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Showing papers in "International Journal on Artificial Intelligence Tools in 2016"


Journal ArticleDOI
TL;DR: A review of research reported on simulated annealing (SA) finds different cooling/annealing schedules are summarized and recent applications of SA in engineering are reviewed.
Abstract: This paper presents a review of research reported on simulated annealing (SA) Different cooling/annealing schedules are summarized Variants of SA are delineated Recent applications of SA in engineering are reviewed

75 citations


Journal ArticleDOI
TL;DR: The quantitative and qualitative results show that the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed.
Abstract: Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM) According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets

66 citations


Journal ArticleDOI
TL;DR: A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herds optimization method.
Abstract: A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves explo...

53 citations


Journal ArticleDOI
TL;DR: CoABCMiner can be used to discover classification rules for the data sets used in experiments, efficiently and is compared with several state-of-the-art algorithms using 14 benchmark data sets.
Abstract: In data mining, classification rule learning extracts the knowledge in the representation of IF_THEN rule which is comprehensive and readable. It is a challenging problem due to the complexity of data sets. Various meta-heuristic machine learning algorithms are proposed for rule learning. Cooperative rule learning is the discovery process of all classification rules with a single run concurrently. In this paper, a novel cooperative rule learning algorithm, called CoABCMiner, based on Artificial Bee Colony is introduced. The proposed algorithm handles the training data set and discovers the classification model containing the rule list. Token competition, new updating strategy used in onlooker and employed phases, and new scout bee mechanism are proposed in CoABCMiner to achieve cooperative learning of different rules belonging to different classes. We compared the results of CoABCMiner with several state-of-the-art algorithms using 14 benchmark data sets. Non parametric statistical tests, such as Friedman test, post hoc test, and contrast estimation based on medians are performed. Nonparametric tests determine the similarity of control algorithm among other algorithms on multiple problems. Sensitivity analysis of CoABCMiner is conducted. It is concluded that CoABCMiner can be used to discover classification rules for the data sets used in experiments, efficiently.

28 citations


Journal ArticleDOI
TL;DR: This paper proposes a new ensemble classifier using Radial Basis Function (RBF) neural networks and fuzzy clustering in order to increase detection accuracy and stability, reduce false positives, and provide higher detection rate for low-frequent attacks.
Abstract: Intrusion Detection Systems have considerable importance in preventing security threats and protecting computer networks against attackers. So far, various classification approaches using data mining and machine learning techniques have been proposed to the problem of intrusion detection. However, using single classifier systems for intrusion detection suffers from some limitations including lower detection rate for low-frequent attacks, detection instability, and complexity in training process. Ensemble classifier systems combine several individual classifiers and obtain a classifier with higher performance. In this paper, we propose a new ensemble classifier using Radial Basis Function (RBF) neural networks and fuzzy clustering in order to increase detection accuracy and stability, reduce false positives, and provide higher detection rate for low-frequent attacks. We also use a hybrid combination method to aggregate the individual predictions of the base classifiers, which helps to increase detection accuracy. The experimental results on NSL-KDD data set demonstrate that our proposed system has a higher detection accuracy compared to other wellknown classification systems. It also performs more effectively for detection of low-frequent attacks. Furthermore, the proposed ensemble method offers better performance compared to popular ensemble methods.

27 citations


Journal ArticleDOI
TL;DR: This paper proposes the use of artificial neural networks (ANN) on programmable UAVs and employs PSO to find near-optimal parameters for static environments and then train a neural network to interpolate PSO solutions in order to improve the UAV route in dynamic environments.
Abstract: Brazil is an agricultural nation whose process of spraying pesticides is mainly carried out by using aircrafts. However, the use of aircrafts with on-board pilots has often resulted in chemicals being sprayed outside the intended areas. The precision required for spraying on crop fields is often impaired by external factors, like changes in wind speed and direction. To address this problem, ensuring that the pesticides are sprayed accurately, this paper proposes the use of artificial neural networks (ANN) on programmable UAVs. For such, the UAV is programmed to spray chemicals on the target crop field considering dynamic context. To control the UAV ight route planning, we investigated several optimization techniques including Particle Swarm Optimization (PSO). We employ PSO to find near-optimal parameters for static environments and then train a neural network to interpolate PSO solutions in order to improve the UAV route in dynamic environments. Experimental results showed a gain in the spraying precisio...

