varunr14/newsummary huggingface dataset:
Genetic algorithm is a branch of evolutionary algorithm has proved its effectiveness in solving constrain based complex real world problems in variety of dimensions . The individual phases of GA are the mimic of the basic biological processes and hence the self adaptability of GA varied in accordance to the adjustable natural processes . In some instances self adaptability in GA fails in identifying adaptable genes to form a solution set after recombination which leads converge toward infeasible solution sometimes this infeasible solution could not be converted into feasible form by means of any of the repairing techniques . In this perspective Gene Suppressor a bio inspired process is being proposed as a new phase after recombination in the classical GA life cycle . This phase works on new individuals generated after recombination to attain self adaptability by adapting best genes in the environment to regulate chromosomes expression for achieving desired phenotype expression . Repairing in this phase converts infeasible solution into feasible solution by suppressing conflicting gene from the environment . Further the solution vector expression is improved by inducing best genes expression in the environment within the set of intended constrains . Multiobjective Multiple Knapsack Problems one of the popular NP hard combinatorial problems is being considered as the test bed for proving the competence of the proposed new phase of GA . The standard MMKP benchmark instances obtained from OR library are used for the experiments reported in this paper . The outcomes of the proposed method is compared with the existing repairing techniques where the analyses proved the proficiency of the proposed GS model in terms of better error and convergence rates for all instances . 


Summary: Gene Suppressor proposed as a new add on phase in GA for attaining self adaption and repairing. This regulates genes dosage and its functional expression with respect to its environment. Identifies suppressor genes to perform suppression activity for attaining specific phenotype. Allows adjustment by gene adaption and repairing to obtain best solution and improving it. Experiment focused on proving single problem but the buildup model can be easily adopted to other problem that uses MKP as a base.
In this paper we propose a H.264 AVC compressed domain human action recognition system with projection based metacognitive learning classifier . The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier . Since compressed domain analysis is done with noisy sparse compression parameters it is a huge challenge to achieve performance comparable to pixel domain analysis . On the positive side compressed domain allows rapid analysis of videos compared to pixel level analysis . The classification results are analyzed for different values of Group of Pictures parameter time window including full videos . The functional relationship between the features and action labels are established using PBL McRBFN with a cognitive and meta cognitive component . The cognitive component is a radial basis function while the meta cognitive component employs self regulation to achieve better performance in subject independent action recognition task . The proposed approach is faster and shows comparable performance with respect to the state of the art pixel domain counterparts . It employs partial decoding which rules out the complexity of full decoding and minimizes computational load and memory usage . This results in reduced hardware utilization and increased speed of classification . The results are compared with two benchmark datasets and show more than 90 accuracy using the PBL McRBFN . The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well known classifiers in machine learning literature . 


Summary: Proposed a fast human action recognition system in H.264 compressed domain. Readily available quantization parameters and motion vectors are used for feature extraction. The functional relationship between the features and action labels are classified using PBL McRBFN which has a cognitive and meta cognitive component. Results are analyzed for various GOP parameters. Achieved more than 90 accuracy using PBL McRBFN on two benchmark datasets KTH and Weizmann.
In this paper we investigate the reduction in total transmission time and the energy consumption of wireless sensor networks using multi hop data aggregation by forming coordination in hierarchical clustering . Novel algorithm handles wireless sensor network in numerous circumstances as in large extent and high density deployments . One of the major purposes is to collect information from inaccessible areas by using factorization of the area into subareas and appointing cluster head in each of the subarea . Coordination and cooperation among the local nodes via relay nodes in local cluster helped to serve each and every node . Routing is based on the predefined path proposed by new transmission algorithm . Transmission distance is minimized by using cluster coordinators for inter cluster communication and relay nodes within the cluster . We show by extended simulations that Chain Based Cluster Cooperative Protocol performs very well in terms of energy and time . To prove it we compare it with LEACH SEP genetic HCR and ERP and found that new protocol consumes six times less energy than LEACH five times less energy than SEP four time less energy than genetic HCR and three times less energy than ERP which further validate our work . 


