neural network research papers

 

research paper on neural network

If you look for a specific paper that gives you the highlights and a short introduction you should check out this one: LeCun, Y., Bengio, Y. and Hinton, G., The results of the research is a confusion matrix to prove the accuracy of Neural Network before Backward Elimination is optimized by % and % after optimize. This proves estimate windowed momentum trials using neural network-based method Backward Elimination more accurate than the individual methods of neural network. CiteScore: ℹ CiteScore: CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. ) to documents published in three previous calendar years (e.g. – 14), divided by the number of documents in these three previous years (e.g. – 14).


Most Downloaded Neural Networks Articles - Elsevier


To browse Academia. Skip to main content. You're using an out-of-date version of Internet Explorer. Log In Sign Up. Papers People. This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutiae's.

The main advantage of the new method is the dimension The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation minutiae points.

In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase, research paper on neural network. Whatever, the degree of fingerprint rotation, the extraction features used to characterize fingerprint remains the same. The characterization efficiency of the proposed method is compared to the method based on the spatial coordinate of fingerprint minutiae's.

The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. Extensive experiments prove that the fingerprint classification based on a novel features and BPNN classifier gives better results in fingerprint classification than several other features and methods. Finally the results of the proposed method are evaluated on the FVC database.

Save to Library. Plant disease analysis is to identify the percentage and the exact location of plant diseases caused by viruses, fungi and bacteria. When one part of the leaf is affected by disease and another part is also affected as soon. In our work, In our work, identifying the disease spot at early stages, the growth level of the diseases.

The classification is one of the most energetic research and relevance area of neural network. Multilayer perception is used to classify the leaf diseases and the back propagation algorithm is used to calculate the disease spot by total area of the leaf.

Implementation has been done by using MATLAB a simulation tool which generates better result for clustering algorithm. Fabrication and biocompatibility of polypyrrole implants suitable for neural prosthetics. Breast Cancer is the most common type of cancer prevalent among female cancer patients, while it also is the second most dreaded disease causing cancer death among women.

A variety of data mining techniques, including Artificial Neural The dataset used in our experiment consists of 23 attributes containing samples obtained from the Mizoram Cancer Institute of Aizawl, Mizoram, India.

We are using data mining classifiers to predict the recurrence of breast cancer over a period of three years evaluated based on the comparison of their performance. Feature and attribute selections have been carried out to enhance the prediction accuracy of the computations. Neural networks: Alternatives to conventional techniques for automatic docking.

Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels.

Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes, research paper on neural network.

Neural networks can reduce the computational time required by existing image processing and control software. Research paper on neural network addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, research paper on neural network, enabling enhanced performance, redundancy, and fault tolerance.

Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.

A counterpropagation neural network for determining target spacecraft orientation. This paper describes a concept that integrates a counterpropagation neural network into a video-based vision system employed for automatic spacecraft docking.

A brief overview of docking phases, the target orientation problem, and A brief overview of docking phases, the target orientation problem, and potential benefits resulting from an automated docking system is provided.

Issues and challenges of automatic target recognition, as applied to automatic docking, are addressed. Following a review of. This paper presented neural network based maximum power point tracking on the design of photovoltaic power input to a DC DC boot converter to the load. Simulink model of photovoltaic array tested the neural network with different Simulink model of photovoltaic array tested the neural network with different temperature and irradiance for maximum power point of a photovoltaic system.

DC DC boot converter is used in load when an average output voltage is stable required which can be lower than the input voltage. At the end, the different temperature and irradiance of the data collected from the photovoltaic array system is used to train the neutral network and output efficiency of the designed DC DC boot converter with MPPT control strategy is accepted the maximum power amount to show the result voltage, current and power output for each different have been presented, research paper on neural network.

The software implementation of iris recognition system introduces in this paper. This system intends to apply for high security required areas. The demand on security research paper on neural network increasing greatly in these years and biometric recognition The demand on security is increasing greatly in these years and biometric recognition gradually becomes a hot field of research. Iris recognition is a branch of biometric recognition method.

In thesis, Iris recognition system consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition.

