Showing papers by "Jadavpur University published in 2013"

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

Abstract: SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.

Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits.

Abstract: Circular RNAs are new players in regulation of post transcriptional gene expression. Animal genomes express many circular RNAs from diverse genomic locations. A recent study has validated a fairly large number of circular RNAs in human, mouse and nematode. Circular RNAs play a crucial role in fine tuning the level of miRNA mediated regulation of gene expression by sequestering the miRNAs. Their interaction with disease associated miRNAs indicates that circular RNAs are important for disease regulation. We studied the potential association of circular RNAs (circRNA) with human diseases in two different ways. First, the interactions of circRNAs with disease associated miRNAs were identified, following which the likelihood of a circRNA being associated with a disease was calculated. For the miRNAs associated with individual diseases, we constructed a network of predicted interactions between the miRNAs and protein coding, long noncoding and circular RNA genes. We carried out gene ontology (GO) enrichment analysis on the set of protein coding genes in the miRNA- circRNA interactome of individual diseases to check the enrichment of genes associated with particular biological processes. Second, disease associated SNPs were mapped on circRNA loci, and Argonaute (Ago) interaction sites on circular RNAs were identified. We compiled a database of disease-circRNA association in Circ2Traits ( http://gyanxet-beta.com/circdb/ ), the first comprehensive knowledgebase of potential association of circular RNAs with diseases in human.

Some case studies on application of "r(m)2" metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data.

Abstract: Quantitative structure-activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The r(m)(2) metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression-based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of r(m)(2). Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the r(m)(2) metrics has been introduced here. The present study reports that the web application can be easily used for computation of r(m)(2) metrics provided observed and QSAR-predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to r(m)(2) calculation.

Three dimensional Ag2O/TiO2 type-II (p-n) nanoheterojunctions for superior photocatalytic activity.

Abstract: Type-II p–n junction three-dimensional Ag2O/TiO2 microspheres have been fabricated by assembling p-type Ag2O nanoparticle on n-type TiO2 3D microsphere. Ag2O/TiO2 microsphere nanoheterojunctions were obtained by hydrothermal synthesis of TiO2 microspheres at 180 °C followed by photoreduction of AgNO3. The samples were carefully characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), field-emission scanning electron microscopy (FESEM), and energy dispersive X-ray analysis (EDX). The photocatalytic activity toward degradation of methyl orange (MO) aqueous solution under UV light was investigated. The result showed that type-II p–n nanoheterojunctions Ag2O/TiO2 significantly enhanced the photocatalytic degradation compared to n-type TiO2 microsphere. It was found that the photocatalytic degradation followed the pseudo first-order reaction model. In particular, heterostructure with molar ratio of TiO2 and AgNO3 of 4:1 exhibited best photocatalytic activity and the corresponding appar...

Luminescent S-doped carbon dots: an emergent architecture for multimodal applications

Abstract: A facile route has been developed to synthesise and isolate sulphur doped fluorescent carbon dots for the first time. Such carbogenic quantum dots exhibit a wide band gap of 4.43 eV with a high open circuit voltage (VOC) of 617 mV along with a fill factor (FF) as high as 37%, using phenyl-C60-butyric acid methyl ester (PCBM) as the electron transporting layer. Besides the wide band gap, which is useful in the fabrication of solar cells, sulphur modified carbon dots also exhibit a high fluorescence quantum yield of 11.8% without any additional surface passivation, producing a unique fluorescent probe for further applications. In addition, the particles have a strong tendency to interact with the surface of gold nanoparticles and produce a thin fluorescent layer over their surfaces. Moreover, as they are completely biocompatible in nature, the highly fluorescent S-doped carbon dots have a strong potential for use in bioimaging applications. Interestingly, owing to the presence of oxygen and sulphur functionality, the highly negatively charged particles can easily bind with positively charged DNA–PEI complexes, simply by mixing them, and after interaction with DNA, bright blue fluorescence has been observed under an excitation wavelength of 405 nm .

A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot

Abstract: This paper provides a new deterministic Q-learning with a presumed knowledge about the distance from the current state to both the next state and the goal. This knowledge is efficiently used to update the entries in the Q-table once only by utilizing four derived properties of the Q-learning, instead of repeatedly updating them like the classical Q-learning. Naturally, the proposed algorithm has an insignificantly small time complexity in comparison to its classical counterpart. Furthermore, the proposed algorithm stores the Q-value for the best possible action at a state and thus saves significant storage. Experiments undertaken on simulated maze and real platforms confirm that the Q-table obtained by the proposed Q-learning when used for the path-planning application of mobile robots outperforms both the classical and the extended Q-learning with respect to three metrics: traversal time, number of states traversed, and 90° turns required. The reduction in 90° turnings minimizes the energy consumption and thus has importance in the robotics literature.

