varunr14/newsummary huggingface dataset:
Cluster ensemble is a powerful method for improving both the robustness and the stability of unsupervised classification solutions . This paper introduced group method of data handling to cluster ensemble and proposed a new cluster ensemble framework which named cluster ensemble framework based on the group method of data handling . CE GMDH consists of three components an initial solution a transfer function and an external criterion . Several CE GMDH models can be built according to different types of transfer functions and external criteria . In this study three novel models were proposed based on different transfer functions least squares approach cluster based similarity partitioning algorithm and semidefinite programming . The performance of CE GMDH was compared among different transfer functions and with some state of the art cluster ensemble algorithms and cluster ensemble frameworks on synthetic and real datasets . Experimental results demonstrate that CE GMDH can improve the performance of cluster ensemble algorithms which used as the transfer functions through its unique modelling process . It also indicates that CE GMDH achieves a better or comparable result than the other cluster ensemble algorithms and cluster ensemble frameworks . 


Summary: A cluster ensemble framework based on the group method of data handling was proposed. The components of the CE GMDH can be chosen according to the target of the application. Three novel transfer functions in CE GMDH were proposed. CE GMDH outperforms the other cluster ensemble algorithms and frameworks.
Minimum class variance support vector machine and large margin linear projection classifier in contrast with traditional support vector machine take the distribution information of the data into consideration and can obtain better performance . However in the case of the singularity of the within class scatter matrix both MCVSVM and LMLP only exploit the discriminant information in a single subspace of the within class scatter matrix and discard the discriminant information in the other subspace . In this paper a so called twin space support vector machine algorithm is proposed to deal with the high dimensional data classification task where the within class scatter matrix is singular . TSSVM is rooted in both the non null space and the null space of the within class scatter matrix takes full advantage of the discriminant information in the two subspaces and so can achieve better classification accuracy . In the paper we first discuss the linear case of TSSVM and then develop the nonlinear TSSVM . Experimental results on real datasets validate the effectiveness of TSSVM and indicate its superior performance over MCVSVM and LMLP . 


Summary: In the case of the singularity of the within class scatter matrix the drawbacks of both MCVSVM and LMLP are analyzed. A novel algorithm TSSVM is proposed to deal with the high dimensional data classification task where the within class scatter matrix is singular. An alternative version of the nonlinear MCVSVM and the nonlinear LMLP are proposed. The nonlinear TSSVM is developed.
Power quality issues have become more important than before due to increased use of sensitive electrical loads . In this paper a new hybrid algorithm is presented for PQ disturbances detection in electrical power systems . The proposed method is constructed based on four main steps simulation of PQ events extraction of features selection of dominant features and classification of selected features . By using two powerful signal processing tools i.e . variational mode decomposition and S transform some potential features are extracted from different PQ events . VMD as a new tool decomposes signals into different modes and ST also analyzes signals in both time and frequency domains . In order to avoid large dimension of feature vector and obtain a detection scheme with optimum structure sequential forward selection and sequential backward selection as wrapper based methods and Gram Schmidt orthogonalization based feature selection method as filter based method are used for elimination of redundant features . In the next step PQ events are discriminated by support vector machines as classifier core . Obtained results of the extensive tests prove the satisfactory performance of the proposed method in terms of speed and accuracy even in noisy conditions . Moreover the start and end points of PQ events can be detected with high precision . 


Summary: Different types of power quality disturbances are discriminated from each other. VMD and ST are used for extraction of dominant features. SVM with simple structure and few adjustable parameters is used as the classifier core. The generalization capability and detection accuracy of the proposed method are increased by elimination of redundant features by using different feature selection methods. The start and end points of PQ events can be detected accurately.
Fabrication of three dimensional structures has gained increasing importance in the bone tissue engineering field . Mechanical properties and permeability are two important requirement for BTE scaffolds . The mechanical properties of the scaffolds are highly dependent on the processing parameters . Layer thickness delay time between spreading each powder layer and printing orientation are the major factors that determine the porosity and compression strength of the 3D printed scaffold . In this study the aggregated artificial neural network was used to investigate the simultaneous effects of layer thickness delay time between spreading each layer and print orientation of porous structures on the compressive strength and porosity of scaffolds . Two optimization methods were applied to obtain the optimal 3D parameter settings for printing tiny porous structures as a real BTE problem . First particle swarm optimization algorithm was implemented to obtain the optimum topology of the AANN . Then Pareto front optimization was used to determine the optimal setting parameters for the fabrication of the scaffolds with required compressive strength and porosity . The results indicate the acceptable potential of the evolutionary strategies for the controlling and optimization of the 3DP process as a complicated engineering problem . 


