Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

A Fuzzy Interval Valued Fusion Technique for Multi- Modal 3D Face Recognition

Soodamani, R & Vimal, V (2016), Vehicle Detection for Traffic Flow Analysis, ICCST2016, Paper presented at the IEEE International Carnahan Conference on Security Technology, 24-27 October 2016, Orlando, Florida. This is the Accepted Manuscript version. The Version of Record is available online at IEEE Explore, doi: https://doi.org/10.1109/CCST.2016.7815709.This paper proposes a fuzzy interval valued multicriteria decision making (MCDM) technique that aggregates information from multi-modal feature sets during decision making in a 3D face recognition system. In this paper, an interval valued fuzzy TOPSIS technique is applied to a 3D face recognition system that is benchmarked against a set of databases. Such a system is shown to be useful in decision making when the choice of alternatives of the feature sets is combinatorial and complex.Final Accepted Versio

Global Research Performance on the Design and Applications of Type-2 Fuzzy Logic Systems: A Bibliometric Analysis

There has been a significant contribution to scientific literature in the design and applications of Type-2 fuzzy logic systems (T2FLS). The T2FLSs found applications in many aspects of our daily lives, such as engineering, pure science, medicine and social sciences. The online web of science was searched to identify the 100 most frequently cited papers published on the design and application of T2FLS from 1980 to 2016. The articles were analyzed based on authorship, source title, country of origin, institution, document type, web of science category, and year of publication. The correlation between the average citation per year (ACY) and the total citation (TC) was analyzed. It was found that there is a strong relationship between the ACY and TC (r = 0.91643, P<0.01), based on the papers consider in this research.  The “Type -2 fuzzy sets made simple” authored by Mendel and John (2002), published in IEEE Transactions on Fuzzy Systems received the highest TC as well as the ACY. The future trend in this research domain was also analyzed. The present analysis may serve as a guide for selecting qualitative literature especially to the beginners in the field of T2FLS

Explainable parts-based concept modeling and reasoning

State-of-the-art artificial intelligence (AI) learning algorithms heavily rely on deep learning methods that exploit correlation between inputs and outputs. While effective, these methods typically provide little insight to the reasoning process used by the machine, which makes it difficult for human users to understand the process, trust the decisions made by the system, and control emergent behaviors in the system. One method to fix this is eXplainable AI (XAI), which aims to create algorithms that perform well while also providing explanations to users about the reasoning process to mitigate the problems outlined above. In this thesis, I focus on advancing the research around XAI techniques by introducing systems that provide explanations through the use of partsbased concept modeling and reasoning. Instead of correlating input to output, I correlate input to sub-parts or features of the overall concept being learned by the system. These features are used to model and reason about a concept using an explicitly defined structure. These structures provide explanations to the user by nature of how they are defined. Specifically, I introduce a shallow and deep Adaptive Neuro-Fuzzy Inference System (ANFIS) that can reason in noisy and uncertain contexts. ANFIS provides explanations in the form of learned rules that combine features to determine the overall output concept. I apply this system to real geospatial parts-based reasoning problems and evaluate the performance and explainability of the algorithm. I discover some drawbacks to the ANFIS system as traditionally defined due to dead and diminishing gradients. This leads me to focus on how to model parts-based concepts and their inherent uncertainty in other ways, namely through Spatially Attributed Relation Graphs (SARGs). I incorporate human feedback to refine the machine learning of concepts using SARGs. Finally, I present future directions for research to build on the progress presented in this thesis.Includes bibliographical references

Review of Intelligent Control Systems with Robotics

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domai

An investigation of a new social networks contact suggestion based on face recognition algorithm

Automated comparison of faces in the photographs is a well established discipline. The main aim of this paper is to describe an approach whereby face recognition can be used in suggestion of a new contacts. The new contact suggestion is a common technique used across all main social networks. Our approach uses a freely available face comparison called "Betaface" together with our automated processig of the user´s Facebook profile. The research´s main point of interest is the comparison of friend´s facial images in a social network itself, how to process such a great amount of photos and what additional sources of data should be used. In this approach we used our automated processing algorithm Betaface in the social network Facebook and for the additional data, the Flickr social network was used. The results and their quality are discussed at the end