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Machine Learning: Algorithms, Real-World Applications and Research Directions

https://doi.org/10.1007/s42979-021-00592-x

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Hybrid Cost-Sensitive Decision Tree

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Finding Association Rules That Trade Support Optimally against Confidence

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Feature Extraction, Construction and Selection

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Reality mining: sensing complex social systems

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Top 10 algorithms in data mining

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A Comprehensive Survey of Clustering Algorithms

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Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

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AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

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Machine Learning: Algorithms, Real-World Applications and Research Directions

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Data Mining: Practical Machine Learning Tools and Techniques

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Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

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Comparison of deep learning approaches to predict COVID-19 infection

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Machine Learning-based traffic prediction models for Intelligent Transportation Systems

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An analytical method for diseases prediction using machine learning techniques

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A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

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Machine learning for internet of things data analysis: a survey

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A simple and fast algorithm for K-medoids clustering

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A stochastic multi-criteria decision analysis for sustainable biomass crop selection

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Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

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Deep learning-based image recognition for autonomous driving

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A machine learning-based framework to identify type 2 diabetes through electronic health records

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Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications

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A machine learning based robust prediction model for real-life mobile phone data

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AppsPred: Predicting context-aware smartphone apps using random forest learning

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ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services

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Automated Product Recommendations with Preference-Based Explanations

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A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

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Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives

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A massively parallel architecture for a self-organizing neural pattern recognition machine

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Advances in neural information processing systems 7

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Confirmation-Guided Discovery of First-Order Rules with Tertius

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Random Forests

inferredcites →https://doi.org/10.1023/a:1018054314350openalex-snapshot

Bagging Predictors

inferredcites →https://doi.org/10.1023/a:1022631118932openalex-snapshot

Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

inferredcites →https://doi.org/10.1023/a:1022643204877openalex-snapshot

Induction of Decision Trees

inferredcites →https://doi.org/10.1023/a:1022689900470openalex-snapshot

Instance-Based Learning Algorithms

inferredcites →https://doi.org/10.1037/h0071325openalex-snapshot

Analysis of a complex of statistical variables into principal components.

inferredcites →https://doi.org/10.1038/nature16961openalex-snapshot

Mastering the game of Go with deep neural networks and tree search

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Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

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Environmental variability supports chimpanzee behavioural diversity

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LIII. On lines and planes of closest fit to systems of points in space

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Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

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The Application of Computers to Taxonomy

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The effects of knowledge transfer on farmers decision making toward sustainable agriculture practices

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The self-organizing map

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Gradient-based learning applied to document recognition

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Scalable algorithms for association mining

inferredcites →https://doi.org/10.1109/access.2018.2836950openalex-snapshot

Machine Learning and Deep Learning Methods for Cybersecurity

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Metabolic Syndrome and Development of Diabetes Mellitus: Predictive Modeling Based on Machine Learning Techniques

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An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

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Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

inferredcites →https://doi.org/10.1109/bigcomp.2019.8679231openalex-snapshot

Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

inferredcites →https://doi.org/10.1109/cinti.2012.6496753openalex-snapshot

Constrained clustering with Minkowski Weighted K-Means

inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot

A detailed analysis of the KDD CUP 99 data set

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Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

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Xception: Deep Learning with Depthwise Separable Convolutions

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State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

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An Empirical Comparison of Supervised Machine Learning Algorithms for Internet of Things Data

inferredcites →https://doi.org/10.1109/icde.1995.380413openalex-snapshot

Set-oriented mining for association rules in relational databases

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Parking availability prediction for sensor-enabled car parks in smart cities

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Sentiment analysis of agricultural product ecommerce review data based on deep learning

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Internet of Things for Smart Cities

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UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

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Manufacturing Analytics and Industrial Internet of Things

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The estimation of the gradient of a density function, with applications in pattern recognition

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Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

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A Survey of the Usages of Deep Learning for Natural Language Processing

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

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Significant Applications of Machine Learning for COVID-19 Pandemic

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UML Fever: Diagnosis and Recovery

inferredcites →https://doi.org/10.1145/1105664.1105679openalex-snapshot

Information Extraction

inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot

Mining association rules between sets of items in large databases

inferredcites →https://doi.org/10.1145/170036.170072openalex-snapshot

Mining association rules between sets of items in large databases

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Behavior-based adaptive call predictor

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Exploiting enriched contextual information for mobile app classification

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Mining Mobile User Preferences for Personalized Context-Aware Recommendation

inferredcites →https://doi.org/10.1145/2632048.2632052openalex-snapshot

MobileMiner

inferredcites →https://doi.org/10.1145/276304.276314openalex-snapshot

Automatic subspace clustering of high dimensional data for data mining applications

inferredcites →https://doi.org/10.1145/276305.276314openalex-snapshot

Automatic subspace clustering of high dimensional data for data mining applications

