varunr14/newsummary huggingface dataset:
Although greedy algorithms possess high efficiency they often receive suboptimal solutions of the ensemble pruning problem since their exploration areas are limited in large extent . And another marked defect of almost all the currently existing ensemble pruning algorithms including greedy ones consists in they simply abandon all of the classifiers which fail in the competition of ensemble selection causing a considerable waste of useful resources and information . Inspired by these observations an interesting greedy Reverse Reduce Error pruning algorithm incorporated with the operation of subtraction is proposed in this work . The RRE algorithm makes the best of the defeated candidate networks in a way that the Worst Single Model is chosen and then its votes are subtracted from the votes made by those selected components within the pruned ensemble . The reason is because for most cases the WSM might make mistakes in its estimation for the test samples . And different from the classical RE the near optimal solution is produced based on the pruned error of all the available sequential subensembles . Besides the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE . Moreover the problem of ties might be solved more naturally with RRE . Finally soft voting approach is employed in the testing to RRE algorithm . The performances of RE and RRE algorithms and two baseline methods i.e . the method which selects the Best Single Model in the initial ensemble and the method which retains all member networks of the initial ensemble are evaluated on seven benchmark classification tasks under different initial ensemble setups . The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms . 


Summary: An interesting RRE pruning algorithm incorporated with the operation of subtraction is proposed in this work. The WSM is chosen and its votes are subtracted from the votes made by those selected components. The backfitting step of RE algorithm is replaced with the selection step of a WSB in RRE. The problem of ties might be solved more naturally with RRE. Soft voting approach is employed in the testing to RRE algorithm.
PieceWise AutoRegressive eXogenous models represent one of the broad classes of the hybrid dynamical systems . Among many classes of HDS PWARX model used as an attractive modeling structure due to its equivalence to other classes . This paper presents a novel fuzzy distance weight matrix based parameter identification method for PWARX model . In the first phase of the proposed method estimation for the number of affine submodels present in the HDS is proposed using fuzzy clustering validation based algorithm . For the given set of input output data points generated by predefined PWARX model fuzzy c means clustering procedure is used to classify the data set according to its affine submodels . The fuzzy distance weight matrix based weighted least squares algorithm is proposed to identify the parameters for each PWARX submodel which minimizes the effect of noise and classification error . In the final phase fuzzy validity function based model selection method is applied to validate the identified PWARX model . The effectiveness of the proposed method is demonstrated using three benchmark examples . Simulation experiments show validation of the proposed method . 


Summary: A novel fuzzy distance weight matrix based parameter identification method. Fuzzy clustering based algorithm used to find sub models of HDS. WLS algorithm is used to identify parameters of sub models. Results validated through simulation experiments.
In this research we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals . The strategy of an agent is modeled by certain simple game theory models with limited information . Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data . The trained model can be used for collective behavior prediction . For modeling individual behavior two simple games the minority game and mixed game are investigated in experiments on the real world stock prices and foreign exchange rate . Experimental results are presented to show the effectiveness of the new proposed model . 


Summary: Collective behaviors are modeled as aggregations of individual behaviors. Individual behavior is modeled by the minority game. Parameters of individual behavior can be learned using genetic algorithms. The new model is tested based on real world financial data.
Large scale software systems are in general difficult to manage and monitor . In many cases these systems display unexpected behavior especially after being updated or when changes occur in their environment . Therefore to handle a changing environment it is desirable to base fault detection and performance monitoring on self adaptive techniques . Several studies have been carried out in the past which inspired on the immune system aim at solving complex technological problems . Among them anomaly detection pattern recognition system security and data mining are problems that have been addressed in this framework . There are similarities between the software fault detection problem and the identification of the pathogens that are found in natural immune systems . Being inspired by vaccination and negative and clonal selection observed in these systems we developed an effective self adaptive model to monitor software applications analyzing the metrics of system resources . 


Summary: Monitoring applications that only use negative selection feature detect too many false positives. The fault injection reinforcement learning process enables the overall model to become robust and efficient. The clonal selection feature promotes adaptation. The model is very flexible because is based on performance indicators and consumption resources.
We propose a new image encryption algorithm which is based on the spatiotemporal non adjacent coupled map lattices . The system of non adjacent coupled map lattices has more outstanding cryptography features in dynamics than the logistic map or coupled map lattices does . In the proposed image encryption we employ a bit level pixel permutation strategy which enables bit planes of pixels permute mutually without any extra storage space . Simulations have been carried out and the results demonstrate the superior security and high efficiency of the proposed algorithm . 