23 citations


Journal ArticleDOI
TL;DR: This work proposes an implementation scheme that triangulates the constraint graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them and generates random scale-free-like qualitative spatial networks using the Barabasi-Albert model with a preferential attachment mechanism.
Abstract: We improve the state-of-the-art method for checking the consistency of large qualitative spatial networks that appear in the Web of Data by exploiting the scale-free-like structure observed in their constraint graphs. We propose an implementation scheme that triangulates the constraint graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them. We generate random scale-free-like qualitative spatial networks using the Barabasi-Albert (BA) model with a preferential attachment mechanism. We test our approach on the already existing random datasets that have been extensively used in the literature for evaluating the performance of qualitative spatial reasoners, our own generated random scale-free-like spatial networks, and real spatial datasets that have been made available as Linked Data. The analysis and experimental evaluation of our method presents significant improvements over the state-of-the-art approach, and establishes our implementation as t...

20 citations


Journal ArticleDOI
TL;DR: An approach to detect communities in signed networks that combines Genetic Algorithms and local search is proposed, which optimizes the concepts of modularity and frustration in order to find network divisions far from random partitions, and having positive and dense intra-Connections, while sparse and negative inter-connections.
Abstract: An approach to detect communities in signed networks that combines Genetic Algorithms and local search is proposed. The method optimizes the concepts of modularity and frustration in order to find network divisions far from random partitions, and having positive and dense intra-connections, while sparse and negative inter-connections. A local search strategy to improve the network division is performed by moving nodes having positive connections with nodes of other communities, to neighboring communities, provided that there is an increase in signed modularity. An extensive experimental evaluation on randomly generated networks for which the ground-truth division is known proves that the method is competitive with a state-of-art approach, and it is capable to find accurate solutions. Moreover, a comparison on a real life signed network shows that our approach obtains communities that minimize the positive inter-connections and maximize the negative intra-connections better than the contestant methods.

19 citations


Journal ArticleDOI
TL;DR: This paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast.
Abstract: Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

17 citations


Journal ArticleDOI
TL;DR: A new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher- Learner- based- Optimization (TLBO) algorithm and it is applied to solve multilevel thresholding problem in which the Kapur's entropy criterion is considered as figure-of-merit.
Abstract: The selection of optimal thresholds is still a challenging task for researchers in case of multilevel thresholding Many swarm and evolutionary computation techniques have been applied for obtaining optimal values of thresholds The performance of all these computation techniques is highly dependent on proper selection of algorithm-specific parameters In this work, a new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher-learner-based-optimization (TLBO) algorithm To prove the efficacy of proposed algorithm, it is applied to solve multilevel thresholding problem in which the Kapur's entropy criterion is considered as figure-of-merit In this experiment, four benchmark test images are considered for multilevel thresholding The optimal values of thresholds are obtained using TLBO, MC and particle swarm optimization (PSO) in addition to proposed algorithm to accomplish the comparative study To support the superiority of proposed algorithm over others, various quantitative and qualitative results are presented in addition to statistical analysis

14 citations


Journal ArticleDOI
TL;DR: Comparative numerical-experiment results of the 4IEPNN using different MATLAB computing routines and the standard multi-layer perceptron (MLP) neural network further verify the superior performance and efficacy of the proposed MIEPNN equipped with the WASD algorithm including PWG and TP techniques in terms of training, testing and predicting.
Abstract: Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal connecting weights between hidden layer and output layer directly, but also obtains the optimal number of hidden-layer neurons. Specifically, a sub-optimal structure is obtained via the PWG technique, then the redundant hidden-layer neurons are further pruned via the TP technique. Consequently, the optimal structure of the MIEPNN is obtained. To provide a reasonable choice in practice, several different MATLAB computing routines related to the WDD method are studied. Comparative numerical-experiment results of the 4IEPNN using these different MATLAB computing routines and the standard multi-layer perceptron (MLP) neural network further verify the superior performance and efficacy of the proposed MIEPNN equipped with the WASD algorithm including PWG and TP techniques in terms of training, testing and predicting.

