A method and/or system for automated generation of dispatch schedule in warehouse outbound operations is disclosed. The method comprising, receiving configuration data comprising information about a warehouse and one or more stores. A variation in demand pattern and frequency pattern for the one or more stores is determined and a weighted score is calculated based on the determined variations. One among the plurality of customized algorithms is selected dynamically based on the calculated weighted score. A dispatch schedule for the warehouse is determined by executing the customized algorithm and the determined dispatch schedule is displayed graphically at a computing device of the user for execution in real-world scenario.

A method for preprocessing data for device operations can include preprocessing measurement data using a machine learning technique, determining, by a Kalman filter and based on (1) the preprocessed measurement data or the measurement data and (2) prediction data from a prediction model predicting a measurement associated with the measurement data, corrected measurement data, and providing the corrected measurement data based on the predicted measurement and the preprocessed measurement data.

A computer-implemented method and system uses a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas. The method and system: (A) use the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each training tumor-biological component pair, a corresponding biological component score; and (B) construct, based on the two atlases and the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on its gene expression data. The method and system may generate the map before using it. The method and system may apply the machine learning perceptron classifier to the input tumor's gene expression data to generate the tumor type of the input tumor.

A data storage medium includes a substrate. The data storage medium also includes an antifreeze layer coated on at least one surface of the substrate. The data storage medium further includes multiple storage containers located on the substrate. The multiple storage containers store different combinations of plant-based molecules representing data.

Methods and systems for security, authentication, tagging, and tracking using nucleic acid (e.g., deoxyribonucleic acid) molecules encoding information. Unique nucleic acid molecules are efficiently produced from pre-fabricated fragments to quickly produce libraries of nucleic acid molecules encoding encrypted or randomized information. Physical objects or artifacts can be tagged with libraries to authenticate the objects, grant access to secured assets or locations, or track the objects or entities. Chemical methods can be applied to verify authenticity, decrypt, or decode information stored in the libraries.

A method and server for optimizing hyperparameter tuples for training production-grade artificial intelligence (AI) models. For each one of the AI models, AI model features are extracted and, for the one AI model, an initial distribution of n hyperparameter tuplesis created considering the extracted AI model features therefor. A loop is repeated, until metric parameters are satisfied, comprising: evaluating latency from training the one AI model for each of the n hyperparameters tuples; evaluating model uncertainty from training the one AI model for each of the n hyperparameters tuples; for each of the n hyperparameters tuples, computing a blended quality measurement from the evaluated latency and evaluated model uncertainty; replacing m hyperparameter tuples having the worst blended quality measurements with m newly generated hyperparameter tuples. The metric parameters include one or more of a threshold value on model uncertainty and blended quality measurement gain between successive loops.

A method and server for optimizing hyperparameter tuples for training production-grade artificial intelligence (AI) models. For each one of the AI models, AI model features are extracted and, for the one AI model, an initial distribution of n hyperparameter tuplesis created considering the extracted AI model features therefor. A loop is repeated, until metric parameters are satisfied, comprising: evaluating latency from training the one AI model for each of the n hyperparameters tuples; evaluating model uncertainty from training the one AI model for each of the n hyperparameters tuples; for each of the n hyperparameters tuples, computing a blended quality measurement from the evaluated latency and evaluated model uncertainty; replacing m hyperparameter tuples having the worst blended quality measurements with m newly generated hyperparameter tuples. The metric parameters include one or more of a threshold value on model uncertainty and blended quality measurement gain between successive loops.

Methods, computer program products, and systems are presented. The methods include, for instance: identifying a training data set and defining a window for an initial beta value representing bias tolerated in formulating expectation conditional to each feature vector from the training data set. The conditional expectations are parallelly regularized by use of DNA computer. Amongst numerous combinations of candidate models, a best fit ensemble is produced as the machine learning model for predicting targeted outcomes based on inputs other than the training data set.

Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update. NMC updates the network, including weight and bias parameters, based on the update, by application of tensor-tensor operations.

The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the updated feature set.