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With the growing popularity of cryptocurrencies and their decentralized nature, the risk of fraudulent activities within these ecosystems has become a pressing concern. This research paper focuses on Ethereum fraud detection using a dataset specifically curated for this purpose. The methodology encompasses essential steps, including data cleaning, correlation analysis, data splitting, and exploratory data analysis to understand the data characteristics. Subsequently, self-optimized machine learning models are trained with the Pycaret library while addressing the class imbalance using SMOTENC (Synthetic Minority oversampling Technique for Nominal and Continuous Data), ADA-SYN (Adaptive Synthetic Algorithm), and K-Means-SMOTE techniques. The performance of the various models is evaluated on test and validation datasets using metrics such as accuracy, precision, recall, and AUC (Area Under Curve). The study reveals that the ensemble models, particularly CATBoost (Categorical Boost) and LGBM (Light Gradient Boost Method), show exceptional efficiency, with accuracy ranging from 97% to 98.42% after oversampling. Moreover, these models exhibit higher F1 scores and AUC values, indicating their potential to detect fraud effectively. The validation metrics also lie in the same range, demonstrating that the models do not suffer from over-fitting. The experiment demonstrates the promise of ensemble models in Ethereum fraud detection, paving the way for deploying robust fraud detection systems in crypto-currency ecosystems. The results show that the K-Means SMOTE oversampling technique has the highest classification accuracy levels of 98.42% with an AUC of 99.82%. • This work reduces the class imbalance in the Ethereum dataset. • The proposed work enhances the accuracy with oversampling techniques. • This work is a Trustworthy and Interpretable Fraud detection technique. • Multiple metrics are used for model evaluation. • Essential features related to the Ethereum frauds are explained using the Shap Explainer. [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]

Loan Facility is a profitable venture for the banking industry and can render great financial support to the beneficiary. The Global banking systems with secured private cloud are making the service reachable around the world around the clock. Loan acceptance and disbursal are governed by the protocol of the banks with the highest degree of privacy and integrity. As per the report of Experian, the loan acceptance rate of the banks has been reduced to 61%–70%, and it is further reduced to 50% post-pandemic since there is a huge financial setback and a higher rate of defaulters. The banks are not in a position to explain the reasoning behind the rejection since the rejection further diminishes the customers' credit scores. With the parallel improvements in Industry 5.0, futuristic banking could evolve around Non Fungible Tokens (NFT) integrated Explainable AI (XAI) framework which can interact with the customer through the human-machine interface in the meta-verse. For such a kind of system, the proposed work could be a driving application which provides explanations for the loan rejection, with the Random Forest integrated XAI framework that provides the reasons for acceptance and rejection of the loan. The proposed Random Forest-based approach rendered the highest accuracy, sensitivity and specificity of 0.998, 0.998, and 0.997, respectively. The LIME and SHAPELY Explainers provide explanations with local and global surrogates of various parameters on the features. • First-ever work on loan disbursement with XAI. • Models of XAI related to financial risk assessment have been discussed. • A recommendation and support system for banks and customers. • Helps customers to focus on credit score improvements. • False-positives and missing data are eliminated for better performance. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a hybrid architecture for developing a system combination model that works in three layers to achieve better translated outputs. In the first layer, we have various machine translation models (i.e. Neural Machine Translation (NMT), Statistical Machine Translation (SMT), etc.). In the second layer, the outputs of these models are combined to leverage the advantages of both the systems (i.e SMT and NMT systems) by using the statistical approach and neural-based approach. But each approach has some advantages and limitations. So, instead of selecting an individual combined system's output as the final one, we apply these outputs in the final layer to produce the target output by assigning appropriate preferences to SMT based and neural-based combinations. Though there are some techniques for system combination but no such approach exists which uses preferences from various system combination models (statistical and neural) for the purpose of better assembling. Empirical results show improved performance in the terms of translation accuracy. Our experiments on two benchmark datasets of English–Hindi and Hindi–English pairs show that the proposed model performs significantly better than the participating models. Apparently, the efficacy of proposed model is significantly better than the state-of-the art machine translation combination systems (6.10 and 4.69 BLEU points for English-to-Hindi, and Hindi-to-English, respectively). Graphical abstract Highlights • A hybrid architecture is proposed to assemble different system combination models. • The architecture is influenced by both statistical and neural network based system combination techniques. • The training datasets are prepared by using different types of MT systems. [ABSTRACT FROM AUTHOR]