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Phone call log as a context source to modeling individual user behavior

inferredcites →https://doi.org/10.1145/2971648.2971747openalex-snapshot

PrefMiner

inferredcites →https://doi.org/10.1145/304181.304187openalex-snapshot

OPTICS

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

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Mining frequent patterns without candidate generation

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Challenges and research opportunities in eCommerce search and recommendations

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Rapid association rule mining

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Improvements to Platt's SMO Algorithm for SVM Classifier Design

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Shape Quantization and Recognition with Randomized Trees

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A survey of transfer learning

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Manufacturing process data analysis pipelines: a requirements analysis and survey

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Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

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Context-aware rule learning from smartphone data: survey, challenges and future directions

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Toward multi-label sentiment analysis: a transfer learning based approach

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Cybersecurity data science: an overview from machine learning perspective

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Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

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Deep Learning applications for COVID-19

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Machine Learning: Algorithms and Applications

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Reinforcement Learning: A Survey

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INDUSTRY 4.0 – ARE WE READY?

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Markov Decision Processes: Discrete Stochastic Dynamic Programming

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Classification and Regression Trees.

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Finding Groups in Data: An Introduction to Cluster Analysis.

inferredcites →https://doi.org/10.2307/2291177openalex-snapshot

Markov Decision Processes: Discrete Stochastic Dynamic Programming.

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Ridge Estimators in Logistic Regression

inferredcites →https://doi.org/10.2307/2530946openalex-snapshot

Classification and Regression Trees.

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Feature Extraction Construction and Selection: A Data Mining Perspective

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A Mobile Intelligent Interruption Management System

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Finding association rules that trade support optimally against confidence

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COVID-19 Outbreak Prediction with Machine Learning

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Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

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Sensor Technologies for Intelligent Transportation Systems

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ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

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IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

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Classification and Regression Trees

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Survey of Machine Learning Algorithms for Disease Diagnostic

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Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1302.4964openalex-snapshot

Estimating Continuous Distributions in Bayesian Classifiers

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Data Science: A Comprehensive Overview

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Finding groups in data: an introduction to cluster analysis

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Pattern recognition and machine learning

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Data mining: concepts and techniques

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1002/9781394249190.ch11openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394271689.ch3openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394280735.ch27openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394287062.ch8openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394287925.ch3openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394302444.ch4openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394303601.ch1openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9781394347070.ch4openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/9783527847358.ch11openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/acs.4075openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/adfm.202408480openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/adma.202510239openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/adom.202203104openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/adom.202503285openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/adts.202500771openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/advs.202507564openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/aenm.202304362openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/agr.70120openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/agt2.70089openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/aidi.202500033openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/aidi.202500122openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/aidi.202600003openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/aisy.202300560openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/ajim.23429openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/ange.202318380openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/anie.202318380openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/apj.3017openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/app.57619openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/arp.70006openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/bab.2491openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/brb3.70219openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/bsd2.70205openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/bte2.70099openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/btm2.10641openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cae.22679openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cae.22780openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cai2.70047openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cend.202400024openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cjce.25258openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cjce.70119openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cpe.70455openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cpe.70547openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/cpe.7637openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/em.22648openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/eng2.70380openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/epd2.70283openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/eqe.70042openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/esp.5833openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/est2.70532openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/hbm.26389openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/hpm.3864openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/hsr2.1656openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/hsr2.72070openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/ima.23059openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/ima.70397openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/inf2.12425openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/int.23088openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/jac5.70003openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/jeo2.12025openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/jeo2.70549openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/ksa.12462openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/lio2.70177openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/lpor.202200357openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/med.21995openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/oca.3237openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/orm2.70039openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/pen.27170openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/puh2.70135openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/rmb2.12584openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/saj2.70057openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/smsc.202400579openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/smtd.202502076openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/spy2.212openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/spy2.295openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/sres.3124openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/suco.202300138openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/suco.70332openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/tre.980openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/uro2.100openalex-snapshot

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inferred← citeshttps://doi.org/10.1002/viw.20230032openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/7355_2023_164openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-1-0716-4690-8_8openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-73317-9_92-1openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-88530-4_1openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-88530-4_10openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-88530-4_3openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-88530-4_4openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-88530-4_8openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-95561-8_5openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-03918-8_51openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-06862-1_3openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-10461-9_42openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-11349-9_6openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-15175-0_36openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-16620-4_4openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-26504-4_8openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-34622-4_1openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-40858-8_509-1openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-41352-0_12openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-42402-1_10openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-42519-6_17openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-43458-7_23openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-43781-6_6openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-44084-7_5openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-45608-4_1openalex-snapshot

Cautious Decision-Making for Tree Ensembles

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