Summary: This paper has presented the our new routing protocol with all results in QoS Quality of service metrics of WSN Wireless Sensor Nodes and it is compared with LEACH SEP genetic HCR and ERP routing protocols. This research is useful for densely deployed networks Terrestrial Environmental and Border surveillance applications . This paper presents the routing protocol in which data is transmitted thorough the CHs and CCOs for intra and inter cluster communication. Our research has optimized many parameters of sensor nodes as Energy Time Reliability Throughput and scalability and results calculated in MATLAB validate our work.
The analysis of internal connective operators of fuzzy reasoning is very significant and the robustness of fuzzy reasoning has been calling for study . An interesting and important question is that how to choose suitable internal connective operators to guarantee good robustness of rule based fuzzy reasoning This paper is intended to answer it . In this paper Lipschitz aggregation property and copula characteristic of t norms and implications are discussed . The robustness of rule based fuzzy reasoning is investigated and the relationships among input perturbation rule perturbation and output perturbation are presented . The suitable t norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning . In 1 Lipschitz operators if both t norm and implication are copulas the rule based fuzzy reasoning is much more stable and more reliable . In copulas if both t norm and implication are 1 l Lipschitz they can guarantee good robustness of fuzzy reasoning . The experiments not only illustrate the ideas proposed in the paper but also can be regarded as applications of soft computing . The approach in the paper also provides guidance for choosing suitable fuzzy connective operators and decision making application in rule based fuzzy reasoning . 


Summary: Lipschitz aggregation property and copula characteristic of t norms and implications are discussed and the robustness of rule based fuzzy reasoning is investigated. According to Lipschitz aggregation property and copula characteristic of t norms and implications suitable t norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning. The approach provides guidance for choosing suitable fuzzy connective operators and decision making application in rule based fuzzy reasoning. The experiments which illustrate the ideas are the applications of intelligent information processing.
Time series forecasting has been widely used to determine future prices of stocks and the analysis and modeling of finance time series is an important task for guiding investors decisions and trades . Nonetheless the prediction of prices by means of a time series is not trivial and it requires a thorough analysis of indexes variables and other data . In addition in a dynamic environment such as the stock market the non linearity of the time series is a pronounced characteristic and this immediately affects the efficacy of stock price forecasts . Thus this paper aims at proposing a methodology that forecasts the maximum and minimum day stock prices of three Brazilian power distribution companies which are traded in the S o Paulo Stock Exchange BM FBovespa . When compared to the other papers already published in the literature one of the main contributions and novelty of this paper is the forecast of the range of closing prices of Brazilian power distribution companies stocks . As a result of its application investors may be able to define threshold values for their stock trades . Moreover such a methodology may be of great interest to home brokers who do not possess ample knowledge to invest in such companies . The proposed methodology is based on the calculation of distinct features to be analysed by means of attribute selection defining the most relevant attributes to predict the maximum and minimum day stock prices of each company . Then the actual prediction was carried out by Artificial Neural Networks which had their performances evaluated by means of Mean Absolute Error Mean Absolute Percentage Error and Root Mean Square Error calculations . The proposed methodology for addressing the problem of prediction of maximum and minimum day stock prices for Brazilian distribution companies is effective . In addition these results were only possible to be achieved due to the combined use of attribute selection by correlation analysis and ANNs . 


