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Continual learning, deep reinforcement learning, and microcircuits: a novel method for clever game playing

Multimedia Tools and Applications 2025年第3期84卷 1537-1559页

作者： Chang, Oscar Ramos, Leo Morocho-Cayamcela, Manuel Eugenio Armas, Rolando Zhinin-Vera, Luis School of Mathematical and Computational Sciences Yachay Tech University Urcuquí100119 Ecuador Yachay Tech University Urcuquí100119 Ecuador Kauel Inc. HoustonTX 77027 United States LoUISE Research Group I3A University of Castilla-La Mancha Albacete02071 Spain MIND Research Group - Model Intelligent Networks Development Ibarra Ecuador Escuela de Ingeniería Eléctrica Universidad Central de Venezuela Caracas1050 Venezuela

Contemporary neural networks frequently encounter the challenge of catastrophic forgetting, wherein newly acquired learning can overwrite and erase previously learned information. The paradigm of continual learning offers a promising solution by enabling intelligent systems to retain and build upon their acquired knowledge over time. This paper introduces a novel approach within the continual learning framework, employing deep reinforcement learning agents that process unprocessed pixel data and interact with microcircuit-like components. These agents autonomously advance through a series of learning stages, culminating in the development of a sophisticated neural network system optimized for predictive performance in the game of tic-tac-toe. Structured to operate in sequential order, each agent is tasked with achieving forward-looking objectives based on Bellman’s principles of reinforcement learning. Knowledge retention is facilitated through the integration of specific microcircuits, which securely store the insights gained by each agent. During the training phase, these microcircuits work in concert, employing high-energy, sparse encoding techniques to enhance learning efficiency and effectiveness. The core contribution of this paper is the establishment of an artificial neural network system capable of accurately predicting tic-tac-toe moves, akin to the observational strategies employed by humans. Our experimental results demonstrate that after approximately 5000 cycles of backpropagation, the system significantly reduced the training loss to LDQN © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Advancing Healthcare: Early Tuberculosis Detection in Chest X-Rays Through Select Convolutional Neural networks

Advancing Healthcare: Early Tuberculosis Detection in Chest...

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International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Cuenca-Dominguez, Stefany Arrobo-Sarango, Victor Quinteros-Sarmiento, Darwin Salinas-Herrera, Santiago Zhinin-Vera, Luis Almeida-Galárraga, Diego Cadena-Morejón, Carolina Tirado-Espín, Andrés Cruz-Varela, Jonathan Ramírez-Cando, Lenin Villalba-Meneses, Fernando School of Biological Sciences and Engineering Yachay Tech University Urcuquí100119 Ecuador LoUISE Research Group University of Castilla-La Mancha Albacete02071 Spain School of Mathematical and Computational Sciences Yachay Tech University Urcuquí100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783031692277

This paper introduces an innovative healthcare approach using convolutional neural networks (CNNs) to detect tuberculosis (TB) in chest X-rays. The primary objective is to develop an advanced neural network solution for TB diagnosis. This work employ ResNet50, VGG16, and MobileNet-V2 architectures to analyze a dataset of 4200 chest radiographs from 700 confirmed TB patients. The experiments show ResNet50’s superiority, achieving an impressive 93% accuracy with a 23% loss. This underscores ResNet50’s potential for early TB detection and its effectiveness in medical imaging. To attain these insights, the team extensively researched modern image classification techniques, object recognition methods, and transfer learning strategies. The CNN models were meticulously assessed deepening our understanding of their strengths and limitations. These findings hold profound implications for TB detection and significantly advance deep learning in medical imaging tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Ecuadorian Sign Language Detection in Real Time

Ecuadorian Sign Language Detection in Real Time

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International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Carlosama, Jeremy Criollo, Solange Játiva, Carolina Mina, Vicky Velastegui, Santiago de-la-A, José Zhinin-Vera, Luis Almeida-Galárraga, Diego Cadena-Morejón, Carolina Tirado-Espín, Andrés Meneses, Fernando Villalba School of Biological Sciences and Engineering Yachay Tech University Urcuquí100119 Ecuador LoUISE Research Group University of Castilla-La Mancha Albacete02071 Spain School of Mathematical and Computational Sciences Yachay Tech University Urcuquí100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783031692277

The paper introduces a groundbreaking approach to bridge the communication divide between Ecuadorian Sign Language users and non-sign language speakers. It employs technology for real-time translation of sign gestures into written language. The study used a dataset encompassing alphabet letters, numbers, and common phrases in Ecuadorian Sign Language, employing an object localization model to recognize hand movements. Our system splits images into a grid, facilitating efficient detection of multiple signs in one go. When sign language gestures are input via images or videos, the system identifies and reproduces them in an app, facilitating effective communication. The results indicate promising accuracy and real-time responsiveness, offering hope for improved inclusivity in the Ecuadorian deaf community. This research not only empowers the deaf but also promotes wider appreciation of Ecuadorian Sign Language. Future work aims to enhance accuracy, expand vocabulary, and integrate the technology into diverse communication platforms for broader impact. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Translation (languages)

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Comparison of 1D Convolutional Neural networks for Arrhythmias Classification Using ECG Signals

Comparison of 1D Convolutional Neural Networks for Arrhyth...

