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We characterize the intrinsic properties of any FRB using its redshift z , spectral index α and energy E 33 in units of |$10^{33} \, {\rm J}$| emitted across 2128–2848 MHz in the FRB's rest frame. Provided that z is inferred from the measured extra-galactic dispersion measure DM Ex, the fluence F of the observed event defines a track in (α, E 33) space which we refer to as the 'energy track'. Here, we consider the energy tracks for a sample of 254 non-repeating low dispersion measure FRBs from the CHIME/FRB catalogue-1, and use these to determine the conditional energy distribution, n (E 33∣α), i.e. the number of FRBs in the interval Δ E 33 given a value of α. For all values of α, we can identify two distinct energy ranges indicating that there are possibly two distinct FRB populations. At high energies, the distribution is well fitted by a modified Schechter function whose slope and characteristic energy both increase with α. At low energies, the number of FRBs are in excess of the predictions of the modified Schechter function indicating that we may have a distinctly different population of low-energy FRBs. We have checked that our main findings are reasonably robust to the assumptions regarding the Galactic Halo and Host galaxy contributions to the dispersion measure. [ABSTRACT FROM AUTHOR]

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits. [ABSTRACT FROM AUTHOR]

We consider a sample of 82 non-repeating fast radio bursts (FRBs) detected at Parkes, ASKAP, CHIME, and UTMOST, each of which operates over a different frequency range and has a different detection criterion. Using simulations, we perform a maximum-likelihood analysis to determine the FRB population model that best fits these data. Our analysis shows that models where the pulse scatter broadening increases moderately with redshift (z) are preferred to those where this increases very sharply or where scattering is absent. Further, models where the comoving event rate density is constant over z are preferred to those where it follows the cosmological star formation rate. Two models for the host dispersion measure (DMHost) distribution (a fixed and a random DMHost) are found to predict comparable results. We obtain the best-fitting parameter values |$\alpha =-1.53^{+0.29}_{-0.19}$|⁠ , |$\overline{E}_{33}=1.55^{+0.26}_{-0.22}$|⁠ , and γ = 0.77 ± 0.24. Here α is the spectral index, γ is the exponent of the Schechter luminosity function, and |$\overline{E}_{33}$| is the mean FRB energy in units of |$10^{33} \, {\rm J}$| across 2128–2848 MHz in the FRB rest frame. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is the study of human perception in any subject of practice. It retrieves data from datasets using natural language processing (NLP) methodologies and algorithms that are either regulation-based, blended, or rely on machine learning approaches. SA is garnering fame for its capacity to fit in a large chunk of data with user evaluations, uncover a trend, and come to a consensus derived from real facts rather than hypotheses established on a limited number of observations. The flexible nature of sentiment gathering has helped in playing a critical role in both commercial and research applications in the last few years. This study presents new sentiment analysis models based on Bidirectional Encoder Representations from Transformers (BERT) for both labeled and unlabeled datasets. The labeled datasets using supervised learning are modeled in a hybrid architecture of fine-tuned BERT and interval Type—2 fuzzy sets. The inclusion of interval Type-2 fuzzy logic for handling reluctance or inaccuracy in data shows commendable results for the labeled datasets. For the prediction of sentiments in unlabeled datasets, they are embedded through a BERT tokenizer with the help of a threshold and activation functions. The coupling of a multi-layer perceptron with the BERT parser substantially decreases the time and complexity compared to supervised learning. Both the models have been implemented on multiple datasets and have outperformed existing state-of-the-art techniques in this field. [ABSTRACT FROM AUTHOR]

This paper proposes an audience selection framework for online brand advertising based on user activities on social media platforms. It is one of the first studies to our knowledge that develops and analyzes implicit brand–brand networks for online brand advertising. This paper makes several contributions. We first extract and analyze implicit weighted brand–brand networks, representing interactions among users and brands, from a large dataset. We examine network properties and community structures and propose a framework combining text and network analyses to find target audiences. As a part of this framework, we develop a hierarchical community detection algorithm to identify a set of brands that are closely related to a specific brand. This latter brand is referred to as the “focal brand.” We also develop a global ranking algorithm to calculate brand influence and select influential brands from this set of closely related brands. This is then combined with sentiment analysis to identify target users from these selected brands. To process large-scale datasets and networks, we implement several MapReduce-based algorithms. Finally, we design a novel evaluation technique to test the effectiveness of our targeting framework. Experiments conducted with Facebook data show that our framework provides significant performance improvements in identifying target audiences for focal brands. [ABSTRACT FROM AUTHOR]