Abstract Shot boundary detection (SBD) is the preliminary and most significant step in Content Based Video Retrieval (CBVR). As such the effectiveness of a CBVR system depends heavily on reliable detection of shot boundaries. In this work, a simple yet effective technique for amalgamating several distance features extracted from video frames has been proposed. The aim here is to develop a technique which is able to produce a better distance feature from the existing ones by hybridizing several distance metrics. In the proposed model, any number of distance features can be incorporated and fused together. The resultant feature is not only more robust but also immune to features which are inefficient. Robustness of the proposed method is tested by combining several low performing features with the more efficient ones. Several statistical amalgamation functions are also tested for determining the most efficient one in terms of F1 score. The power of vague sets has been harnessed to detect the shot boundaries effectively using the resultant distance feature. The proposed method is proved to be effective by means of the results obtained, which show that multiple feature amalgamation can lead to a hybrid distance feature which performs better than the best feature incorporated for SBD. The proposed technique is analyzed using ANOVA. A comparison with the other existing methods portray the efficacy of the proposed approach. This method can also be applied for other research problems where several features are to be fused together for producing superior results than the ones obtained by individual methods. Graphical abstract Highlights • A method for amalgamating several dissimilarity measures is proposed in this work. • Multiple feature amalgamation (fusion) is applied for detection of shot boundaries. • Fusion produces better results compared to the distance features taken individually. • The method is immune to low performing features included without apriori knowledge. • Tests on a diverse set of videos show encouraging results. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Introducing a new algorithm named chaotic-based crow search algorithm (CCSA). • Four variants of the CCSA are introduced and the best one is chosen. • The proposed CCSA algorithm is validated on fifteen FOPs. • The solution of the fractional formulation of the fractional economic, environmental power dispatch problem is investigated. • Results prove that the superiority of CCSA regarding the obtained powerful results. Abstract This paper presents a chaotic crow search algorithm (CCSA) for solving fractional optimization problems (FOPs). To refine the global convergence speed and enhance the exploration/exploitation tendencies, the proposed CCSA integrates chaos theory (CT) into the CSA. CT is introduced to tune the parameters of the standard CSA, yielding four variants, with the best chaotic variant being investigated. The performance of the proposed CCSA is validated on twenty well-known fractional benchmark problems. Moreover, it is validated on a fractional economic environmental power dispatch problem by attempting to minimize the ratio of total emissions to total fuel cost. Finally, the proposed CCSA is compared with the standard CSA, particle swarm optimization (PSO), firefly algorithm (FFA), dragonfly algorithm (DA) and grey wolf algorithm (GWA). Additionally, the efficiency of the proposed CCSA is justified using the non parametric Wilcoxon signed-rank test. The experimental results prove that the proposed CCSA outperforms other algorithms in terms of quality and reliability. [ABSTRACT FROM AUTHOR]

Data Mining is one of the most challenging tasks in a dynamic environment due to rapid growth of data with respect to time. Dimension reduction, the key process of relevant feature selection, is applied prior to extracting interesting patterns or information from large repositories of data. In a dynamic environment, newly generated group of data together with the information extracted from the previous data are analyzed to select the most relevant and important features of the entire data set. As a result, efficiency and acceptability of the incremental feature selection model increase in the field of data mining. In our paper, a group incremental feature selection algorithm is proposed using rough set theory based genetic algorithm for selecting the optimized and relevant feature subset, called reduct. The objective function of the genetic algorithm used for incremental feature selection is defined using the previously generated reduct and positive region of the target set, concepts of rough set theory. The method may be applied in a regular basis in the dynamic environment after small to moderate volume of data being added into the system and thus the computational time, the major issue of the genetic algorithm does not affect the proposed method. Experimental results on benchmark datasets demonstrate that the proposed method provides satisfactory results in terms of number of selected features, computation time and classification accuracies of various classifiers. [ABSTRACT FROM AUTHOR]

This paper aims at providing a multi-objective real-time scheduling algorithm suitable for scheduling of real-time tasks in multiprocessor system with multi-objective criteria. In this proposed Mo-QIGA, scheduling of multi-objective realtime tasks have been targeted for minimizing completion time and total tardiness of each real-time task simultaneously. The exploration of quantum computation in the suggested Mo-QIGA mimics the principles and basic concepts of quantum mechanics. Variable length chromosomes are used in Mo-QIGA and in order to exhibit full Hilbert hyperspace, qubits representation is preferred. In addition, Quantum rotation gate is employed to update the schedules and it obviates classical genetic operators. In order to obtain Pareto-optimal solutions, a random key distribution has been adopted to convert the qubits particles to valid schedule solutions. Moreover, permutation based trimming technique has been introduced for population diversification which leads to good quality schedules. The experiments have been carried out using number of processors varying with real-time tasks priority and processing time. It has been found that the suggested multi-objective quantum inspired genetic algorithm outperforms its classical counterpart in terms of scheduling accuracy and time in multi-objective sense. [ABSTRACT FROM AUTHOR]

