This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.

In this article, quantum inspired incarnations of two swarm based meta-heuristic algorithms, namely, Crow Search Optimization Algorithm and Intelligent Crow Search Optimization Algorithm have been proposed for automatic clustering of colour images. The performance and effectiveness of the proposed algorithms have been judged by experimenting on 15 Berkeley images and five publicly available real life images of different sizes. The validity of the proposed algorithms has been justified with the help of four different cluster validity indices, namely, Pakhira Bandyopadhyay Maulik, I-index, Silhouette and CS-measure. Moreover, Sobol's sensitivity analysis has been performed to tune the parameters of the proposed algorithms. The experimental results prove the superiority of proposed algorithms with respect to optimal fitness, computational time, convergence rate, accuracy, robustness, t -test and Friedman test. Finally, the efficacy of the proposed algorithms has been proved with the help of quantitative evaluation of segmentation evaluation metrics.

Hyperspectral images contain a wide variety of information, varying from relatively large regions to smaller manmade buildings, roads and others. Automatic clustering of various regions in such images is a tedious task. A multilevel quantum inspired fractional order ant colony optimization algorithm is proposed in this paper for automatic clustering of hyperspectral images. Application of fractional order pheromone updation technique in the proposed algorithm produces more accurate results. Moreover, the quantum inspired version of the algorithm produces results faster than its classical counterpart. A new band fusion technique, applying principal component analysis and adaptive subspace decomposition, is successfully proposed for the pre-processing of hyperspectral images. Score Function is used as the fitness function and K-Harmonic Means is used to determine the clusters. The proposed algorithm is implemented on the Xuzhou HYSPEX dataset and compared with classical Ant Colony Optimization and fractional order Ant Colony Optimization algorithms. Furthermore, the performance of each method is validated by peak signal-to-noise ratio which clearly indicates better segmentation in the proposed algorithm. The Kruskal-Wallis test is also conducted along with box plot, which establishes that the proposed algorithm performs better when compared with other algorithms.

In the current era of automation, machines are constantly being channelized to provide accurate interpretations of what people express on social media. The human race nowadays is submerged in the idea of what and how people think and the decisions taken thereafter are mostly based on the drift of the masses on social platforms. This article provides a multifaceted insight into the evolution of sentiment analysis into the limelight through the sudden explosion of plethora of data on the internet. This article also addresses the process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks. The techniques of communalizing user data have also been surveyed in this article. Data, in its different forms, have also been analyzed and presented as a part of survey in this article. Other than this, the methods of evaluating sentiments have been studied, categorized, and compared, and the limitations exposed in the hope that this shall provide scope for better research in the future.

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.

The paper presents an efficient algorithm for image thresholding based on multi-level thresholding. The proposed algorithm employs an effective image thresholding approach as fitness function to compute the threshold. A novel qutrit based quantum algorithm is designed based on the lifestyle of real ants. The novel features of qutrit are incorporated in association with ant colony optimization in the proposed algorithm. The efficacy of the proposed algorithm is exhibited by comparing it with two other popular algorithms in reference with optimal threshold values at different levels, optimal fitness, their mean and standard deviation and the computational time. Moreover, an uniformity measure is introduced to judge the quality of thresholding among different participants at various levels. Furthermore, the Friedman test among all algorithms is performed to statistically establish the supremacy of the proposed algorithm. Finally, the efficacy of the proposed algorithm is visually established by presenting convergence curves for higher levels of thresholding for four test images.

A quantum-inspired self-supervised neural network framework titled Quantum-Inspired Optimized Bi-Directional Self-Organizing Neural Network (Opti-QIBDS Net) suitable for fully automated MR image segmentation is suggested in this article. The suggested Opti-QIBDS Net is characterized by Otsu's multi-class level thresholding scheme based optimized Quantum Inspired Multi-level Sigmoidal (Opti-QIMUSIG) activation function. The network layers of the Opti-QIBDS Net architecture are inter-connected through second order neighborhood based topology and constituted by quantum neurons. The intermediate and output layers of the Opti-QIBDS Net framework are inter-connected through counter propagation of quantum states and pixel intensities are self-organized in counter-propagation fashion obviating any external supervision. Quantum observation is carried out at the end to obtain the segmented tumor from the superposition of quantum states. The proposed optimized self-supervised network architecture has been tested on T1 CE-weighted MR images and found to be very efficient while compared with other supervised and unsupervised approaches.

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pixels. We have proposed a novel image enhancement scheme based on intuitionistic hesitant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the photographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.