scholarly journals Artificial neural network for the recognition of human emotions under a backpropagation algorithm

Athenea ◽  
2021 ◽  
Vol 2 (5) ◽  
pp. 29-34
Author(s):  
Alexander Caicedo ◽  
Anthony Caicedo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Processing ◽  
Emotion Recognition ◽  
Buenos Aires ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Backpropagation Algorithm ◽  
Neural Information ◽  
Neural Information Processing

The era of the technological revolution increasingly encourages the development of technologies that facilitate in one way or another people's daily activities, thus generating a great advance in information processing. The purpose of this work is to implement a neural network that allows classifying the emotional states of a person based on the different human gestures. A database is used with information on students from the PUCE-E School of Computer Science and Engineering. Said information are images that express the gestures of the students and with which the comparative analysis with the input data is carried out. The environment in which this work converges proposes that the implementation of this project be carried out under the programming of a multilayer neuralnetwork. Multilayer feeding neural networks possess a number of properties that make them particularly suitable for complex pattern classification problems [8]. Back-Propagation [4], which is a backpropagation algorithm used in the Feedforward neural network, was taken into consideration to solve the classification of emotions. Keywords: Image processing, neural networks, gestures, back-propagation, feedforward, classification, emotions. References [1]S. Gangwar, S. Shukla, D. Arora. “Human Emotion Recognition by Using Pattern Recognition Network”, Journal of Engineering Research and Applications, Vol. 3, Issue 5, pp.535-539, 2013. [2]K. Rohit. “Back Propagation Neural Network based Emotion Recognition System”. International Journal of Engineering Trends and Technology (IJETT), Vol. 22, Nº 4, 2015. [3]S. Eishu, K. Ranju, S. Malika, “Speech Emotion Recognition using BFO and BPNN”, International Journal of Advances in Science and Technology (IJAST), ISSN2348-5426, Vol. 2 Issue 3, 2014. [4]A. Fiszelew, R. García-Martínez and T. de Buenos Aires. “Generación automática de redes neuronales con ajuste de parámetros basado en algoritmos genéticos”. Revista del Instituto Tecnológico de Buenos Aires, 26, 76-101, 2002. [5]Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. “Handwritten digit recognition with a back-propagation network”. In Advances in neural information processing systems. pp. 396-404, 1990. [6]G. Bebis and M. Georgiopoulos. “Feed-forward neural networks”. IEEE Potentials, 13(4), 27-31, 1994. [7]G. Huang, Q. Zhu and C. Siew. “Extreme learning machine: a new learning scheme of feedforward neural networks”. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference. Vol. 2, pp. 985-990. IEEE, 2004. [8]D. Montana and L. Davis. “Training Feedforward Neural Networks Using Genetic Algorithms”. In IJCAI, Vol. 89, pp. 762-767, 1989. [9]I. Sutskever, O. Vinyals and Q. Le. “Sequence to sequence learning with neural networks”. In Advances in neural information processing systems. pp. 3104-3112, 2014. [10]J. Schmidhuber. “Deep learning in neural networks: An overview”. Neural networks, 61, 85-117, 2015. [11]R. Santos, M. Ruppb, S. Bonzi and A. Filetia, “Comparación entre redes neuronales feedforward de múltiples capas y una red de función radial para detectar y localizar fugas en tuberías que transportan gas”. Chem. Ing.Trans , 32 (1375), e1380, 2013.

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

scholarly journals Advances in verification of ReLU neural networks

2020 ◽  
Author(s):  
Ansgar Rössig ◽  
Milena Petkovic
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Computational Study ◽  
Autonomous Driving ◽  
Mixed Integer ◽  
Data Set ◽  
Approximation Techniques ◽  
Neural Information ◽  
Neural Information Processing ◽  
Algorithmic Approaches

