An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels

Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2%  ±  2.8% measured by zero-one loss for the first evaluation session and 79.6%  ±  3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.

Relevance-based template matching for tracking targets in FLIR imagery

One of the main challenges in automatic target tracking applications is represented by the need to maintain a low computational footprint, especially when dealing with real-time scenarios and the limited resources of embedded environments. In this context, significant results can be obtained by using forward-looking infrared sensors capable of providing distinctive features for targets of interest. In fact, due to their nature, forward-looking infrared (FLIR) images lend themselves to being used with extremely small footprint techniques based on the extraction of target intensity profiles. This work proposes a method for increasing the computational efficiency of template-based target tracking algorithms. In particular, the speed of the algorithm is improved by using a dynamic threshold that narrows the number of computations, thus reducing both execution time and resources usage. The proposed approach has been tested on several datasets, and it has been compared to several target tracking techniques. Gathered results, both in terms of theoretical analysis and experimental data, showed that the proposed approach is able to achieve the same robustness of reference algorithms by reducing the number of operations needed and the processing time.