In thesis, research paper on neural network, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network NN is used for the classification of iris patterns. Research paper on neural network adaptive learning strategy is applied for training of the NN.

The results of simulations illustrate the effectiveness of the neural system in personal identification. Finally, the accuracy of iris recognition system is tested and evaluated with different iris images.

Customary characterization calculations can be constrained in their execution on exceedingly uneven informational collections. A famous stream of work for countering the substance of class inelegance has been the use of an assorted of A famous stream of work for countering the substance of class inelegance has been the use of an assorted of inspecting methodologies.

In this correspondence, we center on planning alterations neural system to properly handle the issue of class irregularity. We consolidate distinctive rebalance heuristics in ANN demonstrating, research paper on neural network, including cost delicate learning, and over and under testing. These ANN based systems are contrasted and different best in class approaches on an assortment of informational collections by utilizing different measurements, including G mean, region under the collector working trademark curve, F measure, and region under the exactness review curve.

Numerous regular strategies, which can be classified into testing, cost delicate, or gathering, research paper on neural network, incorporate heuristic and task subordinate procedures. Its target work is the symphonious mean of different assessment criteria got from a perplexity grid, such criteria as affectability, positive prescient esteem, and others for negatives.

Nitesh Kumar Dr. Research paper on neural network new feature set with new window techniques for customer churn prediction in land-line telecommunications. In order to improve the prediction rates of churn prediction in land-line telecommunication service field, this paper proposes a new set of features with three new input window techniques.

The new features are demographic profiles, The new features are demographic profiles, account information, grant information, Henley segmentation, aggregated call-details, line information, service orders, bill and payment history. The basic idea of the three input window techniques. Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor.

Neural Networks Grown on Organic Semiconductors. The local paradigm for modeling and control: from neuro-fuzzy to lazy learning. Now research paper on neural network the time to defuzzify neuro-fuzzy models.

Is readability compatible with accuracy? From neuro-fuzzy to lazy learning. The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two different local approaches This paper presents a comparative analysis of two different local approaches: the neuro-fuzzy inference system and the lazy learning approach.

In this paper, an integration mission In this paper, research paper on neural network, an integration mission model of both periodical mission and non-periodical mission is built based on the characteristic of research paper on neural network satellite mission and a fault-tolerant scheduling algorithm of static scheduling and dynamic adjustment for small satellite is presented combined with mission allocate algorithm and partial dynamic scheduling algorithm of embedded processor.

Neural network algorithm is used in partial dynamic adjustment of non-periodical mission and backup mission copies that assures the reliability and temporal effectiveness of mission execution.

Simulation results show that it achieves higher schedulability compared to some other methods. Further, in terms of scheduling length and load balancing, it is superior to that of conventional graphic algorithm.

PWR system simulation and parameter estimation with neural networks. In this paper a Gaussian mixture model GMM classifier, called GMM identifier, is proposed as an efficient post-processing method to enhance the performance of a GMM-based speaker verification system; such as Gaussian mixture model In this paper a Gaussian mixture model GMM classifier, called GMM identifier, is proposed as an efficient post-processing method to enhance the performance of a GMM-based speaker verification system; such as Gaussian mixture model universal background model GMM An optical quadratic neural network utilizing 4-wave mixing in KNbO3 has been developed.

This network implements a feed back loop using various optical elements. For training the network employs the supervised quadratic perceptron For training the network employs the research paper on neural network quadratic perceptron algorithm to associate binary-valued input vectors with specified training vectors.

Related Topics. Artificial Neural Networks.

 

Top Research Papers On Recurrent Neural Networks For NLP Enthusiasts

 

research paper on neural network

 

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. This Transactions ceased production in The. This paper studies the method of processing of alarms in an electric substation using artificial neural networks (ANN) as a tool. Whenever there is a fault in the power system, or there is a. Jun 19,  · Recurrent Neural Networks (RNN) have become the de facto neural network architecture for Natural Language Processing (NLP) tasks. Over the last few years, recurrent architecture for neural networks has advanced quite a lot with NLP tasks — from Named Entity Recognition to Language Modeling through Machine TranslationAuthor: Richa Bhatia.