varunr14/newsummary huggingface dataset:
A semi integrated system for driver assistance and pedestrian safety is presented . This system is composed of a single camera which focuses on the driver for picking up visual cues and a stereo rig that focus on the road ahead for the detection of road obstructions and pedestrians . While the car is in motion the driver s viewing direction is obtained and analyzed along with information of road condition and any moving vehicle ahead in order to determine if the current driving condition is safe . In addition when the vehicle is moving slowly the system can also detect the existence of a pedestrian ahead and warns the driver if the pedestrian moves in front of the car . This system contains algorithm based safety analysis as well as fuzzy rules based analysis for interaction between variables . Our experimental results show that the condition for driver safety can be accurately classified in 94.5 of the tested driving conditions and the pedestrians can be identified in 93.18 of the tested cases . These were compared to the results of similar systems and shown to be superior . 


Summary: A driver assistance system that correlates obstacles with driver view is proposed. Fuzzy rules based subsystems are used for analysis under different road conditions. One set of fuzzy rules for when a vehicle or pedestrian ahead is detected. One set of fuzzy rules for when no immediate obstacle is detected. Lab experiments and comparisons using real world data show good performance.
Large number of population based Differential Evolution algorithms has been proposed in the literature . Their good performance is often reported for benchmark problems . However when applied to Neural Networks training for regression these methods usually perform poorer than classical Levenberg Marquardt algorithm . The major aim of the present paper is to clarify why In this research in which Neural Networks are used for a real world regression problem it is empirically shown that various Differential Evolution algorithms are falling into stagnation during Neural Network training . It means that after some time the individuals stop improving or improve very occasionally although the population diversity remains high . Similar behavior of Differential Evolution algorithms is observed for some but not the majority of benchmark problems . In the paper the impact of Differential Evolution population size the initialization range and bounds on Neural Networks performance is also discussed . Among tested algorithms only the Differential Evolution with Global and Local neighborhood based mutation operators performs better than the Levenberg Marquardt algorithm for Neural Networks training . This version of Differential Evolution also shows the symptoms of stagnation but much weaker than the other tested variants . To enhance exploitation in the final stage of Neural Networks training it is proposed to merge the Differential Evolution with Global and Local neighborhood based mutation operators algorithm with the Trigonometric mutation operator . This method does not rule out the stagnation problem but slightly improves the performance of trained Neural Networks . 


Summary: Differential Evolution algorithms applied to ANN training suffer from stagnation. The lack of difference vectors of small magnitude is noted during ANN training by Differential Evolution methods. In case of benchmark problems the lack of difference vectors of small magnitude is only occasionally observed. DEGL algorithm outperforms other Differential Evolution variants for ANN training. Best algorithms found for benchmark problems do not perform well for ANN training.
It is undeniably crucial for a firm to be able to make a forecast regarding the sales volume of new products . However the current economic environments invariably have uncertain factors and rapid fluctuations where decision makers must draw conclusions from minimal data . Previous studies combine scenario analysis and technology substitution models to forecast the market share of multigenerational technologies . However a technology substitution model based on a logistic curve will not always fit the S curve well . Therefore based on historical data and the data forecast by both the Scenario and Delphi methods a two stage fuzzy piecewise logistic growth model with multiple objective programming is proposed herein . The piecewise concept is adopted in order to reflect the market impact of a new product such that it can be possible to determine the effective length of sales forecasting intervals even when handling a large variation in data or small size data . In order to demonstrate the model s performance two cases in the Television and Telecommunication industries are treated using the proposed method and the technology substitution model or the Norton and Bass diffusion model . A comparison of the results shows that the proposed model outperforms the technology substitution model and the Norton and Bass diffusion model . 