This paper presents a content-based image retrieval (CBIR) system with applications in one general purpose and two face image databases using two MPEG-7 image descriptors. The proposed method uses several sophisticated fuzzy-rough feature selection methods and combines the results of these methods to obtain a prominent feature subset for image representation for a particular query. Next, fuzzy-rough upper approximation of the target set (relevant list of images) with respect to the entire database that is represented by the prominent feature subset, is computed for retrieval and ranking. The information table on which every feature selection method works is small in size. Main reasons of performance boost of the proposed method are twofold. One is efficient feature subsets selection. The other reason is the fuzzy-indiscernibility relation based fuzzy-rough framework for computing upper-approximation which supports the approximate equality or similarity sense of CBIR. Fuzzy-rough upper approximation possibly adds more similar images in the relevant list from boundary region to expand the relevant list. The effectiveness of the proposed method is supported by the comparative results obtained from several single dimensionality reduction method, several clustering based retrieval techniques and also tested for face image retrieval. [ABSTRACT FROM AUTHOR]

Thresholding is a commonly used simple and effective technique for image segmentation. The computational time in multi-level thresholding significantly increases with the level of computation because of exhaustive searching, adding to exponential growth of computational complexity. Hence, in this paper, the features of quantum computing are exploited to introduce four different quantum inspired meta-heuristic techniques to accelerate the execution of multi-level thresholding. The proposed techniques are Quantum Inspired Genetic Algorithm, Quantum Inspired Simulated Annealing, Quantum Inspired Differential Evolution and Quantum Inspired Particle Swarm Optimization. The effectiveness of the proposed techniques is exhibited in comparison with the backtracking search optimization algorithm, the composite DE method, the classical genetic algorithm, the classical simulated annealing, the classical differential evolution and the classical particle swarm optimization for ten real life true colour images. The experimental results are presented in terms of optimal threshold values for each primary colour component, the fitness value and the computational time (in seconds) at different levels. Thereafter, the quality of thresholding is judged in terms of the peak signal-to-noise ratio for each technique. Moreover, statistical test, referred to as Friedman test, and also median based estimation among all techniques, are conducted separately to judge the preeminence of a technique among them. Finally, the performance of each technique is visually judged from convergence plots for all test images, which affirms that the proposed quantum inspired particle swarm optimization technique outperforms other techniques. [ABSTRACT FROM AUTHOR]

This article concerns an efficient real-time task scheduling assisted by Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) in multiprocessor environment. Relying on concepts and the principles of quantum mechanics, HQIGA explores the computing power of quantum computation. To drive schedule toward better convergence, HQIGA operates using rotation gate for exploration of variable chromosomes described by qubits in Hilbert hyperspace. A fitness function associated with popularly known heuristic earliest deadline first (EDF) is employed and random key distribution is adopted to convert the qubits chromosomes to valid schedule solutions. In addition to this, permutation based trimming technique is applied to diversify the population which yields good quality schedules. To establish the effectiveness of the suggested HQIGA, it demonstrates using various number of real-time tasks and processors along with arbitrary processing time. Simulation result shows that HQIGA outperforms the classical genetic algorithm (CGA) and Hybrid Particle Swarm Optimization (HPSO) in terms of fitness values obtained using less number of generations and also it improves the scheduling time significantly. HQIGA is also tested separately with the heuristic Shortest Computation Time First (SCTF) technique to show the superiority of EDF over SCTF. [ABSTRACT FROM AUTHOR]

Deep Convolutional Neural Network (CNN)-based image classification systems are often susceptible to noise interruption, i.e. , minor image noise may significantly impact the outcome. On the contrary, classical Spiking Neural Network (SNN) is known for handling noisy data due to the stochastic and temporal behaviour of the spiking neuron signals. However, it is not always feasible to train the weights of the classical SNN due to stochastic and non-differentiable spiking signals. Recent applications of quantum machine learning to stochastic modelling have predicted that quantum computing would prove to be a significant advantage, hence catapulting the study of quantum and neuromorphic computing to a new level of development. Motivated by these observations, this paper introduces a shallow hybrid classical–quantum spiking feedforward neural network referred to as Spiking Quantum Neural Network (SQNN) for dealing with a robust image classification task in the presence of noise and adversarial attacks. The proposed SQNN offers the inherent capabilities of processing unforeseen noisy test images due to its spatial and temporal information. An efficient variational quantum circuit training with the help of a standard back-propagation algorithm obviates the classical Spiking Time-Dependent Plasticity (STDP) and Spike-Prop algorithms, which are often found inefficient in training the feedforward SNN. The proposed SQNN is extensively tested and benchmarked on the PennyLane Quantum Simulator. Experimental results show that the proposed SQNN model supersedes the feedforward SNN (SFNN), Random Quantum Neural Networks (RQNN), AlexNet and ResNet-18 on unseen test images with added noise from the FashionMNIST, MNIST, KMNIST, CIFAR10, and ImageNet datasets. [Display omitted] • A shallow classical–quantum spiking feedforward neural network is introduced. • The Spiking Quantum Neural Network (SQNN) processes unforeseen noisy images. • A variational quantum circuit training with standard back-propagation is used. • SQNN run on PennyLane Quantum Simulator outperforms the classical counterparts. [ABSTRACT FROM AUTHOR]

