Machine Learning is a fast evolving field that has helped almost all types of industries, including healthcare, finance, and technology. Over the past few years, ML has become a popular topic for research, and has achieved numerous breakthroughs.
This article discusses 10 research articles that a researcher needs to read, to keep up with the latest ML approaches and concepts in 2023.
- Novel Machine Learning Algorithm Can Identify Patients at Risk of Poor Overall Survival Following Curative Resection for Colorectal Liver Metastases
(Amygdalos et al., 2023)
Field of Research: Medical and Healthcare
This paper demonstrates the power of a gradient-boosted decision tree model in identifying high-risk patients for poor overall survival after curative resection for colorectal liver metastases. The findings suggest potential benefits of closer follow-up and aggressive treatment strategies for these patients, paving the way for more personalized healthcare interventions.
- A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction
(Khan, Qureshi, Daniyal, & Tawiah, 2023)
Field of Research: Healthcare and Medical
Investigating machine learning methods, such as decision trees and random forests, this study focuses on accurate prediction and decision-making for cardiovascular disease patients. The research aims to improve patient diagnosis and treatment through the integration of predictive algorithms, contributing to the advancement of precision medicine.
- A Novel Machine Learning Algorithm for Interval Systems Approximation Based on Artificial Neural Network
(Zerrougui, Adamou-Mitiche, & Mitiche, 2023)
Field of Research: Structural Dynamics and AI-Based Order Reduction
This paper introduces a novel artificial intelligence technique using artificial neural networks to reduce the degree of large interval systems. The method maintains system stability while achieving a very acceptable approximation, presenting a promising approach for more efficient and reliable structural dynamics analysis.
- A Novel Machine Learning Approach for Solar Radiation Estimation
(Hissou, Benkirane, Guezzaz, Azrour, & Beni-Hssane, 2023)
Field of Research: Renewable Energy and Climate Modeling
Addressing solar energy variability, this paper explores a multivariate time series model with recursive feature elimination to estimate solar radiation. The findings contribute to renewable energy planning and climate modeling efforts, enabling better utilization of solar resources and facilitating sustainable energy transitions.
- A Novel Machine Learning Method for Evaluating the Impact of Emission Sources on Ozone Formation
(Cheng et al., 2023)
Field of Research: Environmental Science and Pollution Control
Using positive matrix factorization and Shapley Additive explanation, this research assesses the impact of emission sources on ozone formation. The findings provide valuable insights into pollution control strategies, aiding in the design of effective air quality management policies.
- AcrPred: A Hybrid Optimization with Enumerated Machine Learning Algorithm to Predict Anti-CRISPR Proteins
(Dao et al., 2023)
Field of Research: Biotechnology and Gene Editing
CRISPR-Cas is a powerful gene editing tool, and this paper focuses on predicting anti-CRISPR proteins using a high-accuracy predictive model based on machine learning. The research provides potential tools for gene editing regulation, opening up new avenues for precision gene therapies.
- Modeling the Mechanical Properties of Recycled Aggregate Concrete Using Hybrid Machine Learning Algorithms
(Peng & Unluer, 2023)
Field of Research: Civil Engineering and Sustainable Construction
This study employs hybrid machine learning models to predict the mechanical properties of recycled aggregate concrete. The research contributes to sustainable construction practices and material optimization, offering greener and more efficient building solutions.
- Deep MCANC: A Deep Learning Approach to Multi-Channel Active Noise Control
(Zhang & Wang, 2023)
Field of Research: Acoustics and Noise Control
This paper introduces deep MCANC, a deep learning-based approach for multi-channel active noise control. The research demonstrates the effectiveness of the method in wideband noise reduction and real-time applications, potentially revolutionizing noise reduction technologies.
- Wheel Defect Detection Using a Hybrid Deep Learning Approach
(Shaikh, Hussain, & Chowdhry, 2023)
Field of Research: Railway Transportation and Vibration Analysis
Addressing the issue of defective wheels in railway transportation, this research proposes a hybrid deep learning approach using accelerometer data for accurate detection of various wheel defects. The findings contribute to improved operational safety and maintenance, ensuring smoother and safer railway operations.
- An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach
(Abdusalomov, Islam, Nasimov, Mukhiddinov, & Whangbo, 2023)
Field of Research: Environmental Monitoring and Fire Safety
In this study, an improved forest fire detection method is proposed, utilizing deep learning approaches and the Detectron2 model. The research demonstrates high precision in detecting fires, which is critical for timely response and fire control, providing a reliable and scalable solution for fire management systems.
Insights
These ten research papers present significant contributions to the field of machine learning, showcasing the transformative potential of this technology across various domains. From improving healthcare outcomes and advancing renewable energy utilization to enhancing environmental monitoring and safety measures, machine learning continues to revolutionize industries and shape a more sustainable and efficient future.
