Pattern classification via linear programming

With the advent of the era of big data, existing computing systems limit the development of new technologies such as cloud computing and artificial intelligence. As the demand for high-performance computing continues to grow, traditional computing models are facing unprecedented challenges. Neural mimicry computing based on artificial neural networks provides a potential solution, and biological computing with advantages such as low energy consumption parallelization is very important for its research, in which gene circuitry will be the key to the construction of artificial neural networks. In this study, we used biological elements to construct gene circuits to form neural network structures to realize neural mimicry calculations. Based on the similarity between the structure of gene circuits and neural networks, we describe a neural network design based on the logic gate calculation of gene circuits, and realize the applications of linear classification, nonlinear classification and pattern classification. In the linear classification of NAND, the four datasets in the space are divided into two categories by hyperplane. The structural design of the neural network for linear classification can be realized by using the genetic circuit, which includes five biological elements and the biological elements between different layers are connected by gene regulation. The simulation results of linear classification showed that the gene circuit realizes the function of NAND by means of the neural network and the output fluorescence value as the detection standard. In the nonlinear classification of XOR, the four datasets in the space are divided into two categories. The structural design of the neural network for nonlinear classification can be realized by using the genetic circuit, which includes six biological elements. The simulation results of nonlinear classification showed that the gene circuit realizes the function of XOR by means of the neural network and the output fluorescence value as the detection standard. In the pattern classification, the neural network with five layers and eight neurons was constructed by a genetic circuit with eight biological elements connected by gene regulation. The simulation results of pattern classification showed that the gene circuit realizes the function of pattern classification by means of the neural network and the output fluorescence value as the detection standard. This study realizes neural mimicry computing by constructing neural network through engineering gene circuits. The integrated molecular computing system is expected to be applied to the manufacturing of artificial intelligence chips, and further applied to aerospace, information security and national defense construction, along with other fields.

The research was carried out in the aspect of studying features of conformation type cows of Ukrainian Black-and-White dairy and Holstein breeds estimated by linear classification method. As for the basis we took the herd of the company "Ukrlandfarming, private enterprise "Burynske", Pidlisniv branch in Sumy region. The aim was to conduct a comparative analysis of experimental breeds according to the assessment of linear classification and determine the impact of final score for type on milk productivity first-born cows. In general, first-born cows of both breeds were characterized by good linear estimation, especially on a 100-score scale. However, animals of Holstein breed in comparison with Ukrainian Black-and-White dairy were better in terms of body parts, which characterize the dairy type by 1.3 score, body - by 0.8 score, udder - by 1.2 score and the final assessment - by 0.8 score (P <0.001).On the trait of chest width, Holstein animals were inferior to their Ukrainian Black-and-White dairy breeds with reliable difference of 1.3 score, but exceeded in angularity - by 1.2 score, strength of fore udder parts attachment - on 0.8 score, central ligament - by 1.1 score (P <0.001).The final estimation level of first-born cows for conformation type directly proportionally determined their milk productivity in 305 days of the first lactation. Cows of Holstein and Ukrainian Black-and-White dairy breed with score - "very good" exceeded their peers with score - "good with plus" for milking by 785 and 589 kg (P˂0.01) respectively, and with score of "good" - by 1705 and 1502 kg (P˂0.001). The use in the selection process of dairy cattle, in determining cows breeding value, linear classification method will be a very effective means of objective definition breed features of conformation type. The presence of correlative relationship between the final score and milk productivity level will contribute to the effectiveness of selection in the indirect sampling of animals on these traits. Key words: Holstein, Ukrainian Black-and-White dairy, linear type assessment, conformation, milk yield.

본 논문에서는 마이크로어레이 (microarray) 자료에 판별분석을 적용 시 나타나는 고차원 및 소표본 문제의 해결방법으로서 직교요인을 새로운 특징변수로 사용한 비모수적 국소선형 로지스틱 판별분석을 제안한다. 제안된 방법은 국소우도에 기반한 것으로서 다범주 판별분석에 적용될 수 있으며, 고려된 직교인자는 주성분 요인, 부분최소제곱 요인, 인자분석 요인 등이다. 대표적인 두 가지 실제 마이크로어레이 자료에 적용한 결과 직교요인들 중에서 부분최소제곱 요인을 특징변수로 사용한 경우 고전적인 통계적 판별분석보다 향상된 분류 능력을 나타내고 있음을 확인하였다. The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

