27 research outputs found
Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation
The Koos grading scale is a classification system for vestibular schwannoma
(VS) used to characterize the tumor and its effects on adjacent brain
structures. The Koos classification captures many of the characteristics of
treatment deci-sions and is often used to determine treatment plans. Although
both contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2)
scanning can be used for Koos Classification, hrT2 scanning is gaining interest
because of its higher safety and cost-effectiveness. However, in the absence of
annotations for hrT2 scans, deep learning methods often inevitably suffer from
performance deg-radation due to unsupervised learning. If ceT1 scans and their
annotations can be used for unsupervised learning of hrT2 scans, the
performance of Koos classifi-cation using unlabeled hrT2 scans will be greatly
improved. In this regard, we propose an unsupervised cross-modality domain
adaptation method based on im-age translation by transforming annotated ceT1
scans into hrT2 modality and us-ing their annotations to achieve supervised
learning of hrT2 modality. Then, the VS and 7 adjacent brain structures related
to Koos classification in hrT2 scans were segmented. Finally, handcrafted
features are extracted from the segmenta-tion results, and Koos grade is
classified using a random forest classifier. The proposed method received rank
1 on the Koos classification task of the Cross-Modality Domain Adaptation
(crossMoDA 2022) challenge, with Macro-Averaged Mean Absolute Error (MA-MAE) of
0.2148 for the validation set and 0.26 for the test set.Comment: 10 pages, 2 figure
Incorporating Machine Learning to Evaluate Solutions to the University Course Timetabling Problem
Evaluating solutions to optimization problems is arguably the most important step for heuristic algorithms, as it is used to guide the algorithms towards the optimal solution in the solution search space. Research has shown evaluation functions to some optimization problems to be impractical to compute and have thus found surrogate less expensive evaluation functions to those problems. This study investigates the extent to which supervised learning algorithms can be used to find approximations to evaluation functions for the university course timetabling problem. Up to 97 percent of the time, the traditional evaluation function agreed with the supervised learning regression model on the result of comparison of the quality of pair of solutions to the university course timetabling problem, suggesting that supervised learning regression models can be suitable alternatives for optimization problems’ evaluation functions
Long-term traffic forecasting in optical networks using Machine Learning
Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-term traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations
Long-term traffic forecasting in optical networks using Machine Learning
Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-term traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations
A Review of Classification Problems and Algorithms in Renewable Energy Applications
Classification problems and their corresponding solving approaches constitute one of the
fields of machine learning. The application of classification schemes in Renewable Energy (RE) has
gained significant attention in the last few years, contributing to the deployment, management and
optimization of RE systems. The main objective of this paper is to review the most important
classification algorithms applied to RE problems, including both classical and novel algorithms.
The paper also provides a comprehensive literature review and discussion on different classification
techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in
RE systems, power quality disturbance classification and other applications in alternative RE systems.
In this way, the paper describes classification techniques and metrics applied to RE problems,
thus being useful both for researchers dealing with this kind of problem and for practitioners
of the field