Journal ArticleDOI
TL;DR: To balance the diversity and convergence ability of the ABC, Mantegna Levy distribution random walk is proposed and incorporated with ABC and the new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm.
Abstract: Data clustering is a common data mining techniques used in many applications such as data analysis and pattern recognition. K-means algorithm is the common clustering method which has fallen into the trap of local optimization and does not always create the optimized response to the problem, although having more advantages such as high speed. Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. However, there are still some problems in ABC algorithm, like inability to find the best solution from all possible solutions. Due to the large step of searching equation in ABC, the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence ability of the ABC, Mantegna Levy distribution random walk is proposed and incorporated with ABC. The new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm. The proposed algorithm benefits from Mantegna Levy distribution to promote the ABC algorithm in solving the number of functional evaluation and also obtaining better convergence speed and high accuracy in a short time. We empirically evaluate the performance of our proposed method on nine standard datasets taken from the UCI Machine Learning Repository. The experimental results show that the proposed algorithm has ability to obtain better results in terms of convergence speed, accuracy, and reducing the number of functional evaluation.

Journal ArticleDOI
TL;DR: AFOA algorithm is a new algorithm with global optimizing capability and high universality, which is therefore very effective in both accelerating the convergence of the swarm to the global optimal front and maintaining diversity of the solutions.
Abstract: With the development of intelligent computation technology, the intelligent evolution algorithms have been widely applied to solve optimization problem in the real world As a novel evolution algorithm, fruit fly optimization algorithm (FOA) has the advantages of simple operation and high efficiency However, FOA also has some disadvantages, such as trapping into local optimal solution easily, failing to traverse the problem domain and limiting the universality In order to cope with the disadvantages of FOA while retain it merits, this paper proposes AFOA, an adaptive fruit fly optimization algorithm AFOA adjusts the swarm range parameter V dynamically and adaptively according to the historical memory of each iteration of the swarm, and adopts the more accurate elitist strategy, which is therefore very effective in both accelerating the convergence of the swarm to the global optimal front and maintaining diversity of the solutions The convergence of the algorithm is firstly analyzed theoretically, and then 14 benchmark functions with different characteristics are executed to compare the performance among AFOA, PSO, FOA, and LGMS-FOA The experimental results have shown that, AFOA algorithm is a new algorithm with global optimizing capability and high universality

Journal ArticleDOI
TL;DR: The proposed ELM regularization method is applied to a series of standard databases for the evaluation of machine learning techniques and the obtained results clearly demonstrate the usefulness of the proposed method and its superiority over a classical approach.
Abstract: Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation of machine learning techniques. Results obtained in terms of classification success rate and training time, are compared to the original ELM, to the well known Least Square Support Vector Machine (LS-SVM) algorithm and with two other methods based on the ELM regularization: Optimally Pruned Extreme Learning Machine (OP-ELM) and Bayesian Extreme Learning Machine (BELM). The obtained results clearly demonstrate the usefulness of the proposed method and its superiority over a classical approach.

Journal ArticleDOI
TL;DR: This work advocate for a more exible approach for the design of multi-purpose tools for decision support able to integrate environmental and behavioral modifications in a linear fashion, and to compare various scenarios built from different hypotheses in terms of actors, behaviors, environment and ows.
Abstract: Among real-system applications of AI, the field of traffic simulation makes use of a wide range of techniques and algorithms. Especially, microscopic models of road traffic have been expanding for several years. Indeed, Multi-Agent Systems provide the capability of modeling the very diversity of individual behaviors. Several professional tools provide comprehensive sets of ready-made, accurate behaviors for several kinds of vehicles. The price in such tools is the difficulty to modify the nature of programmed behaviors, and the specialization in a single purpose, e.g. either studying resulting ows, or providing an immersive virtual reality environment. Thus, we advocate for a more exible approach for the design of multi-purpose tools for decision support. Especially, the use of geographical open databases offers the opportunity to design agent-based traffic simulators which can be continuously informed of changes in traffic conditions. Our proposal also makes decision support systems able to integrate environmental and behavioral modifications in a linear fashion, and to compare various scenarios built from different hypotheses in terms of actors, behaviors, environment and ows. We also describe here the prototype tool that has been implemented according to our design principles.

