varunr14/newsummary huggingface dataset:
The Bayesian neural networks are useful tools to estimate the functional structure in the nonlinear systems . However they suffer from some complicated problems such as controlling the model complexity the training time the efficient parameter estimation the random walk and the stuck in the local optima in the high dimensional parameter cases . In this paper to alleviate these mentioned problems a novel hybrid Bayesian learning procedure is proposed . This approach is based on the full Bayesian learning and integrates Markov chain Monte Carlo procedures with genetic algorithms and the fuzzy membership functions . In the application sections to examine the performance of proposed approach nonlinear time series and regression analysis are handled separately and it is compared with the traditional training techniques in terms of their estimation and prediction abilities . 


Summary: A novel Monte Carlo algorithm is proposed to train Bayesian neural networks. This algorithm is based on the full Bayesian approach of artificial neural networks. Monte Carlo methods are integrated with GAs and fuzzy membership functions. Proposed algorithm is applied to time series and regression analysis in the context of BNNs. Proposed approach is superior to traditional training methods in terms of estimation performance.
Cognitive radio network enables unlicensed users to sense for and opportunistically operate in underutilized licensed channels which are owned by the licensed users . Cognitive radio network has been regarded as the next generation wireless network centered on the application of artificial intelligence which helps the SUs to learn about as well as to adaptively and dynamically reconfigure its operating parameters including the sensing and transmission channels for network performance enhancement . This motivates the use of artificial intelligence to enhance security schemes for CRNs . Provisioning security in CRNs is challenging since existing techniques such as entity authentication are not feasible in the dynamic environment that CRN presents since they require pre registration . In addition these techniques can not prevent an authenticated node from acting maliciously . In this article we advocate the use of reinforcement learning to achieve optimal or near optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs . RL which is an artificial intelligence technique has the ability to learn new attacks and to detect previously learned ones . RL has been perceived as a promising approach to enhance the overall security aspect of CRNs . RL which has been applied to address the dynamic aspect of security schemes in other wireless networks such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs . We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge this is the first survey article that focuses on the use of RL based techniques for security enhancement in CRNs . 


Summary: Cognitive radio leverages on reinforcement learning RL to enhance network security. There is lack of reviews on the application of RL to based security schemes. We cover the challenges characteristics performance enhancements and others.
This paper deals with the problem of parameter estimation in the generalized Mallows model by using both local and global search metaheuristic algorithms . The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings permutations . Several approaches can be found in the literature some of which are based on greedy search and branch and bound search . The greedy approach has the disadvantage of usually becoming trapped in local optima while the branch and bound approach basically A search usually comes down to approximate search because of memory requirements losing in this way its guaranteed optimality . Here we carry out a comparative study of several MH algorithms methods variable neighborhood search methods genetic algorithms and estimation of distribution algorithms and a tailored algorithm A to address parameter estimation in GMMs . We use 22 real datasets of different complexity all but one of which were created by the authors by preprocessing real raw data . We provide a complete analysis of the experiments in terms of accuracy number of iterations and CPU time requirements . 


Summary: We deal with the problem of parameter estimation in Generalized Mallows model GMM . We deal with 22 real datasets all of them but one created by the authors. We have designed two experiments varying the maximum evaluations allowed. Obtained results significantly improve the previous competing approaches.
In this research flexible flow shop scheduling with unrelated parallel machines at each stage are considered . The number of stages and machines vary at each stage and each machine can process specific operations . In other words machines have eligibility and parts have different release times . In addition the blocking restriction is considered for the problem . Parts should pass each stage and process on only one machine at each stage . In the proposed problem transportation of parts loading and unloading parts are done by robots and the objective function is finding an optimal sequence of processing parts and robots movements to minimize the makespan and finding the closest number to the optimal number of robots . The main contribution of this study is to present the mixed integer linear programming model for the problem which considers release times for parts in scheduling area loading and unloading times of parts which transferred by robots . New methodologies are investigated for solving the proposed model . Ant Colony Optimization with double pheromone and genetic algorithm are proposed . Finally two meta heuristic algorithms are compared to each other computational results show that the GA performs better than ACO and the near optimal numbers of robots are determined . 