Summary: We propose an image encryption scheme based on a new spatiotemporal chaotic system. The encryption scheme is not the one time pad encryption. The proposed image encryption has a large key space and high security.
Multilayer perceptron and support vector machine two popular learning machines are increasingly being used as alternatives to classical statistical models for ground level ozone prediction . However employing learning machines without sufficient awareness about their limitations can lead to unsatisfactory results in modeling the ozone evolving mechanism especially during ozone formation episodes . With the spirit of literature review and justification this paper discusses with respect to the concerning of ozone prediction the recently developed algorithms technologies for treating the most prominent model performance degradation limitations . MLP has the black box property i.e . it hardly provides physical explanation for the trained model overfitting and local minima problems and SVM has parameters identification and class imbalance problems . This commentary article aims to stress that the underlying philosophy of using learning machines is by no means as trivial as simply fitting models to the data because it causes difficulties controversies or unresolved problems . This article also aims to serve as a reference point for further technical readings for experts in relevant fields . 


Summary: Address the potential of learning machine to forecast ground level ozone in urban area. Summarize the existing learning machines used to predict ground level ozone. Compare the performance of commented models via practical case in Hong Kong. Address the underlying philosophy of using learning machine in ozone related prediction.
This paper presents fully fuzzy fixed charge multi item solid transportation problems in which direct costs fixed charges supplies demands conveyance capacities and transported quantities are fuzzy in nature . Objective is to minimize the total fuzzy cost under fuzzy decision variables . In this paper some approaches are proposed to find the fully fuzzy transported amounts for a fuzzy solid transportation problem . Proposed approaches are applicable for both balanced and unbalanced FFFCMISTPs . Another fuzzy fixed charge multi item solid transportation problem in which transported amounts are not fuzzy is also presented and solved by some other techniques . The models are illustrated with numerical examples and nature of the solutions is discussed . 


Summary: Fully fuzzy fixed charge multi item solid transportation problem FFFCMISTP is considered. FFFCMISTP with the decision variable are taken as fuzzy. New defuzzification method fuzzy slack and surplus variable is used for FFFCMISTP. Minimization of transportation cost as well as fuzziness of the solution for FFFCMISTP is discussed.
The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images . It is per pixel supervised classification using spectral bands . This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem . Based on 1050 number of experiments we conclude that following two critical issues needs to be addressed selection of most discriminative spectral bands and determination of optimal number of nodes in hidden layer . We propose new methodology based on multiobjective particle swarm optimization technique to determine discriminative spectral bands and the number of hidden layer node simultaneously . The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier . The performance of proposed classifier is evaluated quantitatively using Xie Beni and indexes . The result shows the superiority of the proposed method to the conventional one . 


Summary: We present the work on efficient classification of multispectral images using soft computing approach. Selection of most discriminative spectral bands and determination of the number of hidden layer neurons are the two most critical issues. We proposed a new multiobjective particle swarm optimization based methodology for adaption of neural network structure for pixel classification of Satellite Imagery. It simultaneously estimates the most discriminative spectral features and the optimal number of nodes in hidden layer. Xie Beni and indexes of proposed algorithm are better than MLC and Euclidean Classifier.
In this study two induced generalized hesitant fuzzy hybrid operators called the induced generalized hesitant fuzzy Shapley hybrid weighted averaging operator and the induced generalized hesitant fuzzy Shapley hybrid geometric mean operator are defined . The prominent characteristics of these two operators are that they do not only globally consider the importance of elements and their ordered positions but also overall reflect their correlations . Furthermore when the weight information of the attributes and the ordered positions is partly known using grey relational analysis method and the Shapley function models for the optimal fuzzy measures on an attribute set and on an ordered set are respectively established . Finally an approach to hesitant fuzzy multi attribute decision making with incomplete weight information and interactive conditions is developed and an illustrative example is provided to show its practicality and effectivity . 