Segmentation of magnetic resonance (MR) images plays an important role in the medical science or clinical research. In this article, an application of a genetic algorithm (GA) based segmentation algorithm is presented for automatic grouping of unlabeled pixels of the MR images into different homogeneous clusters. Before the segmentation, the information about the optimal number of segments as well as the underlying pixel distribution of an image is not required in this method. The centroid of different segments is demarcated as active/inactive centroid by the fuzzy intercluster hostility index. After that, the test images are segmented by the selected active centroids. The optimal number of segments and their respective centroids are determined by this method. A performance comparison is manifested between the fuzzy intercluster hostility index based GA method and the well-known automatic clustering using differential evolution (ACDE) algorithm and one genetic algorithm based non-automatic algorithm with the help of two real life MR images. The comparison depicted the superiority of the GA based automatic image segmentation method with the help of fuzzy intercluster hostility index over other two algorithms. [ABSTRACT FROM AUTHOR]

With the crucial growth of technology, data security over the network and internet has achieved immense of prominence today and achieving good security is always a talk of a good security method being in place. Therefore, there is need of better security method with better efficiency in order to increase the security and authenticity and to efficiently decrease computational complexity. Although there are many symmetric key algorithms, we proposed a content-based algorithm, which follows the Symmetric key cryptography method. This is an algorithm implementing binary addition operation, a circular bit shifting operation and folding method and as symmetric key cryptography needs the transmission of the secret key along with the ciphered text through the network, a deep concern has given to make the key secure. [ABSTRACT FROM AUTHOR]

In recent years, gene expression analysis has become crucial in studying microarray and provides several techniques related to computational biology and bioinformatics. This article introduces a novel Intuitionistic fuzzy set under rough multigranulation approximation based feature extraction from microarray gene expression dataset. The proposed model identifies cancer mediating human biomarkers using an Intuitionistic fuzzy soft set based similarity measure. Firstly, Intuitionistic fuzzy rough reduct is devised in the microarray datasets preceded by an entropy based pre-processing step. Intuitionistic fuzzy rough reduct produces the feature sets from the microarray datasets through roughness and accuracy measures that are differentially expressed in a cancerous state. Rough multigranulation approximations reduce the dimensions of the microarray datasets. Thereafter, a weighted similarity measure is designed using the intuitionistic fuzzy soft set for the classification of biomarkers having significant expression patterns from the normal state to the carcinogenic state. The proposed method is demonstrated on six microarray datasets and is validated with different performance metrics resulting in better effectiveness than the state-of-the-art methods. [Display omitted] • Intuitionistic fuzzy rough reduct is aimed for feature selection from microarray. • Initial preprocessing of microarray is made by entropy filtering. • The soft set based IFSSM classification model selects cancer responsible biomarkers. • Parameterization tool builds lower and upper approximations reducing the features. • The rough set deals with boundary regions precisely for fast convergence. • Biomarkers are validated with gene bank, NCBI, biochemical pathway databases. [ABSTRACT FROM AUTHOR]

Removal of miscible hazardous materials from aqueous solutions is an alarming problem for the environmental scientists. Several linear and nonlinear regression models like Langmuir, Freundlich, D–R, Tempkin isotherm models are in vogue for determining the adsorbing capacity of standard adsorbents used for this purpose. In this article, we propose a novel quantum inspired backpropagation multilayer perceptron (QBMLP) based on quantum gates (single qubit rotation gates and two qubit controlled-not gates) for the prediction of this adsorption behavior exhibited by calcareous soil oftentimes used in adsorbing miscible iron from aqueous solutions. The backpropagation learning formulae for the proposed QBMLP architecture has also been generalized for multiple number of layers in both field homogeneous and field heterogeneous configurations characterized by three standard activations, viz., sigmoid , tanh and tan 1.5h functions. Applications of the efficiency of the proposed QBMLP over the regression models are demonstrated with regards to the prediction behavior of the adsorption of iron by calcareous soil from an aqueous solution with effect to various characteristic adsorbent parameters. The adsorption process is considered to be a physical one since the activation energy ( E A ) of ferrous ion adsorption is 9.469 kJ mol −1 due to Arrhenius. Moreover, the thermodynamic parameters of Gibb's free energy ( G 0 ), enthalpy ( H 0 ) and entropy ( S 0 ) values indicate it be spontaneous. Results indicate that QBMLP predicts the adsorption behavior of calcareous soil to a very closer extent thereby obviating the need for further regression/experimental analysis. Comparison with the performance of a similar classical multilayer perceptron (MLP) architecture also reveals the prediction and time efficiency of the proposed QBMLP architecture. [ABSTRACT FROM AUTHOR]