Summary: We predict maximum and minimum day stock prices of power companies. The methodology is based on attribute selection and time series prediction. The most relevant attributes are determined by correlation analysis. The actual time series prediction is carried out by neural networks. The proposed methodology provides very good results.
The main aim in network anomaly detection is effectively spotting hostile events within the traffic pattern associated to network operations by distinguishing them from normal activities . This can be only accomplished by acquiring the a priori knowledge about any kind of hostile behavior that can potentially affect the network or more easily by building a model that is general enough to describe the normal network behavior and detect the violations from it . Earlier detection frameworks were only able to distinguish already known phenomena within traffic data by using pre trained models based on matching specific events on pre classified chains of traffic patterns . Alternatively more recent statistics based approaches were able to detect outliers respect to a statistic idealization of normal network behavior . Clearly while the former approach is not able to detect previously unknown phenomena the latter one has limited effectiveness since it can not be aware of anomalous behaviors that do not generate significant changes in traffic volumes . Machine learning allows the development of adaptive non parametric detection strategies that are based on understanding the network dynamics by acquiring through a proper training phase a more precise knowledge about normal or anomalous phenomena in order to classify and handle in a more effective way any kind of behavior that can be observed on the network . Accordingly we present a new anomaly detection strategy based on supervised machine learning and more precisely on a batch relevance based fuzzyfied learning algorithm known as U BRAIN aiming at understanding through inductive inference the specific laws and rules governing normal or abnormal network traffic in order to reliably model its operating dynamics . The inferred rules can be applied in real time on online network traffic . This proposal appears to be promising both in terms of identification accuracy and robustness flexibility when coping with uncertainty in the detection classification process as verified through extensive evaluation experiments . 


Summary: Adaptive network anomaly detection strategy based on a batch relevance based fuzzified learning algorithm. Couples the capability of inferring decisional structures from incomplete observations with the flexibility of a fuzzy based uncertainty management strategy. Infers the laws and rules governing normal or abnormal network traffic in order to model its operating dynamics. Based on a rule based detection strategy is more effective against previously unknown phenomena and robust against obfuscation mechanisms.
In this paper a new approach called evolving principal component clustering is applied to a data stream . Regions of the data described by linear models are identified . The method recursively estimates the data variance and the linear model parameters for each cluster of data . It enables good performance robust operation low computational complexity and simple implementation on embedded computers . The proposed approach is demonstrated on real and simulated examples from laser range finder data measurements . The performance complexity and robustness are validated through a comparison with the popular split and merge algorithm . 


Summary: A novel approach for data stream clustering to linear model prototypes. Good performance robust operation low computational complexity and simple implementation. Validation of results by comparison to well known algorithms.
Surface Electromyography is a non invasive easy to record signal of superficial muscles from the skin surface . The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition prosthetics and rehabilitation applications . Considering the nonlinear and non stationary characteristics of sEMG hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent or ensemble Empirical Mode Decomposition based MLEs . In this research we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE . The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD . The nonlinear MLE features are classified using Flexible Neural Tree which can solve highly structured dependent problems of the Artificial Neural Network . The testing has been conducted using several experiments with five participants . The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD based MLE through simulations . Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6 accuracy . 


Summary: We use nonlinear and non stationary features of sEMG to identify various gestures. In order to reduce the complexity of EMD nonlinear MLE method is used. In order to improve characteristic stability we use a method based on nonlinear MLE. The proposed method classifies six different hand gestures up to 97.6 accuracy. The method can be used for prosthetics and other rehabilitation applications.
Despite the wide application of evolutionary computation techniques to rule discovery in stock algorithmic trading a comprehensive literature review on this topic is unavailable . Therefore this paper aims to provide the first systematic literature review on the state of the art application of EC techniques for rule discovery in stock AT . Out of 650 articles published before 2013 51 relevant articles from 24 journals were confirmed . These papers were reviewed and grouped into three analytical method categories and three EC technique categories . A significant bias toward the applications of genetic algorithm based and genetic programming based techniques in technical trading rule discovery is observed . Other EC techniques and fundamental analysis lack sufficient study . Furthermore we summarize the information on the evaluation scheme of selected papers and particularly analyze the researches which compare their models with buy and hold strategy . We observe an interesting phenomenon where most of the existing techniques perform effectively in the downtrend and poorly in the uptrend and considering the distribution of research in the classification framework we suggest that this phenomenon can be attributed to the inclination of factor selections and problem in transaction cost selections . We also observe the significant influence of the transaction cost change on the margins of excess return . Other influenced factors are also presented in detail . The absence of ways for market trend prediction and the selection of transaction cost are two major limitations of the studies reviewed . In addition the combination of trading rule discovery techniques and portfolio selection is a major research gap . Our review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research . 


