varunr14/newsummary huggingface dataset:
Recently a novel probabilistic model building evolutionary algorithm named probabilistic model building genetic network programming has been proposed . PMBGNP uses graph structures for its individual representation which shows higher expression ability than the classical EDAs . Hence it extends EDAs to solve a range of problems such as data mining and agent control . This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems . Different from the other continuous EDAs the proposed algorithm evolves the continuous variables by reinforcement learning . We compare the performance with several state of the art algorithms on a real mobile robot control problem . The results show that the proposed algorithm outperforms the others with statistically significant differences . 


Summary: This paper proposes a novel continuous estimation of distribution algorithm EDA . A recent EDA named PMBGNP is extended from discrete domain to continuous domain. Reinforcement Learning RL is applied to construct the probabilistic model. Experiments on real mobile robot control show the superiority of the proposed algorithm. It bridges the gap between EDA and RL.
The evaluation of bone development is a complex and time consuming task for the physicians since it may cause intraobserver and interobserver differences . In this study we present a new training algorithm for support vector machines in order to determine the bone age in young children from newborn to 6 years old . By the new algorithm we aimed to assist the radiologists so as to eliminate the disadvantages of the methods used in bone age determination . To achieve this purpose primarily feature extraction procedure was performed to the left hand wrist X ray images by using image processing techniques and the features related with the carpal bones and distal epiphysis of radius bone were obtained . Then these features were used for the input arguments of the classifier . In the classification process a new training algorithm for support vector machines was proposed by using particle swarm optimization . When training support vector machines particle swarm optimization was used for generating a new training instance which will represent the whole training set of the related class by using the training set . Finally these new instances were used as the support vectors and classification process was carried out by using these new instances . The performance of the proposed method was compared with the naive Bayes k nearest neighborhood support vector machines and C4.5 algorithms . As a result it was determined that the proposed method was found successful than the other methods for bone age determination with a classification performance of 74.87 . 


Summary: This paper proposes a new approach for training support vector machines with a bone age determination system. The proposed approach is a combination of particle swarm optimization PSO and support vector machines SVMs . The performance and accuracy of the proposed PSO SVM algorithm are examined on a bone age data set. The results obtained by PSO SVM show that PSO SVM is more effective than the previous study based on conventional SVM.
This paper deals with Hamilton Jacobi Bellman equation based stabilized optimal control of hybrid dynamical systems . This paper presents the fuzzy clustering based event wise multiple linearized modeling approaches for HDS to describe the continuous dynamic in each event . In the present work a fuzzy clustering validation approach is presented for the selection of number of linearized models which span entire HDS . The method also describes how to obtain event wise operating point using fuzzy membership function which is used to find the event wise model bank by linearizing the first principles model . The event wise linearized models are used for the formulation of the optimal control law . The HJB equation is formulated using a suitable quadratic term in the objective function . By use of the direct method of Lyapunov stability the control law is shown to be optimal with respect to objective functional and stabilized the event wise linearized models . The global Lyapunov function is proposed with discrete variables which stabilized the HDS . The proposed modeling and control algorithm have been applied on two HDSs . Necessary theoretical and simulation experiments are presented to demonstrate the performance and validation of the proposed algorithm . 


Summary: Hamilton Jacobi Bellman HJB equation based stabilized optimal control of hybrid dynamical systems HDS is presented. The fuzzy validity based method is used to find the number of linear models present in the HDS. Stability proof for the event wise and generalized HJB solution based optimal control is proposed. The proposed modeling and control algorithm have been applied on two HDSs.
We present an optimization based unsupervised approach to automatic document summarization . In the proposed approach text summarization is modeled as a Boolean programming problem . This model generally attempts to optimize three properties namely relevance summary should contain informative textual units that are relevant to the user redundancy summaries should not contain multiple textual units that convey the same information and length summary is bounded in length . The approach proposed in this paper is applicable to both tasks single and multi document summarization . In both tasks documents are split into sentences in preprocessing . We select some salient sentences from document to generate a summary . Finally the summary is generated by threading all the selected sentences in the order that they appear in the original document . We implemented our model on multi document summarization task . When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets we found that our method improves the summarization results significantly . This is because first when extracting summary sentences this method not only focuses on the relevance scores of sentences to the whole sentence collection but also the topic representative of sentences . Second when generating a summary this method also deals with the problem of repetition of information . The methods were evaluated using ROUGE 1 ROUGE 2 and ROUGE SU4 metrics . In this paper we also demonstrate that the summarization result depends on the similarity measure . Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately . 