Summary: Use historical data and the data investigated by the Scenario and Delphi methods. Propose a two stage fuzzy piecewise logistic growth model for sale forecasting. Forecast the market shares of the optimistic pessimistic and most possible scenarios. Demonstrate two cases in the Television and Telecommunication industries of the global market. Outperform the technology substitution model or the Norton and Bass diffusion model according to MAE MSE and MAPE.
This paper presents a detailed study about a biomedical image retrieval framework by extracting Phase Congruency features from L a b triplets of images and representing them in fuzzy feature space . These features correspond to an edge corner map of the given image . The resulting map is then processed by Scale Invariant Feature Transform to derive keypoints that are invariant to affine transformations . The ensuing features were vector quantized to build a codebook of keypoints . The codebook was produced using a Spherical Self Organizing Map built with a geodesic data structure termed as GeoSOM . Then keypoints of the query image are mapped with the codebook and their occurrences are counted to formulate a histogram termed as Phase Congruency based Bag of Keypoints . This histogram is generated offline for target images and a similarity measure was performed with the query image to yield the nearest match based on a global fuzzy membership function . Exhaustive experiments of the proposed framework named as BIRS were performed on a diverse medical image collection . Finally performance of BIRS demonstrates the advantage of the proposed image representation approach in terms of Precision Recall parameters . Furthermore relative comparison of the proposed scheme with existing feature descriptors depicts improved P R values . The proposed feature extraction and representation scheme was also robust against quantization errors . 


Summary: BIRS Biomedical Image Retrieval System framework for diverse medical image collection. Phase Congruency based features for acute image characterizations. Building codebook using GeoSOM for image encoding thereby achieving dimensionality reduction. SIFT features combined with fuzzy function for significant improvement in information retrieval. Performance analysis indicates reduction in retrieval time with efficient indexing and effective retrieval.
This research addresses system reliability analysis using weakest t norm based approximate intuitionistic fuzzy arithmetic operations where failure probabilities of all components are represented by different types of intuitionistic fuzzy numbers . Due to the incomplete imprecise vague and conflicting information about the component of system the present study evaluates the reliability of system in terms of membership function and non membership function by using weakest t norm based approximate intuitionistic fuzzy arithmetic operations on different types of intuitionistic fuzzy numbers . In general interval arithmetic operations have been used to analyze the fuzzy system reliability . In complicated systems interval arithmetic operations may occur the accumulating phenomenon of fuzziness . In order to overcome the accumulating phenomenon of fuzziness this research adopts approximate intuitionistic fuzzy arithmetic operations under the weakest t norm arithmetic operations to analyze fuzzy system reliability . The approximate intuitionistic fuzzy arithmetic operations employ principle of interval arithmetic under the weakest t norm arithmetic operations . The proposed novel fuzzy arithmetic operations may obtain fitter decision values which have smaller fuzziness accumulating and successfully analyze the system reliability . Also weakest t norm arithmetic operations provide more exact fuzzy results and effectively reduce fuzzy spreads . Using proposed approach fuzzy reliability of series system and parallel system are also constructed . For numerical verification of proposed approach a malfunction of printed circuit board assembly is presented as a numerical example . The result of the proposed method is compared with the listing approaches of reliability analysis methods . 


Summary: Weakest t norm T based approximate intuitionistic fuzzy arithmetic operations on different types of intuitionistic fuzzy numbers to evaluate fault interval and reliability interval. The proposed novel fuzzy arithmetic operations may obtain fitter decision values which have smaller fuzziness accumulating and successfully analyze the system reliability. Also weakest t norm arithmetic operations provide more exact fuzzy results and effectively reduce fuzzy spreads fuzzy intervals . Fuzzy reliability of PCBA fault has been analyzed using the proposed approach.
The present paper proposes a multidimensional coupled chaotic map as a pseudo random number generator . Based on an introduced dynamical systems a watermark scheme is presented . By modifying the original image and embedding a watermark in the difference values within the original image the proposed scheme overcomes the problem of embedding a watermark in the spatial domain . As the watermark extraction does not require the original image the introduced model can be employed for practical applications . This algorithm tries to improve the problem of failure of embedding in small key space embedding speed and level of security . 


