From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review

From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review

There are promising prospects on the way to widespread use of AI, as well as problems that need to be overcome to adapt AI&ML technologies in industries. The paper systematizes the AI sections and calculates the dynamics of changes in the number of scientific articles in machine learning section...

Full description

Bibliographic Details
Main Authors:	Ravil I. Mukhamediev, Adilkhan Symagulov, Yan Kuchin, Kirill Yakunin, Marina Yelis
Format:	Article
Language:	English
Published:	MDPI AG 2021-06-01
Series:	Applied Sciences
Subjects:	artificial intelligence machine learning deep learning explainable machine learning transfer learning convolution neural networks
Online Access:	https://www.mdpi.com/2076-3417/11/12/5541

Similar Items

Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges
by: Ravil I. Mukhamediev, et al.
Published: (2022-07-01)

One method of generating synthetic data to assess the upper limit of machine learning algorithms performance
by: Yan I. Kuchin, et al.
Published: (2020-01-01)

Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods
by: Ravil I. Mukhamediev, et al.
Published: (2023-11-01)

Application of Machine Learning Methods to Assess Filtration Properties of Host Rocks of Uranium Deposits in Kazakhstan
by: Yan Kuchin, et al.
Published: (2023-10-01)

Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning
by: Ravil I. Mukhamediev, et al.
Published: (2023-11-01)

Using Pseudo-Color Maps and Machine Learning Methods to Estimate Long-Term Salinity of Soils
by: Ravil I. Mukhamediev, et al.
Published: (2024-09-01)

Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images
by: Ravil Mukhamediev, et al.
Published: (2023-05-01)

Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
by: Ravil I. Mukhamediev, et al.
Published: (2021-10-01)

Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation
by: Francesco Mercaldo, et al.
Published: (2023-09-01)

Tabular machine learning using conjunctive threshold neural networks
by: Weijia Wang, et al.
Published: (2022-12-01)

Saliency-assisted multi-label classification for explainable deep learning applications in endoscopic ENT navigation
by: Bieck Richard, et al.
Published: (2022-09-01)

Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8,9 OLI Data with Machine Learning Models
by: Ravil I. Mukhamediev, et al.
Published: (2023-08-01)

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification
by: Daniel Probst
Published: (2023-11-01)

A detailed study of interpretability of deep neural network based top taggers
by: Ayush Khot, et al.
Published: (2023-01-01)

Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
by: Gomathy Ramaswami, et al.
Published: (2022-09-01)

Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals
by: Noor Kamal Al-Qazzaz, et al.
Published: (2024-01-01)

Wildfire Danger Prediction and Understanding With Deep Learning
by: Spyros Kondylatos, et al.
Published: (2022-09-01)

Editorial: Deep Learning in Brain-Computer Interface
by: Minkyu Ahn, et al.
Published: (2022-05-01)

mSHAP: SHAP Values for Two-Part Models
by: Spencer Matthews, et al.
Published: (2021-12-01)

EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
by: Evandro S. Ortigossa, et al.
Published: (2024-01-01)

Deep transfer learning with Bayesian inference
by: Gambardella, A
Published: (2021)

The use of machine learning “black boxes” explanation systems to improve the quality of school education
by: R. Muhamedyev, et al.
Published: (2020-01-01)

Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare
by: Han Yuan, et al.
Published: (2024-09-01)

Explainable machine learning for public policy: Use cases, gaps, and research directions
by: Kasun Amarasinghe, et al.
Published: (2023-01-01)

Distributional Prototypical Methods for Reliable Explanation Space Construction
by: Hyungjun Joo, et al.
Published: (2023-01-01)

Effective depression detection and interpretation: Integrating machine learning, deep learning, language models, and explainable AI
by: Gazi Hasan Al Masud, et al.
Published: (2025-03-01)

Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence
by: Hela Elmannai, et al.
Published: (2023-04-01)

Plant Disease Identification Using Shallow Convolutional Neural Network
by: Sk Mahmudul Hassan, et al.
Published: (2021-11-01)

Automated Classification of Auroral Images with Deep Neural Networks
by: Zhiyuan Shang, et al.
Published: (2023-02-01)

Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification
by: Simona Moldovanu, et al.
Published: (2024-09-01)

Enhancing intrusion detection performance using explainable ensemble deep learning
by: Chiheb Eddine Ben Ncir, et al.
Published: (2024-09-01)

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network
by: Jinmo Gu, et al.
Published: (2021-08-01)

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning
by: Ali H. Al-Timemy, et al.
Published: (2023-05-01)

Colon cancer diagnosis by means of explainable deep learning
by: Marcello Di Giammarco, et al.
Published: (2024-07-01)

Mass Media as a Mirror of the COVID-19 Pandemic
by: Kirill Yakunin, et al.
Published: (2021-12-01)

Shallow and deep learning classifiers in medical image analysis
by: Francesco Prinzi, et al.
Published: (2024-03-01)

Automation of quantum dot measurement analysis via explainable machine learning
by: Daniel Schug, et al.
Published: (2025-01-01)

A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context
by: Catalina Lozano-Murcia, et al.
Published: (2023-07-01)

Machine Translation in Low-Resource Languages by an Adversarial Neural Network
by: Mengtao Sun, et al.
Published: (2021-11-01)

Making Expert Decisions Easier to Fathom: On the Explainability of Visual Object Recognition Expertise
by: Jay Hegdé, et al.
Published: (2018-10-01)