Summary: We model document summarization as a quadratic Boolean programming problem. We create a modified differential evolution to solve the optimization problem. Experimental study shows that the model improves the summarization results.
In this paper a Multitree Genetic Programming based method is developed to learn an INTerpretable and ACcurate Takagi Sugeno Kang fuzzy rule based sYstem for dynamic portfolio trading . The MGP INTACTSKY utilizes a TSK model with a new structure to develop a more interpretable and accurate system for dynamic portfolio trading . In the new structure of TSK disjunctive normal form rules with variable structured consequent parts are developed in which the absence of some input variables is allowed . Input variables are the most influential technical indices which are selected by stepwise regression analysis . The technical indices are computed using wavelet transformed stock price series to eliminate the noise . The proposed system directly induces the preferred portfolio weights from the stock s technical indices through time . Here genetic programming with the multitree structure is applied to learn the TSK fuzzy rule bases with the Pittsburgh approach . With this approach the correlation of different stocks is properly considered during the evolutionary process . To evaluate the performance of the MGP INTACTSKY for portfolio trading the proposed model is implemented on the Tehran Stock Exchange as an emerging market as well as Toronto and Frankfurt Stock Exchanges as two mature markets . The experimental results show that the proposed model outperforms other methods such as the momentum strategy the multitree genetic programming based crisp system the genetic algorithm based first order TSK system the buy and hold approach and the market s main index in terms of accuracy and interpretability . 


Summary: MGP INTACTSKY is a fuzzy rule based system for dynamic portfolio trading. Multitree Genetic Programming MGP is applied to learn the TSK fuzzy rule bases. The new TSK structure leads to a more interpretable and accurate system. Input variables are technical indices selected by stepwise regression analysis. The results are based on testing of the model on one emerging and two mature markets.
In this article the dynamic multi swarm particle swarm optimizer and a new cooperative learning strategy are hybridized to obtain DMS PSO CLS . DMS PSO is a recently developed multi swarm optimization algorithm and has strong exploration ability for the use of a novel randomly regrouping schedule . However the frequently regrouping operation of DMS PSO results in the deficiency of the exploitation ability . In order to achieve a good balance between the exploration and exploitation abilities the cooperative learning strategy is hybridized to DMS PSO which makes information be used more effectively to generate better quality solutions . In the proposed strategy for each sub swarm each dimension of the two worst particles learns from the better particle of two randomly selected sub swarms using tournament selection strategy so that particles can have more excellent exemplars to learn and can find the global optimum more easily . Experiments are conducted on some well known benchmarks and the results show that DMS PSO CLS has a superior performance in comparison with DMS PSO and several other popular PSO variants . 


Summary: A new cooperative learning strategy is hybridized with DMS PSO. Information can be exchanged among sub swarms before the regrouping process. Experimental results show that DMS PSO CLS has a superior performance.
Designing of scalable routing protocol with prolonged network lifetime for a wireless sensor network is a challenging task . WSN consists of large number of power communication and computational constrained inexpensive nodes . It is difficult to replace or recharge battery of a WSN node when operated in a hostile environment . Cluster based routing is one of the techniques to provide prolonged network lifetime along with scalability . This paper proposes a technique for cluster formation derived from Grid based method . We have also proposed a new decentralized cluster head election method based on Bollinger Bands . Bollinger Bands are based on Upper Bollinger Band and Lower Bollinger Band both of these bands are extremely reactive to any change in the inputs provided to them . We have used this property of Bollinger Bands to elect CH . Simulation result shows significant improvement in the network lifetime in comparison with other decentralized and ant based algorithms . 


Summary: Used Grid Based Method to create clusters. Derived cluster head selection method based on Bollinger Bands BB that is a technical trading tool. Simulation results shows significant improvement in the network life time that is measured using First Node Dies FND Half of the Nodes Alive HNA and Last Node Dies LND . Proposed algorithm is compared with seven different algorithms using three different node deployment pattern and two different positions of Sink.
The pyramidal dual tree directional filter bank transform is a new image decomposition which has many advantages such as multiscale and multidirectional transform efficient implementation high angular resolution low redundant ratio and shiftable subbands . In this paper we present a new color image segmentation algorithm based on PDTDFB domain hidden Markov tree model . Firstly the joint statistics and mutual information of the PDTDFB coefficients are studied . Then the PDTDFB coefficients are modeled using a HMT model with Gaussian mixtures which can effectively capture the intra scale inter scale and inter direction dependencies . Finally a color image segmentation using PDTDFB domain HMT model is developed in which expectation maximization parameter estimation Bayesian multiscale raw segmentation context based multiscale fusion and majority vote based color component fusion are used . Experimental evidence shows that the proposed color image segmentation algorithm has very effective segmentation results in comparison with the state of the art segmentation methods recently proposed in the literature . 


Summary: The joint statistics and mutual information of the PDTDFB transform coefficients are studied. The PDTDFB transform coefficients are modeled using a HMT statistical model with Gaussian mixtures. The intra scale inter scale and inter direction dependencies are captured comprehensively. A PDTDFB domain HMT statistical model based color image segmentation scheme is developed.
This paper presents a modified version of the water cycle algorithm . The fundamental concepts and ideas which underlie the WCA are inspired based on the observation of water cycle process and how rivers and streams flow to the sea . New concept of evaporation rate for different rivers and streams is defined so called evaporation rate based WCA which offers improvement in search . Furthermore the evaporation condition is also applied for streams that directly flow to sea based on the new approach . The ER WCA shows a better balance between exploration and exploitation phases compared to the standard WCA . It is shown that the ER WCA offers high potential in finding all global optima of multimodal and benchmark functions . The WCA and ER WCA are tested using several multimodal benchmark functions and the obtained optimization results show that in most cases the ER WCA converges to the global solution faster and offers more accurate results than the WCA and other considered optimizers . Based on the performance of ER WCA on a number of well known benchmark functions the efficiency of the proposed method with respect to the number of function evaluations and accuracy of function value are represented . 


Summary: WCA concepts are inspired by nature and based on water cycle process. New concepts of evaporation rate for rivers and streams are applied in the ER WCA. Variable evaporation rate is utilized in the ER WCA to evaporate the water adaptively. ER WCA forces new generated streams to search near sea using the concept of variance. ER WCA outperforms or equals other methods in terms of function evaluations and solution quality.
In this paper a hybrid gravitational search algorithm and pattern search technique is proposed for load frequency control of multi area power system . Initially various conventional error criterions are considered the PI controller parameters for a two area power system are optimized employing GSA and the effect of objective function on system performance is analyzed . Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation . After that PS is employed to fine tune the best solution provided by GSA . Further modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS approach . The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm differential evolution bacteria foraging optimization algorithm particle swarm optimization hybrid BFOA PSO NSGA II and genetic algorithm for the same interconnected power system . Additionally sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values . Also the proposed approach is extended to two area reheat thermal power system by considering the physical constraints such as reheat turbine generation rate constraint and governor dead band nonlinearity . Finally to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA PS technique . 


Summary: Selection of objective function and controller structure is vital for controller design. An objective function using ITAE damping ratio and settling times is proposed. The concept is applied to design an hGSA PS based PI PID controller for LFC. Nonlinear interconnected power system model with GRC GDB and time delay is considered.
In this paper we present a clustering framework for type 2 fuzzy clustering which covers all steps of the clustering process including clustering algorithm parameters estimation and validation and verification indices . The proposed clustering algorithm is developed based on dual centers type 2 fuzzy clustering model . In this model the centers of clusters are defined by a pair of objects rather than a single object . The membership values of the objects to the clusters are defined by type 2 fuzzy numbers and there are not any type reduction or defuzzification steps in the proposed clustering algorithm . In addition the relation among the size of the cluster bandwidth distance between dual centers and fuzzifier parameter are indicated and analyzed to facilitate the parameters estimation step . To determine the optimum number of clusters we develop a new validation index which is compatible with the proposed model structure . A new compatible verification index is also defined to compare the results of the proposed model with existing type 1 fuzzy clustering model . Finally the results of computational experiments are presented to show the efficiency of the proposed approach . 