Summary: Flexible flow shop scheduling problem with robotic transportation is presented. Minimization of maximum completion time of all parts is considered as objective function. A mixed integer linear programming model is proposed for the problem. Two meta heuristic algorithm GA and ACO is presented.
The theoretical studies of differential evolution algorithm have gradually attracted the attention of more and more researchers . According to recent researches the classical DE can not guarantee global convergence in probability except for some special functions . Along this perspective a problem aroused is that on which functions DE can not guarantee global convergence . This paper firstly addresses that DE variants are difficult on solving a class of multimodal functions identified by two characteristics . One is that the global optimum of the function is near a boundary of the search space . The other is that the function has a larger deceptive optima set in the search space . By simplifying the class of multimodal functions this paper then constructs a Linear Deceptive function . Finally this paper develops a random drift model of the classical DE algorithm to prove that the algorithm can not guarantee global convergence on the class of functions identified by the two above characteristics . 


Summary: We constructed a Linear Deceptive function as the representative of a class of multimodal functions. DE cannot guarantee convergence in probability on the above class of multimodal functions. A random drift model was firstly used to analyze the convergence of a real coded evolutionary algorithm. DE s mutation operators prefer to search in the aggregating region of the target individuals.
Detecting discontinuities in electrical signals from recorded oscillograms makes it possible to segment them . This is the first step in implementing automated methods which will ensure disturbances in electrical power systems are detected classified and stored . In this context this paper presents a way of determining an adaptive threshold based on the decomposition of electrical signals through the Discrete Wavelet Transform using Daubechies family filter banks allowing for the segmentation of signals and as a consequence the analysis of disturbances related to Power Quality . Considering this the proposed approach was initially evaluated for signals originating from mathematical models representing short term voltage fluctuations transients and harmonic distortions . In the synthetic signal database either single or combined occurrences of more than one disturbance were considered . By applying the DWT the amount of energy and entropy of energy were then calculated for the leaves of the second level of decomposition . Based on these calculations a unique adaptive threshold could be determined for each analyzed signal . Afterwards the amount of existing intersections between the threshold and the curve of details obtained for the second level of decomposition was then defined . Thus the intersections determine the beginning and end of the segments . In order to validate the approach the performance of the proposed methodology was analyzed considering the signals obtained from oscillograms provided by IEEE 1159.3 Task Force as well as real oscillograms obtained from a regional distribution utility . After these analyses it was observed that the proposed approach is efficient and applicable to automatic segmentation of events related to PQ . 


Summary: An adaptive threshold is determined for segmentation of power quality signals. The power quality signals are based on mathematical models and acquired in field. Wavelet transforms are used to decompose the signals. The intersections between the adaptive threshold and the wavelet transform detail curves determine the start and the end of the segments. The adaptive threshold was accurate in more than 96 percent of the signals.
Time series forecasting is an important and widely popular topic in the research of system modeling and stock index forecasting is an important issue in time series forecasting . Accurate stock price forecasting is a challenging task in predicting financial time series . Time series methods have been applied successfully to forecasting models in many domains including the stock market . Unfortunately there are 3 major drawbacks of using time series methods for the stock market some models can not be applied to datasets that do not follow statistical assumptions most time series models that use stock data with a significant amount of noise involutedly have worse forecasting performance and the rules that are mined from artificial neural networks are not easily understandable . To address these problems and improve the forecasting performance of time series models this paper proposes a hybrid time series adaptive network based fuzzy inference system model that is centered around empirical mode decomposition to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Stock Index . To measure its forecasting performance the proposed model is compared with Chen s model Yu s model the autoregressive model the ANFIS model and the support vector regression model . The results show that our model is superior to the other models based on root mean squared error values . 


