Intelligent Diagnosis Method for New Diseases Based on Fuzzy SVM Incremental Learning

The diagnosis of new diseases is a challenging problem. In the early stage of the emergence of new diseases, there are few case samples; this may lead to the low accuracy of intelligent diagnosis. Because of the advantages of support vector machine (SVM) in dealing with small sample problems, it is selected for the intelligent diagnosis method. The standard SVM diagnosis model updating needs to retrain all samples. It costs huge storage and calculation costs and is difficult to adapt to the changing reality. In order to solve this problem, this paper proposes a new disease diagnosis method based on Fuzzy SVM incremental learning. According to SVM theory, the support vector set and boundary sample set related to the SVM diagnosis model are extracted. Only these sample sets are considered in incremental learning to ensure the accuracy and reduce the cost of calculation and storage. To reduce the impact of noise points caused by the reduction of training samples, FSVM is used to update the diagnosis model, and the generalization is improved. The simulation results on the banana dataset show that the proposed method can improve the classification accuracy from 86.4% to 90.4%. Finally, the method is applied in COVID-19’s diagnostic. The diagnostic accuracy reaches 98.2% as the traditional SVM only gets 84%. With the increase of the number of case samples, the model is updated. When the training samples increase to 400, the number of samples participating in training is only 77; the amount of calculation of the updated model is small.

Tracking cell lineages in 3D by incremental deep learning

Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software’s performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.

Learning-based dynamic capabilities in closed-loop supply chains: an expert study

Purpose Product returns information gives firms an opportunity for continuous strategic adaptation by allowing them to understand the reasons for product returns, learning from them and improving their products and processes accordingly. By applying the Dynamic Capabilities (DCs) view in the context of closed-loop supply chains (CLSC), this study explores how firms can continuously learn from product returns information.Design/methodology/approach This study adopts a qualitative Delphi study-inspired approach. Experts from industry and academia are interviewed in two interview rounds. First round of interviews are based on extant research, while the second round allows the experts to elaborate and correct the results.Findings This study culminates into a conceptual model for incremental learning from product returns information. The results indicate incremental learning from product returns can potentially lead to a competitive advantage. Additionally, the authors identify the sources of information, capabilities along with their microfoundations and the manifestations of product return information. Three propositions are formulated embedding the findings in DC theory.Research limitations/implications This study supports extant literature in confirming the value of product returns information and opens concrete avenues for research by providing several propositions.Practical implications This research elucidates the practices, processes and resources required for firms to utilize product returns information for continuous strategic adaptation. Practitioners can use these results while implementing continuous learning practices in their organizations.Originality/value This study presents the first systematic framework for incremental learning from product returns information. The authors apply the DC framework to a new functional domain, namely CLSC management and product returns management. Furthermore, the authors offer a concrete example of how organizational learning and DC intersect, thus advancing DC theoretical knowledge.