Several classical techniques have evolved over the years for the purpose of denoising binary images. But the main disadvantages of these classical techniques lie in that an a priori information regarding the noise characteristics is required during the extraction process. Among the intelligent techniques in vogue, the multilayer self organizing neural network (MLSONN) architecture is suitable for binary image preprocessing tasks. In this article, we propose a quantum version of the MLSONN architecture. Similar to the MLSONN architecture, the proposed quantum multilayer self organizing neural network (QMLSONN) architecture comprises three processing layers viz., input, hidden and output layers. The different layers contains qubit based neurons. Single qubit rotation gates are designated as the network layer interconnection weights. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a quantum backpropagation algorithm. Results of application of the proposed QMLSONN are demonstrated on a synthetic and a real life binary image with varying degrees of Gaussian and uniform noise. A comparative study with the results obtained with the MLSONN architecture and the supervised Hopfield network reveals that the QMLSONN outperforms the MLSONN and the Hopfield network in terms of the computation time. [ABSTRACT FROM AUTHOR]

From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Therefore, this paper constructs a fuzzy classifier, which aims to detect infected subjects by observing and analyzing the CT images of suspected patients. Firstly, a deep learning algorithm is used to extract the low-level features of CT images in the COVID-CT dataset. Subsequently, we analyze the extracted feature information with attribute reduction algorithm to obtain features with high recognition. Then, some key features are selected as the input for the fuzzy diagnosis model to the training model. Finally, several images in the dataset are used as the test set to test the trained fuzzy classifier. The obtained accuracy rate is 94.2%, and the F1-score is 93.8%. Experimental results show that, compared with the deep learning diagnosis methods widely used in medical image analysis, the proposed fuzzy model improves the accuracy and efficiency of diagnosis, which consequently helps to curb the spread of COVID-19. • Proposal of a deep fuzzy model for diagnosing COVID-19 using CT images. • Design of deep learning framework for extracting CT image features. • Attribute reduction algorithm is used to filter the features and extract the most expressed features. • The extracted features were used to train the fuzzy diagnosis model and optimize the model performance. • The training time and parameters of the model are less, so it has certain clinical value. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of gray-level images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter. [Copyright &y& Elsevier]

Abstract: Segmentation of the different feature based data in a dataset is a challenging proposition in the image processing community. There exist different techniques to solve this problem satisfactorily. A color image is an example of three-dimensional dataset and it consists of a collection of three primary color intensity features. In this article, we focus on the segmentation of true color test images, based on all possible combination of color intensity features. A multilevel sigmoidal (MUSIG) activation function that is applied in the self-organizing neural network architecture is quite efficient enough to segment multilevel gray level intensity images. The function uses equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function for the self-organizing neural network architecture can be generated with the optimized class responses from the image content and can be used to effectively segment multilevel gray level intensity images as well. This article proposes a parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with the optimized class responses for the individual features with a parallel self-organizing neural network architecture to segment true color images. The optimized class responses are generated in parallel using a genetic algorithm based optimization technique. A standard objective function is applied to measure the quality of the segmented images in the proposed genetic algorithm-based optimization method. Results of segmentation of two real life true color images by the ParaOptiMUSIG activation function show better performances over those obtained with a conventional non-optimized MUSIG activation applied separately on the color gamut. [Copyright &y& Elsevier]