Summary: The aggregated artificial neural network was used to investigate the simultaneous effects of printing parameters on the compressive strength and porosity of scaffolds. Particle swarm optimization algorithm was implemented to obtain the optimum topology of the AANN. Pareto front optimization was used to determine the optimal setting parameters. The presented results and discussion can give informative information to practitioners who want to design a porous structure and need to know the impact of influential design parameters.
In this paper novel interval and general type 2 self organizing fuzzy logic controllers are proposed for the automatic control of anesthesia during surgical procedures . The type 2 SOFLC is a hierarchical adaptive fuzzy controller able to generate and modify its rule base in response to the controller s performance . The type 2 SOFLC uses type 2 fuzzy sets derived from real surgical data capturing patient variability in monitored physiological parameters during anesthetic sedation which are used to define the footprint of uncertainty of the type 2 fuzzy sets . Experimental simulations were carried out to evaluate the performance of the type 2 SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for anesthesia under signal and patient noise . Results show that the type 2 SOFLCs can perform well and outperform previous type 1 SOFLC and comparative approaches for anesthesia control producing lower performance errors while using better defined rules in regulating anesthesia set points while handling the control uncertainties . The results are further supported by statistical analysis which also show that zSlices general type 2 SOFLCs are able to outperform interval type 2 SOFLC in terms of their steady state performance . 


Summary: Type 2 self organizing fuzzy logic controllers for automatic anesthesia control. Type 2 SOFLC use type 2 fuzzy sets to handle anesthesia control uncertainties. Data capturing inter and intra patient variability used to define type 2 fuzzy sets. Simulations show effectiveness of type 2 SOFLC in control of anesthetic infusion under noisy and uncertain surgical conditions. Type 2 SOFLC are able to outperform the existing type 1 SOFLC.
This paper proposes relaxed conditions for control synthesis of discrete time Takagi Sugeno fuzzy control systems under unreliable communication links . To widen the applicability of the fuzzy control approach under network environments a novel fuzzy controller which is homogenous polynomially parameter dependent on both the current time normalized fuzzy weighting functions and the multi steps past normalized fuzzy weighting functions is provided to make much more use of the information of the underlying system . Moreover a new kind of slack variable approach is also developed and thus the algebraic properties of these multi instant normalized fuzzy weighting functions are collected into some augmented matrices . As a result the conservatism of control synthesis of discrete time Takagi Sugeno fuzzy control systems under unreliable communication links can be significantly reduced . Two illustrative examples are presented to demonstrate the effectiveness of the theoretical development . 


Summary: A novel fuzzy controller and a new kind of slack variable approach are developed. The criterion takes the form of an LMI which is computationally tractable. The obtained stabilization conditions are less conservative.
An accurate contour estimation plays a significant role in classification and estimation of shape size and position of thyroid nodule . This helps to reduce the number of false positives improves the accurate detection and efficient diagnosis of thyroid nodules . This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level sets for accurate and effective segmentation of thyroid nodules in ultrasound images . The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set is based on Neutrosophic L Means clustering which incorporates spatial information for Level Set evolution . The SNDRLS takes rough estimation of region of interest as input provided by Spatial NLM clustering for precise delineation of one or more nodules . The performance of the proposed method is compared with level set NLM clustering Active Contour Without Edges Fuzzy C Means clustering and Neutrosophic based Watershed segmentation methods using the same image dataset . To validate the SNDRLS method the manual demarcations from three expert radiologists are employed as ground truth . The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures . The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively . The proposed method achieves the automated nodule boundary even for low contrast blurred and noisy thyroid ultrasound images without any human intervention . Additionally the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering . 


Summary: The proposed method integrates SNLM clustering and level set. Able to delineate thyroid nodules in ultrasound images accurately and automatically. Can be applied without preprocessing due to its indeterminacy handling capability. The parameters of SNDRLS are determined adaptively from SNLM clustering. Experimental results show the effectiveness of the proposed method.
Two soft computing techniques are implemented to model and optimize the compressive strength of carbon polymer composites . Artificial neural network is used to establish a relationship between the uniaxial compressive strength of fabricated materials and the most significant processing parameters . To put together a database three different types of wood are carbonized at various heat treatment temperatures in specific pyrolysis time periods . Compression tests are then conducted at room temperature on the composites at a constant strain rate . The collected data of compressive strength and the related fabrication parameters are used as sets of data for training a neural network . A nested cross validation scheme is used to ensure the efficiency of the network . Results are indicative of a very good network which generalizes very well . Next an attempt is made to optimize the compressive behavior of the composites by controlling carbonization temperature time and also starting material type with the aid of a genetic algorithm coupled with the trained network . The optimization system yields promising results significantly enhancing the compressive strength . The validity of the optimal experiment as proposed by the soft computing system is verified by subsequent laboratory testing . 


Summary: Neural network is trained to predict the compressive strength of composites from experimental variables. Input variables are optimized to maximize compressive strength by genetic algorithm. Experimental constraints are considered during optimization. The consistency of the analysis is verified by laboratory experiments.
The present study applies a Hybrid method for identification of unknown parameters in a semi empirical tire model the so called Magic Formula . Firstly the Hybrid method used a Genetic Algorithm as a global search methodology with high exploration power . Secondly the results of the Genetic Algorithm were used as starting values for the Levenberg Marquardt algorithm as a gradient based method with high exploitation power . In this way the beneficial aspects of both methods are simultaneously exploited and their shortcomings are avoided . In order to establish the effectiveness of the proposed method performance of the Hybrid method has been compared with other methods available in the literature . In addition the use of GA as a Heuristic method for tire parameters identification has been discussed . Moreover the extrapolation power of Magic Formula identified with Hybrid method has been properly investigated . Finally the performance of the Hybrid method has been examined through tire parameter identification with priori known model . The results indicated that the Hybrid method has outstanding benefits such as high convergence speed high accuracy and null sensitivity to the starting values of unknown parameters . 


Summary: A Hybrid method for identifying tire parameters in Magic Formula has been applied. Firstly the Hybrid method uses Genetic Algorithm GA with a high exploration power. Then the best result of GA is used as starting values SVs for Levenberg Marquardt. The method shows high convergence speed and accuracy and null sensitivity to SVs.
In this paper we studied fuzzy linear fractional differential equations under Riemann Liouville H differentiability as the fuzzy initial value problems . Some of the previous results on solutions of these equations are concreted . We obtained new solutions by using the fractional hyperbolic functions and their properties in details . Finally an application and two examples are given to illustrate our results . 


Summary: Fuzzy linear fractional differential equations in form under Riemann Liouville H differentiability are studied. Some of the previous results on solutions of these equations are concreted. New solutions are obtained using the fractional hyperbolic functions and their properties. An application and two examples are given to illustrate our results.
In this paper we have made a humble attempt to automate an ophthalmologic diagnostic system based on signal processing using wavelets . Electroretinographic signals indicate the activity of the retinal cells from different layers of the inner retina and therefore these signals are used to predict various dreadful diseases . In this work we have analyzed 95 subjects from four different classes viz . Controls Congenital Stationery Night Blindness rod cone dystrophy and Central Retinal Vein Occlusion . The signal features extracted by wavelets are used for morphological and statistical analysis and for getting the subtle parameters like entropy . The results found comprises of difference in the values of wavelet coefficients a wave and b wave amplitude in the case of normal and pathological signals . The colour intensity distribution of scalograms shows highlighting variations in the case of maximum response and oscillatory potentials of the ERG signals for specific type of diseases . Furthermore we propose an Electroretinographic Index from different entropy parameters which can be used to distinguish between the normal and abnormal classes . This new method based on ERG signal analysis can be reliable enough to build a solution for the constraints in the field of ophthalmology . 