As aspiring researchers and practitioners delve into these informative papers, they will gain a deeper appreciation for the multifaceted applications of machine learning, inspiring further exploration and innovation in this dynamic and evolving field. Embracing the insights from these research papers, the machine learning community can continue pushing the boundaries of AI technology, ushering in a new era of intelligent solutions for complex challenges.
Additional Reading: Review Papers on Machine Learning
- Machine Learning and Deep Learning: A Review of Methods and Applications
(Sharifani & Amini, 2023)
Field of Research: Artificial Intelligence and Machine Learning
This comprehensive review article explores the methods and applications of both machine learning and deep learning. It delves into the strengths and weaknesses of these technologies and discusses their potential future directions. Additionally, the article addresses the challenges associated with data privacy, ethical considerations, and the importance of transparency in decision-making processes. As machine learning and deep learning continue to reshape industries and human-computer interactions, this review provides a valuable resource for understanding their transformative impact and potential for future innovation.
- Machine Learning Algorithms to Forecast Air Quality: A Survey
(Méndez, Merayo, & Núñez, 2023)
Field of Research: Environmental Science and Air Quality Forecasting
Air pollution poses significant health risks, making the accurate forecasting of pollutant concentrations crucial for public health and environmental management. This survey reviews machine learning algorithms, particularly deep learning models, that have been applied to air quality forecasting from 2011 to 2021. The paper classifies the contributions based on geographical distribution, predicted values, predictor variables, evaluation metrics, and machine learning models used, providing valuable insights into the state-of-the-art techniques in this important environmental domain.
The two additional review papers provide readers with broader perspectives on the state-of-the-art in machine learning and its diverse applications. Together, these review papers complement the top 10 research papers by providing readers with comprehensive insights into the broader landscape of machine learning research and its real-world applications. As the field continues to evolve, staying updated with both cutting-edge research and broader trends will be essential for leveraging machine learning's potential to tackle complex problems and create a positive impact on society.
References
Abdusalomov, A. B., Islam, B. M. S., Nasimov, R., Mukhiddinov, M., & Whangbo, T. K. (2023). An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach. Sensors, 23(3), 1512. https://doi.org/10.3390/s23031512
Amygdalos, I., Müller‐Franzes, G., Bednarsch, J., Czigany, Z., Ulmer, T. F., Bruners, P., … Lang, S. A. (2023). Novel machine learning algorithm can identify patients at risk of poor overall survival following curative resection for colorectal liver metastases. Journal of Hepato-Biliary-Pancreatic Sciences, 30(5), 602–614. https://doi.org/10.1002/jhbp.1249
Cheng, Y., Huang, X.-F., Peng, Y., Tang, M.-X., Zhu, B., Xia, S.-Y., & He, L.-Y. (2023). A novel machine learning method for evaluating the impact of emission sources on ozone formation. Environmental Pollution, 316, 120685. https://doi.org/10.1016/j.envpol.2022.120685
Dao, F.-Y., Liu, M.-L., Su, W., Lv, H., Zhang, Z.-Y., Lin, H., & Liu, L. (2023). AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins. International Journal of Biological Macromolecules, 228, 706–714. https://doi.org/10.1016/j.ijbiomac.2022.12.250
Hissou, H., Benkirane, S., Guezzaz, A., Azrour, M., & Beni-Hssane, A. (2023). A Novel Machine Learning Approach for Solar Radiation Estimation. Sustainability, 15(13), 10609. https://doi.org/10.3390/su151310609
Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023). A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction. Health & Social Care in the Community, 2023, 1–10. https://doi.org/10.1155/2023/1406060
Méndez, M., Merayo, M. G., & Núñez, M. (2023). Machine learning algorithms to forecast air quality: a survey. Artificial Intelligence Review, 56(9), 10031–10066. https://doi.org/10.1007/s10462-023-10424-4
Peng, Y., & Unluer, C. (2023). Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms. Resources, Conservation and Recycling, 190, 106812. https://doi.org/10.1016/j.resconrec.2022.106812
Shaikh, K., Hussain, I., & Chowdhry, B. S. (2023). Wheel Defect Detection Using a Hybrid Deep Learning Approach. Sensors, 23(14), 6248. https://doi.org/10.3390/s23146248
Sharifani, K., & Amini, M. (2023). Machine Learning and Deep Learning: A Review of Methods and Applications. World Information Technology and Engineering Journal, 10(7), 3897–3904. Retrieved from https://ssrn.com/abstract=4458723
Zerrougui, R., Adamou-Mitiche, A. B. H., & Mitiche, L. (2023). A novel machine learning algorithm for interval systems approximation based on artificial neural network. Journal of Intelligent Manufacturing, 34(5), 2171–2184. https://doi.org/10.1007/s10845-021-01874-0
Zhang, H., & Wang, D. (2023). Deep MCANC: A deep learning approach to multi-channel active noise control. Neural Networks, 158, 318–327. https://doi.org/10.1016/j.neunet.2022.11.029
