the proceedings contain 38 papers. the topics discussed include: Ttime series prediction with wavelet neuralnetworks;learning in neuralnetworks by normalized stochastic gradient algorithm: local convergence;support ...
ISBN:
(纸本)0780355121
the proceedings contain 38 papers. the topics discussed include: Ttime series prediction with wavelet neuralnetworks;learning in neuralnetworks by normalized stochastic gradient algorithm: local convergence;support vector machines trained by linear programming: theory and applications in image compression and data classification;neurocomputing in teletraffic: multifractal spectrum approximation;cellular neuralnetworks based on terminal dynamics;global asymptotic stability for RNNs with a bipolar activation function;the binary vectors storing procedure in an analog feedback associative memory;on training with slope adaptation for feedforward NNs;force learn algorithm - training a neural net with patterns which have highest errors;behavior of neural maximum likelihood decoder in the hard and soft decoding of telecommunication channels;and potato creams recognition from electronic nose and tongue signals: feature extraction/selection and RBF neuralnetwork classifiers.
Machine Translation (MT) has made significant advancements due to the rapid expansion of natural language processing. Even though several machine translation methods have been released in recent years, there hasn'...
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Machine Translation (MT) has made significant advancements due to the rapid expansion of natural language processing. Even though several machine translation methods have been released in recent years, there hasn't been enough focus on automated and intelligent quality detection for translation outcomes. neural Machine Translation (NMT), a machine translation method that is datadriven, is more effective in large corpora but with restricted corpus resources, there is still a sizable range of opportunity for advancement. To overcome these issues, design a system module of sentence translator using neural Machine Translation is presented. this analysis will construct a translation system depending on the GRNN (Gated Recurrent neuralnetwork) deep learning algorithm and comprises the creation of the attention, pre-processing, coding, and decoding modules. this system does the Language translation with improved Accuracy and performance.
the proceedings contain 40 papers. the topics discussed include: whole chromosome features of genomic signals;statistical learning: data mining and prediction;a neural nonlinear adaptive filter with a trainable activa...
ISBN:
(纸本)0780375939
the proceedings contain 40 papers. the topics discussed include: whole chromosome features of genomic signals;statistical learning: data mining and prediction;a neural nonlinear adaptive filter with a trainable activation function;perlustration of error surfaces for nonlinear stochastic gradient descent algorithms;bridging of layers of neuralnetworks;a simple biologically inspired principal component analyzer - MoDH neuron model;modeling non-stationary dynamic system using recurrent radial basis function networks;evolutionary neuro autonomous agents;recent trends in neuralnetworks for multimedia processing;foundations of predictive data mining;ionospheric storm forecasting technique by artificial neuralnetwork;daily load forecasting based on previous day load;and electric load forecasting with multilayer perceptron and Elman neuralnetwork.
Signal processing has made extensive use of neuralnetworks. this study examines neuralnetwork training using the PSO and BP algorithms. To train the neuralnetwork, the PSO algorithm alone, the BP algorithm alone, a...
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the proceedings contain 45 papers. the topics discussed include: data mining, neuralnetworks and rule extraction;nikola tesla in history of wireless;asymmetric and normalized cuts for image clustering and segmentatio...
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ISBN:
(纸本)1424404339
the proceedings contain 45 papers. the topics discussed include: data mining, neuralnetworks and rule extraction;nikola tesla in history of wireless;asymmetric and normalized cuts for image clustering and segmentation;minor component analysis (MCA) applied to image classification in CBIR system;evaluating the influence of image modifications upon content-based multimedia retrieval;GA-based feature extraction for clapping sound detection;real-time face detection and tracking of animals;on rotation invariant texture classification using two-grid coupled CNNs;improvements in image segmentation by applying Hopfield neuralnetwork;face detection approach in neuralnetwork based method for video surveillance;face recognition by using unitary vector spaces;application of neuralnetworks in network intrusion detection;and routing in packet-switched networks by using neuralnetwork.
Low-power and low-complexity Direction-of-Arrival (DOA) estimation is a critical challenge in various signal processing applications. this paper presents a data-driven approach using a neuralnetwork framework designe...
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People from all around the world are afflicted by the fatal condition known as diabetes mellitus (DM). A timely diagnosis of DM is particularly advantageous because it may be managed before the beginning of the condit...
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People from all around the world are afflicted by the fatal condition known as diabetes mellitus (DM). A timely diagnosis of DM is particularly advantageous because it may be managed before the beginning of the condition. In this work, different pre-processing methods for the diagnosis of DM are discussed and contrasted. Additionally, the accuracy of several methods for data mining has been evaluated depending on missing scores, with a particular emphasis on artificial neuralnetworks (ANN) to handle missing data utilizing z-value and MinMax procedures. IoT (Internet of things) devices are used in this research to track the patients' situations. Statistics are sent from IoT gadgets to smartphones for surveillance, and subsequently from smartphones to the web, where categorization is done. Employing a Python instrument, the simulation is carried out on the data sets that were obtained. the simulation findings demonstrate that the suggested strategy outperforms current state-of-the-art ensemble approaches in terms of accuracy pace, recall, precision, and f-value.
Lung cancer is a major public health concern, and accurate prediction models are essential for early detection and effective treatment. However, existing prediction models often suffer from low accuracy and robustness...
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Lung cancer is a major public health concern, and accurate prediction models are essential for early detection and effective treatment. However, existing prediction models often suffer from low accuracy and robustness, especially in the presence of outliers and overfitting. this research study proposes a novel approach for lung cancer prediction that integrates variational autoencoders (VAEs), early stopping, neuralnetwork clustering, and optimal tuning. the proposed model was evaluated on a dataset of 10,000 lung cancer patients. the model achieved an accuracy of 92.1% on the test set, which is a significant improvement over the accuracy of previous models. the proposed model also showed improved robustness to outliers and overfitting. the main contributions of this work include the development of a new method for predicting lung cancer that combines various techniques such as Variational Autoencoders (VAEs), early stopping, neuralnetwork clustering, and optimal tuning. the proposed approach achieves higher accuracy compared to previous models and demonstrates improved robustness to potential issues like outliers and overfitting. this study represents a significant advancement in the field of lung cancer prediction. the proposed model offers the potential to revolutionize early detection and treatment strategies, leading to improved patient care and outcomes. Additionally, it serves as a foundation for future research in lung cancer diagnostics and management, paving the way for further breakthroughs in this crucial area. this study paves the way for personalized medicine approaches in lung cancer treatment by enabling more precise patient stratification and targeted therapies. the proposed model's potential for early detection translates to significant cost savings for healthcare systems and improved quality of life for patients.
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