Abstract: Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

Abstract: Techniques are provided for detecting counterfeit products. Measurements and images corresponding to a product are obtained, wherein at least a portion of the measurements or images are obtained from a mobile/IoT/IoB device. The measurements and images are provided to a trained machine learning model to progressively analyze the measurements and images. Based on the progressive analysis, a determination/prediction is made with an associated confidence score as to whether the product is real or counterfeit.

Abstract: A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

Abstract: An approach for providing a cap with an embedded high-resolution lens and a sampling insert that is used during an authentication of a composition of a liquid in a container sealed by the cap. The cap has a top portion of a cap with an opening, a sampling insert inside the opening in the top portion of the cap, and a high-resolution lens inside an opening in the sampling insert.

Abstract: Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

Abstract: Techniques are provided for detecting counterfeit products. Measurements and images corresponding to a product are obtained, wherein at least a portion of the measurements or images are obtained from a mobile/IoT/IoB device. The measurements and images are provided to a trained machine learning model to progressively analyze the measurements and images. Based on the progressive analysis, a determination/prediction is made with an associated confidence score as to whether the product is real or counterfeit.

Abstract: A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.