varunr14/newsummary huggingface dataset:
Materials informatics is a growing field in materials science . Materials scientists have begun to use soft computing techniques to discover novel materials . In order to apply these techniques the descriptors of a material must be selected thereby deciding the resulting performance . As a way of describing a material the properties of each element in the material are used directly as the features of the input variable . Depending on the number of elements in the material the dimensionality of the input may differ . Hence it is not possible to apply the same model to materials with different numbers of elements for tasks such as regression or discrimination . In the present paper we present a novel method of uniforming the dimensionality of the input that allows regression or discriminative tasks to be performed using soft computing techniques . The main contribution of the proposed method is to provide a solution for uniforming the dimensionality among input vectors of different size . The proposed method is a variant of the denoising autoencoder Vincent et al . using neural networks and gives a latent representation with uniformed dimensionality of the input . In the experiments of the present study we consider compounds with ionic conductivity and hydrogen storage materials . The results of the experiments indicate that the regression tasks can be performed using the uniformed latent data learned by the proposed method . Moreover in the clustering task using these latent data we observed distance preservation in data space which is also the case for the denoising autoencoder . This result may enable the proposed method to be used in a broad range of applications . 


Summary: We introduced two representation in respect to materials naive representation and expert representation. We proposed a uniforming method of dimensionality of data with different size input vectors using a neural network. The proposed method outperformed the conventional methods the multi layer autoencoder the denoising autoencoder and kernel PCA for the linear regression task on synthetic data. The experimental results showed the robustness for data size and number of constituent elements. In the linear regression task on ion conductivity data of bulk materials and hydrogen storage materials the good fitting performance was obtained in terms of the latent data uniformed by the proposed method.
We study the theoretical performance of using Electrical Impedance Tomography to measure the conductivity of the main tissues of the head . The governing equations are solved using the Finite Element Method for realistically shaped head models with isotropic and anisotropic electrical conductivities . We focus on the Electroencephalography signal frequency range since EEG source localization is the assumed application . We obtain the Cram r Rao Lower Bound to find the minimum conductivity estimation error expected with EIT measurements . The more convenient electrode pairs selected for current injection from a typical EEG array are determined from the CRLB . Moreover using simulated data the Maximum Likelihood Estimator of the conductivity parameters is shown to be close to the CRLB for a relatively low number of measurements . The results support the idea of using EIT as a low cost and practical tool for individually measure the conductivity of the head tissues and to use them when solving the EEG source localization . Even when the conductivity of the soft tissues of the head is available from Diffusion Tensor Imaging EIT can complement the electrical model with the estimation of the skull and scalp conductivities . 


Summary: Parametric estimation of scalp skull and brain conductivities is analyzed. Realistic head models including anisotropy are adopted. Convenient electrode pairs for the current injection are determined. The Maximum Likelihood Estimator is advisable in practical situations.
Conventionally optimal reactive power dispatch is described as the minimization of active power transmission losses and or total voltage deviation by controlling a number of control variables while satisfying certain equality and inequality constraints . This article presents a newly developed meta heuristic approach chaotic krill herd algorithm for the solution of the ORPD problem of power system incorporating flexible AC transmission systems devices . The proposed CKHA is implemented and its performance is tested successfully on standard IEEE 30 bus test power system . The considered power system models are equipped with two types of FACTS controllers . Simulation results indicate that the proposed approach yields superior solution over other popular methods surfaced in the recent state of the art literature including chaos embedded few newly developed optimization techniques . The obtained results indicate the effectiveness for the solution of ORPD problem of power system considering FACTS devices . Finally simulation is extended to some large scale power system models like IEEE 57 bus and IEEE 118 bus test power systems for the same objectives to emphasis on the scalability of the proposed CKHA technique . The scalability the robustness and the superiority of the proposed CKHA are established in this paper . 