Summary: A multidimensional coupled chaotic map as a pseudo random number generator is proposed. A watermark scheme is presented. The proposed scheme is subjected to some security analysis as well as statistical tests suites. Watermark extraction does not require the original image.
Inspired by the ideas of multi swarm information sharing and elitist perturbation guiding a novel multi swarm cooperative multistage perturbation guiding particle swarm optimizer is proposed in this paper . The multi swarm information sharing idea is to harmoniously improve the evolving efficiency via information communicating and sharing among different sub swarms with different evolution mechanisms . It is possible to drive a stagnated sub swarm to revitalize once again with the beneficial information obtained from other sub swarms . Multistage elitist perturbation guiding strategy aims to slow down the learning speed and intensity in a certain extent from the global best individual while keeping the elitist learning mechanism . It effectively enlarges the exploration domain and diversifies the flying tracks of particles . Extensive experiments indicate that the proposed strategies are necessary and cooperative both of which construct a promising algorithm MCpPSO when comparing with other particle swarm optimizers and state of the art algorithms . The ideas of central position perturbation along the global best particle different computing approaches for central position and important parameters influence analysis are presented and analyzed . 


Summary: Propose a novel multi swarm cooperative multistage perturbation guiding particle swarm optimizer. Multi swarm information sharing idea aims to improve the evolving efficiency via information communicating and sharing among different sub swarms. Multistage perturbation guiding strategy aims to slow down the learning speed and intensity. Comprehensive studies on algorithm are presented.
In actuality for example the review of the National Science Foundation and the blind peer review of doctoral dissertation in China the evaluation experts are requested to provide two types of information such as the performance of the evaluation objects and the familiarity with the evaluation areas . However existing information aggregation research achievements can not be used to fusion the two types information described above effectively . In this paper we focus on the information aggregation issue in the situation where there are confidence levels of the aggregated arguments under intuitionistic fuzzy environment . Firstly we develop some confidence intuitionistic fuzzy weighted aggregation operators such as the confidence intuitionistic fuzzy weighted averaging operator and the confidence intuitionistic fuzzy weighted geometric operator . Then based on the Einstein operations we proposed the confidence intuitionistic fuzzy Einstein weighted averaging operator and the confidence intuitionistic fuzzy Einstein weighted geometric operator . Finally a practical example about the review of the doctoral dissertation in Chinese universities is provided to illustrate the developed intuitionistic fuzzy information aggregation operators . 


Summary: The confidence intuitionistic fuzzy weighted averaging CIFWA operator and the confidence intuitionistic fuzzy weighted geometric CIFWG operator are proposed. The confidence intuitionistic fuzzy Einstein weighted averaging CIFEWA operator and the confidence intuitionistic fuzzy Einstein weighted geometric CIFEWG operator are proposed. The properties of the CIFWA and the CIFWG operators are studied in detail. The application of the proposed method to the review of the doctoral dissertation in Chinese universities is provided.
Autonomy and adaptability are key features of intelligent agents . Many applications of intelligent agents such as the control of ambient intelligence environments and autonomous intelligent robotic systems require the processing of information coming in from many available sensors to produce adequate output responses in changing scenarios . Autonomy in these cases applies not only to the ability of the agent to produce correct outputs without human guidance but also to its ubiquity and or portability low power consumption and integrability . In this sense an embedded electronic system implementation paradigm can be applied to the design of autonomous intelligent agents in order to satisfy the above mentioned characteristics . However processing complex computational intelligence algorithms with tight delay constraints in resource constrained and low power embedded systems is a challenging engineering problem . In this paper a single chip intelligent agent based on a computationally efficient neuro fuzzy information processing core is described . The system has been endowed with an information preprocessing module based on Principal Component Analysis that permits a substantial reduction of the input space dimensionality with little loss of modeling capability . Moreover the PCA module has been tested as a means to achieve deep adaptability in changing environment dynamics and to endow the agent with fault tolerance in the presence of sensor failures . For data driven trials and research a data set obtained from an experimental intelligent inhabited environment has been used as a benchmark system . 


Summary: Single chip embedded MIMO autonomous intelligent agent with on line learning capability small footprint low power transparent device. Dynamic deep adaptability through an integrated PCA analyzer. Dynamic on chip significant feature space reduction for adaptation speed up and enhanced generalization capability. Fault tolerance in the presence of sensor failures by minimum input feature space re computing in the integrated PCA module. Real implementation figures and real operation verification.
Traditional parametric software reliability growth models are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances . Non parametric models like the artificial neural network based models can predict software reliability based on only fault history data without any assumptions . In this paper initially we propose a robust feedforward neural network based dynamic weighted combination model for software reliability prediction . Four well known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN . Based on this proposed FFNN architecture we also propose a robust recurrent neural network based dynamic weighted combination model to predict the software reliability more justifiably . A real coded genetic algorithm is proposed to train the ANNs . Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets . We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs . Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models . Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM . 


