A new structure-preserving dimensionality reduction approach and OI-net implementation

Merits of genetic algorithms (GAs), an efficient evolutionary searching paradigm, are utilized for pattern classification in /spl Rfr//sup N/ by fitting hyperplanes to model the decision boundaries in the feature space. Theoretical analysis establishes that as the size of the training set (n) goes towards infinity, the error probability and the decision boundary of the GA based classifier will approach those of Bayes (optimum) classifier.

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.

One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical [Formula: see text]-means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN’s accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

ABSTRACTPurpose To construct a predicting model for urosepsis risk for patients with upper urinary tract calculi based on ultrasound and urinalysis.Materials and Methods A retrospective study was conducted in patients with upper urinary tract calculi admitted between January 2016 and January 2020. The patients were randomly grouped into the training and validation sets. The training set was used to identify the urosepsis risk factors and construct a risk prediction model based on ultrasound and urinalysis. The validation set was used to test the performance of the artificial neural network (ANN).Results Ultimately, 1716 patients (10.8% cases and 89.2% control) were included. Eight variables were selected for the model: sex, age, body temperature, diabetes history, urine leukocytes, urine nitrite, urine glucose, and degree of hydronephrosis. The area under the receiver operating curve in the validation and training sets was 0.945 (95% CI: 0.903-0.988) and 0.992 (95% CI: 0.988-0.997), respectively. Sensitivity, specificity, and Yuden index of the validation set (training set) were 80.4% (85.9%), 98.2% (99.0%), and 0.786 (0.849), respectively.Conclusions A preliminary screening model for urosepsis based on ultrasound and urinalysis was constructed using ANN. The model could provide risk assessments for urosepsis in patients with upper urinary tract calculi.

Reservoir parameter estimation using a hybrid neural network

In machine learning-based fractional cover estimation, the fractional cover distribution in training samples critically influences model construction and, consequently the accuracy of the estimations. While some studies have descriptively compared the accuracies of machine learning-based estimations across training sets derived from different sampling methods, a significant gap remains in quantitatively analyzing how the fractional cover distribution in training samples affects accuracy. This study aims to bridge this gap by introducing descriptors for fractional cover distribution in the training set and establishing mathematical relationships between these descriptors and the accuracy of fractional cover estimation. We employed the Dirichlet distribution to characterize the joint fractional cover of multiple land classes and the Beta distribution for single-class cover. Subsequently, two descriptors were developed: the Kullback-Leibler (KL) divergence, measuring the similarity of fractional cover distributions for the target class between the training and test sets, and the geometric angle, representing the fractional cover distributions of the target class in the training set at the same KL divergence. Fractional cover estimation was performed using random forest regression, with accuracy assessed on an independent test set. The relationships between the KL divergence and accuracy, and between the geometric angle and accuracy at the same KL divergence, were modeled using univariate linear models and harmonic models, respectively. The combined effects of these descriptors on accuracy were further analyzed using coupled harmonic analysis and generalized additive models. Our experimental results, using both simulated and real data, demonstrated the effectiveness of these models. Given the strong explanatory power of the KL divergence in the accuracy of fractional cover estimation, we encourage researchers to report detailed statistical information of both training and test sets, enriching the understanding of model performance in fractional cover estimation.

Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification

A computationally efficient algorithm is proposed for modeling and coding the time-varying spectra of musical sounds. The aim is to encode individual data sets and not the statistical properties of the sounds. A given sequence of acoustic feature vectors is modeled by finding such a set of “states” (anchor points in the feature space) that the input data can be efficiently represented by interpolating between them. The achieved modeling accuracy for a database of musical sounds was approximately two times better than that of a conventional “vector quantization” model where the input data was k-means clustered and the input data vectors were then replaced by their corresponding cluster centroids. The computational complexity of the proposed algorithm as a function of the input sequence length T is O(TlogT).

Intuitionistic Fuzzy C-means (IFCM) is a robust clustering method which is based upon intuitionistic fuzzy set theory. It uses Euclidean distance as a distance metric, hence can only cluster hyper spherically distributed data-sets in data space or in feature space. FCM and KFCM with a new distance measure (FCM-σ and KFCM-σ) can detect non-hyperspherical clusters in data space and feature space but they are sensitive to noise and produce inefficient results in the presence of noise. This paper present a robust Intuitionistic Fuzzy c-means(IFCM-σ) and a robust kernel Intutitionistic Fuzzy C-Means(KIFCM-σ) with a new distance metric that incorporates the distance variation in a cluster to regularize the distance between data point and the cluster centroid. Propose algorithms are the hybridization of IFCM, kernel function, and new distance metric in the data space and in the feature space which avoid various problems of IFCM and FCM-σ. Experiments are done using two-dimensional synthetic data-sets and noisy digital images, and results are compared with IFCM, KIFCM, FCM-σ and KFCM-σ. The results show that our proposed algorithms, especially KIFCM-σ are more effective.