Journal ArticleDOI
TL;DR: Post-processing results conducted with MATLAB indicate that the evolving community discovery algorithm approaches the performance of its deterministic counterpart with considerably less complexity.
Abstract: k-Means is among the most significant clustering algorithms for vectors chosen from an underlying space S. Its applications span a broad range of fields including machine learning, image and signal processing, and Web mining. Since the introduction of k-Means, two of its major design parameters remain open to research. The first is the number of clusters to be formed and the second is the initial vectors. The latter is also inherently related to selecting a density measure for S. This article presents a two-step framework for estimating both parameters. First, the underlying vector space is represented as a fuzzy graph. Afterwards, two algorithms for partitioning a fuzzy graph to non-overlapping communities, namely Fuzzy Walktrap and Fuzzy Newman-Girvan, are executed. The former is a low complexity evolving heuristic, whereas the latter is deterministic and combines a graph communication metric with an exhaustive search principle. Once communities are discovered, their number is taken as an estimate of the true number of clusters. The initial centroids or seeds are subsequently selected based on the density of S. The proposed framework is modular, allowing thus more initialization schemes to be derived. The secondary contributions of this article are HI, a similarity metric for vectors with numerical and categorical entries and the assessment of its stochastic behavior, and TD, a metric for assessing cluster confusion. The aforementioned framework has been implemented mainly in C# and partially in C++ and its performance in terms of efficiency, accuracy, and cluster confusion was experimentally assessed. Post-processing results conducted with MATLAB indicate that the evolving community discovery algorithm approaches the performance of its deterministic counterpart with considerably less complexity.

Journal ArticleDOI
TL;DR: This work considers the problem of automatically human face recognition from frontal views with occlusion and disguise, and proposes a new rule for the selection of block and the coefficients for the reconstruction based on the distribution of block labels and the corresponding residual.
Abstract: We consider the problem of automatically human face recognition from frontal views with occlusion and disguise. Since the occlusion can make the query image deviated from the normal distribution, most prior block-based methods focus on reducing the occlusion influence on the global-based representation. Our method is also block-based, but the blocks are non-uniform. Each block contains a certain face component such as eyes, cheek, and forehead, which makes the block more physically meaningful. Recently, sparse representation-based classification (SRC) and Collaborative representation-based classification (CRC) are applied to image recognition successfully, so we classify each block by SRC or CRC respectively. Then the occlusion pixels can be estimated by residuals. Besides, based on the distribution of block labels and the corresponding residual, we propose a new rule for the selection of block and the coefficients for the reconstruction. The final identification is performed on the un-occluded part of each image. Experiments on the AR, extended Yale B and CMU-PIE database verify the robustness and effectiveness of our method.

Journal ArticleDOI
TL;DR: Experimental results clearly demonstrate MOABCLS’s ability of finding a set of well converged and appropriately distributed non-dominated solutions, and the performance promotion by introducing the local search method.
Abstract: This paper proposes a new multi-objective artificial bee colony (ABC) algorithm called MOABCLS by combining ABC with a polynomial mutation based local search method. In this algorithm, an external archive is used to store the non-dominated solutions found so far which are maintained by the crowding distance method. A global best food source gbest is selected and used to produce new food sources in both employed and onlooker bee phases. The aim of adopting a local search is to keep good balance between exploration and exploitation. The MOABCLS is able to deal with both unconstrained and constrained problems, and it is evaluated on test functions (with up to five objectives) taken from the CEC09 competition. The performance of MOABCLS is compared with that of eight state-of-the-art multi-objective algorithms with respect to IGD metric. It is shown by the Wilcoxon test results that MOABCLS performs competitively or even better than the peer algorithms. Further experimental results clearly demonstrate MOABCLS’s ability of finding a set of well converged and appropriately distributed non-dominated solutions, and the performance promotion by introducing the local search method.

Journal ArticleDOI
TL;DR: An improved chaotic binary bat algorithm to solve the QoS multicast routing problem and demonstrates the superiority, effectiveness and efficiency of the proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particles Swarmoptimization (JPSO, and Binary Bat Al algorithm (BBA).
Abstract: The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter β of the pulse frequency fi. Second, we use a dynamic formulation to update the parameter α of the loudness Ai. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

Journal ArticleDOI
TL;DR: A practical application case for the papaya milk sales forecasting has showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.
Abstract: This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.