Summary: CKHA algorithm is applied for ORPD problems considering FACTS devices. CKHA is implemented on three IEEE standard test systems. Two different objective functions are considered. The results of CKHA are compared to other algorithms surfaced recently. Effectiveness of CKHA is established for ORPD problem with FACTS devices.
This work proposes a comparative study of a pair of electrocardiographic 2D representations the frontal plane and a preferential plane obtained from ECG data . During depolarization and repolarization main electrical vectors were analyzed and compared between healthy subjects and patients referred for percutaneous transluminal coronary angioplasty . Recordings were obtained at rest . Many patients from the latter group presented normal ECGs thus the hypothesis to prove was that electrical axis in any of the studied planes would effectively discriminate silent ischemia records from healthy ones . The FP was constructed with I and aVF leads while the PP used the two first eigenvectors of the spatial correlation matrix of the ECG . Although the depolarization and repolarization vectors from both groups resulted normal those from the silent ischemia group appeared strongly biased to the left closer to the limit of the normality range . This slight change originated a significant separation between health and disease in the FP . Here most of the parameters resulted highly informative even those related to the depolarization phase . The cardiac vector integrating both depolarization and repolarization information presented the highest performance . Parameters in the PP however did not produce an acceptable discrimination except for the amplitude of the T wave . Additionally the repolarization orientation in the FP was the only marker that simultaneously discriminated three different groups of patients according to their occlusion sites . In conclusion the FP offered a 2D representation general enough to enable the separation of silent ischemia versus health populations while the PP did not due mainly to its individually optimized nature failing to provide a unique referencial frame for all the subjects . 


Summary: We compared cardiac vectors of ischemic and healthy patients in two different planes. Planes under study the Einthoven s plane FP and a PCA transformed plane PP . We compared the ability to discriminate ischemia from health of both representations. The FP enabled the discrimination of ischemic patients while the PP did not.
Due to noises speckles etc . automatic prostate segmentation is rather challenging and using only low level information such as intensity gradient is insufficient and unable to tackle the problem . In this paper we propose an automatic prostate segmentation method combining intrinsic properties of TRUS images with the high level shape prior information . First intrinsic properties of TRUS images such as the intensity transition near the prostate boundary as well as the speckle induced texture features obtained by Gabor filter banks are integrated to deform the model to the target contour . These properties make our method insensitive to high gradient regions introduced by noises and speckles . Then the preliminary segmentation is fine tuned by the non parametric shape prior which is optimally distilled by non parametric kernel density estimation as it can approximate arbitrary distributions . The refinement is along the direction of mean shift vector and considerably strengthens the robustness of the method . The performance of our method is validated by experimental results . Compared with the state of the art the accuracy and robustness of the method is quite promising and the mean absolute distance is only 1.21 0.85mm . 


Summary: An explicit prostate segmentation model utilizing non parametric shape prior is proposed. The method can model the shape that is not statistically significant in the shape space. The method can escape from local minima by using a fairly large detection range during freely deforming. Compared with recently proposed sparse shape composition method training data set with a large number of instances is not necessary. The intrinsic properties of TRUS images are integrated into the model.
A spectral angle based feature extraction method Spectral Clustering Independent Component Analysis is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images . SC ICA provides equal priority to global and local features thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction . First input multispectral MRI is divided into different clusters by a spectral distance based clustering . Then Independent Component Analysis is applied on the clustered data in conjunction with Support Vector Machines for brain tissue analysis . Normal and abnormal datasets consisting of real and synthetic T1 weighted T2 weighted and proton density fluid attenuated inversion recovery images were used to evaluate the performance of the new method . Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC ICA based classification especially in reproduction of small abnormalities . Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index accuracy values 0.75 98.8 observed against ICA based SVM results 0.17 96.1 for reproduced lesions . Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases . 