Self-adaptive differential evolution for feature selection in hyperspectral image data

Abstract: Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Each pixel of an image is represented by a vector where the components of the vector constitute the reflectance value of the surface for each of the bands. The length of the vector is equal to the number of bands. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. Moreover, higher correlation among neighboring bands increases the redundancy among them. As a result, feature selection becomes very essential for reducing the dimensionality. In the proposed work, an attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Self-adaptive differential evolution (SADE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on three sets of data and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.

Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search

Abstract: Authentication is very important in validating a medical content in the domain of telemedicine; however, there are many challenges. Accurate verification is paramount, and any misuse of personal information may have serious consequences. Many authentication processes tried to design various methods to minimise such discrepancies. In this current work, we propose a new approach to design a robust biomedical content authentication system by embedding logo of the hospital within the electrocardiogram signal by means of both discrete wavelet transformation and cuckoo search CS. An adaptive meta-heuristic cuckoo search is used to find the optimal scaling factor settings for logo embedding. Results show that the proposed method can serve as a secure and accurate authentication system.

Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future.

Abstract: In order to protect the life of all creatures living in the environment, the toxicity arising from various hazardous chemicals must be controlled. This imposes a serious responsibility on different chemical, pharmaceutical, and other biological industries to produce less harmful chemicals. Among various international initiatives on harmful aspects of chemicals, the 'Green Chemistry' ideology appears to be one of the most highlighted concepts that focus on the use of eco-friendly chemicals. Ionic liquids are a comparatively new addition to the huge garrison of chemical compounds released from the industry. Extensive research on ionic liquids in the past decade has shown them to be highly useful chemicals with a good degree of thermal and chemical stability, appreciable task specificity and minimal environmental release resulting in a notion of 'green chemical'. However, studies have also shown that ionic liquids are not intrinsically non-toxic agents and can pose severe degree of toxicity as well as the risk of bioaccumulation depending upon their structural components. Moreover, ionic liquids possess issues of waste generation during synthesis as well as separation problems. Predictive quantitative structure-activity relationship (QSAR) models constitute a rational opportunity to explore the structural attributes of ionic liquids towards various physicochemical and toxicological endpoints and thereby leading to the design of environmentally more benevolent analogues with higher process selectivity. Such studies on ionic liquids have been less extensive compared to other industrial chemicals. The present review attempts to summarize different QSAR studies performed on these chemicals and also highlights the safety, health and environmental issues along with the application specificity on the dogma of 'green chemistry'.

Molecular architecture using novel types of non-covalent π-interactions involving aromatic neutrals, aromatic cations and π-anions

Abstract: A solid-state complex utilizing non-covalent interactions between two aromatic cations is synthesized and characterized. The X-ray study of the structure shows that the anion templated π+–π+ interactions are the major driving force in the crystal packing, while π+–π, π–π, π–anion and π+–anion interactions assist the overall stabilization of self-assembly. In addition, we also identify the cation-mediated non-covalent interaction between two π anions (π−–π− interaction). The interaction energies of the important driving forces (π+–π+, π+–π, π–anion, π+–anion, and π−–π− interactions) observed in the crystal structure are calculated using dispersion-corrected density functional theory (DFT-D).

A Wideband Minkowski Fractal Dielectric Resonator Antenna

Abstract: A CPW slot loop fed Minkowski shaped fractal dielectric resonator antenna is proposed. Self similar property of fractal geometry is utilized to bring higher order modes close together to realize a wide impedance bandwidth. A comparative study of the resonant frequency behavior between the well established fractal electrical boundary antennas and fractal magnetic boundary antennas is presented to provide insight into the functionality of the proposed antenna. The comparison highlights the fact that electrical boundary lowers resonance frequency, whereas fractal magnetic boundary increases the resonant frequency. Minkowski fractal is also compared with Koch fractal and Sierpinski curve geometries. It is observed that the Minkowski fractal DRA yields the widest impedance bandwidth along with stable gain amongst the three proposed geometries. The proposed antenna exhibits a fractional bandwidth of 64% (5.52 -10.72 GHz) and a maximum gain of 4.9 dBi.

A successive layer-by-layer assembly of supramolecular frameworks driven by a novel type of face-to-face π+–π+ interactions

Abstract: The solid-state complex [PTPH3](NO3)3·2(HNO3) (1) has been synthesized and characterized by X-ray studies, where PTPH3 is the triply protonated form of 4′-(4-pyridyl)-2,2′:6′,2′′-terpyridine (PTP). The solid-state structure of the complex reveals that the π+–π+ interactions are the major driving force in the crystal packing while π+–π, π–π and π–anion interactions assist the overall stabilization of self-assembly. Complex 1 exhibits two different π-stack layers, where layer 1 is generated through π+–π+ interactions and the mutual forces of π+–π+ and π+–π form layer 2. The interaction energies of the main driving forces (π+–π+, π+–π and π–anion interactions) observed in the crystal structure have been calculated using dispersion-corrected density functional theory (DFT-D). An analysis of the Hirshfeld surface of complex 1 shows the intermolecular interactions involved within the crystal structure and corresponding quantitative information are presented by fingerprint plots.

