A Review of Machine Learning and Deep Learning Applications

Shinde, Pramila; Shah, Seema

doi:10.1109/iccubea.2018.8697857

Cited by 445 publications

(229 citation statements)

References 9 publications

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“…The algorithms learn from training data and are then given test data to see how well it is accurately predicting what an image is showing, presented through an accuracy percentage. The user then analyses these answers and any errors are corrected and re-learned, helping train the model and increasing the accuracy of a given algorithm [69].…”

Section: A) Supervised Learningmentioning

confidence: 99%

Digital Twin: Enabling Technologies, Challenges and Open Research

et al. 2020

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Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

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Section: A) Supervised Learningmentioning

confidence: 99%

Digital Twin: Enabling Technologies, Challenges and Open Research

et al. 2020

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“…F 1 = F 10 + F 11 is the number of features that contain i (w i = 1), and we count features independent of class membership w i ∈ f0, 1g . F = F 00 + F 01 + F 10 + F 11 is the total number of documents [42]. X 2 is a measure of how much expected counts E and observed counts N deviate from each other.…”

Section: 27mentioning

confidence: 99%

Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

Onesime

Yang

Dai

2021

Computational and Mathematical Methods in Medicine

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Genomic islands are related to microbial adaptation and carry different genomic characteristics from the host. Therefore, many methods have been proposed to detect genomic islands from the rest of the genome by evaluating its sequence composition. Many sequence features have been proposed, but many of them have not been applied to the identification of genomic islands. In this paper, we present a scheme to predict genomic islands using the chi-square test and random forest algorithm. We extract seven kinds of sequence features and select the important features with the chi-square test. All the selected features are then input into the random forest to predict the genome islands. Three experiments and comparison show that the proposed method achieves the best performance. This understanding can be useful to design more powerful method for the genomic island prediction.

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“…Support vector machine, random forest, logistic regression and decision tree are extensively used machine learning algorithms in real time applications. Various real time applications such as information retrieval, medical diagnosis, natural language processing, classification and prediction are the key application precincts of machine learning [37]. In Figure 1, domain knowledge based machine learning feature selection is illustrated.…”

Section: A Study Of Machine Deep and Reinforcement Learningmentioning

confidence: 99%

“…There are extensive applications in image-based pattern recognition in deep learning. Computer vision, object detection, video surveillance, medical diagnosis are primary applications of deep learning [37].…”

Section: A Study Of Machine Deep and Reinforcement Learningmentioning

confidence: 99%

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

Th¹

2021

Preprint

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Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

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A Review of Machine Learning and Deep Learning Applications

Cited by 445 publications

References 9 publications

Digital Twin: Enabling Technologies, Challenges and Open Research

Digital Twin: Enabling Technologies, Challenges and Open Research

Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

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