Assuming that Fast Radio Bursts (FRBs) are of extragalactic origin, we have developed a formalism to predict the FRB detection rate and the redshift distribution of the detected events for a telescope with given parameters. We have adopted FRB 110220, for which the emitted pulse energy is estimated to be E0 =5.4×1033 J, as the reference event. The formalism requires us to assume models for (a) pulse broadening due to scattering in the ionized intergalactic medium - we consider two different models for this, (b) the frequency spectrum of the emitted pulse - we consider a power-law model Ev αv -α with -5 ≤ α ≤ 5, and (c) the comoving number density of the FRB occurrence rate n(E, wi, z) - we ignore the z dependence and assume a fixed intrinsic pulse width wi = 1 ms for all the FRBs. The distribution of the emitted pulse energy E is modelled through (a) a delta function where all the FRBs have the same energy E = E0, and (b) a Schechter luminosity function where the energies have a spread around E0. The models are all normalized using the four FRBs detected by Thornton et al. Our model predictions for the Parkes telescope are all consistent with the inferred redshift distribution of the 14 FRBs detected there to date. We also find that scattering places an upper limit on the redshift of the FRBs detectable by a given telescope; for the Parkes telescope, this is z ∼ 2. Considering the upcoming Ooty Wide Field Array, we predict an FRB detection rate of ∼0.01 to ∼103 d-1. [ABSTRACT FROM AUTHOR]

Abstract: The parallel self-organizing neural network (PSONN) architecture uses bilevel sigmoidal activation functions for the purpose of extraction of embedded objects from pure color noisy perspectives. The process of extraction often involves an enhancement of the images under consideration. The network employs multilevel sigmoidal activation function to segment true color images. Both these activation functions are characterized by fixed thresholding parameters, which do not incorporate the underlying heterogeneity in the image intensity gamut. Methods for incorporating dynamic thresholding mechanisms into the thresholding characteristics of the PSONN architecture are investigated in this paper. We also propose a parallel bi-directional self-organizing neural network (PBDSONN) architecture to address the limitations of the PSONN architecture. Three constituent BDSONNs in the proposed architecture process color component information by embedded adaptive fuzzy context sensitive thresholding (CONSENT) mechanisms. A source layer feeds the BDSONNs with input color component information. Another sink layer fuses the processed color component information into resultant outputs. Comparative results of the quality of the extracted/segmented images indicate the efficacy of the proposed PBDSONN architecture over the PSONN architecture with fixed as well as dynamic thresholding mechanisms. [Copyright &y& Elsevier]

The conventional multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, thereby ignoring the heterogeneity of image information content. In this article, a novel approach for generating optimised class responses of the MUSIG activation function is proposed so that image content heterogeneity can be incorporated in the segmentation procedure. Four different types of objective function are used to measure the quality of the segmented images in the proposed genetic algorithm-based optimisation method. Results of segmentation of one synthetic and two real-life images by the proposed optimised MUSIG (OptiMUSIG) activation function with optimised class responses show better performances over the conventional MUSIG counterpart with equal and fixed responses. Comparative studies with the standard fuzzy c-means (FCM) algorithm, efficient in clustering of multidimensional data, also reveal better performances of the proposed function. [ABSTRACT FROM AUTHOR]

This paper presents the design, development and validation methodology of an agent-based computational model of the B2C electronic auction marketplace. It aims at a comprehensive understanding of the varied issues governing a B2C electronic auction, incorporating the behavior of all relevant agents such as the auctioneer, the consumer and the retailer; and the environment in which these agents operate and interact. In contrast with conventional methods, agent based modeling employs a bottom-up modeling approach where behaviors of individual agents and rules for their interaction, specified at the micro level, give rise to emergent macro level phenomenon. The development methodology should ensure that agent models are aligned with theory, current knowledge of the field and observed phenomena, and output validity of the model also needs to be ascertained. Beginning with a general introduction to agent based computational modeling, this paper formalizes this alignment and validation methodology and elaborates each step, noting the rationale and means for achieving these. The manner in which this process was used in modeling B2C auctions is then described. [ABSTRACT FROM AUTHOR]

Data analysts in direct marketing seek models to identify the most promising individuals to mail to and thus maximize returns from solicitations. A variety of criterion can be used to assess model performance, including response to or revenue generated from earlier solicitations. Given budgetary limitations, typically a fraction of the total customer database is selected for mailing. This depth-of-file that is to be mailed to provides potentially useful information that should be considered in model determination. This article presents a genetic algorithm-based approach for obtaining models in explicit consideration of this mailing depth. Issues related to overfitting, common in application of machine learning techniques, are examined, and experiments are based on a real-life data set. [ABSTRACT FROM AUTHOR]

Presents a complete generalization of the Vose genetic algorithm from binary to higher cardinality case. Reference made to Boolean AND and EXCLUSIVE-OR operators; Generalization of Walsh matrices with finite Fourier transforms for higher cardinality usage.