The problems of neural network algorithms designing for modeling of the switching technical systems are considered. The method for designing of dynamic models using polynomial approximation is proposed. The generalized models of switching systems taking into account nonstationary perturbations are constructed. The heuristic optimization algorithms implemented in the form of computer libraries are developed. The problems of neural network algorithm implementation using high-level hybrid computing are considered. The effect of the application of the algorithms proposed in the paper is to reduce the time and energy costs on creating vector thrust. Algorithmic support based on artificial neural networks with training is proposed. The library of high-parallel training algorithms for neural networks in the problems of optimal trajectories constructing for switching technical systems is developed. The obtained results can be used in problems of researching models of controlled technical systems with switching operating modes, in particular, in modeling the dynamics of various classes of aircraft and in modeling intelligent transport systems #CSOC1120.

Discrimination of Brazilian artisanal and inspected pork sausages: Application of unsupervised, linear and non-linear supervised chemometric methods

In general, there are two kinds of linear classification methods: one is MSE, and the other is FLD. Because linear methods are not sufficient to analyze the data with nonlinear patterns, the nonlinear methods KMSE and KFD are hence developed from MSE and FLD, respectively. Both transform the instances from the original attribute space to the high-dimensional feature space and then linear methods are applied. The objective of FLD and KFD is to find the directions on which the projection of training instances can provide the maximal separability of classes. FLD and KFD are known to be inefficient for datasets with a large amount of attributes and instances, respectively. To improve the computing efficiency, we use MSE for linear classification problems. However, MSE, like SVM, can use only the one-against-one or the one-against-the-rest approach to solve the multi-class problems. Both are inefficient compared to FLD and KFD where only one model is built to discriminate multiple classes simultaneously. Thus, we develop the multi-class MSE with Sherman-Woodbury formula to improve the computation efficiency. It can deal with multiple classes simultaneously by a class-labeling scheme. The different class-labeling schemes are determined by the Gram-Schmidt process. The nonlinear application, multi-class KMSE, is also developed from the multi-class MSE. Then, a simulated example is used to show how the proposed method works and to visualize the meaning of the class-labeling scheme. Finally, two real-world datasets are used for comparing the proposed method with other conventional methods.

The recent upsurge in research activities into artificial neural networks (ANNs) has proven that neural networks have powerful pattern classification and prediction capabilities. ANNs have been successfully used for a variety of tasks in many fields of business, industry, and science. researchers and practitioners. Interest in neural networks is evident from the growth in the number of papers published in journals of diverse scientific disciplines. A search of several major databases can easily result in hundreds or even thousands of “neural networks” articles published in one year.One of the major application areas of ANNs is forecasting. There is an increasing interest in forecasting using ANNs in recent years. Forecasting has a long history, and the importance of this old subject is reflected by the diversity of its applications in different disciplines ranging from business to engineering. The ability to accurately predict the future is fundamental to many decision processes in planning, scheduling, purchasing, strategy formulation, policymaking, and supply chain operations. As such, forecasting is an area where a lot of effort has been invested in the past. Yet, it is still an important and active field of human activity at present and will continue to be in the future. Forecasting has been dominated by linear methods for many decades. Linear methods are easy to develop and implement and they are also relatively simple to understand and interpret. However, linear models have serious limitations in that they are not able to capture any nonlinear relationships in the data. The approximation of linear models to complicated nonlinear relationships is not always satisfactory. In the early 1980s, Makridakis (1982) organized a large-scale forecasting competition (often called M-competition) where a majority of commonly used linear methods were tested with more than 1,000 real-time series. The mixed results show that no single linear model is globally the best, which may be interpreted as the failure of linear modeling in accounting for a varying degree of nonlinearity that is common in real-world problems.ANNs provide a promising alternative tool for forecasters. The inherently nonlinear structure of neural networks is particularly useful for capturing the complex underlying relationship in many real-world problems. Neural networks are perhaps more versatile methods for forecasting applications in that not only can they find nonlinear structures in a problem, they can also model linear processes. For example, the capability of neural networks in modeling linear time series has been studied and confirmed by several researchers.Research efforts on neural networks as forecasting models are considerable and applications of ANNs for forecasting have been reported in many studies. Although some theoretical and empirical issues remain unsolved, the field of neural network forecasting has surely made significant progress during the last decade. It will not be surprising to see even greater advancement and success in the next decade.The purpose of this paper is to use recurrent neural networks to curve the fitting of France electricity exports annually. It is recommended that further research be undertaken in the following areas Intelligent forecasting methods are being used as an alternative to traditional forecasting methods.

