scholarly journals Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines

2021 ◽  
pp. 4-34
Author(s):  
G. Dodig-Crnkovic
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Science ◽  
Philosophy Of Mind ◽  
Computational Models ◽  
Common Ground ◽  
Natural Philosophy ◽  
Learning To Learn ◽  
Natural Systems ◽  
Integrative View

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

scholarly journals Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

Philosophies ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 17 ◽  
Author(s):  
Gordana Dodig-Crnkovic
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Science ◽  
Philosophy Of Mind ◽  
Computational Models ◽  
Common Ground ◽  
Natural Philosophy ◽  
Learning To Learn ◽  
Natural Systems ◽  
Integrative View

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

scholarly journals Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews

2021 ◽  
Vol 11 (2) ◽  
pp. 103-109
Author(s):  
Pumrapee Poomka ◽  
◽  
Nittaya Kerdprasop ◽  
Kittisak Kerdprasop
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Computational Models ◽  
Shopping Mall ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Review Section

At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

scholarly journals An Overview Studying of Deep Learning

2020 ◽  
pp. 394-398
Author(s):  
Mya Sandar Kyin ◽  
Zaw Lin Oo ◽  
Khin Mar Cho
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Unstructured Data ◽  
Levels Of Abstraction ◽  
Large Numbers ◽  
Multiple Levels ◽  
Multiple Processing

Deep learning is a subfield of machine learning however both drop under the broad category of artificial intelligence. Deep learning is what powers the most human-like artificial intelligence that consents computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning is making major advances in solving problems hence categorized in wider section of artificial intelligence. The main advantage of Deep Learning is to create an artificial neural network that can learn and make intelligent decisions on its own and to process large numbers of features makes deep learning very powerful when dealing with unstructured data.

scholarly journals Identification of Autism Spectrum Disorder (ASD) using Autoencoder

2020 ◽  
Vol 9 (4) ◽  
pp. 945-948
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Great Degree ◽  
Computational Models ◽  
Autism Spectrum ◽  
Benchmark Dataset ◽  
Spectrum Disorder ◽  
Learning Techniques ◽  
3D Representations

Deep Learning (DL) techniques are computational models based on representation learnings. They are demonstrated to be the best reasonable strategies to deal with information with various portrayals and with numerous degrees of reflection. Recognizable proof of ASD has been a test as there is no demonstrated reason for it. The issue has been tended to by numerous specialists with the utilization of fMRI. As MRI and its varieties have 3D representations, Machine Learning and Deep Learning techniques are appropriate to deal with and handle them. This paper extends the recognizable proof of ASD from fMRI pictures utilizing Autoencoder organize. The examinations are led on the benchmark dataset ABIDE II. Results uncover that DL strategies are bringing out better classifiers delivering a great degree of arrangement exactness.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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