Of late, convolutional neural networks (CNNs) find great attention in hyperspectral image (HSI) classification since deep CNNs exhibit commendable performance for computer vision-related areas. CNNs have already proved to be very effective feature extractors, especially for the classification of large data sets composed of 2-D images. However, due to the existence of noisy or correlated spectral bands in the spectral domain and nonuniform pixels in the spatial neighborhood, HSI classification results are often degraded and unacceptable. However, the elementary CNN models often find intrinsic representation of pattern directly when employed to explore the HSI in the spectral–spatial domain. In this article, we design an end-to-end spectral–spatial squeeze-and-excitation (SE) residual bag-of-feature (S3EResBoF) learning framework for HSI classification that takes as input raw 3-D image cubes without engineering and builds a codebook representation of transform feature by motivating the feature maps facilitating classification by suppressing useless feature maps based on patterns present in the feature maps. To boost the classification performance and learn the joint spatial–spectral features, every residual block is connected to every other 3-D convolutional layer through an identity mapping followed by an SE block, thereby facilitating the rich gradients through backpropagation. Additionally, we introduce batch normalization on every convolutional layer (ConvBN) to regularize the convergence of the network and scale invariant BoF quantization for the measure of classification. The experiments conducted using three well-known HSI data sets and compared with the state-of-the-art classification methods reveal that S3EResBoF provides competitive performance in terms of both classification and computation time. [ABSTRACT FROM AUTHOR]

The massive number of research articles on the Web makes it troublesome for researchers to identify related works that could meet their preferences and interests. Consequently, various network representation learning-based models have been proposed to produce citation recommendations. Nevertheless, these models do not exploit semantic relations and contextual information between the objects of bibliographic papers' networks, which can result in inadequate citation recommendations. Moreover, existing citation recommendation methods face problems such as lack of personalization, cold-start, and network sparsity. To mitigate such problems and produce individualized citation recommendations, we propose a heterogeneous network embedding model that jointly learns node representations by exploiting semantics corresponding to the author, time, context, field of study, citations, and topics. Compared to baseline models, the results produced by the proposed model over the DBLP datasets prove 10% and 12% improvement on mean average precision (MAP) and normalized discounted cumulative gain (nDCG@10) metrics, respectively. Also, the effectiveness of our model is analyzed on the cold-start papers and network sparsity problems, where it gains 12% and 9% better MAP and recall@10 scores, respectively. [ABSTRACT FROM AUTHOR]

Image thresholding is well accepted and one of the most imperative practices to accomplish image segmentation. This has been widely studied over the past few decades. However, as the multi-level thresholding computationally takes more time when level increases, hence, in this article, quantum mechanism is used to propose six different quantum inspired meta-heuristic methods for performing multi-level thresholding faster. The proposed methods are Quantum Inspired Genetic Algorithm, Quantum Inspired Particle Swarm Optimization, Quantum Inspired Differential Evolution, Quantum Inspired Ant Colony Optimization, Quantum Inspired Simulated Annealing and Quantum Inspired Tabu Search. As a sequel to the proposed methods, we have also conducted experiments with the two-Stage multithreshold Otsu method, maximum tsallis entropy thresholding, the modified bacterial foraging algorithm, the classical particle swarm optimization and the classical genetic algorithm. The effectiveness of the proposed methods is demonstrated on fifteen images at the different level of thresholds quantitatively and visually. Thereafter, the results of six quantum meta-heuristic methods are considered to create consensus results. Finally, statistical test, called Friedman test, is conducted to judge the superiority of a method among them. Quantum Inspired Particle Swarm Optimization is found to be superior among the proposed six quantum meta-heuristic methods and the other five methods are used for comparison. A Friedman test again conducted between the Quantum Inspired Particle Swarm Optimization and all the other methods to justify the statistical superiority. Finally, the computational complexities of the proposed methods have been elucidated for the sake of finding out the time efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

• A Qutrit Genetic Algorithm is proposed for thresholding of hyperspectral images. • A new quantum mutation procedure is implemented to bring diversity in the offsprings. • Interactive Information method and BS-NET-Conv Network reduce the number of bands. • Modified Otsu Criterion and Masi entropy are used as fitness functions. • A quantum disaster operation prevents the population to get stuck into local optima. Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and S ø rensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency. [ABSTRACT FROM AUTHOR]

Parkinson's Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson's Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Weight Distribution. The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%. The presented model can be used not only for disease diagnosis but is also likely to find its applications in various other fields as well. [ABSTRACT FROM AUTHOR]

• Smoke detection and localization in both clear and hazy outdoor environments. • Using a lightweight CNN architecture called EfficientNet for smoke detection. • Employing DeepLabv3+ semantic segmentation architecture for smoke localization. • Pixel-wise annotation of a new benchmark dataset for smoke semantic segmentation. • Outperformed existing smoke detection and segmentation methods. Fire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings. [ABSTRACT FROM AUTHOR]