Journal ArticleDOI
TL;DR: The objective is to build a non-invertible transformation, based on random projection, which meets the requirements of revocability, diversity, security and performance, and uses the chaotic behavior of logistic map to build the projection vectors using a methodology that makes the construction of the projection matrix depend on the biometric template and its identity.
Abstract: Personal authentication systems based on biometrics have given rise to new problems and challenges related to the protection of personal data, issues of less concern in traditional authentication systems. The irrevocability of biometric templates makes biometric systems very vulnerable to several attacks. In this paper we present a new approach for biometric template protection. Our objective is to build a non-invertible transformation, based on random projection, which meets the requirements of revocability, diversity, security and performance. In this context, we use the chaotic behavior of logistic map to build the projection vectors using a methodology that makes the construction of the projection matrix depend on the biometric template and its identity. The proposed approach has been evaluated and compared with Biohashing and BioPhasor using a rigorous security analysis. Our extensive experimental results using several databases (e.g., face, finger-knuckle and iris), show that the proposed technique has the ability to preserve and increase the performance of protected systems. Moreover, it is demonstrated that the security of the proposed approach is sufficiently robust to possible attacks keeping an acceptable balance between discrimination, diversity and non-invertibility.

Journal ArticleDOI
TL;DR: This paper proposes a novel probabilistic Network scheme that employs the aforementioned topic identification method, in order to modify ranking of results as the users select documents, and evaluated in practice the implemented prototype with extensive experiments with the ClueWeb09 dataset.
Abstract: It is widely known that search engines are the dominating tools for finding information on the web. In most of the cases, these engines return web page references on a global ranking taking in mind either the importance of the web site or the relevance of the web pages to the identified topic. In this paper, we focus on the problem of determining distinct thematic groups on web search engine results that other existing engines provide. We additionally address the problem of dynamically adapting their ranking according to user selections, incorporating user judgments as implicitly registered in their selection of relevant documents. Our system exploits a state of the art semantic web data mining technique that identifies semantic entities of Wikipedia for grouping the result set in different topic groups, according to the various meanings of the provided query. Moreover, we propose a novel probabilistic Network scheme that employs the aforementioned topic identification method, in order to modify ranking of results as the users select documents. We evaluated in practice our implemented prototype with extensive experiments with the ClueWeb09 dataset using the TREC’s 2009, 2010, 2011 and 2012 Web Tracks’ where we observed improved retrieval performance compared to current state of the art re-ranking methods.

Journal ArticleDOI
TL;DR: A novel approach to analyze the problem of multi-document summarization based on a mixture model, consisting of a contextual topic model from a Bayesian hierarchical topic modeling family for selecting candidate summary sentences, and a regression model in machine learning for generating the summary.
Abstract: Oft-decried information overload is a serious problem that negatively impacts the comprehension of information in the digital age. Text summarization is a helpful process that can be used to alleviate this problem. With the aim of seeking a novel method to enhance the performance of multi-document summarization, this study proposes a novel approach to analyze the problem of multi-document summarization based on a mixture model, consisting of a contextual topic model from a Bayesian hierarchical topic modeling family for selecting candidate summary sentences, and a regression model in machine learning for generating the summary. By investigating hierarchical topics and their correlations with respect to the lexical co-occurrences of words, the proposed contextual topic model can determine the relevance of sentences more effectively, recognize latent topics, and arrange them hierarchically. The quantitative evaluation results from a practical application demonstrates that a system implementing this model can significantly improve the performance of summarization and make it comparable to state-of-the-art summarization systems.

Journal ArticleDOI
TL;DR: This paper introduces a prognostic framework based on concepts originating from the machine learning universe and proceeds to assess the performance of the prognostics algorithms with statistical methods aiming to formulate a linear predictor whose coefficients are the solution of a multi-objective optimization problem.
Abstract: Integration of energy systems with machine intelligence technologies advances the new generation of intelligent energy systems. One feature of intelligent energy systems is their ability to predict a future fault state (prognosis), and thus support control actions. This paper introduces a prognostic framework based on concepts originating from the machine learning universe and proceeds to assess the performance of the prognostics algorithms with statistical methods aiming to formulate a linear predictor whose coefficients are the solution of a multi-objective optimization problem. Prediction is achieved through independent Gaussian process kernel regressors put together as terms of a linear forecaster. In this novel framework the available data is used to train the regression models whose degradation predictions cover a predetermined time period and have the form of a predictive distribution whose mean and variance values are computed for future moments. Thus, given the observed data points one may search for the most probable values of other quantities of interest, or the values at different points from those measured. The feasibility of the cascading prognostics methodology is demonstrated via a turbine blade degradation example. This implementation is characterized by advantages that include the utilization of an optimization process that simultaneously determines the lowest possible values in a set of different statistical measures and the employment of a set of kernels for modeling various data features and capturing the system dynamics.

