# Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2101.03655 · HTML: https://mlsanity.com/paper/2101.03655/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 161 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 22 of 22)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset](https://mlsanity.com/paper/1711.07831/) (2017)
- [Edge-enabled V2X Service Placement for Intelligent Transportation Systems](https://mlsanity.com/paper/2001.06288/) (2020)
- [Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection](https://mlsanity.com/paper/2008.02327/) (2020)
- [Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach](https://mlsanity.com/paper/2012.11325/) (2020)
- [DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach](https://mlsanity.com/paper/2012.13604/) (2020)
- [Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection](https://mlsanity.com/paper/2006.09272/) (2020)
- [Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining](https://mlsanity.com/paper/2006.05031/) (2020)
- [Predicting drug response of tumors from integrated genomic profiles by deep neural networks](https://mlsanity.com/paper/1805.07702/) (2018)
- [Systematic Ensemble Model Selection Approach for Educational Data Mining](https://mlsanity.com/paper/2005.06647/) (2020)
- [Optimized Random Forest Model for Botnet Detection Based on DNS Queries](https://mlsanity.com/paper/2012.11326/) (2020)
- [The Predictive Power of Social Media: On the Predictability of U.S. Presidential Elections using Twitter](https://mlsanity.com/paper/1407.0622/) (2014)
- [Relationship between Student Engagement and Performance in e-Learning Environment Using Association Rules](https://mlsanity.com/paper/2101.02006/) (2020)

## Built on by (most cited first, 14 of 14)

- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://mlsanity.com/paper/2208.03399/) (2022)
- [Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.05079/) (2021)
- [Arabic Fake News Detection Based on Deep Contextualized Embedding Models](https://mlsanity.com/paper/2205.03114/) (2022)
- [Ontology in Hybrid Intelligence: a concise literature review](https://mlsanity.com/paper/2303.17262/) (2023)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems](https://mlsanity.com/paper/2407.02914/) (2024)
- [An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data](https://mlsanity.com/paper/2109.13828/) (2021)
- [Anomaly Detection in Intra-Vehicle Networks](https://mlsanity.com/paper/2205.03537/) (2022)
- [Optimized Ensemble Model Towards Secured Industrial IoT Devices](https://mlsanity.com/paper/2401.05509/) (2024)

## Abstract (arXiv)

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.