Abstract: The disclosure relates generally to methods and systems for generating an optimized planting schedule of a crop to overcome storage capabilities. Conventional techniques in the art are limited in dealing with actual planting schedule of the crops in accordance with the storage capacities, thus leading to suboptimal harvest cycles that fail to meet the optimal storage requirements. In accordance with the present disclosure, the optimization model makes use of the cumulative maturity value (CMV) data for each day of the planting period and the harvest period of the crop, and optimizes the planning associated with planting of crops for a set of farms so that interval between harvest period is minimized and the end-of-harvest produce volumes meet certain thresholds to facilitate storage of all procurement without wastage and as per the market demand.

Abstract: Statistical pattern recognition relies on substantial amount of annotated samples for better learning and learning is insufficient in low resource scenarios. Creating annotated databases itself is a challenging task, requires lot of effort and cost, which may not always be feasible. Such challenges are addressed by the present disclosure by generating synthetic samples through automatic transformation using Deep Autoencoders (DAE). An autoencoder is trained using all possible combination of pairs between a plurality of classes that could be formed from a limited number of handful samples in a low resource database, and then the DAE is used to generate new samples when one class samples are given as input to the autoencoder. Again, the system of the present disclosure can be configured to generate number of training samples as required. Also, the deep autoencoder can be dynamically configured to meet requirements.

Abstract: The disclosure presents herein a method to train a classifier in a machine learning using more than one simultaneous sample to address class imbalance problem in any discriminative classifier. A modified representation of the training dataset is obtained by simultaneously considering features based representations of more than one sample. A modification to an architecture of a classifier is needed into handling the modified date representation of the more than one samples. The modification of the classifier directs same number of units in the input layer as to accept the plurality of simultaneous samples in the training dataset. The output layer will consist of units equal to twice the considered number of classes in the classification task, therefore, the output layer herein will have four units for two-class classification task. The disclosure herein can be implemented to resolve the problem of learning from low resourced data.

Abstract: Statistical pattern recognition relies on substantial amount of annotated samples for better learning and learning is insufficient in low resource scenarios. Creating annotated databases itself is a challenging task, requires lot of effort and cost, which may not always be feasible. Such challenges are addressed by the present disclosure by generating synthetic samples through automatic transformation using Deep Autoencoders (DAE). An autoencoder is trained using all possible combination of pairs between a plurality of classes that could be formed from a limited number of handful samples in a low resource database, and then the DAE is used to generate new samples when one class samples are given as input to the autoencoder. Again, the system of the present disclosure can be configured to generate number of training samples as required. Also, the deep autoencoder can be dynamically configured to meet requirements.

Abstract: The disclosure presents herein a method to train a classifier in a machine learning using more than one simultaneous sample to address class imbalance problem in any discriminative classifier. A modified representation of the training dataset is obtained by simultaneously considering features based representations of more than one sample. A modification to an architecture of a classifier is needed into handling the modified date representation of the more than one samples. The modification of the classifier directs same number of units in the input layer as to accept the plurality of simultaneous samples in the training dataset. The output layer will consist of units equal to twice the considered number of classes in the classification task, therefore, the output layer herein will have four units for two-class classification task. The disclosure herein can be implemented to resolve the problem of learning from low resourced data.