Abstract We consider the problem of verifying linear properties of neural networks. Despite their success in many classification and prediction tasks, neural networks may return unexpected results for certain inputs. This is highly problematic with respect to the application of neural networks for safety-critical tasks, e.g. in autonomous driving. We provide an overview of algorithmic approaches that aim to provide formal guarantees on the behaviour of neural networks. Moreover, we present new theoretical results with respect to the approximation of ReLU neural networks. On the other hand, we implement a solver for verification of ReLU neural networks which combines mixed integer programming with specialized branching and approximation techniques. To evaluate its performance, we conduct an extensive computational study. For that we use test instances based on the ACAS Xu system and the MNIST handwritten digit data set. The results indicate that our approach is very competitive with others, i.e. it outperforms the solvers of Bunel et al. (in: Bengio, Wallach, Larochelle, Grauman, Cesa-Bianchi, Garnett (eds) Advances in neural information processing systems (NIPS 2018), 2018) and Reluplex (Katz et al. in: Computer aided verification—29th international conference, CAV 2017, Heidelberg, Germany, July 24–28, 2017, Proceedings, 2017). In comparison to the solvers ReluVal (Wang et al. in: 27th USENIX security symposium (USENIX Security 18), USENIX Association, Baltimore, 2018a) and Neurify (Wang et al. in: 32nd Conference on neural information processing systems (NIPS), Montreal, 2018b), the number of necessary branchings is much smaller. Our solver is publicly available and able to solve the verification problem for instances which do not have independent bounds for each input neuron.

Parallel Distributed Approaches to Combinatorial Optimization: Benchmark Studies on Traveling Salesman Problem

1990 ◽  
Vol 2 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Carsten Peterson
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Information Processing ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Traveling Salesman ◽  
Neural Information ◽  
Neural Information Processing

We present and summarize the results from 50-, 100-, and 200-city TSP benchmarks presented at the 1989 Neural Information Processing Systems (NIPS) postconference workshop using neural network, elastic net, genetic algorithm, and simulated annealing approaches. These results are also compared with a state-of-the-art hybrid approach consisting of greedy solutions, exhaustive search, and simulated annealing.

scholarly journals Backpropagation Algorithm on Implementation of Signature Recognition

2021 ◽  
pp. 21-29
Author(s):  
Rini Sovia ◽  
Musli Yanto ◽  
Widya Nursanty
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Identification System ◽  
Backpropagation Algorithm ◽  
Processing Information ◽  
Signature Recognition

Many things are required by all parties, especially in the process of recognition of one's identity, ranging from health care, maintenance of bank accounts, aviation services, immigration and others.Many ways of proving one's identity and the most popular one is using a signature.The signature is used as an identification system which serves to recognize a person's identity.Recognition process is still done manually by matching the signature by the person concerned.Therefore, the very need for a system that is able to analyze and identify the characteristics of the signature, so it can be used as an alternative to simplify the process of introducing people’s signature.Artificial neural networks can be used as one of the solutions in identification of signatures.Artificial neural network is a branch of science of artificial intelligence that is capable of processing information with the performance of certain characteristics.Artificial neural networks have some method such as perceptron, Hopfield discrete, Adaline, Backpropagation, and Kohonen.In this paper, the artificial neural network with back propagation method is applied in the process of signature and patternrecognition which provided a solution that is able to analyze and recognize people's signature.Implementation of the application of neural networks in pattern recognition signature can further be applied to any computer that handles problems in the process of matching one's data.

Chaos Control and Neural Classification

1994 ◽  
Vol 49 (4-5) ◽  
pp. 589-593
Author(s):  
Axel A. Hoff
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Chaos Control ◽  
Chaotic Systems ◽  
Active Role ◽  
Time Signal ◽  
Unstable Periodic Orbits ◽  
Neutral Information ◽  
Neural Information ◽  
Neural Information Processing

Abstract Chaotic behaviour in biological neural networks is known from various experiments. The recent finding that it is possible to "control" chaotic systems may help answer the question whether chaos plays an active role in neutral information processing. It is demonstrated that a method for chaos control which was proposed by Pyragas can be used to let a chaotic system act like an autoassociative memory for time signal inputs. Specifically a combined chaotic and chaos control system can reconstruct unstable periodic orbits from incomplete information. The potential relevance of these findings for neural information processing is pointed out.

scholarly journals The Neural Network Zoo

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 9 ◽  
Author(s):  
Stefan Leijnen ◽  
Fjodor van Veen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Information Processing ◽  
Network Architectures ◽  
Learning Models ◽  
The Neural Network ◽  
Neural Information ◽  
Neural Information Processing ◽  
Practical Tool ◽  
Neural Network Architectures

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.

scholarly journals The Neural Network Zoo

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 9
Author(s):  
Stefan Leijnen ◽  
Fjodor van Veen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Information Processing ◽  
Network Architectures ◽  
Learning Models ◽  
The Neural Network ◽  
Neural Information ◽  
Neural Information Processing ◽  
Practical Tool ◽  
Neural Network Architectures

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.

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