Summary: Spectral clustering extension to independent component analysis has been proposed. Synthetic and clinical MRI data analyzed to verify the potential of the algorithm. High performance tissue classification observed in multispectral brain MRI study. The proposed method shows 98.8 accuracy in clinical abnormality analysis.
Automatic analysis of biomedical time series such as electroencephalogram and electrocardiographic signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine . In this work a simple yet effective bag of words representation that is originally developed for text document analysis is extended for biomedical time series representation . In particular similar to the bag of words model used in text document domain the proposed method treats a time series as a text document and extracts local segments from the time series as words . The biomedical time series is then represented as a histogram of codewords each entry of which is the count of a codeword appeared in the time series . Although the temporal order of the local segments is ignored the bag of words representation is able to capture high level structural information because both local and global structural information are well utilized . The performance of the bag of words model is validated on three datasets extracted from real EEG and ECG signals . The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag of words model such as local segment length and codebook size but also robust to noise . 


Summary: A novel feature representation is proposed for biomedical time series classification. The representation is extended from the bag of words model in text document analysis. The proposed bag of words representation is effective and discriminative. The representation is able to capture both global and local structural information.
Technology credit scoring models have been used to screen loan applicant firms based on their technology . Typically a logistic regression model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology . However these attributes are evaluated in linguistic expressions represented by fuzzy number . Besides the possibility of loan default can be described in verbal terms as well . To handle these fuzzy input and output data we proposed a fuzzy credit scoring model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology . The method of fuzzy logistic regression as an appropriate prediction approach for credit scoring with fuzzy input and output was presented in this study . The performance of the model is improved compared to that of typical logistic regression . This study is expected to contribute to practical utilization of the technology credit scoring with linguistic evaluation attributes . 


Summary: We propose a technology credit scoring model based on fuzzy logistic regression. Fuzzy predictor fuzzy binary responses with crisp coefficients are considered. Fuzzy least square method is used to estimate parameters. The performance of proposed fuzzy logistic regression model is improved compared to the logistic regression.
Diaphragmatic electromyogram signal plays an important role in the diagnosis and analysis of respiratory diseases . However EMGdi recordings are often contaminated by electrocardiographic interference which posing serious obstacle to traditional denoising approaches due to overlapped spectra of these signals . In this paper a novel method based on wavelet transform and independent component analysis is proposed to remove the ECG interference from noisy EMGdi signals . With the proposed method the original independent components of contaminated EMGdi signal were first obtained with ICA . Then the ECG components contained were removed by a specially designed wavelet domain filter . After that the purified independent components were reconstructed back to the original signal space by ICA to obtain clean EMGdi signals . Experimental results achieved on practical clinical data show that the proposed approach is better than several traditional methods include wavelet transform ICA digital filter and adaptive filter in ECG interference removing . 


Summary: A novel de noising method is proposed to remove the ECG interference from noisy EMGdi signals. Experimental results achieved on practical clinical data show that the proposed approach is better than traditional methods include wavelet transform WT ICA digital filter and adaptive filter in ECG interference removing. The original independent components of contaminated EMGdi signal were firstly obtained with independent component analysis ICA . Then the ECG components contained were removed by a specially designed wavelet domain filter. Finally the purified independent components were reconstructed back to the original signal space by ICA to obtain the clean EMGdi signals.
The minimum vertex cover problem is a classical combinatorial optimization problem . This paper studies this problem based on rough sets . We show that finding the minimal vertex cover of a graph can be translated into finding the attribute reduction of a decision information table . At the same time finding a minimum vertex cover of graphs is equivalent to finding an optimal reduct of a decision information table . As an application of the theoretical framework a new algorithm for the minimum vertex cover problem based on rough sets is constructed . Experiments show that the proposed algorithm gets better performance in terms of the ratio value when compared with some other algorithms . 


Summary: We study the NP hard problem of finding a minimum vertex cover of graphs based on rough sets. The problem of finding a minimum vertex cover of graphs can be translated into the problem of finding an optimal reduct of a decision information table in rough sets. A new method based on rough sets is proposed to compute the minimum vertex cover of a given graph.
In this paper I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform . Initially fast finite shearlet transform was performed over mammogram images and feature vectors were built using coefficients of the transform . In subsequent calculations features were ranked according to t test statistics and capabilities were distinguished between different classes . To maximize differences between class representatives a thresholding process was implemented as a final stage of feature extraction and classifications were calculated over the optimal feature set using 5 fold cross validation and a support vector machine classifier . The present results show that the proposed method provides satisfactory classification accuracy . 


