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Volume 12 (2022): Issue 3 (July 2022)

Volume 12 (2022): Issue 2 (April 2022)

Volume 12 (2022): Issue 1 (January 2022)

Volume 11 (2021): Issue 4 (October 2021)

Volume 11 (2021): Issue 3 (July 2021)

Volume 11 (2021): Issue 2 (April 2021)

Volume 11 (2021): Issue 1 (January 2021)

Volume 10 (2020): Issue 4 (October 2020)

Volume 10 (2020): Issue 3 (July 2020)

Volume 10 (2020): Issue 2 (April 2020)

Volume 10 (2020): Issue 1 (January 2020)

Volume 9 (2019): Issue 4 (October 2019)

Volume 9 (2019): Issue 3 (July 2019)

Volume 9 (2019): Issue 2 (April 2019)

Volume 9 (2019): Issue 1 (January 2019)

Volume 8 (2018): Issue 4 (October 2018)

Volume 8 (2018): Issue 3 (July 2018)

Volume 8 (2018): Issue 2 (April 2018)

Volume 8 (2018): Issue 1 (January 2018)

Volume 7 (2017): Issue 4 (October 2017)

Volume 7 (2017): Issue 3 (July 2017)

Volume 7 (2017): Issue 2 (April 2017)

Volume 7 (2017): Issue 1 (January 2017)

Volume 6 (2016): Issue 4 (October 2016)

Volume 6 (2016): Issue 3 (July 2016)

Volume 6 (2016): Issue 2 (April 2016)

Volume 6 (2016): Issue 1 (January 2016)

Volume 5 (2015): Issue 4 (October 2015)

Volume 5 (2015): Issue 3 (July 2015)

Volume 5 (2015): Issue 2 (April 2015)

Volume 5 (2015): Issue 1 (January 2015)

Volume 4 (2014): Issue 4 (October 2014)

Volume 4 (2014): Issue 3 (July 2014)

Volume 4 (2014): Issue 2 (April 2014)

Volume 4 (2014): Issue 1 (January 2014)

Volume 3 (2013): Issue 4 (October 2013)

Volume 3 (2013): Issue 3 (July 2013)

Volume 3 (2013): Issue 2 (April 2013)

Volume 3 (2013): Issue 1 (January 2013)

Journal Details
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

Search

Volume 12 (2022): Issue 2 (April 2022)

Journal Details
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

Search

5 Articles
Open Access

Machine Learning and Traditional Econometric Models: A Systematic Mapping Study

Published Online: 23 Feb 2022
Page range: 79 - 100

Abstract

Abstract

Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods.

Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject.

Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications.

Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.

Keywords

  • machine learning
  • econometric models
  • regression
  • prediction
Open Access

A Progressive and Cross-Domain Deep Transfer Learning Framework for Wrist Fracture Detection

Published Online: 23 Feb 2022
Page range: 101 - 120

Abstract

Abstract

There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require expertise which is both expensive and time-consuming, especially in the medical field. Furthermore, in health care systems where mistakes can have catastrophic consequences, there is a general mistrust in the black-box aspect of AI models. In this work, we focus on improving the performance of medical imaging applications when limited data is available while focusing on the interpretability aspect of the proposed AI model. This is achieved by employing a novel transfer learning framework, progressive transfer learning, an automated annotation technique and a correlation analysis experiment on the learned representations.

Progressive transfer learning helps jump-start the training of deep neural networks while improving the performance by gradually transferring knowledge from two source tasks into the target task. It is empirically tested on the wrist fracture detection application by first training a general radiology network RadiNet and using its weights to initialize RadiNetwrist, that is trained on wrist images to detect fractures. Experiments show that RadiNetwrist achieves an accuracy of 87% and an AUC ROC of 94% as opposed to 83% and 92% when it is pre-trained on the ImageNet dataset.

This improvement in performance is investigated within an explainable AI framework. More concretely, the learned deep representations of RadiNetwrist are compared to those learned by the baseline model by conducting a correlation analysis experiment. The results show that, when transfer learning is gradually applied, some features are learned earlier in the network. Moreover, the deep layers in the progressive transfer learning framework are shown to encode features that are not encountered when traditional transfer learning techniques are applied.

