Networks Data Transfer Classification Based On Neural Networks

Data transmission classification is an important issue in networks communications, since the data classification process has the ultimate impact in organizing and arranging it according to size and area to prepare it for transmission to minimize the transmission bandwidth and enhancing the bit rate. There are several methods and mechanisms for classifying the transmitted data according to the type of data and to the classification efficiency. One of the most recent classification methods is the classification of artificial neural networks (ANN). It is considered one of the most dynamic and up-to-date research in areas of application. ANN is a branch of artificial intelligence (AI). The neural network is trained by backpropagation algorithm. Various combinations of functions and their effect while utilizing ANN as a file, classifier was studied and the validity of these functions for different types of datasets was analyzed. Back propagation neural university (BPNN) supported with Levenberg Marqurdte (LM) activation function might be utilized with as a successful data classification tool with a suitable set of training and learning functions which operates, when the probability is maximum. Whenever the maximum likelihood method was compared with backpropagation neural network method, the BPNN supported with Levenberg Marqurdte (LM) activation function was further accurate than maximum likelihood method. A high predictive ability against stable and well-functioning BPNN is possible. Multilayer feed-forward neural network algorithm is also used for classification. However BPNN supported with Levenberg Marqurdte (LM) activation function proves to be more effective than other classification algorithms.

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of "features" (e.g., proposed biosignatures) and novelties (uncharacterized, "new" features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

ARDIS: a Swedish historical handwritten digit dataset

This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red–green–blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of machine learning methods on several digit datasets is carried out. Additionally, correlation between ARDIS and existing digit datasets Modified National Institute of Standards and Technology (MNIST) and US Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset. Accordingly, convolutional neural network trained on MNIST and USPS and tested on ARDIS provide the highest accuracies $$58.80\%$$ 58.80 % and $$35.44\%$$ 35.44 % , respectively. Consequently, the results reveal that machine learning methods trained on existing datasets can have difficulties to recognize digits effectively on our dataset which proves that ARDIS dataset has unique characteristics. This dataset is publicly available for the research community to further advance handwritten digit recognition algorithms.

A scalable stagewise approach to large-margin multiclass loss-based boosting

We present a scalable and effective classification model to train multiclass boosting for multiclass classification problems. A direct formulation of multiclass boosting had been introduced in the past in the sense that it directly maximized the multiclass margin. The major problem of that approach is its high computational complexity during training, which hampers its application to real-world problems. In this paper, we propose a scalable and simple stagewise multiclass boosting method which also directly maximizes the multiclass margin. Our approach offers the following advantages: 1) it is simple and computationally efficient to train. The approach can speed up the training time by more than two orders of magnitude without sacrificing the classification accuracy and 2) like traditional AdaBoost, it is less sensitive to the choice of parameters and empirically demonstrates excellent generalization performance. Experimental results on challenging multiclass machine learning and vision tasks demonstrate that the proposed approach substantially improves the convergence rate and accuracy of the final visual detector at no additional computational cost compared to existing multiclass boosting.

A compact 3D VLSI classifier using bagging threshold network ensembles

A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote. Such a combination improves generalization performance but can require large amounts of memory and computation, a serious drawback for addressing portable real-time pattern recognition applications. We report here a compact three-dimensional (3D) multiprecision very large-scale integration (VLSI) implementation of a bagging ensemble. In our circuit, individual classifiers are decision trees implemented as threshold networks - one layer of threshold logic units (TLUs) followed by combinatorial logic functions. The hardware was fabricated using 0.7-/spl mu/m CMOS technology and packaged using MCM-V micro-packaging technology. The 3D chip implements up to 192 TLUs operating at a speed of up to 48 GCPPS and implemented in a volume of (/spl omega/ /spl times/ L /spl times/ h) = (2 /spl times/ 2 /spl times/ 0.7) cm/sup 3/. The 3D circuit features a high level of programmability and flexibility offering the possibility to make an efficient use of the hardware resources in order to reduce the power consumption. Successful operation of the 3D chip for various precisions and ensemble sizes is demonstrated through an electronic nose application.

The discriminant pixel approach: a new tool for the rational interpretation of GCxGC-MS chromatograms

GCxGC is now recognized as the most suited analytical technique for the characterization of complex mixtures of volatile compounds; it is implemented worldwide in academic and industrial laboratories. However, in the frame of comprehensive analysis of non-target analytes, going beyond the visual examination of the color plots remains challenging for most users. We propose a strategy that aims at classifying chromatograms according to the chemical composition of the samples while determining the origin of the discrimination between different classes of samples: the discriminant pixel approach. After data pre-processing and time-alignment, the discriminatory power of each chromatogram pixel for a given class was defined as its correlation with the membership to this class. Using a peak finding algorithm, the most discriminant pixels were then linked to chromatographic peaks. Finally, crosschecking with mass spectrometry data enabled to establish relationships with compounds that could consequently be considered as candidate class markers. This strategy was applied to a large experimental data set of 145 GCxGC-MS chromatograms of tobacco extracts corresponding to three distinct classes of tobacco.

Attractor networks for shape recognition

We describe a system of thousands of binary perceptrons with coarse-oriented edges as input that is able to recognize shapes, even in a context with hundreds of classes. The perceptrons have randomized feedforward connections from the input layer and form a recurrent network among themselves. Each class is represented by a prelearned attractor (serving as an associative hook) in the recurrent net corresponding to a randomly selected subpopulation of the perceptrons. In training, first the attractor of the correct class is activated among the perceptrons; then the visual stimulus is presented at the input layer. The feedforward connections are modified using field-dependent Hebbian learning with positive synapses, which we show to be stable with respect to large variations in feature statistics and coding levels and allows the use of the same threshold on all perceptrons. Recognition is based on only the visual stimuli. These activate the recurrent network, which is then driven by the dynamics to a sustained attractor state, concentrated in the correct class subset and providing a form of working memory. We believe this architecture is more transparent than standard feedforward two-layer networks and has stronger biological analogies.

Shape Quantization and Recognition with Randomized Trees

We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures.

No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labeled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions.

The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred [Formula: see text] symbols. State-of-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on [Formula: see text] symbols is to analyze invariance, generalization error and related issues, and a comparison with artificial neural networks methods is presented in this context.

[Figure: see text]

A new recurrent neural-network architecture for visual pattern recognition

We propose a new type of recurrent neural-network architecture, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks with respect to function and architecture, because it has been originally extended from being a mere multilayer feedforward neural network, to improve discrimination and generalization powers. We also prove the convergence properties of the learning algorithm in the proposed recurrent neural network, and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeric database of Concordia University, Montreal, Canada. Experimental results have confirmed that the proposed recurrent neural network improves discrimination and generalization powers in the recognition of visual patterns.