Summary: An improvement ranking method for HFEs is proposed. Two induced generalized hesitant fuzzy Shapley hybrid operators are defined. Models for the optimal fuzzy measures are built. An approach to hesitant fuzzy multi attribute decision making is developed.
Bibliometrics is a discipline that analyzes bibliographic material from a quantitative perspective . It is very useful for classifying information according to different variables including journals institutions and countries . This paper presents a general overview of research in the fuzzy sciences using bibliometric indicators . The main advantage is that these indicators provide a general picture identifying some of the most influential research in this area . The analysis is divided into key sections focused on relevant journals papers authors institutions and countries . Most of the results are in accordance with our common knowledge although some unexpected results are also found . Note that the aim of this paper is to be informative and these indicators identify most of the fundamental research in this field . However some very influential issues may be omitted if they are not included in the Web of Science database which is used for carrying out the bibliometric analysis . 


Summary: Bibliometrics in fuzzy research. List of most cited papers on fuzzy topics of all time. An overview of influential authors institutions and countries. Journal analysis in fuzzy research.
In this paper we investigate the deviation of the priority weights from hesitant multiplicative preference relations in group decision making environments . As basic elements of HMPRs hesitant multiplicative elements usually have different numbers of possible values . To correctly compute or compare HMEs there are two principles to normalize them i.e . the normalization and the normalization . Based on the normalization we develop a new goal programming model to derive the priority weights from HMPRs in group decision making environments . Based on the normalization a consistent HMPR and an acceptably consistent HMPR are defined and their desired properties are studied . A convex combination method is then developed to obtain interval weights from an acceptably consistent HMPR . This approach is further extended to group decision making situations in which the experts evaluate their preferences as several HMPRs . Finally some numerical examples are provided to illustrate the validity and applicability of the proposed models . 


Summary: We investigate the deviation of the priority weights from HMPRs under GDM. Based on the normalization and normalization we develop two models to derive the weights from HMPRs. Some numerical examples are given to illustrate the proposed models.
Clustering is an efficient topology control method which balances the traffic load of the sensor nodes and improves the overall scalability and the life time of the wireless sensor networks . However in a cluster based WSN the cluster heads consume more energy due to extra work load of receiving the sensed data data aggregation and transmission of aggregated data to the base station . Moreover improper formation of clusters can make some CHs overloaded with high number of sensor nodes . This overload may lead to quick death of the CHs and thus partitions the network and thereby degrade the overall performance of the WSN . It is worthwhile to note that the computational complexity of finding optimum cluster for a large scale WSN is very high by a brute force approach . In this paper we propose a novel differential evolution based clustering algorithm for WSNs to prolong lifetime of the network by preventing faster death of the highly loaded CHs . We incorporate a local improvement phase to the traditional DE for faster convergence and better performance of our proposed algorithm . We perform extensive simulation of the proposed algorithm . The experimental results demonstrate the efficiency of the proposed algorithm . 


Summary: The proposed work is a novel DE based clustering scheme for WSNs. The algorithm incorporates an additional step to enhance the performance. Experimental results demonstrate the superiority over existing algorithms. The performance is shown in terms of network life energy consumption etc.
Evolutionary algorithms start with an initial population vector which is randomly generated when no preliminary knowledge about the solution is available . Recently it has been claimed that in solving continuous domain optimization problems the simultaneous consideration of randomness and opposition is more effective than pure randomness . In this paper it is mathematically proven that this scheme called opposition based learning also does well in binary spaces . The proposed binary opposition based scheme can be embedded inside many binary population based algorithms . We applied it to accelerate the convergence rate of binary gravitational search algorithm as an application . The experimental results and mathematical proofs confirm each other . 


Summary: We introduce the concept of opposition based learning in binary spaces.It is proven that utilizing random numbers and their opposite is beneficial in evolutionary algorithms.Opposite numbers are applied to accelerate the convergence rate of Binary Gravitational Search Algorithm BGSA .The results show that OBGSA possesses superior performance in accuracy as compared to the BGSA.
Application of machine learning techniques to the functional Magnetic Resonance Imaging data is recently an active field of research . There is however one area which does not receive due attention in the literature preparation of the fMRI data for subsequent modelling . In this study we focus on the issue of synchronization of the stream of fMRI snapshots with the mental states of the subject which is a form of smart filtering of the input data performed prior to building a predictive model . We demonstrate investigate and thoroughly discuss the negative effects of lack of alignment between the two streams and propose an original data driven approach to efficiently address this problem . Our solution involves casting the issue as a constrained optimization problem in combination with an alternative classification accuracy assessment scheme applicable to both batch and on line scenarios and able to capture information distributed across a number of input samples lifting the common simplifying i.i.d . assumption . The proposed method is tested using real fMRI data and experimentally compared to the state of the art ensemble models reported in the literature outperforming them by a wide margin . 