Summary: This paper presents an approach for breast cancer diagnosis in digital mammograms using shearlet transform. A new approach is represented for feature extraction using shearlet transform t test statistic and dynamic thresholding. The system uses two datasets ROIs obtained from two different database MIAS and DDSM database. The datasets include all abnormalites for breast cancer. The classification section was performed to distinguish the ROIs as normal abnormal and benign malignant.
Eye activity has larger electrical potential than the average electroencephalogram recording thus making it one of the major sources of artefacts . Ocular artefacts must be removed as completely as possible with little or no loss of EEG to obtain a higher quality EEG . Using independent component analysis the EEG is separated into independent components and the contaminated component is removed thus removing the OA . However ICA does not separate the sources completely and some of the meaningful EEG is lost . In this paper a new method combining ICA and wavelet neural networking is proposed . In this method WNN is applied to the contaminated ICs correcting the OA and thus lowering the data lost . The method was evaluated using simulated and real datasets and the results show that the OA are successfully removed with very little data loss . 


Summary: A combination of ICA and WNN is introduced to eliminate EOG artefacts. This method corrects only EOG artefacts within a contaminated component. Minimal low frequency and underlying cerebral activity is lost. EEG quality is increased.
Pulse transit time and pulse wave velocity are the markers most widely used to evaluate the vascular effects of aging hypertension arterial stiffness and atherosclerosis . To calculate these markers it is necessary to determine the location of the onset and systolic peak of the arterial pulse wave . In this paper a method employed for electrocardiography R peak detection with a slight modification is applied for both the onset and systolic peak detections in APW . The method employs Shannon energy envelope estimator Hilbert transform and moving average filter . The minimum value and the positive zero crossing points of the odd symmetry function of the HT correspond to the locations of the onset and systolic peak respectively . The algorithm was evaluated using expert s annotations with 10 records of 5min length and different signal to noise ratios and achieved a good performance and precision . When compared to expert s annotation the algorithm detected these fiducial points with average sensitivity positive predictivity and accuracy of 100 and presented errors less than 10ms . In APW signals contaminated with noise in both cases the relative error is less than 2 respect to pulse wave periods of 800ms . The performance of algorithm was compared with both foot approximation and adaptive threshold methods and the results show that the algorithm outperforms theses reported methods with respect to manuals annotation . The results are promising suggesting that the method provides a simple but accurate onset and systolic peak detection and can be used in the measurement of pulse transit time pulse wave velocity and pulse rate variability . 


Summary: Hilbert transform method can be used for detecting both the onset and systolic peak in pulse wave signals. It achieves good performance and precision when compared to expert annotation. It is robust when noise and interference are present in pulse wave and also. Shows a low detection error rate. It is computationally simple.
We propose a methodological study for the optimization of surface EMG based hand gesture classification effective to implement a human computer interaction device for both healthy subjects and transradial amputees . The widely commonly used unsupervised Principal Component Analysis approach was compared to the promising supervised common spatial pattern methodology to identify the best classification strategy and the related tuning parameters . A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures . Twenty healthy subjects were recruited to compute optimized parameters for and to compare between the two strategies . The system was also tested on a transradial amputee subject in order to assess the robustness of the optimization in recognizing disabled users gestures . Results show that RMS WA ANN is the best feature vector classifier pair for the PCA approach whereas M RMS WA ANN is the best pair for the CSP methodology . Statistical analysis on classification results shows no significant differences between the two strategies . Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject . This motivates further investigation of the proposed methodology on a larger sample of amputees . Our results are useful to boost EMG based hand gesture recognition and constitute a step toward the definition of an efficient EMG controlled system for amputees . 


Summary: An optimization study on EMG based hand gesture recognition is proposed. We compare unsupervised PCA vs. supervised CSP preprocessing technique. Artificial neural network achieves best classification accuracies in both cases. The study identifies the best parameters to choose in pattern recognition stages. The optimization algorithm is robust enough to be used on amputees as well.