Recognition of partly occluded patterns: a neural network model

 Human beings are often able to read a letter or word partly occluded by contaminating ink stains. However, if the stains are completely erased and the occluded areas of the letter are changed to white, we usually have difficulty in reading the letter. In this article I propose a hypothesis explaining why a pattern is easier to recognize when it is occluded by visible objects than by invisible opaque objects. A neural network model is constructed based on this hypothesis. The visual system extracts various visual features from the input pattern and then attempts to recognize it. If the occluding objects are not visible, the visual system will have difficulty in distinguishing which features are relevant to the original pattern and which are newly generated by the occlusion. If the occluding objects are visible, however, the visual system can easily discriminate between relevant and irrelevant features and recognize the occluded pattern correctly. The proposed model is an extended version of the neocognitron model. The activity of the feature-extracting cells whose receptive fields cover the occluding objects is suppressed in an early stage of the hierarchical network. Since the irrelevant features generated by the occlusion are thus eliminated, the model can recognize occluded patterns correctly, provided the occlusion is not so large as to prevent recognition even by human beings. Received: 21 February 2000 / Accepted in revised form: 11 September 2000     

Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.     

A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed.     

Recognition of culture state using two-dimensional gel electrophoresis with an artificial neural network

Proteomic technologies were applied to the examination of nutrient components in culture broth. In bioprocesses, many types of media have been proposed and used on the commercial scale. Natural nutrients, the chemical components of which cannot be identified completely, are often used in fermentation processes such as in the production of baker's yeast, alcoholic beverages, amino acids, and pharmaceuticals. The catabolic activities of the microorganisms in these processes vary with the species used. We used an artificial neural network (ANN) to recognize the sufficiency of chemical elements based on the protein spots resolved in 2-DE, and we evaluated this technique using the leave-one-out method. We also attempted to reduce the number of input data for spot selection based on sensitivity analysis of the ANN, and the selected data were used to improve accuracy.     

A recurrent neural network for partitioning of hand drawn characters into strokes of different orientations.

A neural network is described which is intended to extract orientation features that should be used for recognition of hand drawn characters. The network partitions the input hand drawn characters into separate line segments (strokes) according to their orientations. The network consists of several neural layers; each layer serves for extracting strokes of a certain orientation. Every neural layer has one-to-one correspondence with an input screen. The network uses an iterative update procedure which includes interactions of neurons inside each layer through oriented excitatory connections and inhibitory interrelations between the corresponding neurons of different layers. Computer simulation of the network was performed. Experiments showed that the network efficiently classifies all pixels of any hand drawn characters according to the orientations of the strokes constituting these characters and performs, as a result of that, a reasonable segmentation of characters.     

Recognition method of soybean leaf diseases using residual neural network based on transfer learning

Leaf disease is an important factor restricting the high quality and high yield of the soybean plant. Insufficient control of soybean diseases will destroy the local ecological environment and break the stability of the food chain. To overcome the low accuracy in recognizing soybean leaf diseases using traditional deep learning models and complexity in chemical analysis operations, in this study, a recognition model of soybean leaf diseases was proposed based on an improved deep learning model. First, four types of soybean diseases (Septoria Glycines Hemmi, Soybean Brown Leaf Spot, Soybean Frogeye Leaf Spot, and Soybean Phyllosticta Leaf Spot) were taken as research objects. Second, image preprocessing and data expansion of original images were carried out using image registration, image segmentation, region calibration and data enhancement. The data set containing 53, 250 samples was randomly divided into the training set, verification set, and test set according to the ratio of 7:2:1. Third, the convolution layer weight of the pre-training model based on the ImageNet open data set was transferred to the convolution layer of the ResNet18 model to reconstruct the global average pooling layer and the fully connected layer for constructing recognition model of TRNet18 model. Finally, the recognition accuracy of the four leaf diseases reached 99.53%, the Macro-F1 was 99.54%, and the average recognition time was 0.047184 s. Compared with AlexNet, ResNet18, ResNet50, and TRNet50 models, the recognition accuracy and Macro-F1 of the TRNet18 model were improved by 6.03% and 5.99% respectively, and the model recognition time was saved by 16.67%, The results showed that the proposed TRNet18 model had higher classification accuracy and stronger robustness, which can not only provide a reference for accurate recognition of other crop diseases, but also be transplanted to the mobile terminal for recognition of crop leaf diseases.     

On the probabilistic optimization of spiking neural networks

The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN.     

