has gone into studying this problem in order to understand how individual droplets contribute to the accretion process. One approach has been to capture the moment of impact of a supercooled droplet onto a surface placed in an icing tunnel. However, this produces a large number of images that must be analysed manually. This paper describes the development of an automated analysis system, employing image processing techniques, that is capable of classifying the impact images without operator input. Using a carefully chosen feature vector and K-means clustering algorithm, the classification results from the automated system are comparable with that achieved using the manual approach. [ABSTRACT FROM AUTHOR]

Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case. [ABSTRACT FROM AUTHOR]

This paper presents a multi step-size monic normalization equation-error linear filter. We also extend the idea to nonlinear adaptive filter and derive a multi step-size monic normalization equation-error bilinear filter (MSS MNEEBF). The algorithms enjoy fast convergence behavior and can remove biased estimates associated with conventional equation-error adaptive filters. Simulation results validate the usefulness of our algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to develop image watermarking algorithms which are portable to a variety of applications such as copyright protection, fingerprinting and identification. Therefore, we require that the watermark be binary and be not only detectable but also extractable. The embedding technique is based on the parent-child structure of the multiwavelet transform called "triple tree" and this technique does not require the original image in the watermark extraction. The experimental results show that the watermark. [ABSTRACT FROM AUTHOR]

In this paper incomplete quantitative data has been dealt by using the concept of fuzzy entropy. Fuzzy entropy has been used to extrapolate the data pertaining to the compressor current. Certain attributes related to the compressor current have been considered. Test data of compressor current used in this knowledge discovery algorithm knows the entire attribute clearly. The developed algorithm is very effective and can be used in the various application related to knowledge discovery and machine learning. The developed knowledge discovery algorithm using fuzzy entropy has been tested on a multi-compressor system for incomplete compressor current data and it is found that the error level is merely ± 4.40%, which is far better than other available knowledge discovery algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an energy back-propagation algorithm (EBP). Learning and convergence processes of the standard backpropagation algorithm (SBP) are based on the energy function. The energy function is used with the convergence process to extract the nearest image for the unknown tested image. The EBP algorithm shows considerably better performance in terms of time of learning, time of convergence, and size of input image compared to the SBP algorithm. [ABSTRACT FROM AUTHOR]

In order to be successful in multi-agent electronic negotiating environments, intelligent agents should be capable of adapting their negotiation strategies and tactics so that they can achieve an agreement with optimized profit. In this paper, some findings are going to be shown in which negotiating intelligent agents in electronic commerce start negotiating using a simplified standard protocol in conjunction with a combined negotiation. Taking advantage of a new developed evolutionary algorithm, agents configure their negotiation strategies somehow they can get more profit. They can use fuzzy fairness function to behavior with fairness or without fairness. [ABSTRACT FROM AUTHOR]

In this paper, a Neuro-Predictive (NP) controller is designed and implemented on a highly no-linear system, a model helicopter in a constrained situation. It is observed that the closed loop system with the NP controller has a significant overshoot and a long setting time in comparison to the same system with an existing fuzzy controller. In order to improve the undesired system performance, s Sugeno-type fuzzy compensator, having only two rules, is added to the control loop to adjust control input. The newly designed Neuro-Predictive control with Fuzzy Compensator (NPFC) improves the system performance in both overshoot and settling time. Furthermore, it is shown that the NPFC controlled system is robust to disturbance and parameter changes. [ABSTRACT FROM AUTHOR]

In this paper, a new method for image noise cancellation by designing the templates of cellular neural network (CNN) is introduced. We propose a modified clonal selection algorithm (MCSA) which has an adaptive maturation strategy based on affinity and clone framework to search approximate optimal solution. By MCSA, we could optimize the templates of CNN for image noise cancelling. Finally, the MCSA-CNN method is compared with the Zeng stack smoother method in image noise cancellation, and we take Computed Tomography image for example. [ABSTRACT FROM AUTHOR]

