Abstract: A gem imaging system includes a stage having a platform configured to hold an object. The system further includes a light source configured to illuminate the object. The system further includes a housing configured to at least partially enclose the object. The housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: A gem imaging system includes a stage having a platform configured to hold an object. The system further includes a light source configured to illuminate the object. The system further includes a housing configured to at least partially enclose the object. The housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: In an embodiment, a gem imaging system includes a stage having a platform configured to hold an object. In an embodiment, the system further includes a light source configured to illuminate the object. In an embodiment, the system further includes a housing configured to at least partially enclose the object, wherein the housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

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 example operation may include one or more of receiving a request which identifies a data value, reading, from a distributed blockchain storage, one or more data other data values that are related to the identified data value and which are previously stored thereon, determining whether the identified data value satisfies one or more compliance attributes based on the one or more other data values, and generating an output based on the determination.

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: Embodiments of the present invention disclose a method, computer program product, and system for mapping one or more inclusions in a mineral crystal. A set of image data associated with the mineral crystal is receiving. The received set of image data is analyzed. One or more inclusions associated with the mineral crystal is identified based on the analyzed image data. The identified one or more inclusions of the mineral crystal are mapped to a tree structure representing the surface of the mineral crystal. The mapped one or more inclusions are encoded as a chain-code associated with the mineral crystal. A radial distance between a center of mass value of the mineral crystal and a center of mass value of the identified one or more inclusions is calculated and a mineral crystal fingerprint is generated.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

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: 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: An example operation may include one or more of receiving a request which identifies a data value, reading, from a distributed blockchain storage, one or more data other data values that are related to the identified data value and which are previously stored thereon, determining whether the identified data value satisfies one or more compliance attributes based on the one or more other data values, and generating an output based on the determination.

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: In an embodiment, a gem imaging system includes a stage having a platform configured to hold an object. In an embodiment, the system further includes a light source configured to illuminate the object. In an embodiment, the system further includes a housing configured to at least partially enclose the object, wherein the housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for mapping one or more inclusions in a mineral crystal. A set of image data associated with the mineral crystal is receiving. The received set of image data is analyzed. One or more inclusions associated with the mineral crystal is identified based on the analyzed image data. The identified one or more inclusions of the mineral crystal are mapped to a tree structure representing the surface of the mineral crystal. The mapped one or more inclusions are encoded as a chain-code associated with the mineral crystal. A radial distance between a center of mass value of the mineral crystal and a center of mass value of the identified one or more inclusions is calculated and a mineral crystal fingerprint is generated.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for mapping one or more inclusions in a mineral crystal. A set of image data associated with the mineral crystal is receiving. The received set of image data is analyzed. One or more inclusions associated with the mineral crystal is identified based on the analyzed image data. The identified one or more inclusions of the mineral crystal are mapped to a tree structure representing the surface of the mineral crystal. The mapped one or more inclusions are encoded as a chain-code associated with the mineral crystal. A radial distance between a center of mass value of the mineral crystal and a center of mass value of the identified one or more inclusions is calculated and a mineral crystal fingerprint is generated.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.