State-of-the-Art Techniques in Artificial Intelligence for Continual Learning: A Review

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

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State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Diagnostics ◽

10.3390/diagnostics11091545 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1545

Author(s):

Pichet Ruenchit

Keyword(s):

Artificial Intelligence ◽

Dna Barcoding ◽

State Of The Art ◽

Shape Change ◽

Geometric Morphometric ◽

Artificial Intelligence Technology ◽

Critical Problems ◽

Human Interpretation ◽

Sensitivity Specificity ◽

Art Techniques

Conventional methods such as microscopy have been used to diagnose parasitic diseases and medical conditions related to arthropods for many years. Some techniques are considered gold standard methods. However, their limited sensitivity, specificity, and accuracy, and the need for costly reagents and high-skilled technicians are critical problems. New tools are therefore continually being developed to reduce pitfalls. Recently, three state-of-the-art techniques have emerged: DNA barcoding, geometric morphometrics, and artificial intelligence. Here, data related to the three approaches are reviewed. DNA barcoding involves an analysis of a barcode sequence. It was used to diagnose medical parasites and arthropods with 95.0% accuracy. However, this technique still requires costly reagents and equipment. Geometric morphometric analysis is the statistical analysis of the patterns of shape change of an anatomical structure. Its accuracy is approximately 94.0–100.0%, and unlike DNA barcoding, costly reagents and equipment are not required. Artificial intelligence technology involves the analysis of pictures using well-trained algorithms. It showed 98.8–99.0% precision. All three approaches use computer programs instead of human interpretation. They also have the potential to be high-throughput technologies since many samples can be analyzed at once. However, the limitation of using these techniques in real settings is species coverage.

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Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges

Artificial Intelligence Review ◽

10.1007/s10462-021-09999-7 ◽

2021 ◽

Author(s):

Sanjeev Kumar Saini ◽

Rashmi Gupta

Keyword(s):

Artificial Intelligence ◽

State Of The Art ◽

Cardiac Abnormalities ◽

Electrocardiogram Signals ◽

Artificial Intelligence Methods ◽

Future Challenges

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Gamma-ray Spectrometry in Geothermal Exploration: State of the Art Techniques

Energies ◽

10.3390/en7084757 ◽

2014 ◽

Vol 7 (8) ◽

pp. 4757-4780 ◽

Cited By ~ 17

Author(s):

Alistair McCay ◽

Thomas Harley ◽

Paul Younger ◽

David Sanderson ◽

Alan Cresswell

Keyword(s):

Gamma Ray ◽

State Of The Art ◽

Gamma Ray Spectrometry ◽

Geothermal Exploration ◽

Art Techniques

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Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition

Sensors ◽

10.3390/s21124233 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4233

Author(s):

Bogdan Mocanu ◽

Ruxandra Tapu ◽

Titus Zaharia

Keyword(s):

Emotion Recognition ◽

Loss Function ◽

State Of The Art ◽

Disease Diagnosis ◽

Data Representation ◽

Speech Emotion Recognition ◽

Audio Features ◽

Global Accuracy ◽

Space Data ◽

Art Techniques

Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human–machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%.

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State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020

Current Gastroenterology Reports ◽

10.1007/s11894-021-00810-9 ◽

2021 ◽

Vol 23 (5) ◽

Author(s):

Jiyoung Lee ◽

Michael B. Wallace

Keyword(s):

Artificial Intelligence ◽

State Of The Art ◽

The Impact

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Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

IEEE Access ◽

10.1109/access.2021.3107316 ◽

2021 ◽

Vol 9 ◽

pp. 118584-118605

Author(s):

Munyaradzi Munochiveyi ◽

Arjun Chakravarthi Pogaku ◽

Dinh-Thuan Do ◽

Anh-Tu Le ◽

Miroslav Voznak ◽

...

Keyword(s):

State Of The Art ◽

Open Issues ◽

Art Techniques

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A Survey on Bias and Fairness in Machine Learning

ACM Computing Surveys ◽

10.1145/3457607 ◽

2021 ◽

Vol 54 (6) ◽

pp. 1-35

Author(s):

Ninareh Mehrabi ◽

Fred Morstatter ◽

Nripsuta Saxena ◽

Kristina Lerman ◽

Aram Galstyan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Real World ◽

State Of The Art ◽

Future Directions ◽

Discriminatory Behavior ◽

Real World Applications ◽

Near Future ◽

Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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