Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

School of Artificial Intelligence and Data Science (AIDE)

The real-world decision making often involves a comparison of uncertain systems or alternatives based on fuzzy evaluations. The concept of fuzzy entropy is quite useful in such situations. However, fuzzy entropy and the conventional probabilistic entropy differ in their semantics. This article critically examines the existing fuzzy entropy functions and redefine them to bring them closer to the probabilistic entropy. More specifically, new variants of the extant Luca and Termini, and Pal and Pal fuzzy entropy functions are proposed. The proposed fuzzy entropy functions are extended for the probabilistic-fuzzy uncertainty, commonly observed in the real world. A real application is included to demonstrate the usefulness of the proposed entropy functions in decision making applications. © 1993-2012 IEEE.

IEEE Transactions on Fuzzy Systems

Bridging the Gap between Probabilistic and Fuzzy Entropy

A novel uncertainty representation framework is introduced based on the inter-linkage between the inherent fuzziness and the agent’s confusion in its representation. The measure of fuzziness and this confusion is considered to be directly related to the lack of distinction between membership and non-membership grades. We term the proposed structure as confidence fuzzy set (CFS). It is further generalized as generalized CFS, quasi CFS and interval-valued CFS to take into consideration the DM’s individualistic bias in the representation of the underlying fuzziness. The operations on CFSs are investigated. The usefulness of CFS in multi-criteria decision making is discussed, and a real application in supplier selection is included. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

Representing uncertainty about fuzzy membership grade

In multi attribute decision making (MADM), the choice of a decision-maker (DM) is often the result of an interplay between the DM's unique attitude, the utilities (specific to the DM) for the attributes, and the degree of positive (or negative) interaction among the attributes. Based on this conglomeration of information, we introduce a few choice models to give the probabilities for different possible choices of a DM. More specifically, the conventional multinomial logit (MNL) model is extended to consider the interaction among the attributes, and also the DM's unique attitudinal character. The usefulness of the proposed models is illustrated in a real world application. © 2019

Information Sciences

Logit choice models for interactive attributes

Background: Functional integration or connectivity in brain is directional, non-linear as well as variable in time-lagged dependence. Deep neural networks (DNN) have become an indispensable tool everywhere, by learning higher levels of abstract and complex patterns from raw data. However, in neuroscientific community they generally work as black-boxes, leading to the explanation of results difficult and less intuitive. We aim to propose a brain-connectivity measure based on an explainable NN (xNN) approach. New Method: We build a NN-based predictor for regression problem. Since we aim to determine the contribution/relevance of past data-point from one region i in the prediction of current data-point from another region j, i.e. the higher-order connectivity between two brain-regions, we employ layer-wise relevance propagation (Bach et al., 2015) (LRP, a method for explaining DNN predictions), which has not been done before to the best of our knowledge. Specifically, we propose a novel score depending on weights as a quantitative measure of connectivity, called as relative relevance score (xNN-RRS). The RRS is an intuitive and transparent score. We provide an interpretation of the trained NN weights with-respect-to the brain-connectivity. Results: Face validity of our approach is demonstrated with experiments on simulated data, over existing methods. We also demonstrate construct validity of xNN-RRS in a resting-state fMRI experiment. Comparison: Our approach shows superior performance, in terms of accuracy and computational complexity, over existing state-of-the-art methods for brain-connectivity estimation. Conclusion: The proposed method is promising to serve as a first post-hoc explainable NN-approach for brain-connectivity analysis in clinical applications. © 2019 Elsevier B.V.

Journal of Neuroscience Methods

Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach

This paper examines the predictors of immunization coverages of children across Indian states and evaluates the role of Universal Immunization Program (UIP) - a comprehensive policy of the Indian government in that light. Employing Machine Learning methods such as LASSO and hierarchical LASSO, we find that not the UIP expenditure by itself, but health infrastructure turns out to be a robust predictor of immunization coverage. The policy prescription that follows from our study is that the immunization program should focus on promoting the required health infrastructure in addition to monitoring the usage of funds closely for facilitating effective usage of the money. We also scrutinize performances of `BIMARU', states that are considered traditionally underperforming states in terms of health and education.

World University root node

Economics Bulletin

India's Universal Immunization Program: a lesson from Machine Learning

The recent information set theory provides a useful mechanism to represent an agent's perceived information values. However, often a decision-maker (DM) considers multiple evaluations for the same information source value. To this end, we extend the recent information set as hesitant information set (HIS). It gives the multiple perceived information values, corresponding to an information source value. In the context of multi-attribute decision making, HIS represents a set of different possible subjective utilities that an agent may perceive as an evaluation of an alternative-attribute pair. The basic operations, and properties of HIS are investigated. A few information measures based on HIS are presented. Besides many illustrative examples, a real application in group multi attribute decision making problem is included. © 2018 Elsevier B.V.

Applied Soft Computing Journal

Hesitant information sets and application in group decision making

An information source value is perceived differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the notion of relations in SIS is devised. The significance of the proposed SIS in aiding multi criteria decision making is illustrated through real case-studies. © 2019 Wiley Periodicals, Inc.

International Journal of Intelligent Systems

Soft information set for multicriteria decision making

An entropy-based method is presented to model a decision-maker's (DM's) subjective utility for a criterion value. The proposed method considers distribution of all the values that the criterion takes for the given set of alternatives. Based on the utility so modeled, and and the DM's attitudinal character, a multi criteria decision aiding (MCDA) approach is developed to find the best alternative. The proposed method and the approach are applied in a real car selection case-study. © 2018 Elsevier Inc.

Decision aiding model with entropy-based subjective utility

A new data structure for the representation of an agent’s assessment of an information source value, along with the associated confidence degree is presented. The proposed structure is termed as confidence information set (CIS). It gives multiple information values, as perceived, corresponding to an information source value, along with the associated confidence degree. The usefulness of the proposed CIS in multi criteria decision making is shown through illustrative examples. © 2019 IADIS Press. All rights reserved.

Proceedings of the 12th IADIS International Conference Information Systems 2019, IS 2019

Confidence information sets

Visual dual-stream theory posits that two distinct neural pathways of specific functional significance originate from primary visual areas and reach the inferior temporal (ventral) and posterior parietal areas (dorsal). However, there are several unresolved questions concerning the fundamental aspects of this theory. For example, is the functional dissociation between ventral and dorsal stream driven by features in input stimuli or is it driven by categorical differences between visuoperceptual and visuomotor functions? Is the dual stream rigid or flexible? What is the nature of the interactions between the two streams? We addressed these questions using fMRI recordings on healthy human volunteers and employing stimuli and tasks that can tease out the divergence between visuoperceptual and visuomotor variants of dual-stream theory. fMRI scans were repeated after seven practice sessions that were conducted in a non-MRI environment to investigate the effects of neuroplasticity. Brain activation analysis supports an input-based functional dissociation and existence of context-dependent neuroplasticity in dual-stream areas. Intriguingly, premotor cortex activation was observed in the position perception task and distributed deactivated regions were observed in all perception tasks, thus warranting a network-level analysis. Dynamic causal modeling analysis incorporating activated and deactivated brain areas during perception tasks indicates that the brain dynamics during visual perception and actions could be interpreted within the framework of predictive coding. Effectively, the network-level findings point toward the existence of more intricate context-driven functional networks selective of “what” and “where” information rather than segregated streams of processing along ventral and dorsal brain regions. © 2020 Massachusetts Institute of Technology.