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International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Escobar-Ordoñez, Julián Zhinin-Vera, Luis Guerrero-Ligña, Alejandra Rosero-Astudillo, Ibeth Valencia-Cevallos, Camila Almeida-Galárraga, Diego Cadena-Morejón, Carolina Tirado-Espín, Andrés Cruz-Varela, Jonathan Ramírez-Cando, Lenin Villalba-Meneses, Fernando School of Biological Sciences and Engineering Yachay Tech University Urcuquí100119 Ecuador LoUISE Research Group University of Castilla-La Mancha Albacete02071 Spain School of Mathematical and Computational Sciences Yachay Tech University Urcuquí100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783031692277

Cardiovascular Diseases (CVD), encompassing a range of heart and blood vessel conditions, have long been a significant global health concern. Among these, arrhythmias, disruptions in the heart’s rhythm, hold substantial importance due to their potential impact on morbidity and mortality. Atrial fibrillation (AF), the most common arrhythmia worldwide, affects millions and is associated with increased morbidity and mortality. This study focuses on enhancing arrhythmia detection using neural networks and hyperparameter tuning. By exploring various model dimensions, layers, batch sizes, and optimizers, we rigorously evaluated their impact on performance using electrocardiogram (ECG) signal data. Results showed that a hybrid CNN+LSTM architecture with 6 layers, utilizing the Adam optimizer and a batch size of 32, achieved the best accuracy in arrhythmia detection. These findings emphasize the importance of hyperparameter tuning for effective model generalization and its potential to improve cardiac care in the face of significant global health challenges posed by cardiovascular diseases, including arrhythmias. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Detecting Parkinson’s Disease with Convolutional Neural networks: Voice Analysis and Deep Learning 11th

Detecting Parkinson’s Disease with Convolutional Neural Ne...

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11th Ecuadorian Congress of Information and Communication Technologies, TICEC 2023

作者： Saltos, Kevin Zhinin-Vera, Luis Godoy, Cristina Chachalo, Roberth Almeida-Galárraga, Diego Cadena-Morejón, Carolina Tirado-Espín, Andrés Cruz-Varela, Jonathan Villalba Meneses, Fernando School of Biological Sciences and Engineering Yachay Tech University Urcuquí100119 Ecuador LoUISE Research Group University of Castilla-La Mancha Albacete02071 Spain School of Mathematical and Computational Sciences Yachay Tech University Urcuquí100650 Ecuador MIND Research Group - Model Intelligent Networks Development Ibarra Ecuador

ISBN: (纸本)9783031454370

Parkinson’s disease, the second most prevalent neurodegenerative disorder among individuals over the age of 65, poses challenges in early-stage detection, often requiring multiple tests to confirm diagnosis. However, advancements in neural network technology have facilitated the analysis, simplification, and prediction of complex problems that surpass human capacity in terms of speed and efficiency. With its diverse array of physical symptoms, we have specifically focused on vocal pitch changes and alterations, as they manifest in approximately 90% of Parkinson’s patients. Furthermore, the acquisition of vocal data is noninvasive and easily accessible. In this study, we employ a convolutional neural network (CNN) to assess the predictive accuracy of Parkinson’s disease using voice data from affected individuals. Our results demonstrate promising accuracy, achieving a classification accuracy of 95%. By training our CNN model, we aim to provide an affordable and convenient solution for detecting this neurodegenerative condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Q-Learning in a Multidimensional Maze Environment 10th

Q-Learning in a Multidimensional Maze Environment

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10th Ecuadorian Congress of Information and Communication Technologies, TICEC 2022

作者： Chang, Oscar Niemes, Stadyn Román Pijal, Washington Armijos, Arianna Zhinin-Vera, Luis School of Mathematical and Computational Sciences Yachay Tech University Urcuquí100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuqui Ecuador LoUISE Research Group I3A University of Castilla-La Mancha Albacete Spain

ISBN: (纸本)9783031182716

Experiments with rodents in mazes demonstrate that, in addition to visual cues, spatial localization and olfactory sense play a key role in orientation, foraging and eventually survival. Simulation at some level and understanding of this unique behavior is important for solving optimal routing problems. This article proposes a Reinforcement Learning (RL) agent that learns optimal policies for discovering food sources in a 2D maze using space location and olfactory sensors. The proposed Q-learning solution uses a dispersion formula to generate a cheese smell matrix S, tied in space time to the reward matrix R and the learning matrix Q. RL is performed in a multidimensional maze environment, in which location and odor sensors cooperate in making decisions and learning optimal policies for foraging activities. The proposed method is computationally evaluated using location and odor sensor in two different scenarios: random and Deep-Search First (DFS), showing positive results in both cases. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Time Series Prediction by Using Convolutional Neural networks

Time Series Prediction by Using Convolutional Neural Network...