Summary: This paper proposes a hybrid time series ANFIS model based on EMD to forecast stock price. In order to evaluate the forecasting performances the proposed model is compared with other models. The experimental results show that proposed model is superior to the listing models.
We consider the problem faced by a company that must outsource reverse logistics activities to third party providers . Addressing RL outsourcing problems has become increasingly relevant issue in the management science and decision making literatures . The correct evaluation and ranking of the decision criteria priorities determining the selection of the best third party RL providers is essential for the competitive performance of the outsourcing company . The method proposed in this study allows to identify and classify these decision criteria . First the relevant criteria and sub criteria are identified using a SWOT analysis . Then Intuitionistic Fuzzy AHP is used to evaluate the relative importance weights among the criteria and the corresponding sub criteria . These relative weights are implemented in a novel extension of Mikhailov s fuzzy preference programming method to produce local weights for all criteria and sub criteria . Finally these local weights are used to assign a global weight to each sub criterion and create a ranking . We discuss the results obtained by applying the proposed model to a case study of a real company . In particular these results show that the most important priority for the company when delegating RL activities to 3PRLPs is to focus on the core business while reducing costs constitutes one of its least important priorities . 


Summary: We study a criteria evaluation system for outsourcing reverse logistics ORL . A hybrid SWOT and intuitionistic fuzzy AHP model evaluates strategic factors in ORL. Triangular intuitionistic fuzzy numbers are used to model ambiguity and uncertainty. The proposed model is validated through a case study. Focusing on core business is shown to be the organization s strategic priority.
This paper presents a new algorithm designed to find the optimal parameters of PID controller . The proposed algorithm is based on hybridizing between differential evolution and Particle Swarm Optimization with an aging leader and challengers algorithms . The proposed algorithm is tested on twelve benchmark functions to confirm its performance . It is found that it can get better solution quality higher success rate in finding the solution and yields in avoiding unstable convergence . Also ALC PSODE is used to tune PID controller in three tanks liquid level system which is a typical nonlinear control system . Compared to different PSO variants genetic algorithm differential evolution and Ziegler Nichols method the proposed algorithm achieve the best results with least standard deviation for different swarm size . These results show that ALC PSODE is more robust and efficient while keeping fast convergence . 


Summary: Propose a modified PSO DV algorithm using aging mechanism. The algorithm is tested on 12 benchmark functions. The algorithm is used for determining the optimal parameters of PID controller. Comparisons with different evolutionary algorithms show that the algorithm is efficient and robust.
Many variants of particle swarm optimization both enhance the performance of the original method and greatly increase its complexity . Motivated by this fact we investigate factors that influence the convergence speed and stability of basic PSO without increasing its complexity from which we develop an evaluation index called Control Strategy PSO . The evaluation index is based on the oscillation properties of the transition process in a control system . It provides a method of selection parameters that promote system convergence to the optimal value and thus helps manage the optimization process . In addition it can be applied to the characteristic analyses and parameter confirmation processes associated with other intelligent algorithms . We present a detailed theoretical and empirical analysis in which we compare the performance of CSPSO with published results on a suite of well known benchmark optimization functions including rotated and shifted functions . We used the convergence rates and iteration numbers as metrics to compare simulation data and thereby demonstrate the effectiveness of our proposed evaluation index . We applied CSPSO to antenna array synthesis and our experimental results show that it offers high performance in pattern synthesis . 


