A quantum mutation-based backtracking search algorithm

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.

     

Rough-fuzzy collaborative clustering.

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.     

Satellite image segmentation with Shadowed C-Means

Satellite images often require segmentation in the presence of uncertainty, caused due to factors like environmental conditions, poor resolution and poor illumination. Since any subsequent image analysis depends on the quality of such segmentation, one has to obtain an efficient algorithm for the purpose. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper we map the newly developed shadowed clustering algorithm to the problem of segmenting remotely sensed images.It is observed that shadowed clustering can efficiently handle overlapping among segments while modeling uncertainty among the boundaries. Unlike rough clustering, here the choice of user-defined parameters is fully eliminated. The number of segments is automatically optimized in terms of validity indices. The algorithm is robust in the presence of outliers. The superiority of the system is demonstrated in segmenting a synthetic image, along with land cover types from the Indian Remote Sensing (IRS) images of the cities of Mumbai and Kolkata and the SPOT image around Kolkata. The algorithm is found to efficiently and accurately extract the different homogeneous regions in the presence of uncertainty. The results are analyzed both qualitatively and quantitatively.     

Annealing-Induced Morphological Changes in Nanocrystalline Quantum Dots and Their Impact on Charge Transport Properties

The effects of thermal annealing on the morphological and photoconductive properties of cadmium selenide quantum dots coated with zinc sulfide are studied. The results of transmission electron microscopy with in situ annealing show a number of events taking place simultaneously, including aggregation of dots, changes in the size and shape distribution, and reduction in interdot separation. Transient absorption results indicate that there is a small redshift of the spectrum. There is a shortening of the absorption decay lifetimes due to annealing. Higher photocurrents are measured in the annealed compared with unannealed dots at room temperature.     

LSER analysis of reactive extraction of Zn and Cu with bis (2-ethylhexyl) phosphate (HDEHP) in six different diluents

An equilibrium study on the reactive extraction of Zn and Cu from aqueous solution using bis(2-ethylhexyl) phosphate (HDEHP) in six different diluents including n-hexane, n-heptane, benzene, toluene, chloroform and dichloromethane (DCM) was carried out. Additionally, the extraction equilibrium results were correlated with a linear solvation energy relationship (LSER) model. The extraction efficiency of HDEHP in terms of distribution coefficient was found to vary in the order of diluents as DCM> chloroform> toluene> benzene> n-heptane> n-hexane. The highest value of the distribution coefficient, K_D, was observed with DCM as 21.55 (Zn) and 18.97 (Cu). The experimental data were correlated with the LSER model, and a close resemblance was observed between them.     

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?     

A social network approach to trust management in VANETs

In the past few years, vehicular ad hoc networks(VANETs) was studied extensively by researchers. VANETs is a type of P2P network, though it has some distinct characters (fast moving, short lived connection etc.). In this paper, we present several limitations of current trust management schemes in VANETs and propose ways to counter them. We first review several trust management techniques in VANETs and argue that the ephemeral nature of VANETs render them useless in practical situations. We identify that the problem of information cascading and oversampling, which commonly arise in social networks, also adversely affects trust management schemes in VANETs. To the best of our knowledge, we are the first to introduce information cascading and oversampling to VANETs. We show that simple voting for decision making leads to oversampling and gives incorrect results in VANETs. To overcome this problem, we propose a novel voting scheme. In our scheme, each vehicle has different voting weight according to its distance from the event. The vehicle which is more closer to the event possesses higher weight. Simulations show that our proposed algorithm performs better than simple voting, increasing the correctness of voting.     

Mapping the research landscape of Covid-19 from social sciences perspective: a bibliometric analysis

Scientometrics - COVID-19 has emerged as a widely researched topic and the academia has taken interest in the effects of COVID-19 in various sectors of human life and society. Most of the...