Abstract: The pragmatic realism of the high dimensionality incurs limitations in many pattern recognition arena such as text classification,data mining, information retrieval and face recognition. The unsupervised PCA ante up no attention to the class labels of the existing training data. LDA is not stable due to the small sample size problem and but corroborate best directions when each class has a Gaussian density with a common covariance matrix. But it can flop if the class densities are more general and interpreted class separability in between-class-matrices are inadequate. Maximum Margin Criterion (MMC) having lower computational cost, is more efficient than LDA for calculating the discriminant vectors barring the computation for inverse of within-class-scatter matrix. But traditional MMC disregards the discriminative information within the local structure of samples and performance is depended on choosing of a coefficient. In this paper we delineate the locality of data points by counting a distances among data points considering the supervised knowledge. We have computed the entire scatter matrix in Laplacian graph embedded space and finally the produced stastically uncorrelated discriminant vectors reduces redundancy among the extracted features and there is no constant to be chosen. Our experiment with Reauter dataset recommends this algorithm is more efficient than LDA, MMC and it manifests similar or sometimes better result than other locality based algorithm like LPP and LSDA. [Copyright &y& Elsevier]

Abstract: Neural network based image segmentation techniques primarily focus on the selection of appropriate thresholding points in the image feature space. Research initiatives in this direction aim at addressing this problem of effective threshold selection for activation functions. Multilevel activation functions resort to fixed and uniform thresholding mechanisms. These functions assume homogeneity of the image information content. In this paper, we propose a collection of adaptive thresholding approaches to multilevel activation functions. The proposed thresholding mechanisms incorporate the image context information in the thresholding process. Applications of these mechanisms are demonstrated on the segmentation of real life multilevel intensity images using a self-supervised multilayer self-organizing neural network (MLSONN) and a supervised pyramidal neural network (PyraNet). We also present a bi-directional self-organizing neural network (BDSONN) architecture suitable for multilevel image segmentation. The architecture uses an embedded adaptive thresholding mechanism to a characteristic multilevel activation function. The segmentation efficiencies of the thresholding mechanisms evaluated using four unsupervised measures of merit, are reported for the three neural network architectures considered. [Copyright &y& Elsevier]

Inventions – concepts, devices, procedures – are often created by networks of interacting agents in which the agents can be individuals (as in a scientific discipline) or they can themselves be collectives (as in firms interacting in a market). Different collectives create and invent at different rates. It is plausible that the rate of invention is jointly determined by properties of the agents (e.g., their cognitive capacity) and by properties of the network of interactions (e.g., the density of the communication links), but little is known about such two-level interactions. We present an agent-based model of social creativity in which the individual agent captures key features of the human cognitive architecture derived from cognitive psychology, and the interactions are modeled by agents exchanging partial results of their symbolic processing of task information. We investigated the effect of agent and network properties on rates of invention and diffusion in the network via systematic parameter variations. Simulation runs show, among other results, that (a) the simulation exhibits network effects, i.e., the model captures the beneficial effect of collaboration; (b) the density of connections produces diminishing returns in term of the benefits on the invention rate; and (c) limits on the cognitive capacity of the individual agents have the counterintuitive consequence of focusing their efforts. Limitations and relations to other computer simulation models of creative collectives are discussed. [Copyright &y& Elsevier]

Abstract: A time efficient technique for real-time tracking of high-speed objects in a video sequence is presented in this article. The technique is primarily based on the segmentation of the optical flow field computed between the successive image frames of a video sequence, followed by the tracking of a detected point of interest (POI) within the segmented flow field. In the initial phase of the technique, the optical flow field between the first two successive image frames acquired from a video sequence, is computed. A fuzzy hostility index indicative of the degree of coherence of the moving objects in the image frames, is used to segment the optical flow field. This yields different coherent regions of interest (ROIs) in the segmented flow field. A POI is then detected in the different ROIs obtained. Tracking of the moving object is then carried out by computing the flow fields between predefined ROIs in the neighborhood of the detected POI in the subsequent image frames. Since the selected ROIs are smaller than the image frames, a fair amount of reduction in the time required for the computation of the optical flow field is achieved, thereby facilitating real-time operation. An application of the proposed technique is demonstrated on three video sequences of high-speed flying fighter aircrafts. [Copyright &y& Elsevier]