Selection of an optimal subset of classifiers in designing classifier ensembles is an important problem. The search algorithms used for this purpose maximize an objective function which may be the combined training accuracy or diversity of the selected classifiers. Taking into account the fact that there is no benefit in using multiple copies of the same classifier, it is generally argued that the classifiers should be diverse and several measures of diversity are proposed for this purpose. In this paper, the relative strengths of combined training accuracy and diversity based approaches are investigated for the plurality voting based combination rule. Moreover, we propose a diversity measure where the difference in classification behavior exploited by the plurality voting combination rule is taken into account. [ABSTRACT FROM AUTHOR]

Local search (LS) methods for solving constraint satisfaction problems (CSP) such as GSAT, WalkSAT and DLM starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment, using the penalty function to determine a better neighbour valuations to move to. It repeats this process until it finds a solution that satisfies all constraints. ICM considers some of the constraints as hard constraints that are always satisfied. In this way, the constraints reduce the possible neighbours in each move and hence the overall search space. We choose the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach any solution from any valuation in this space. In this paper, we incorporate ICM into one of the most recent local search algorithm, ESG, and we show the improvement of the new algorithm. [ABSTRACT FROM AUTHOR]

The assessment and forecast for atmospheric quality have become the key problem in the study of the quality of atmospheric environment. In order to evaluate the grade of the atmospheric pollution, a model based on the particle swarm optimization (PSO) algorithm is proposed in this paper. Experimental results show the advantages of the proposed models, such as pellucid principle and physical explication, predigested formula and low computation complexity. In addition, an improved Elman neural network, namely, the output-input feedback Elman (OIF Elman) neural network is also applied to forecast the atmospheric quality. Simulations show that the OIF Elman neural network has great potential in the field of forecasting the atmospheric quality. [ABSTRACT FROM AUTHOR]

An evolutionary algorithm based on Random Keys to represent Golomb Rulers segments, has been found to be a reliable option for finding Optimal Golomb Rulers in a short amount of time, when comparing with standard methods. This paper presents a modified version of this evolutionary algorithm where the maximum segment length for a Golomb Ruler is also part of the evolutionary process. Attained experimental results shows us that this alteration does not seems to provide significant benefits to the static version of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes the use of Artificial Neural Networks (ANN) for the prediction of the maximum surface crack width of precast reinforced concrete beams joined by steel coupler connectors and anchor bars (jointed beams). Two different training algorithms are used in this study and their performances are compared. The first approach used Back propagation (BPANN) and the second one includes Genetic Algorithms (GANN) during the training process. Input and output vectors are designed on the basis of empirical equations available in the literature to estimate crack widths in common reinforced concrete (RC) structures and parameters that characterize the mechanical behavior of RC beams with overlapped reinforcement. Two well-defined points of loading are considered in this study to demonstrate the suitability of this approach in both, a linear and a highly nonlinear stage of the mechanical response of this type of structures. Remarkable results were obtained, however, in all cases the combined Genetic Artificial Neural Network approach resulted in improved prediction performance over networks trained by error back propagation. [ABSTRACT FROM AUTHOR]

In this paper, we study the difference between two ways of setting synaptic weights in a “temporal” neural network. Used as a controller of a simulated mobile robot, the neural network is alternatively evolved through an evolutionary algorithm or trained via an hebbian reinforcement learning rule. We compare both approaches and argue that in the last instance only the learning paradigm is able to exploit meaningfully the temporal features of the neural network. [ABSTRACT FROM AUTHOR]

The main focus is on using computational intelligence to model the distributed decision agents and to study the dynamic behavior under game-theoretic framework in allocating network resources. By viewing a network as a collection of resources which users are selfishly competing for, authors' research aims at finding efficient, decentralized algorithms, leading to network architectures which provide explicit Quality of Service guarantees, the crucial issue in high speed multimedia networks. There are several parallel research projects from different institutions dealing with this complicated issue.