In addition to the empirical results, a clinical study is conducted and the performance of RadiNetwrist is compared to that of an expert radiologist. We found that RadiNetwrist exhibited similar performance to that of radiologists with more than 20 years of experience.

This motivates follow-up research to train on more data to feasibly surpass radiologists’ performance, and investigate the interpretability of AI models in the healthcare domain where the decision-making process needs to be credible and transparent.

Keywords

  • deep learning
  • transfer learning
  • wrist fracture detection
  • medical informatics
  • progressive transfer learning
Open Access

Position-Encoding Convolutional Network to Solving Connected Text Captcha

Published Online: 23 Feb 2022
Page range: 121 - 133

Abstract

Abstract

Text-based CAPTCHA is a convenient and effective safety mechanism that has been widely deployed across websites. The efficient end-to-end models of scene text recognition consisting of CNN and attention-based RNN show limited performance in solving text-based CAPTCHAs. In contrast with the street view image and document, the character sequence in CAPTCHA is non-semantic. The RNN loses its ability to learn the semantic context and only implicitly encodes the relative position of extracted features. Meanwhile, the security features, which prevent characters from segmentation and recognition, extensively increase the complexity of CAPTCHAs. The performance of this model is sensitive to different CAPTCHA schemes. In this paper, we analyze the properties of the text-based CAPTCHA and accordingly consider solving it as a highly position-relative character sequence recognition task. We propose a network named PosConv to leverage the position information in the character sequence without RNN. PosConv uses a novel padding strategy and modified convolution, explicitly encoding the relative position into the local features of characters. This mechanism of PosConv makes the extracted features from CAPTCHAs more informative and robust. We validate PosConv on six text-based CAPTCHA schemes, and it achieves state-of-the-art or competitive recognition accuracy with significantly fewer parameters and faster convergence speed.

Keywords

  • deep neural network
  • position encoding CNN
  • text-based CAPTCHA recognition
  • character recognition
Open Access

Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity

Published Online: 23 Feb 2022
Page range: 135 - 148

Abstract

Abstract

1Most reinforcement learning benchmarks – especially in multi-agent tasks – do not go beyond observations with simple noise; nonetheless, real scenarios induce more elaborate vision pipeline failures: false sightings, misclassifications or occlusion. In this work, we propose a lightweight, 2D environment for robot soccer and autonomous driving that can emulate the above discrepancies. Besides establishing a benchmark for accessible multi-agent reinforcement learning research, our work addresses the challenges the simulator imposes. For handling realistic noise, we use self-supervised learning to enhance scene reconstruction and extend curiosity-driven learning to model longer horizons. Our extensive experiments show that the proposed methods achieve state-of-the-art performance, compared against actor-critic methods, ICM, and PPO.

Keywords

  • deep reinforcement learning
  • multi-agent environment
  • autonomous driving
  • robot soccer
  • self-supervised learning
Open Access

An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection

Published Online: 23 Feb 2022
Page range: 149 - 163

Abstract

Abstract

Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoen-coder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together.

We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.

Keywords

  • intrusion detection
  • neural network
  • ensemble classifiers
  • hyperparameter optimization
  • sparse autoencoder
  • NSL-KDD
  • machine learning
5 Articles
Open Access

Machine Learning and Traditional Econometric Models: A Systematic Mapping Study

Published Online: 23 Feb 2022
Page range: 79 - 100

Abstract

Abstract

Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods.

Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject.

Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications.

Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.

Keywords

  • machine learning
  • econometric models
  • regression
  • prediction
Open Access

A Progressive and Cross-Domain Deep Transfer Learning Framework for Wrist Fracture Detection

Published Online: 23 Feb 2022
Page range: 101 - 120

Abstract

Abstract

There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require expertise which is both expensive and time-consuming, especially in the medical field. Furthermore, in health care systems where mistakes can have catastrophic consequences, there is a general mistrust in the black-box aspect of AI models. In this work, we focus on improving the performance of medical imaging applications when limited data is available while focusing on the interpretability aspect of the proposed AI model. This is achieved by employing a novel transfer learning framework, progressive transfer learning, an automated annotation technique and a correlation analysis experiment on the learned representations.