A Hyperspectral Image (HSI) is a data cube consisting of hundreds of spatial images. Each captured spatial band is an image at a particular wavelength. Thresholding of these images is itself a tedious task. Two procedures, viz., Qubit Fractional Order Particle Swarm Optimization and Qutrit Fractional Order Particle Swarm Optimization are proposed in this paper for HSI thresholding. The Improved Subspace Decomposition Algorithm, Principal Component Analysis, and a Band Selection Convolutional Neural Network are used in the preprocessing stage for band reduction or informative band selection. For optimal segmentation of the HSI, modified Otsu's criterion, Masi entropy and Tsallis entropy are used. A new method for quantum disaster operation is implemented to prevent the algorithm from getting stuck into local optima. The implementations are carried out on three well known datasets viz., the Indian Pines, the Pavia University and the Xuzhou HYSPEX. The proposed methods are compared with state-of-the-art methods viz., Particle Swarm Optimization (PSO), Ant Colony Optimization, Darwinian Particle Swarm Optimization, Fractional Order Particle Swarm Optimization, Exponential Decay Weight PSO and Heterogeneous Comprehensive Learning PSO concerning the optimal thresholds, best fitness value, computational time, mean and standard deviation of fitness values. Furthermore, the performance of each method is validated with Peak signal-to-noise ratio and Sørensen–Dice Similarity Index. The Kruskal–Wallis test, a statistical significance test, is conducted to establish the superiority in favor of the proposed methods. The proposed algorithms are also implemented on some benchmark functions and real life images to establish their universality. [Display omitted] • Qubit and Qutrit Fractional Order Particle Swarm Optimization are proposed. • ISD algorithm, PCA , BS-Net-Conv and SC CNN networks are used for band reduction. • Modified Otsu, Masi and Tsallis entropy criteria are used as objective functions. • A new quantum disaster operation is also introduced. • Three well-known datasets are used for the experiments. [ABSTRACT FROM AUTHOR]

This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering. • Development of three novel ELM-based PSO models using adaptive and time varying accelerations coefficients. • Development of high-performance AI-based models in predicting CBR using real-life data. • Introduction of Accuracy Matrix for graphical assessment of performance indices. • Determination of best performing model using Uncertainty analysis and statistical testing. • Experimental validation of models using separate laboratory tests. [ABSTRACT FROM AUTHOR]

Most of the image preprocessing techniques by existing neighborhood neural networks, suffer from the problem of false classification of the image features. This is mainly due to the redundancy in the interconnectivity patterns of the networks. The larger number of interconnections in these networks implies a larger network complexity as well. A fuzzy set-theoretic pruning algorithm to refine the interconnection pattern of neighborhood neural network architectures, is presented. The algorithm is primarily focussed on the judicious selection of the participating neurons of the network topology. Efficiency of the pruning algorithm is demonstrated with the segmentation and extraction of synthetic and real life images. The universality of the algorithm is evident from its application in the binary, multilevel and color domains. [ABSTRACT FROM AUTHOR]

COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%. • This paper reveals that related tweets failed to guide people on COVID-19 pandemic. • This study analyzes two types of tweets gathered during the pandemic times. • The research demonstrates that no useful words are found in WordCloud or word frequency in tweets. • Claims are validated by a proposed deep learning classifier model yielding accuracy up to 81%. • A designed Gaussian membership based fuzzy rule base correctly identifies sentiments from tweets. [ABSTRACT FROM AUTHOR]

Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. • CALOFCM is proposed for automatic hepatic lesion segmentation from CT scans. • ALO is employed to guide FCM for optimal cluster centroids with more accurate result. • CALOFCM increases stability, reduces sensibility and gets best cluster centroids. • CALOFCM is validated on a group of patients and compared with other techniques. • CALOFCM has the potential to help medical experts in their decision making. [ABSTRACT FROM AUTHOR]

Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods. • Proposed a new enhancement scheme based on the intuitionistic fuzzy soft sets (IFSSs) for colonoscopy images. • The intuitionistic fuzzy image (IFM) is constructed by optimizing the intuitionistic fuzzy hesitant entropy (IHFE). • The intuitionistic fuzzy soft matrix (IFSM) and a fuzzy soft score (FSS) are measured using fuzzy soft set (FSS) with local features. • A contrast intensification operator adopts the fuzzy soft score and hesitant entropy to find the fittest membership functions (MFs). • The proposed method outperforms state-of-the-art methods while obtaining reasonable enhancement outcomes for colonoscopy imaging. [ABSTRACT FROM AUTHOR]

The classical self-supervised neural network architectures suffer from slow convergence problem and incorporation of quantum computing in classical self-supervised networks is a potential solution towards it. In this article, a fully self-supervised novel quantum-inspired neural network model referred to as Quantum-Inspired Self-Supervised Network (QIS-Net) is proposed and tailored for fully automatic segmentation of brain MR images to obviate the challenges faced by deeply supervised Convolutional Neural Network (CNN) architectures. The proposed QIS-Net architecture is composed of three layers of quantum neuron (input, intermediate and output) expressed as qbits. The intermediate and output layers of the QIS-Net architecture are inter-linked through bi-directional propagation of quantum states, wherein the image pixel intensities (quantum bits) are self-organized in between these two layers without any external supervision or training. Quantum observation allows to obtain the true output once the superimposed quantum states interact with the external environment. The proposed self-supervised quantum-inspired network model has been tailored for and tested on Dynamic Susceptibility Contrast (DSC) brain MR images from Nature data sets for detecting complete tumor and reported promising accuracy and reasonable dice similarity scores in comparison with the unsupervised Fuzzy C-Means clustering, self-trained QIBDS Net, Opti-QIBDS Net, deeply supervised U-Net and Fully Convolutional Neural Networks (FCNNs). • A fully self-supervised novel Quantum-Inspired Self-Supervised Network (QISNet) is proposed. • The proposed QIS-Net architecture is composed of three layers of quantum neuron (input, intermediate and output) expressed as qubits. • The intermediate and output layers of the QIS-Net architecture are inter-linked through bi-directional propagation of quantum states. • Quantum observation allows to obtain the true output once the superimposed quantum states interact with the external environment. [ABSTRACT FROM AUTHOR]