Object selection by an oscillatory neural network

We describe a new solution to the problem of consecutive selection of objects in a visual scene by an oscillatory neural network with the global interaction realised through a central executive element (central oscillator). The frequency coding is used to represent greyscale images in the network. The functioning of the network is based on three main principles: (1) the synchronisation of oscillators via phase-locking, (2) adaptation of the natural frequency of the central oscillator, and (3) resonant increase of the amplitudes of the oscillators which work in-phase with the central oscillator. Examples of network simulations are presented to show the reliability of the results of consecutive selection of objects under conditions of constant and varying brightness of the objects.     

重新认识神经胶质细胞

在神经生物学研究领域,胶质细胞过去被认为仅仅是为神经元提供支持而受到冷落。近年来的研究使人们认识到,胶质细胞在整个神经系统中发挥着远比以往所知的更为活跃、更为重要的作用。     

Modulation of neural network activity by patterning.

Using neuronal cultures on microelectrode arrays, researchers have shown that recordable electrical activity can be influenced by chemicals in the culture environment, thus demonstrating potential applicability to biosensors or drug screening. Since practical success requires the design of robust networks with repeatable, reliable responses understanding the sources of variation is important. In this report, we used lithographic technologies to confine neurons to highly defined patterns (40 microm wide stripes); in turn these patterns gave us a measure of control over the local density of neurons (100-500 cells/mm(2)). We found that the apparent electrical activity of the network, as measured by the fraction of electrodes from which signals were recordable, increases 8-10-fold with greater local density. Also, average-firing rates of the active neurons increased 3-5-fold. We conclude that patterned networks offer one means of controlling and enhancing the responsiveness of cultured neural networks.     

Modular analysis of the probabilistic genetic interaction network

MOTIVATION: Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. RESULTS: Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.     

Validation of protein models by a neural network approach

Background  

The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction.     

Suboptimum solutions obtained by the Hopfield-Tank neural network algorithm

The neural network method of Hopfield and Tank claims to be able to find nearly-optimum solutions for discrete optimization problems, e.g. the travelling salesman problem. In the present paper, an example is given which shows that the Hopfield-Tank algorithm systematically prefers certain solutions even if the energy values of these solutions are clearly higher than the energy of the global minimum.     

基于卷积神经网络的细菌转录终止子预测

在遗传学中,终止子是位于poly(A)位点下游、长度在数百碱基以内、包含多个回文序列、具有终止转录功能的DNA结构域,其主要作用是使转录终止。在原核生物基因组中有两类转录终止子,即Rho-dependent因子和Rho-independent因子。在本项研究中,提出了一种新的预测模型(TermCNN)来快速准确地识别细菌转录终止子。该模型将具有代表性的6-mer特征子集(2 537个特征)和电子—离子相互作用伪电位(EIIP)作为输入向量,利用卷积神经网络(CNN)构建预测模型。五折交叉验证和独立测试的结果表明该模型优于最新的预测模型iTerm-PseKNC。值得注意的是,该模型在跨物种试验中具有明显的优势。它可以高度精确地预测大肠杆菌(E. coli)和枯草芽孢杆菌(B. subtilis)的转录终止子。     

Recognition of general patterns using neural networks

The Hopfield model of neural network stores memory in its symmetric synaptic connections and can only learn to recognize sets of nearly orthogonal patterns. A new algorithm is put forth to permit the recognition of general (non-orthogonal) patterns. The algorithm specifies the construction of the new network's memory matrix T _ij, which is, in general, asymmetrical and contains the Hopfield neural network (Hopfield 1982) as a special case. We find further that in addition to this new algorithm for general pattern recognition, there exists in fact a large class of T _ij memory matrices which permit the recognition of non-orthogonal patterns. The general form of this class of T _ij memory matrix is presented, and the projection matrix neural network (Personnaz et al. 1985) is found as a special case of this general form. This general form of memory matrix extends the library of memory matrices which allow a neural network to recognize non-orthogonal patterns. A neural network which followed this general form of memory matrix was modeled on a computer and successfully recognized a set of non-orthogonal patterns. The new network also showed a tolerance for altered and incomplete data. Through this new method, general patterns may be taught to the neural network.     

A probabilistic network for the diagnosis of acute cardiopulmonary diseases

In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented in detail. A panel of expert physicians collaborated to specify the qualitative part, which is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables into univariate conditional distributions. The quantitative part, which is a set of parametric models defining these univariate conditional distributions, was estimated following the Bayesian paradigm. In particular, we exploited an original reparameterization of Beta and categorical logistic regression models to elicit the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital records via Markov chain Monte Carlo simulation. Refinement was iteratively performed until the probabilistic network provided satisfactory concordance index values for several acute diseases and reasonable diagnosis for six fictitious patient cases. The probabilistic network can be employed to perform medical diagnosis on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167 patient findings.