Abstract: A method and system for emotion recognition and forecasting are disclosed. The method may include obtaining an audio data of a conversation involving a plurality of speakers and identifying a plurality of turns of the conversation from the plurality of utterances. The method may further include extracting audio embedding features from the plurality of turns, obtaining a plurality of text segments associated with the audio data, extracting text embedding features from the plurality of text segments, obtaining and concatenating speaker embedding features associated with the audio data, obtaining and concatenating a plurality of emotion features corresponding to the plurality of turns. The method further comprises executing a tree-based prediction model to predict emotion features of the plurality of speakers for a subsequent turn of the ongoing conversation based on the audio embedding features, text embedding features, the concatenated speaker embedding features, and the concatenated emotion features.

Abstract: A method and system for emotion recognition and forecasting are disclosed. The method may include obtaining an audio data of a conversation involving a plurality of speakers and identifying a plurality of turns of the conversation from the plurality of utterances. The method may further include extracting audio embedding features from the plurality of turns, obtaining a plurality of text segments associated with the audio data, extracting text embedding features from the plurality of text segments, obtaining and concatenating speaker embedding features associated with the audio data, obtaining and concatenating a plurality of emotion features corresponding to the plurality of turns. The method further comprises executing a tree-based prediction model to predict emotion features of the plurality of speakers for a subsequent turn of the ongoing conversation based on the audio embedding features, text embedding features, the concatenated speaker embedding features, and the concatenated emotion features.

Abstract: The present invention is an organic manure which is prepared by mixing or blending natural ingredients along with sufficient amount of nitrogen, phosphorous and potassium and other micro-nutrients. Moreover, this invention also relates to the process of preparing the above organic manure.

Abstract: System and method for data cleansing are disclosed. The method comprises receiving one or more data records pre-categorized into one or more categories. Identifying at least one concept associated with one or more data records, and grouping, the at least one concept associated with the one or more data records into a plurality of category lists based on the predefined category associated with each of the one or more data records. Determining, one or more intersection sets based on a comparison between each pair of the plurality of category lists, wherein each intersection set comprises a set of one or more common concepts associated with a pair of category lists. The method comprises replacing each of at least one common concept of the set of one or more common concepts associated with each intersection set by a category name based on an occurrence rate of the common concepts.

Abstract: In one embodiment, a method for transforming training data to improve data classification is disclosed. The method comprises extracting concepts from a training data set. The method comprises computing frequency of occurrence of each concept in each category and removing concepts from the data records when the frequency of occurrence of a concept in a category is less than a threshold frequency value. Further, the method comprises computing a percentage contribution of each concept of remaining concepts in each category upon removing the concepts and eliminating concepts, from the remaining concepts, contributing equally to each category based on the percentage contribution of each concept to provide a reformed training data set. Further, the method comprises appending a category name to a corresponding data record in the reformed training data set based on a normalized frequency of occurrence of the concept in a category to improve data classification.

Abstract: This disclosure relates to systems and method for creating concept maps using concept gravity matrix. The method includes extracting a plurality of n-grams from the text corpus; creating a gravity matrix based on a frequency of occurrence of each of the plurality of n-grams within the text corpus and word-distance amongst the plurality of n-grams; calculating a corpus gravity based on the gravity matrix; determining a concept gravity and a concept influence for each of the plurality of n-grams in the gravity matrix based on the corpus gravity, a row aggregate associated with each of the plurality of n-grams in the gravity matrix, and a column aggregate associated with each of the plurality of n-grams in the gravity matrix; and creating the concept map based on the concept gravity and the concept influence determined for each of the plurality of n-grams.

Abstract: System and method for data cleansing are disclosed. The method comprises receiving one or more data records pre-categorized into one or more categories. Identifying at least one concept associated with one or more data records, and grouping, the at least one concept associated with the one or more data records into a plurality of category lists based on the predefined category associated with each of the one or more data records. Determining, one or more intersection sets based on a comparison between each pair of the plurality of category lists, wherein each intersection set comprises a set of one or more common concepts associated with a pair of category lists. The method comprises replacing each of at least one common concept of the set of one or more common concepts associated with each intersection set by a category name based on an occurrence rate of the common concepts.

Abstract: In one embodiment, a method for transforming training data to improve data classification is disclosed. The method comprises extracting concepts from a training data set. The method comprises computing frequency of occurrence of each concept in each category and removing concepts from the data records when the frequency of occurrence of a concept in a category is less than a threshold frequency value. Further, the method comprises computing a percentage contribution of each concept of remaining concepts in each category upon removing the concepts and eliminating concepts, from the remaining concepts, contributing equally to each category based on the percentage contribution of each concept to provide a reformed training data set. Further, the method comprises appending a category name to a corresponding data record in the reformed training data set based on a normalized frequency of occurrence of the concept in a category to improve data classification.

Abstract: The present invention discloses a buried object detection system. The detection system has an acoustic emitter capable of generating a non-linear acoustic impulse or a continuous acoustic signal of variable amplitude and frequency. Sensors are deployed on an appropriate surface or surfaces of a granular medium, which are capable of detecting the backscattered and, if possible, forward scattered signals of the original impulse or wave from a buried inclusion or inclusions. The information received by the sensors may be transmitted to a computer for further manipulation and analysis.