Summary: The first systematic literature review on evolutionary rule discovery in stock algorithmic trading. A clear demonstrate of studies in this field based on a classification framework. A precise analysis of gaps and limitations in existing studies based on detail of evaluation scheme. The most important factors influencing profitability of models are presented in detail. Targeted suggestions for future improvements based on the review are proposed.
This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns . A discriminative common vector based method is utilized for feature selection . In the literature there is no study using whole MJP for identity authentication exceptionally a work using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study . The originality of this approach is that whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP . The developed system performs some basic tasks like image acquisition image pre processing feature extraction matching and performance evaluation . The feasibility and effectiveness of this approach is rigorously evaluated using the k fold cross validation technique on two different databases a publicly available database and a specially established database . The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33 and 100.00 . 


Summary: A new biometric identifier whole MJP pattern is introduced. An effective fast and robust MJP based biometric system is developed and presented. Discriminative common vector based method is firstly applied to obtain the feature sets of MJPs.
This paper introduces a novel approach to detect and classify power quality disturbance in the power system using radial basis function neural network . The proposed method requires less number of features as compared to conventional approach for the identification . The feature extracted through the wavelet is trained by a radial basis function neural network for the classification of events . After training the neural network the weight obtained is used to classify the Power Quality problems . For the classification 20 types of disturbances are taken into account . The classification performance of RBFNN is compared with feed forward multilayer network learning vector quantization probabilistic neural network and generalized regressive neural network . The classification accuracy of the RBFNN network is improved just by rewriting the weights and updating the weights with the help of cognitive as well as the social behavior of particles along with fitness value . The simulation results possess significant improvement over existing methods in signal detection and classification . 


Summary: The power quality disturbance detection and classification is important for improving the power quality. Various disturbances were taken in to account over 20 events. Wavelet is used to extract features from a MATLAB simulated disturbance waveforms. Radial Basis Function Neural Network has been used to detect and classify the disturbance and compared with other considered approach. The entire work has been presented with particle swarm optimization.
In this paper using the Dempster Shafer theory of evidence a new decision criterion is proposed which can quickly classify airborne objects without any a priori knowledge whose data are laced with environmental noise characteristics within 10seconds from the time it is detected . Kinematic parameters of an airborne object received from radars are used to classify it into one of the six classes which include three levels of ballistic target discrimination aerodynamic satellite and unknown . The DST is chosen as it can suitably handle the element of uncertainty limited a priori data and short observation times that exist with the data acquired for the purpose of classification . The focus of the work is on ballistic targets in a theater of war . The approach is compared with the popularly known k NN and decision tree techniques and is found to perform better with the chosen data sets . This approach is tested using both real flight test data and simulated data . 


Summary: Approach presents classification of ballistic missiles using evidential theory. Airborne objects classified without prior knowledge in few seconds after detection. Decision criterion proposed to categorize airborne objects into six major classes. Approach performs better than k NN and decision tree methods with chosen data sets. Validated with real and simulated data sets from single and multiple radars sources.
In this paper a new version of the particle swarm optimization algorithm suitable for discrete optimization problems is presented and applied for the solution of the capacitated location routing problem and for the solution of a new formulation of the location routing problem with stochastic demands . The proposed algorithm combines three different topologies which are incorporated in a constriction particle swarm optimization algorithm and thus a very effective new algorithm the global and local combinatorial expanding neighborhood topology particle swarm optimization was developed . The algorithm was tested initially in the three classic sets of benchmark instances for the capacitated location routing problem with discrete demands and then as there are no benchmark instances for the location routing problem with stochastic demands these instances were transformed appropriately in order to be suitable for the problem with stochastic demands . The algorithm was tested in the problem with the stochastic demands using these transformed sets of benchmark instances . The algorithm was compared with a number of different implementations of the PSO and with metaheuristic evolutionary and nature inspired algorithms from the literature for the location routing problem with discrete and stochastic demands . 


Summary: An improved variant of the particle swarm optimization algorithm is presented. A new formulation of the location routing problem with stochastic demands is given. A new neighborhood topology for PSO suitable for combinatorial optimization problems is proposed. The proposed algorithm is tested in the CLRP and in the LRPSDs. Comparisons with other algorithms from the literature are performed.