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MyJournals org - Science - Entropy, Vol 25, Pages 559: Entropy and Fractal Techniques for Monitoring Fish Behaviour and Welfare in Aquacultural Precision Fish Farming—A Review (Entropy)

MyJournals.org - Science - Entropy, Vol. 25, Pages 559: Entropy and Fractal Techniques for Monitoring Fish Behaviour and Welfare in Aquacultural Precision Fish Farming—A Review (Entropy)

MutexMatch: Semi-Supervised Learning With Mutex-Based Consistency Regu by Yue Duan, Zhen Zhao et al

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this article, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict “what it is” by the conventional true-positive classifier (TPC), while low-confidence samples are employed to achieve a simpler goal to predict with ease “what it is not” by the true-negative classifier (TNC). In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by the consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., Canadian Institute for Advanced Research (CI

Decision Tree Evaluation on Sensitive Datasets for Secure E-Healthcare by Mingwu Zhang, Yu Chen et al

Collecting and analyzing patients' e-healthcare data in Medical Internet-of-Things (MIOT), e-Healthcare providers can offer reliable medical services that will achieve better treatment for patients. For example, the diagnosis of disease and predictions of health offer an alternative and helpful evaluation of the risk of diseases, thereby helping patients lead a healthier life. However, e-Healthcare providers cannot cope with the huge volumes of data and respond to this online service such that a feasible solution is adopted to outsource the medical data to powerful medical cloud servers. Since medical data are very sensitive and outsourced servers are not fully trusted, a direct outsourcing decision tree evaluation service will inevitably result in huge privacy risks with regards to patient identity or original medical data. It is hard to hide the results of an evaluation from the single-server model unless a fully homomorphic cryptosystem is used, or the requester must communicat

LBank Exchange Will List Entropy on February 16, 2023

Road Town, British Virgin Islands (Newsfile Corp. - February 14, 2023) - LBank Exchange, a global digital asset trading platform, will list Entropy (ENT) on February 16, 2023. For all users of LBank

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