Journal ArticleDOI
TL;DR: The paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities.
Abstract: The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naive Bayesian classifier from uncertain objects. So the paper studies Naive Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naive Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances.

Journal ArticleDOI
TL;DR: This paper presents a strategy for extrapolating data from limited uncertain information to ensure a certain level of robustness in the solutions obtained and is motivated and evaluated with real-world applications of harvesting and supplying timber from forests to mills and the well known knapsack problem with uncertainty.
Abstract: Data uncertainty in real-life problems is a current challenge in many areas, including Operations Research (OR) and Constraint Programming (CP). This is especially true given the continual and accelerating increase in the amount of data associated with real-life problems, to which Large Scale Combinatorial Optimization (LSCO) techniques may be applied. Although data uncertainty has been studied extensively in the literature, many approaches do not take into account the partial or complete lack of information about uncertainty in real-life settings. To meet this challenge, in this paper we present a strategy for extrapolating data from limited uncertain information to ensure a certain level of robustness in the solutions obtained. Our approach is motivated and evaluated with real-world applications of harvesting and supplying timber from forests to mills and the well known knapsack problem with uncertainty.

Journal ArticleDOI
TL;DR: In the hybrid ABC, an improved search operator learned from Differential Evolution is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum.
Abstract: Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

Journal ArticleDOI
TL;DR: The objective of the work is to highlight the key features and afford finest future directions in the research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to 2016 by inspecting articles, papers from scientific and standard publications.
Abstract: The objective the work is intend to highlight the key features and afford finest future directions in the research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to 2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management has progressively increased in the past decade by inspecting articles, papers from scientific and standard publications. Survey materialized in three fold process. Firstly, investigate on the amalgamation of Resource Allocation, Resource Scheduling and then proceeded with Resource management. Secondly, we performed a structural analysis on different author’s prominent contributions in the form of tabulation by categories and graphical representation. Thirdly, huddle with conceptual similarity in the field and also impart a summary on all resource allocations. In cloud computing environments, there are two players: cloud providers and cloud users. On one hand, providers hold massive computing resources in their large datacenters and rent resources out to users on a per-usage basis. On the other hand, there are users who have applications with fluctuating loads and lease resources from providers to run their applications. Further, delivers conclusions by conferring future research directions in the field of cloud computing, such as reduce clouds early in the Internet, combining Resource Allocation, Resource Scheduling and Resource management rather than a Cloud model for providing high quality results, etc.

Journal ArticleDOI
TL;DR: A method for qualitative and quantitative analysis of WorkFlow nets based on the proof trees of Linear Logic, concerned with the resource planning for each task of the workflow process and the computation of symbolic date intervals for task execution.
Abstract: This article presents a method for qualitative and quantitative analysis of WorkFlow nets based on the proof trees of Linear Logic. The qualitative analysis is concerned with the proof of Soundness correctness criterion defined for WorkFlow nets. To prove the Soundness property, a proof tree of Linear Logic is built for each different scenario of the WorkFlow net. The quantitative analysis is concerned with the resource planning for each task of the workflow process and is based on the computation of symbolic date intervals for task execution. In particular, such symbolic date intervals are computed using the proof trees used to prove Soundness property.

Journal ArticleDOI
TL;DR: The results showed that the proposed system can effectively identify the correct DVR brands/models with a high accuracy and it was observed that the combination of the traditional speech features with subband Teager energy cepstral parameters (STEC) and short time frame energy as a feature improved recognition accuracy under both silent and noisy recording conditions.
Abstract: Identification of the speech signal origin is an important issue since it may play a vital role for criminal and forensic investigations. Yet, in the media forensics field, source digital voice recorder (DVR) identification has not been given much attention. In this paper we study the effect of subband based features obtained using uniform wavelet packet decomposition and Teager energy operator on the DVR model and brand identification performance. In order to assess the effects of these features on the proposed system, one-class classifiers (OCCs) with two reference multi-class classifiers were carried out. The performance of the DVR identification system is tested on a custom database of twelve portable DVRs of six different brands. The results showed that the proposed system can effectively identify the correct DVR brands/models with a high accuracy. Moreover, it was observed that the combination of the traditional speech features with subband Teager energy cepstral parameters (STEC) and short time frame energy as a feature improved recognition accuracy under both silent and noisy recording conditions.