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Future Technologies Conference, FTC 2020

作者： Velastegui, Ronny Zhinin-Vera, Luis Pilliza, Gissela E. Chang, Oscar School of Mathematical and Computational Sciences Yachay Tech University Urcuqui100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783030631277

All companies need an effective method to predict future sales, and several classic statistical methods exist and are heavily used in the industry. This work proposes a novel sales prediction method based on Convolutional Neural networks. This type of neural network is generally used for image processing tasks. But in this work, we explore new applications and develop models that produce good results in sales prediction for real pharmaceutical product data. Also, we implemented several classical and statistical prediction methods, and we compared them with our proposed model. For this, we used three comparison metrics: prediction accuracy, number of weights, and number of iterations. Finally, we proceeded to determine which prediction method is better both in accuracy and efficiency terms. © 2021, Springer Nature Switzerland AG.

关键词： Sales

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A Wise Up Visual Robot Driven by a Self-taught Neural Agent

A Wise Up Visual Robot Driven by a Self-taught Neural Agent

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Future Technologies Conference, FTC 2020

作者： Chang, Oscar Zhinin-Vera, Luis School of Mathematical and Computational Sciences Yachay Tech University Urcuqui100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783030631277

This paper presents a biological inspired robot capable of learning by itself high level Tic-Tac-Toe playing policies and then use this knowledge to advantageously compete with humans. The robot comprises a robotic arm, an artificial vision system and a self-motivated neural agent which has the capability to explore in a simulated ambient, new forms of game episodes that conduce toward bigger rewards. During the training phase a three terms reinforcement learning scheme is proposed, where the agent memory resources are sustained by adviser neural sub-networks, noise-balanced trained as to satisfy the look for future conditions in the control optimization predicted by the Bellman equation. In the operating phase the components merge into a wised up robot, with look ahead capacities, that mimic the abilities of ingenious human players. The achieved look ahead robotic intelligence could be useful in other complex robotic mechanisms. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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A Deep Learning Approach for Symmetric-Key Cryptography System

A Deep Learning Approach for Symmetric-Key Cryptography Sys...

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Future Technologies Conference, FTC 2020

作者： Quinga-Socasi, Francisco Zhinin-Vera, Luis Chang, Oscar School of Mathematical and Computational Sciences Yachay Tech University Urcuqui100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783030631277

Cryptography is the art and science of protecting information form intruders of data by making the information unintelligible (encryption), as well as, to retrieve the original data (decryption). Good cryptography means that the information is encrypted in such a way that a brute force attack against the key or cryptography algorithm are all impossible. Up to date, several ciphers utilizing complex mathematics have been proposed. But none of them are entirely secure and their vulnerabilities have been exposed. Therefore, novel cryptography algorithms, capable of provide superior protection, are highly desirable. In proposed work, a method for generating a key from an alphanumeric login password is introduced and implementation of symmetric-key encryption and decryption using an autoencoder neural network. Our experiments show that proposed method overcome traditional cryptography algorithms, at lest when small text file are used, and it is extremely hard to crack. © 2021, Springer Nature Switzerland AG.

关键词： Cryptography

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Self-taught Neural Agents in Clever Game Playing

Self-taught Neural Agents in Clever Game Playing

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Future Technologies Conference, FTC 2020

作者： Chang, Oscar Zhinin-Vera, Luis Quinga-Socasi, Francisco School of Mathematical and Computational Sciences Yachay Tech University Urcuqui100650 Ecuador MIND Research Group - Model Intelligent Networks Development Urcuquí Ecuador

ISBN: (纸本)9783030631277

In the Tic-Tac-Toe game community, brilliant children and some adults discover, through playing experience, exceptional game situations where the current player declares him/herself a winner, no matter what the opponent does in the next moves. This paper proposes a Tic-Tac-Toe learning environment based on a self-motivated neural agent that learns by itself these exceptional game situations and then use this knowledge in real world tournaments, where it mimics a Markov model. The used reinforcement learning method involves a reward gaining strategy where indexed sub networks are noise balanced trained, as to clearly point toward found rewards, thus endorsing a successful future search for maximal recompenses. During training the agent explores far ahead movements based on the Bellman equation and memorizes game patterns that assure winning-ahead situations. During the operating phase the neural agent receives advising from the trained networks and realizes aperture moves that mimic the abilities of clever human players. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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