Export diversification, composition, and economic growth: Evidence from cross-country analysis

Abstract: We investigate the export-growth relationship at disaggregate levels – disaggregation both at the country level and at the level of exports – focusing on the diversification and the composition of exports of countries. In a sample of 65 countries for the period 1965–2005 the dynamic panel estimation reveals that both diversification and composition of exports are important determinants of economic growth after controlling for the impacts of other variables like lagged income, investment, and infrastructure. There is a critical level of export concentration beyond which increasing export specialization leads to higher growth. Below this critical level, diversification of exports matters for gross domestic product (GDP) growth. Growth of high technology exports also contributes tothe output growth; the relationship becomes stronger for countries that have share of manufacturing exports in their total exports greater than the world average. These results are robust even when the dataset isclassified in four ...

A Relativistic Model for Strange Quark Star

Abstract: We propose a relativistic model for strange quark stars within the framework of MIT Bag model. In our model, we assume that the highly compact strange stars are anisotropic in nature which is an expected feature in the ultra high density regime. We discuss various physical features of the model and show that the model satisfies all the regularity conditions. By estimating the value of the Bag constant for strange star candidates like 4U 1820-30, Her X-1 and SAX J 1808.4-3658, we show that a wire range of values of the Bag constant are possible for such stars though, in the case of 4U 1820-30, the estimated value of the Bag constant has been found to be very close to its currently acceptable range. Nevertheless, our results are in agreement with the recent CERN-SPS and RHIC data.

A Cluster-Based Differential Evolution Algorithm With External Archive for Optimization in Dynamic Environments

Abstract: This paper presents a Cluster-based Dynamic Differential Evolution with external Ar chive (CDDE_Ar) for global optimization in dynamic fitness landscape The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions The clusters are evolved separately using a standard differential evolution algorithm The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations Accordingly, the total population is redistributed into a new number of clusters In this way, a certain sharing of information occurs periodically during the optimization process The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers

Realization of an Adaptive Memetic Algorithm Using Differential Evolution and Q-Learning: A Case Study in Multirobot Path Planning

Abstract: Memetic algorithms (MAs) are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolutions. An adaptive MA (AMA) incorporates an adaptive selection of memes (units of cultural transmission) from a meme pool to improve the cultural characteristics of the individual member of a population-based search algorithm. This paper presents a novel approach to design an AMA by utilizing the composite benefits of differential evolution (DE) for global search and Q-learning for local refinement. Four variants of DE, including the currently best self-adaptive DE algorithm, have been used here to study the relative performance of the proposed AMA with respect to runtime, cost function evaluation, and accuracy (offset in cost function from the theoretical optimum after termination of the algorithm). Computer simulations performed on a well-known set of 25 benchmark functions reveal that incorporation of Q-learning in one popular and one outstanding variants of DE makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed AMA has been studied on a real-time multirobot path-planning problem. Experimental results obtained for both simulation and real frameworks indicate that the proposed algorithm-based path-planning scheme outperforms the real-coded genetic algorithm, particle swarm optimization, and DE, particularly its currently best version with respect two standard metrics defined in the literature.

General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

Abstract: Facial expressions of a person representing similar emotion are not always unique. Naturally, the facial features of a subject taken from different instances of the same emotion have wide variations. In the presence of two or more facial features, the variation of the attributes together makes the emotion recognition problem more complicated. This variation is the main source of uncertainty in the emotion recognition problem, which has been addressed here in two steps using type-2 fuzzy sets. First a type-2 fuzzy face space is constructed with the background knowledge of facial features of different subjects for different emotions. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face space. Both interval and general type-2 fuzzy sets (GT2FS) have been used separately to model the fuzzy face space. The interval type-2 fuzzy set (IT2FS) involves primary membership functions for m facial features obtained from n-subjects, each having l-instances of facial expressions for a given emotion. The GT2FS in addition to employing the primary membership functions mentioned above also involves the secondary memberships for individual primary membership curve, which has been obtained here by formulating and solving an optimization problem. The optimization problem here attempts to minimize the difference between two decoded signals: the first one being the type-1 defuzzification of the average primary membership functions obtained from the n-subjects, while the second one refers to the type-2 defuzzified signal for a given primary membership function with secondary memberships as unknown. The uncertainty management policy adopted using GT2FS has resulted in a classification accuracy of 98.333% in comparison to 91.667% obtained by its interval type-2 counterpart. A small improvement (approximately 2.5%) in classification accuracy by IT2FS has been attained by pre-processing measurements using the well-known interval approach.