Summary: To automate an ophthalmologic diagnostic system based on signal processing. Wavelet transform is used to extract the features. Entropy analysis is used and we have formulated an Electroretinographic Index to discriminate between controls and suspects. It will assist the physicians to cross check their diagnosis.
Complex biological systems such as the human brain can be expected to be inherently nonlinear and hence difficult to model . Most of the previous studies on investigations of brain function have either used linear models or parametric nonlinear models . In this paper we propose a novel application of a nonlinear measure of phase synchronization based on recurrences correlation between probabilities of recurrence to study seizures in the brain . The advantage of this nonparametric method is that it makes very few assumptions thus making it possible to investigate brain functioning in a data driven way . We have demonstrated the utility of CPR measure for the study of phase synchronization in multichannel seizure EEG recorded from patients with global as well as focal epilepsy . For the case of global epilepsy brain synchronization using thresholded CPR matrix of multichannel EEG signals showed clear differences in results obtained for epileptic seizure and pre seizure . Brain headmaps obtained for seizure and pre seizure cases provide meaningful insights about synchronization in the brain in those states . The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value . Comparative studies with linear correlation have shown that the nonlinear measure CPR outperforms the linear correlation measure . 


Summary: Phase synchronization in brain using recurrence plot based technique is studied. A novel application of the technique to multichannel seizure EEG data is made. Contour plots of Correlation between Probability of Recurrence CPR matrix show clear contrast between seizure and pre seizure signals and a classification accuracy of 100 has been achieved. CPR could identify the focus of epilepsy and the identification is much better than that obtained using linear correlation. The paper has shown the utility of a nonparametric nonlinear model in studying synchronization in the brain.
A novel quadrature clutter rejection approach based on multivariate empirical mode decomposition which is an extension of empirical mode decomposition to multivariate for processing multichannel signals is proposed in this paper to suppress the quadrature clutter signals induced by the vascular wall and the surrounding stationary or slowly moving tissues in composite Doppler ultrasound signals and extract more blood flow components with low velocities . In this approach the MEMD algorithms which include the bivariate empirical mode decomposition with a nonuniform sampling scheme for adaptive selection of projection directions and the trivariate empirical mode decomposition with noise assistance are directly employed to adaptively decompose the complex valued quadrature composite signals echoed from both bidirectional blood flow and moving wall into a small number of zero mean rotation components which are defined as complex intrinsic mode functions . Then the relevant CIMFs contributed to blood flow components are automatically distinguished in terms of the break of the CIMFs power and then directly added up to give the quadrature blood flow signal . Specific simulation and human subject experiments are taken up to demonstrate the advantages and limitations of this novel method for quadrature clutter rejection in bidirectional Doppler ultrasound signals . Due to eliminating the extra errors induced by the Hilbert transform or complex FIR filter algorithms used in the traditional clutter rejection approaches based on the directional separation process the proposed method provides improved accuracy for clutter rejection and preserve more slow blood blow components which could be helpful to early diagnose arterial diseases . 


Summary: We proposed a novel quadrature clutter rejection approach to suppress the quadrature clutter signals induced by the slowly moving vessel walls and tissues in composite Doppler ultrasound signals. The proposed clutter rejection approach is based on multivariate empirical mode decomposition MEMD . The phase information between the quadrature demodulated Doppler ultrasound signals is keeping well during the decomposition by MEMD. The extra errors induced by the traditional clutter rejection approaches could be removed effectively. The NA TEMD provides the best performance for conserving more bidirectional blood flow components with low velocities.
The analysis of heart rate variability is central for cardiac diagnostics but its essential nonstationarity has started to gain attention only recently . The aim of this work is to develop a method for finding mathematical indicators of HRV spectral properties considering frequency nonstationarity . The analysis is done both for the new model of rhythmogram taking into account frequency modulation and for the true rhythmogram record during head up tilt test . Continuous wavelet transformation of the frequency modulated signal has been derived in analytical form . The local frequency of heart rhythm giving the maximum of CWT has been determined . Treated as another non stationary signal this frequency has been subjected to CWT following double CWT procedure . The transient periods for local frequency the frequencies of local frequency fluctuation against the main trend and the periods of emergence and attenuation of such fluctuations have been defined by estimating the spectral integrals in the ranges ULF VLF LF HF . The presented technique allows to use HRV control in the cases of arrhythmia ectopic beats heart turbulence and other non stationary violence of heart rhythm and also while studying long term cardiac records . 


Summary: We propose a new model of non stationary rhythmogram as a frequency modulated signal. Double continuous wavelet transformation is performed to analyze non stationary HRV. Various scenarios of oscillation rearrangement are studied for frequency modulated rhythmogram signal. The transient periods and other spectral characteristics are calculated basing on head up tilt test real records.