Summary: We challenge a popular approach to labelling fMRI data for predictive modelling. We propose a new labelling method based on data stream synchronization. We validate the proposed method experimentally on real fMRI data. We observe major classification accuracy improvement and model complexity reduction.
Global positioning system is the most widely used military and commercial positioning tool for real time navigation and location . Geometric dilution of precision stands as a relevant measure of positioning accuracy and consequently the performance quality of the GPS positioning algorithm . Since the calculation of GPS GDOP has a time and power burden that involves complicated transformation and inversion of measurement matrices in this paper we propose hybrid intelligent methods namely adaptive neuro fuzzy inference system improved ANFIS and radial basis function for GPS GDOP classification . Through investigation it is verified that the ANFIS is a high performance and valuable classifier . In the ANFIS training the radius vector has very important role for its recognition accuracy . Therefore in the optimization module bee algorithm is proposed for finding the optimum vector of radius . In order to improve the performance of the proposed method a new improvement for the BA is used . In addition to enhance the accuracy of the method principal component analysis is utilized as a pre processing step . Experimental results clearly indicate that the proposed intelligent methods have high classification accuracy rates comparing with conventional ones . 


Summary: GDOP stands as a relevant measure of positioning accuracy. We propose hybrid intelligent methods namely ANFIS improved ANFIS and RBF for GPS GDOP classification. Bee algorithm BA and improved BA are proposed for finding the optimum radius vector of the ANFIS. To enhance the classification accuracy PCA is utilized.
Removal of miscible hazardous materials from aqueous solutions is an alarming problem for the environmental scientists . Several linear and nonlinear regression models like Langmuir Freundlich D R Tempkin isotherm models are in vogue for determining the adsorbing capacity of standard adsorbents used for this purpose . In this article we propose a novel quantum inspired backpropagation multilayer perceptron based on quantum gates for the prediction of this adsorption behavior exhibited by calcareous soil oftentimes used in adsorbing miscible iron from aqueous solutions . The backpropagation learning formulae for the proposed QBMLP architecture has also been generalized for multiple number of layers in both field homogeneous and field heterogeneous configurations characterized by three standard activations viz . sigmoid tanh and tan1.5h functions . Applications of the efficiency of the proposed QBMLP over the regression models are demonstrated with regards to the prediction behavior of the adsorption of iron by calcareous soil from an aqueous solution with effect to various characteristic adsorbent parameters . The adsorption process is considered to be a physical one since the activation energy of ferrous ion adsorption is 9.469kJmol 1 due to Arrhenius . Moreover the thermodynamic parameters of Gibb s free energy enthalpy and entropy values indicate it be spontaneous . Results indicate that QBMLP predicts the adsorption behavior of calcareous soil to a very closer extent thereby obviating the need for further regression experimental analysis . Comparison with the performance of a similar classical multilayer perceptron architecture also reveals the prediction and time efficiency of the proposed QBMLP architecture . 


Summary: The ability of calcareous soil to remove Fe II from aqueous solution is investigated. The maximum adsorption of Fe II of 2.475mg g at 6.0mgL 1 initial Fe II concentration was found at pH 6.0 and a temperature of 303K after 120min. As an inhouse alternative an intelligent technique has been envisaged by a conventional MLP and a proposed QBMLP architecture. Both the MLP and QBMLP are found to be efficient in the compared to the batch study process.
Traditionally clustering is the task of dividing samples into homogeneous clusters based on their degrees of similarity . As samples are assigned to clusters users need to manually give descriptions for all clusters . In this paper a rapid fuzzy rule clustering method based on granular computing is proposed to give descriptions for all clusters . A new and simple unsupervised feature selection method is employed to endow every sample with a suitable description . Exemplar descriptions are selected from sample s descriptions by relative frequency and data granulation is guided by the selected exemplar fuzzy descriptions . Every cluster is depicted by a single fuzzy rule which make the clusters understandable for humans . The experimental results show that our proposed model is able to discover fuzzy IF THEN rules to obtain the potential clusters . 


Summary: A rapid fuzzy rule clustering method based on granular computing is proposed. Exemplar descriptions are selected from sample s descriptions by relative frequency. Data granulation is guided by the selected exemplar descriptions.