Progressive transfer learning helps jump-start the training of deep neural networks while improving the performance by gradually transferring knowledge from two source tasks into the target task. It is empirically tested on the wrist fracture detection application by first training a general radiology network RadiNet and using its weights to initialize RadiNetwrist, that is trained on wrist images to detect fractures. Experiments show that RadiNetwrist achieves an accuracy of 87% and an AUC ROC of 94% as opposed to 83% and 92% when it is pre-trained on the ImageNet dataset.

This improvement in performance is investigated within an explainable AI framework. More concretely, the learned deep representations of RadiNetwrist are compared to those learned by the baseline model by conducting a correlation analysis experiment. The results show that, when transfer learning is gradually applied, some features are learned earlier in the network. Moreover, the deep layers in the progressive transfer learning framework are shown to encode features that are not encountered when traditional transfer learning techniques are applied.

In addition to the empirical results, a clinical study is conducted and the performance of RadiNetwrist is compared to that of an expert radiologist. We found that RadiNetwrist exhibited similar performance to that of radiologists with more than 20 years of experience.

This motivates follow-up research to train on more data to feasibly surpass radiologists’ performance, and investigate the interpretability of AI models in the healthcare domain where the decision-making process needs to be credible and transparent.

Keywords

  • deep learning
  • transfer learning
  • wrist fracture detection
  • medical informatics
  • progressive transfer learning
Open Access

Position-Encoding Convolutional Network to Solving Connected Text Captcha

Published Online: 23 Feb 2022
Page range: 121 - 133

Abstract

Abstract

Text-based CAPTCHA is a convenient and effective safety mechanism that has been widely deployed across websites. The efficient end-to-end models of scene text recognition consisting of CNN and attention-based RNN show limited performance in solving text-based CAPTCHAs. In contrast with the street view image and document, the character sequence in CAPTCHA is non-semantic. The RNN loses its ability to learn the semantic context and only implicitly encodes the relative position of extracted features. Meanwhile, the security features, which prevent characters from segmentation and recognition, extensively increase the complexity of CAPTCHAs. The performance of this model is sensitive to different CAPTCHA schemes. In this paper, we analyze the properties of the text-based CAPTCHA and accordingly consider solving it as a highly position-relative character sequence recognition task. We propose a network named PosConv to leverage the position information in the character sequence without RNN. PosConv uses a novel padding strategy and modified convolution, explicitly encoding the relative position into the local features of characters. This mechanism of PosConv makes the extracted features from CAPTCHAs more informative and robust. We validate PosConv on six text-based CAPTCHA schemes, and it achieves state-of-the-art or competitive recognition accuracy with significantly fewer parameters and faster convergence speed.

Keywords

  • deep neural network
  • position encoding CNN
  • text-based CAPTCHA recognition
  • character recognition
Open Access

Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity

Published Online: 23 Feb 2022
Page range: 135 - 148

Abstract

Abstract

1Most reinforcement learning benchmarks – especially in multi-agent tasks – do not go beyond observations with simple noise; nonetheless, real scenarios induce more elaborate vision pipeline failures: false sightings, misclassifications or occlusion. In this work, we propose a lightweight, 2D environment for robot soccer and autonomous driving that can emulate the above discrepancies. Besides establishing a benchmark for accessible multi-agent reinforcement learning research, our work addresses the challenges the simulator imposes. For handling realistic noise, we use self-supervised learning to enhance scene reconstruction and extend curiosity-driven learning to model longer horizons. Our extensive experiments show that the proposed methods achieve state-of-the-art performance, compared against actor-critic methods, ICM, and PPO.

Keywords

  • deep reinforcement learning
  • multi-agent environment
  • autonomous driving
  • robot soccer
  • self-supervised learning
Open Access

An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection

Published Online: 23 Feb 2022
Page range: 149 - 163

Abstract

Abstract

Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoen-coder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together.

We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.

Keywords

  • intrusion detection
  • neural network
  • ensemble classifiers
  • hyperparameter optimization
  • sparse autoencoder
  • NSL-KDD
  • machine learning

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