This paper is intended to identify the optimal number of clusters automatically from an image dataset using some quantum behaved nature inspired meta-heuristic algorithms. Due to the lack of sufficient information, it is difficult to identify the appropriate number of clusters from a dataset, which has enthused the researchers to solve the problem of automatic clustering and to open up a new era of cluster analysis with the help of several natures inspired meta-heuristic algorithms. In this paper, three quantum inspired meta-heuristic techniques, viz., Quantum Inspired Particle Swarm Optimization (QIPSO), Quantum Inspired Spider Monkey Optimization (QISMO) and Quantum Inspired Ageist Spider Monkey Optimization (QIASMO), have been proposed. A comparison has been outlined between the quantum inspired algorithms with their corresponding classical counterparts. The efficiency of the quantum inspired algorithms has been established over their corresponding classical counterparts with regards to fitness, mean, standard deviation, standard errors of fitness, convergence curves (for benchmarked mathematical functions) and computational time. Finally, the results of two statistical superiority tests, viz., t- test and Friedman test have been provided to prove the superiority of the proposed methods. The superiority of the proposed methods has been established on five publicly available real life image datasets, five Berkeley image datasets of different dimensions and four benchmark mathematical functions both visually and quantitatively. • It is difficult to identify the appropriate number of clusters from a dataset. • We propose three quantum inspired meta-heuristics for automatic determination of number of clusters. • The proposed algorithms perform better than their classical counterparts. • Statistical superiority tests also prove the superiority of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and effective texture recognition method that uses a new class of jet texton learning. In this approach, first a Jet space representation of the image is derived from a set of derivative of Gaussian (DtGs) filter responses upto 2nd order (R 6), so called local jet vector (Ljv), which satisfies the scale space properties, where the combinations of local jets preserve the intrinsic local structure of the image in a hierarchical way and are invariant to image translation, rotation and scaling. Next, the jet textons dictionary is learned using K-means clustering algorithm from DtGs responses, followed by a contrast Weber law normalization pre-processing step. Finally, the feature distribution of jet texton is considered as a model which is utilized to classify texture using a non-parametric nearest regularized subspace (Nrs) classifier. Extensive experiments on three large and well-known benchmark database for texture classification like KTH-TIPS, Brodatz and CUReT show that the proposed method achieves state-of-the-art performance, especially when the number of available training samples is limited. The source code of complete system is made publicly available at https://github.com/swalpa/JetTexton. • This paper presents a efficient jet texton learning for texture classification. • The jet texton dictionary is learned using K -means clustering algorithm. • The jet texton is invariant to image scaling, translation, and rotation. • The proposed method achieves state-of-the-art performance on benchmark databases. [ABSTRACT FROM AUTHOR]

Image enhancement algorithms are commonly used to increase the contrast and visual quality of low-dose x-ray images. This paper proposes an automated enhancement method using soft fuzzy sets with a new decision-making scheme based on Dempster-Shafer theory of evidence for the visual interpretation of pneumonia malformation in low-dose x-ray images, called as XEFSDS. The XEFSDS model first generates an original source x-ray image into a complementary image, then each original and complement image is applied to the characterized image object and background areas of fuzzy space. The S-function is utilized to define fuzzy soft sets for the classification of gray level ambiguity in both images, and hence a decision criterion via Dempster-Shafer approach and fuzzy interval has been adapted to discriminate uncertainties on the pixel intensity and the spatial information. Modified membership grade operations have been performed on each object/background area, and Werner's AND/OR operator (an aggregation operator) has been utilized to build a new membership function from two modified membership functions. Finally, an enhanced image is obtained from the new membership function via defuzzification. Experiments on different pneumonia X-ray images demonstrate that the XEFSDS scheme produces better results than the existing methods. To show the advantages of the XEFSDS scheme, we have executed a segmentation based examination on enhanced image for the detection of pneumonia malformation as well as abnormal lobe (lobar pneumonia) or bronchopneumonia. [ABSTRACT FROM AUTHOR]