This paper compares known architectures and training algorithms of multi-layered artificial neural networks (ANNs) and their ability to solve one specific type of chess problem (the Hamiltonian Cycles known as the Knight's Tours). The networks used here are trained to solve tours starting from the center of a five-by- five chessboard. The ANN architectures focused on are the function approximation feedforward architecture; the classification feedforward architecture; and two customized variations of the function approximation feedforward architecture. The training algorithms compared include Levenberg-Marquardt; variable learning (adaptive) gradient descent; resilient backpropagation; the Fletcher-Reeves conjugate gradient; and the Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton algorithm. These five training algorithms are used for each of the four architectures. The real world application of this research is limited only by the functions given and the parameters used to define the environment. This can be expanded to include a variety of chess pieces (functions) in a variety of environments (any m-by-n sized board or even multidimensional problem solving). The practical limitation on the application is the software necessary to run extremely large neural networks.

Diffusion tensor imaging (DTI) provides rich information about brain tissue structure especially in the white matter, which is known to be affected in several diseases like schizophrenia. Identifying patterns of brain changes induced by pathology is therefore crucial to clinical studies. However, the high dimensionality and complex structure of DTI make it difficult to apply conventional linear statistical and pattern classification methods to identify such patterns. In this paper, we present a novel framework that uses a combination of DTI-based anisotropy and geometry features to effectively identify brain regions with pathology-induced abnormality, and to classify brains into the diseased and healthy groups. Our method first directly estimates the underlying overlap between the patient and control groups, based on a semi-parametric Bayes error estimation method. By ranking voxels based on these estimation results, the method identifies abnormal brain regions from which features are extracted through Kernel Principal Component Analysis (KPCA) for subsequent classification. Application of the method to a dataset of controls and patients with schizophrenia, demonstrates promising accuracy of this framework in identifying brain patterns to separate two groups, and hence aiding in prognosis and treatment.

The object of the study is methods based on neural networks for analyzing the tonality of a corpus of texts. To achieve the goal set in the work, it is necessary to solve the following tasks: study the theoretical material for learning deep neural networks and their features in relation to natural language processing; study the documentation of the Tensorflow library; develop models of convolutional and recurrent neural networks; to develop the implementation of linear and non-linear classification methods on bag of words and Word2Vec models; to compare the accuracy and other quality indicators of implemented neural network models with classical methods. Tensorboard is used for learning visualization. The work shows the superiority of classifiers based on deep neural networks over classical classification methods, even if the Word2Vec model is used for vector representations of words. The model of recurrent neural network with LSTM blocks has the highest accuracy for this corpus of texts.

Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.

Risk assessment of refinery unit maintenance based on fuzzy second generation curvelet neural network

Along with the rapid development of computer and image processing technology, it is definitely convenient to obtain various images for subjects, which can be more robust to classification as more feature information is contained. However, how to effectively exploit the rich discriminative information within image sets is the key problem. In this paper, based on the concept of dual linear regression classification method for image set classification, we propose a novel discriminative framework to exploit the superiority of discriminant regression mechanism. We aim to learn a projection matrix to force the represented image points from the same class to be close and those from different class are better separated. The feature extraction strategy in our discriminative framework can appropriately work with the corresponding classification strategy, thus, better classification performance can be achieved. Moreover, we propose a kernel discriminative extension method to address the non-linearity problem by adopting the kernel trick. From the experimental results, our proposed method can obtain competitive recognition rates on face recognition tasks via mapping the original image sets into a more discriminative feature space. Besides, it also shows the effectiveness for object classification task with small image sizes and different number of frames.

Ecological Applications of Fuzzy Logic.- Ecological Applications of Qualitative Reasoning.- Ecological Applications of Non-supervised Artificial Neural Networks.- Ecological Applications of Genetic Algorithms.- Ecological Applications of Evolutionary Computation.- Ecological Applications of Adaptive Agents.- Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata.- Prediction and Elucidation of Stream Ecosystems.- Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks.- Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- Prediction and Elucidation of River Ecosystems.- Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- Prediction and Elucidation of Lake and Marine Ecosystems.- A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation.- Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication.- Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication.- Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks.- Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach.- Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura.- Classification of Ecological Images at Micro and Macro Scale.- Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- Age Estimation of Fish Using a Probabilistic Neural Network.- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.

Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm.