Rishemjit Kaur

Human opinion dynamics: An inspiration to solve complex optimization problems

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.

Towards biological plausibility of electronic noses

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).

Bacteriorhodopsin–ZnO hybrid as a potential sensing element for low-temperature detection of ethanol vapour

Summary Zinc oxide (ZnO) and bacteriorhodopsin (bR) hybrid nanostructures were fabricated by immobilizing bR on ZnO thin films and ZnO nanorods. The morphological and spectroscopic analysis of the hybrid structures confirmed the ZnO thin film/nanorod growth and functional properties of bR. The photoactivity results of the bR protein further corroborated the sustainability of its charge transport property and biological activity. When exposed to ethanol vapour (reducing gas) at low temperature (70 °C), the fabricated sensing elements showed a significant increase in resistivity, as opposed to the conventional n-type behaviour of bare ZnO nanostructures. This work opens up avenues towards the fabrication of low temperature, photoactivated, nanomaterial–biomolecule hybrid gas sensors.

Quantifying moral foundations from various topics on Twitter conversations

Moral foundations theory explains variations in moral behavior using innate moral foundations: Care, Fairness, Ingroup, Authority, and Purity, along with experimental supports. However, little is known about the roles of and relationships between those foundations in everyday moral situations. To address these, we quantify moral foundations from a large amount of online conversations (tweets) about moral topics on the social media site Twitter. We measure moral loadings using latent semantic analysis of tweets related to topics on abortion, homosexuality, immigration, religion, and immorality in general, showing how the five moral foundations function in spontaneous conversations about moral violating situations. The results indicate that although the five foundations are mutually related, Purity is the most distinctive foundation and Care is the most dominant foundation in everyday conversations on immorality. Our study shows a new possibility of natural language processing and social big data for moral psychology.

Quality assessment of engine oil: An impedance spectroscopy based approach

A.C impedance spectroscopy is a very popular and decisive method to determine certain physicochemical properties of samples. In this work we use classification algorithms to identify the used or fresh class of engine oils, based on their impedance spectra. A proposed equivalent circuit, with multiple combination of resistors, capacitors and constant phase element (CPE), along with raw impedance data constituted the input feature matrix for the classifiers. Classifiers, based on Support Vector Machine (SVM) and Artificial Neural Network applied separately, were able to identify between used samples and fresh samples with an accuracy of about 98 to 100 percent. The qualitative performance of the method was defined by false positive rate, false negative rate, sensitivity rate and specificity rate.

MAI Mitigation in MC-CDMA Systems Using Social Impact Based Wireless Communication Algorithm

In this paper a novel optimization technique i.e. Social Impact based Wireless Communication Algorithm (SIWCA) has been applied on multi-carrier code division multiple access (MC-CDMA) communication systems to mitigate multiple access interference (MAI). MC-CDMA is being researched as an alternate technology for fourth generation (4G) as well as fifth generation (5G) mobile systems. MAI has been a major concern for the CDMA based systems. MAI increases the bit error rate in a MC-CDMA system, which in turn degrades the system performance. The SIWCA is based on the social impact theory of human behavior in the society. The proposed approach combines the social sciences with the communication technology. The simulation results show that SIWCA based MC-CDMA detector is capable of significantly reducing the MAI and gives a near-optimum performance. Further SIWCA is compared with popular optimization techniques like genetic algorithm (GA) and binary particle swarm optimization (PSO) for different parameters. Simulation results show that SIWCA converges fast and works with lesser number of control parameters as compared to GA and PSO.

Human Opinion Inspired Feature Selection Strategy for Predicting the Pleasantness of a Molecule

The identification of features responsible for smell of a molecule has been a long-standing challenge. We use cheminformatics and opinion dynamics based optimization algorithm to identify feature subsets of a molecule, which can predict how pleasant a molecule will smell. We have also compared it to standard feature selection techniques. The features identified reveal that three classes of features are primarily responsible for pleasantness. The work may open up some innovative inroads into feature identification and their physical understanding into the olfactory stimulus-percept problem.

Effects of Topological Variations on Opinion Dynamics Optimizer

Continuous opinion dynamics optimizer CODO is an algorithm based on human collective opinion formation process for solving continuous optimization problems. In this paper, we have studied the impact of topology and introduction of leaders in the society on the optimization performance of CODO. We have introduced three new variants of CODO and studied the efficacy of algorithms on several benchmark functions. Experimentation demonstrates that scale free CODO performs significantly better than all algorithms. Also, the role played by individuals with different degrees during the optimization process is studied.

Social Impact Theory-Based Node Placement Strategy for Wireless Sensor Networks

The network density, energy consumption, and connectivity are the most important design parameters for a self-organizing wireless sensor network. This paper presents a social impact theory-based multi-objective strategy for optimizing these parameters. The proposed strategy optimizes the clustering schemes and signal strengths along with the operational modes of the sensor nodes. The algorithm has been implemented in MATLAB using an open source social impact theory Optimization toolbox (http://mloss.org/software/view/457/). The suggested algorithm offers the achievement of optimal designs and satisfies the different design parameters.