公开(公告)号：US20240096492A1

公开(公告)日：2024-03-21

申请号：US18367546

申请日：2023-09-13

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Trisrota DEB ,  Ishan SAHU ,  Sai Chander RACHA ,  Sundeep KHANDELWAL ,  Arpan PAL ,  Utpal GARAIN ,  Soumadeep SAHA

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: The present invention relates to the field of evaluating clinical diagnostic models. Conventional metrics does not consider context dependent clinical principles and is unable to capture critically important features that ought to be present in a diagnostic model. Thus, present disclosure provides a method and system for evaluating clinical efficacy of multi-label multi-class computational diagnostic models. Diagnosis for a given dataset of diagnostic samples is obtained from the diagnostic model which is then classified as wrong, missed, over or right diagnosis, based on which a first penalty is calculated. A second penalty is calculated for each diagnostic sample using a contradiction matrix. The first and second penalties are summed up to compute a pre-score for each diagnostic sample. Finally, the diagnostic model is evaluated using a metric that is based on sum of pre-scores, and scores from a perfect and a null multi-label multi-class computational diagnostic model.

公开(公告)号：US20190205778A1

公开(公告)日：2019-07-04

申请号：US16179771

申请日：2018-11-02

Applicant: Tata Consultancy Services Limited

Inventor： Ishan SAHU ,  Snehasis BANERJEE ,  Tanushyam CHATTOPADHYAY ,  Arpan PAL ,  Utpal GARAIN

IPC: G06N7/00 ,  G06N99/00 ,  G06K9/62 ,  G06F17/18

CPC classification number: G06F17/148

Abstract: Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks. The traditional systems and methods provide for selecting a mother wavelet and signal classification using some traditional techniques and methods but none them provide for selecting an optimal mother wavelet to facilitate machine learning tasks. Embodiments of the present disclosure provide for obtaining an optimal mother wavelet to facilitate machine learning tasks by computing values of energy and entropy based upon labelled datasets and a probable set of mother wavelets, computing values of centroids and standard deviations based upon the values of energy and entropy, computing a set of distance values and normalizing the set of distance values and obtaining the optimal mother wavelet based upon the set of distance values for performing a wavelet transform and further facilitating machine learning tasks by classifying or regressing, a new set of signal classes, corresponding to a new set of signals.

公开(公告)号：US20240143630A1

公开(公告)日：2024-05-02

申请号：US18383930

申请日：2023-10-26

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Arpan PAL ,  Soumadeep SAHA ,  Utpal GARAIN

IPC: G06F16/28

CPC classification number: G06F16/285

Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model. The disclosed method is used for electrocardiogram classification, shape classification and so on.