1176 OUTCOME PREDICTION IN PATIENTS WITH BLADDER CANCER AFTER RADICAL CYSTECTOMY USING ARTIFICIAL NEURAL NETWORKS

To build up an artificial neural network (ANN) model in the diagnosis of urothelial cell carcinoma (UCC) in urine cytology smears. We randomly selected a total of 115 urine cytology samples, out of which 59 were histopathology proven UCC cases and remaining 56 were benign cases from routine cytology samples. All the carcinoma cases were proven on histopathology. Image morphometric analysis was performed on Papanicolaou's stained smears to study nuclear area, diameter, perimeter, standard deviation of nuclear area, and integrated gray density. Detailed cytological features were also studied in each case by two independent observers and were semi-quantitatively graded. The back propagation ANN model was designed as 17-11-3 with the help of heuristic search. The cases were randomly partitioned as training, validation, and testing sets by the program. There were 79 cases for training set, 18 cases for validation set and 18 cases for test set. In the training set, ANN was able to diagnose all the malignant and benign cases. In the test set, all the benign and malignant cases were diagnosed correctly. However, one of the low grade cases was diagnosed as high grade UCC by ANN model. We successfully built an ANN model in urine from the visual and morphometric data to identify the benign and malignant cases. In addition, the system can also identify the low grade and high grade UCC cases.

Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment.

Most of the existing methods for pedestrian detection work well, only when the following assumption is satisfied: the features extracted from the training dataset and the testing dataset have very similar distributions in the feature space. However, in practice, this assumption does not hold because of the scene complexity and variation. In this paper, a new method is proposed for detecting pedestrians in various scenes based on the transfer learning technique. Our proposed method employs the following two strategies for improving the pedestrian detection performance. First, a new sample screening method based on manifold learning is proposed. The basic idea is to choose samples from the training set, which may be similar to the samples from the unseen scene, and then merge the selected samples into the unseen set. Second, a new classification model based on transfer learning is proposed. The advantage of the classification model is that only a small number of samples need to be used from the unseen scenes. Most of the training samples are still obtained from the training scene, which take up to 90% of the entire training samples. Compared to the traditional pedestrian detection methods, the proposed algorithm can adapt to different scenes for detecting pedestrians. Experiments on two pedestrian detection benchmark datasets, DC and NICTA, showed that the method can obtain better performance as compared to other previous methods.

In digital breast tomosynthesis (DBT), quasi-three-dimensional (3D) structural information is reconstructed from a small number of 2D projection view (PV) mammograms acquired over a limited angular range. The authors developed preliminary computer-aided diagnosis (CADx) methods for classification of malignant and benign masses and compared the effectiveness of analyzing lesion characteristics in the reconstructed DBT slices and in the PVs. A data set of MLO view DBT of 99 patients containing 107 masses (56 malignant and 51 benign) was collected at the Massachusetts General Hospital with IRB approval. The DBTs were obtained with a GE prototype system which acquired 11 PVs over a 50 degree arc. The authors reconstructed the DBTs at 1 mm slice interval using a simultaneous algebraic reconstruction technique. The region of interest (ROI) containing the mass was marked by a radiologist in the DBT volume and the corresponding ROIs on the PVs were derived based on the imaging geometry. The subsequent processes were fully automated. For classification of masses using the DBT-slice approach, the mass on each slice was segmented by an active contour model initialized with adaptive k-means clustering. A spiculation likelihood map was generated by analysis of the gradient directions around the mass margin and spiculation features were extracted from the map. The rubber band straightening transform (RBST) was applied to a band of pixels around the segmented mass boundary. The RBST image was enhanced by Sobel filtering in the horizontal and vertical directions, from which run-length statistics texture features were extracted. Morphological features including those from the normalized radial length were designed to describe the mass shape. A feature space composed of the spiculation features, texture features, and morphological features extracted from the central slice alone and seven feature spaces obtained by averaging the corresponding features from three to 19 slices centered at the central slice were compared. For classification of masses using the PV approach, a feature extraction process similar to that described above for the DBT approach was performed on the ROIs from the individual PVs. Six feature spaces obtained from the central PV alone and by averaging the corresponding features from three to 11 PVs were formed. In each feature space for either the DBT-slice or the PV approach, a linear discriminant analysis classifier with stepwise feature selection was trained and tested using a two-loop leave-one-case-out resampling procedure. Simplex optimization was used to guide feature selection automatically within the training set in each leave-one-case-out cycle. The performance of the classifiers was evaluated by the area (Az) under the receiver operating characteristic curve. The test Az values from the DBT-slice approach ranged from 0.87 +/- 0.03 to 0.93 +/- 0.02, while those from the PV approach ranged from 0.78 +/- 0.04 to 0.84 +/- 0.04. The highest test Az of 0.93 +/- 0.02 from the nine-DBT-slice feature space was significantly (p = 0.006) better than the highest test Az of 0.84 +/- 0.04 from the nine-PV feature space. The features of breast lesions extracted from the DBT slices consistently provided higher classification accuracy than those extracted from the PV images.