Summary: The clustering model considers dual centers rather than single centers. The dual centers type 2 clustering model and algorithm are proposed. The relations among parameters of the proposed model are explained. The degrees of belonging to the clusters are defined by type 2 fuzzy numbers. The verification and verification indices are developed for model evaluation.
Unplanned dilution and ore loss are the most critical challenges in underground stoping operations . These problems are the main cause behind a mine closure and directly influencing the productivity of the underground stope mining and the profitability of the entire operation . Despite being aware of the significance of unplanned dilution and ore loss prediction of these phenomena is still unexplained as they occur through complex mechanisms and causative factors . Current management practices primarily rely on similar stope reconciliation data and the intuition of expert mining engineers . In this study an innovative unplanned dilution and ore loss management system is established using a neuro fuzzy system . The aim of the proposed decision support system is to overcome the UB phenomenon in underground stope blasting which provides quantitative prediction of unplanned dilution and ore loss with practical recommendations simultaneously . To achieve the method proposed an uneven break prediction system was developed by an artificial neural network considering 1076 datasets covering 10 major UB causative factors collected from three underground stoping mines in Western Australia . In succession the UB consultation system was established via a fuzzy expert system in reference to surveyed results of fifteen underground mining experts . The UB prediction and consultation system were combined as one concurrent neuro fuzzy system that is named the uneven break optimiser . Because the current UB prediction systems in investigated mines were highly unsatisfactory with correlation coefficient of 0.088 and limited to only unplanned dilution the performance of the proposed UB prediction system is a remarkable achievement . The uneven break optimiser can be directly employed to improve underground stoping production and this tool will be beneficial not only for underground stope planning and design but also for production management . 


Summary: Focus on a minimisation unplanned dilution and ore loss in underground stope. Comprehensive datasets were collected from three mines. Prediction model was built via artificial neural network ANN . Consultation model was built via Fuzzy expert system FES . ANN and FES are combined as a concurrent neuro fuzzy model.
Feature selection is often required as a preliminary step for many pattern recognition problems . However most of the existing algorithms only work in a centralized fashion i.e . using the whole dataset at once . In this research a new method for distributing the feature selection process is proposed . It distributes the data by features i.e . according to a vertical distribution and then performs a merging procedure which updates the feature subset according to improvements in the classification accuracy . The effectiveness of our proposal is tested on microarray data which has brought a difficult challenge for researchers due to the high number of gene expression contained and the small samples size . The results on eight microarray datasets show that the execution time is considerably shortened whereas the performance is maintained or even improved compared to the standard algorithms applied to the non partitioned datasets . 


Summary: Feature selection is indispensable when dealing with microarray data. A new method for distributing the filtering process is proposed. The data is distributed by features and then merged in a final subset. The method is tested on 8 microarray datasets. The classification accuracy is maintained and the time considerably shortened.
In this paper we aim at proposing a switching adaptive control scheme using a Hopfield based dynamic neural network for nonlinear systems with external disturbances . In our proposed scheme an auxiliary direct adaptive controller ensures the system stability when the indirect adaptive controller is failed that is approaches to zero where is the denominator of an indirect adaptive control law . The IAC s limitation of then can be solved by simply switching the IAC to the DAC where is a positive desired value . The Hopfield dynamic neural network is used to not only design DAC but also approximate the unknown plant nonlinearities in IAC design . The designed simple structure of HDNN keeps the tracking performance well and also makes the practical implementation much easier because of the use of less and fixed number of neurons . 


Summary: A switching adaptive control scheme using a Hopfield based dynamic neural network SACHNN for nonlinear systems with external disturbances is proposed. The IAC s limitation of can be solved by simply switching the IAC to the DAC where is a positive desired value. The Hopfield dynamic neural network HDNN is used to not only design DAC but also approximate the unknown plant nonlinearities in IAC design.