A four dukkha state-space model for hand tracking

In this paper, we propose a hand tracking method which was inspired by the notion of the four dukkha: birth, aging, sickness and death (BASD) in Buddhism. Based on this philosophy, we formalize the hand tracking problem in the BASD framework, and apply it to hand track hand gestures in isolated sign language videos. The proposed BASD method is a novel nature-inspired computational intelligence method which is able to handle complex real-world tracking problem. The proposed BASD framework operates in a manner similar to a standard state-space model, but maintains multiple hypotheses and integrates hypothesis update and propagation mechanisms that resemble the effect of BASD. The survival of the hypothesis relies upon the strength, aging and sickness of existing hypotheses, and new hypotheses are birthed by the fittest pairs of parent hypotheses. These properties resolve the sample impoverishment problem of the particle filter. The estimated hand trajectories show promising results for the American sign language

A New RBFNDDA-KNN Network and Its Application to Medical Pattern Classification

Abstract. In this paper, a new variant of the Radial Basis Function Network with Dynamic Decay Adjust (RBFNDDA) is introduced for undertaking pattern classification problems with noisy data. The RBFNDDA network is integrated with the k-nearest neighbours algorithm to form the proposed RBFNDDA-KNN model. Given a set of labelled data samples, the RBFNDDA network undergoes a constructive learning algorithm that exhibits a greedy insertion behaviour. As a result, many prototypes (hidden neurons) that represent small (with respect to a threshold) clusters of labelled data are introduced in the hidden layer. This results in a large network size. Such small prototypes can be caused by noisy data, or they can be valid representatives of small clusters of labelled data. The KNN algorithm is used to identify small prototypes that exist in the vicinity (with respect to a distance metric) of the majority of large prototypes from different classes. These small prototypes are treated as noise, and are, therefore, pruned from the network. To evaluate the effectiveness of RBFNDDA-KNN, a series of experiments using pattern classification problems in the medical domain is conducted. Benchmark and real medical data sets are experimented, and the results are compared, analysed, and discussed. The outcomes show that RBFNDDA-KNN is able to learn information with a compact network structure and to produce fast and accurate classification results

Honokiol Induces Calpain-Mediated Glucose-Regulated Protein-94 Cleavage and Apoptosis in Human Gastric Cancer Cells and Reduces Tumor Growth

Background. Honokiol, a small molecular weight natural product, has been shown to possess potent anti-neoplastic and anti-angiogenic properties. Its molecular mechanisms and the ability of anti-gastric cancer remain unknown. It has been shown that the anti-apoptotic function of the glucose-regulated proteins (GRPs) predicts that their induction in neoplastic cells can lead to cancer progression and drug resistance. We explored the effects of honokiol on the regulation of GRPs and apoptosis in human gastric cancer cells and tumor growth. Methodology and Principal Findings. Treatment of various human gastric cancer cells with honokiol led to the induction of GRP94 cleavage, but did not affect GRP78. Silencing of GRP94 by small interfering RNA (siRNA) could induce cell apoptosis. Treatment of cells with honokiol or chemotherapeutics agent etoposide enhanced the increase in apoptosis and GRP94 degradation. The calpain activity and calpain-II (m-calpain) protein (but not calpain-I (mu-calpain)) level could also be increased by honokiol. Honokiol-induced GRP94 down-regulation and apoptosis in gastric cancer cells could be reversed by siRNA targeting calpain-II and calpain inhibitors. Furthermore, the results of immunofluorescence staining and immunoprecipitation revealed a specific interaction of GRP94 with calpain-II in cells following honokiol treatment. We next observed that tumor GRP94 over-expression and tumor growth in BALB/c nude mice, which were inoculated with human gastric cancer cells MKN45, are markedly decreased by honokiol treatment. Conclusions and Significance. These results provide the first evidence that honokiol-induced calpain-II-mediated GRP94 cleavage causes human gastric cancer cell apoptosis. We further suggest that honokiol may be a possible therapeutic agent to improve clinical outcome of gastric cancer

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Novel Improvement Techniques To Fuzzy Artmap And Their Evolutionary Models For Pattern Classification

This thesis is focused on the development of evolutionary artificial neural network (EANN) models for pattern classification

A hybrid FAM–CART model and its application to medical data classification

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability–plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM–CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM–CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability–plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM–CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM–CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool

An extended fuzzy-kNN approach to solving class-imbalanced problems

In this paper, for solving imbalanced classification problem, more attention is placed on data points in the boundary area between two classes. The fuzzy k-nearest neighbors algorithm, which has good performance in conventional classification problems, is adapted here to solve imbalanced classification problems, where G-mean accuracy is used to evaluate our proposal method and compare it with other approaches

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum\u27 setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant

Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular

Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a &ldquo;compressor&rdquo; to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts&rsquo; opinions used in maintaining the CW system.<br /