The disclosure generally relates to methods and systems for identifying presence of abnormal heart sounds from heart sound signals of a subject being monitored. Conventional Artificial intelligence (AI) based abnormal heart sounds detection models with supervised learning requires a substantial amount of accurate training datasets covering all heart disease types for the training, which is quiet challenging. The present methods and systems solve the problem solves the problem of identifying presence of the abnormal heart sounds using an efficient semi-supervised learning model. The semi-supervised learning model is generated based on probability distribution of spectrographic properties obtained from heart sound signals of healthy subjects. A Kullback-Leibler (KL) divergence between a predefined Gaussian distribution and an encoded probability distribution of the semi-supervised learning model is determined as an anomaly score for identifying the abnormal heart sounds.

The present disclosure encompasses systems and methods for predicting embryo ploidy. Specific embodiments encompass methods of non-invasively predicting ploidy status of an embryo, by receiving a dataset with a static image of the embryo, analyzing the static image by one or more machine and/or deep learning model via one or more classification task applied to the dataset; and generating an output prediction of the ploidy status of the embryo. Particular methods relate to methods wherein the dataset additionally includes one or more clinical and/or morphological features for the embryo. Embodiments also relate to predicting embryo viability and/or improving embryo selection, such as during in vitro fertilization, and uses thereof.

In an approach to AI explaining for natural language processing, responsive to receiving an input text for a machine learning model, an output is generated from the machine learning model. A plurality of alteration techniques are applied to the input text to generate one or more alternate outputs, where each alternate output corresponds to an alteration technique. A variation rate of the alternate output is calculated for each alteration technique. A preferred technique of generating neighboring data of the input text is generated based on a comparison of the variation rate of the alternate output for each alteration technique.

A label-free cell classification and screening system based on hybrid transfer learning, including a data preprocessing module for acquiring 2D light scattering video data and for digital cell filtering, is made public here; the data preprocessing module includes the label-free high-content video flow cytometry, which has the optical excitation module, the sheath flow control module, and the data acquisition and processing module; the image archiving module is used to sort and set labels for cells; in the feature extraction module, the first convolutional neural network is used to obtain image data feature vectors; in the cell classification and screening module, a support vector machine model is used to obtain the cell screening results.