Summary: We propose robust feedforward and recurrent neural network based dynamic weighted combination models. We combine four software reliability growth models by dynamically evaluated weights. We propose genetic algorithm based learning algorithm to train the proposed ANNs. Experimental results demonstrate that proposed models have fairly accurate predictability. PRNNDWCM has best software reliability prediction capability.
This paper proposes a new evolutionary algorithm for continuous non linear optimization problems . This optimization algorithm is inspired by the procedure of trading the shares on stock market and it is called exchange market algorithm . Evaluation of how the stocks are traded on the stock market by elites has formed this evolutionary as an optimization algorithm . In the proposed method there are two different modes in EMA . In the first mode there is no oscillation in the market whereas in the second mode the market has oscillation . It is noticeable that at the end of each mode the individuals finesses are evaluated . For the first mode the algorithm s duty is to recruit people toward successful individuals while in the second case the algorithm seeks optimal points . In this algorithm the generation and organization of random numbers are performed in the best way due to the existence of two absorbent operators and two searching operators leading to high capability in global optimum point extraction . To evaluate the performance of the proposed algorithm this algorithm has been implemented on 12 different benchmark functions with 10 20 30 and 50 dimension variables . The results obtained by 30 dimension variables are compared with the results obtained by the eight new and efficient algorithms . The results indicate the ability of the proposed algorithm in finding the global optimum point of the functions for each run of the program . 


Summary: This paper proposes a new heuristic algorithm for solving optimization problems. This optimization algorithm is inspired of trading the shares on stock market. The proposed algorithm is successfully implemented on 12 benchmark functions. Result shows the high ability of proposed algorithm in global optimum extraction.
Mechanical and physical properties of sandstone are interesting scientifically and have great practical significance as well as their relations to the mineralogy and pore features . These relations are however highly nonlinear and can not be easily formulated by conventional methods . This paper investigates the potential of the technique named as the relevance vector machine for prediction of the elastic compressibility of sandstone based on its characteristics of physical properties . Based on the fact that the hyper parameters may have effects on the RVM performance an iteration method is proposed in this paper to search for optimal hyper parameter value so that it can produce best predictions . Also the qualitative sensitivity of the physical properties is investigated by the backward regression analysis . Meanwhile the hyper parameter effect of the RVM approach is discussed in the prediction of the elastic compressibility of sandstone . The predicted results of the RVM demonstrate that hyper parameter values have evident effects on the RVM performance . Comparisons on the results of the RVM the artificial neural network and the support vector machine prove that the proposed strategy is feasible and reliable for prediction of the elastic compressibility of sandstone based on its physical properties . 


Summary: The relevance vector machine is evaluated for estimation of rock compressibility based on physical properties. An iteration strategy is proposed to optimize the hyper parameters of the relevance vector machine. The parameter effect is demonstrated on the performance of the relevance vector machine. The adaptive relevance vector machine is compared to the artificial neural networks and the support vector machine in the estimation.
Human performance evaluation is one of the most important fields to analyze for the continuity of an organization . Evaluation files filled by the managers generally end up in dusty folders where no one looks . This decreases the credibility of the evaluators and the process itself . Whereas the management thinks that they are taking the valuable time from the people who can do better things instead of these evaluations . In this paper we add an engineering point of view to this process by giving a Hybrid Multicriteria Decision Making approach to evaluate employees performances working for a same task and explain an efficient way of handling the qualitative and quantitative data simultaneously . The real life situations where performance criteria show interaction will be possible to solve and the different types of interactions will be handled with the proposed hybrid method using Analytical Network Process and Choquet Integral simultaneously . We also give a numerical illustration at the end of the study with the appropriate concluding remarks including the advantages of the proposed method . 


Summary: We propose a performance evaluation method for human resources. We use a Hybrid Multicriteria Decision Making Model fort his purpose. All possible interaction types are considered and handled with the proposed method. Ignoring the interactions may lead to erroneous decisions.