Summary: An evaluation index called Control Strategy PSO is developed. It can be applied to other intelligent algorithms. We present a detailed theoretical and empirical analysis.
Advanced sensing technologies have produced a significant amount of discrete point data in the past decade . Measurement uncertainty frequently occurs at the geometric discontinuity of mechanical parts . In this paper a genetic search algorithm is developed for optimally constrained multiple line fitting of discrete data points . It contains two important technical components constrained least squares fitting of multiple lines and genetic search for optimal corner edge points . The algorithm is designed for both two dimensional and three dimensional cases . Numerical experiments demonstrate the effectiveness of the proposed approach compared to the conventional least squares fitting method as well as exhaustive search method . A comparative study with a particle swarm method indicates that both the genetic search and particle swarm search produce similar results in terms of minimum fitting errors . It can be used for the effective determination of sharp edges or corners based on discrete data points measured for high precision industrial inspection and manufacturing . direction numbers of a line in a three dimensional space binary numbers for a chromosome cognitive and social parameters decimal value corresponding to binary bits the shortest distance of the ith data point and the fitting line fitness function in two dimensional cases fitness function in three dimensional cases the fitness value of the ith candidate corner mean and maximum fitness the probability of crossover the probability of mutation the position of the globally best individual the best previous position of the ith particle random numbers that are uniformly distributed within a range the kth position of the ith particle in 2D The kth position of the ith particle in 3D the coordination of the ith data point in a three dimension space The coordination of start point for genetic search orthogonal projection of data points to fitted lines the real increment in a given genetic search the coordination of candidate constrained corner the size of genetic search in three coordinate directions the coordinates of the point passed by a line the coordinates of any point in a three dimension space the kth velocity of the ith particle inertial weight the coefficients of a line equation in two and three dimensions the sum of squared coordinate difference in x coordinate between data points and their corresponding points on the fitted line the sum of squared coordinate difference in y coordinate between data points and their corresponding points on the fitted line the gradient of E the gradient of E standard deviation of data points 


Summary: Numerical computations were conducted under many conditions including different chromosome lengths and search sizes. The good experimental performance of two schemes indicates that the genetic search proposed in this paper is a better way to locate exactly a constrained point at geometric discontinuity. Our algorithm is invariant of chromosome length and search space size to a certain degree.
As social media and e commerce on the Internet continue to grow opinions have become one of the most important sources of information for users to base their future decisions on . Unfortunately the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time . The users have to read a large number of opinions of different entities before making any decision . Recently a new retrieval task in information retrieval known as Opinion Based Entity Ranking has emerged . OpER directly ranks relevant entities based on how well opinions on them are matched with a user s preferences that are given in the form of queries . With such a capability users do not need to read a large number of opinions available for the entities . Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity . Entity relevance scores are computed primarily on the basis of occurrences of query keywords match by assuming all opinions of an entity as a single field of text . Intuitively entities that have positive judgments and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments . This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user s query keywords . As a useful ranking model may be constructed from many ranking features we apply learning to rank approach based on genetic programming to combine features in order to develop an effective retrieval model for OpER task . The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach . 


Summary: In this paper we address Opinion Based Entity Rank OpER task. OpER ranks entities based on how well opinion of entities match with users queries. We have outlined an extensive list of ranking features that can be used to capture the notion of query keyword relevance with individual opinions. Our experiments indicate that these ranking features have significantly high effectiveness for OpER task than standard retrieval models. For further improving the effectiveness we combine these ranking feature using genetic programming GP and learning to rank approach.
Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications . In recent years some itemset representations based on node sets have been proposed which have shown to be very efficient for mining frequent itemsets . In this paper we propose DiffNodeset a novel and more efficient itemset representation for mining frequent itemsets . Based on the DiffNodeset structure we present an efficient algorithm named dFIN to mining frequent itemsets . To achieve high efficiency dFIN finds frequent itemsets using a set enumeration tree with a hybrid search strategy and directly enumerates frequent itemsets without candidate generation under some case . For evaluating the performance of dFIN we have conduct extensive experiments to compare it against with existing leading algorithms on a variety of real and synthetic datasets . The experimental results show that dFIN is significantly faster than these leading algorithms . 


Summary: In this paper we propose an algorithm named dFIN for frequent itemset mining. dFIN achieves high performance by employing DiffNodesets to represent itemsets. Experiment results on real datasets show that dFIN is effective and outperforms state of the art algorithms.