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Their privacy-preserving AI method can overcome ethical, legal and political obstacles - thus paving the way for widespread use of AI, says Brraren. To train good AI algorithms, we need good data, says Kaissis. AI algorithms can support medical personnel in diagnosing illnesses. [Extracted from the article]

Recently, Artificial Neural Network (ANN) has been developed as an alternative to classical statistical methods in animal production. The methods can do classification or prediction by analyzing the information in the data set with the help of the neural network without requiring any preconditions (for example, distribution of data, non-linear data, highly correlated variables, etc.). In this context, we hypothesized that ANN, which is not only used in large and complex data sets but also estimates better in small data sets compared to classical statistical methods. The ability of ANN of Bayesian Regularization (BR-ANN) or Levenberg-Marquardt (LM-ANN) algorithms and Multiple Regression (MR) model to predict meat quality traits were assessed in a comparative study. The multilayer ANN algorithms obtained prediction data of meat quality measurements from pre-slaughter information using 1, 2, 4, 6 and 8 neurons in the hidden layer applied 10 times for each model. The performance of the methods was assessed according to the coefficient of determination (R2) criteria, root mean squared error (RMSE) and residual prediction deviation (RPD). The comparison of the findings of BR-ANN and ML-ANN algorithms showed a similar ability to predict meat quality traits (error of prediction and R2 values between 0.32-2.72 and 0.19-0.49, respectively). However, MR model predictions had lower performance than ANN algorithms, resulting in a wider error of prediction interval (0.4-3.44) and low R2 (0.16-0.44). The RPD for meat quality traits was fair for BR-ANN and LM-ANN but was poor for MR. Based on our results, the ANN algorithms produced more reasonable prediction values than the MR model. ANN algorithms can be used as an acceptable alternative method for simple physical measurements of meat quality. ANN algorithms can be used reliably in small data sets. [ABSTRACT FROM AUTHOR]

Classification of digital image content is mainly done by identifying low level image features such as corners and edges. The literature shows variety of algorithms for the identification of corner and non-corner pixels, important for objects' identification and image segmentation. However, all of these algorithms produce different results for same data and therefore, suitable for limited applications. This paper proposes a hybrid solution of combining complementary corner detection algorithms to improve image pixels' classification. This has been done by identifying the best detection algorithm for corner points with small and large angles and producing a hybrid algorithm by combining the latter two. Results have shown that Harris detector combined with Global and Local Curvature Points (GLC) improved the detection rate by 28% in synthetic images, but 50% in real images whereas, the combination of Shi's detection algorithm with GLC enhanced the detection rate by 25.9% in synthetic images and 123% in real images, showing a significant improvement. [ABSTRACT FROM AUTHOR]

ABSTRACT: Bioinformatics is a new area of research in which DNA, RNA and proteins sequences are dealt. To store, retrieve, analyze, match, align, search, and process these sequences different techniques are existed. Currently a lot of advancements in sequence analysis cause the drastic increase in the DNA database sizes that require more efficient approaches encompass accuracy. Search and analysis of DNA patterns can be performed by using various pattern matching algorithms in the computational biology. The aim of present study is to present taxonomy and performance evaluation of these pattern matching algorithms. The objective of this SLR is to set a research trend and to find a mathematical model to estimate execution search time before scanning whole DNA sequence by following a search strategy. INDEX TERMS: DNA, DNA pattern matching, DNA string matching, DNA pattern matching algorithm, time complexity, and space complexity. [ABSTRACT FROM AUTHOR]