Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine Learning Analysis
Hector J. Castro, Maitham G. Yousif
Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques
Cristiana Bolchini, Luca Cassano, Antonio Miele
Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python
Heinrich Peters, Michael Parrott
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle, Oliver Neumann, Markus Reischl, Ralf Mikut
Method and Validation for Optimal Lineup Creation for Daily Fantasy Football Using Machine Learning and Linear Programming
Joseph M. Mahoney, Tomasz B. Paniak
Monitoring Machine Learning Models: Online Detection of Relevant Deviations
Florian Heinrichs
Automated Detection of Persistent Inflammatory Biomarkers in Post-COVID-19 Patients Using Machine Learning Techniques
Ghizal Fatima, Fadhil G. Al-Amran, Maitham G. Yousif
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR
Petr Philonenko, Vladimir Kokh, Pavel Blinov
Predicting environment effects on breast cancer by implementing machine learning
Muhammad Shoaib Farooq, Mehreen Ilyas
Driving behavior-guided battery health monitoring for electric vehicles using machine learning
Nanhua Jiang, Jiawei Zhang, Weiran Jiang, Yao Ren, Jing Lin, Edwin Khoo, Ziyou Song
Can neural networks count digit frequency?
Padmaksh Khandelwal
Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research
Md Abu Sufian, Jayasree Varadarajan
How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning
Farah Abu Hamad, Rama Hasiba, Deema Shahwan, Huthaifa I. Ashqar
Machine Learning with Chaotic Strange Attractors
Bahadır Utku Kesgin, Uğur Teğin
Predicting Temperature of Major Cities Using Machine Learning and Deep Learning
Wasiou Jaharabi, MD Ibrahim Al Hossain, Rownak Tahmid, Md. Zuhayer Islam, T. M. Saad Rayhan
An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine
Abdullah Al Hasib, Ashikur Rahman, Mahpara Khabir, Md. Tanvir Rouf Shawon
Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models
Dangxing Chen
FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides, Tong Xia, Niels van Berkel
StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors
Inez Okulska, Daria Stetsenko, Anna Kołos, Agnieszka Karlińska, Kinga Głąbińska, Adam Nowakowski