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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 $\mathcal {S}$ -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.

This study aims to circumvent the operation of conducting laboratory tests of soil shear strength through a hybrid machine learning approach. The proposed approach integrates extreme learning machine (ELM) and particle swarm optimisation (PSO) with adaptive acceleration coefficients. Three hybrid ELMs, namely PSO optimised ELM with time-varying acceleration coefficients (ELM-TP), ELM optimised by improved PSO (ELM-IP), and ELM optimised by modified PSO (ELM-MP), have been established. Subsequently, the concept of mean PSO has been incorporated, and three additional hybrid models, namely ELM-TP integrated with mean PSO (ELM-TMP), ELM-IP integrated with mean PSO (ELM-IMP), and ELM-MP integrated with mean PSO (ELM-MMP), are constructed. The proposed concept is also used to construct six artificial neural network (ANN)-based hybrid models (i.e. ANN-TP, ANN-IP, ANN-MP, ANN-TMP, ANN-IMP, and ANN-MMP). Experimental results exhibit that the constructed ELM-IMP and ANN-IMP models can achieve the most desired accuracies in predicting the shear strength of soils.

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. [ABSTRACT FROM AUTHOR]

Although advanced algorithms are needed to enable increasingly autonomous civil aviation applications, there are limitations in assurance technologies, which must be addressed to gain trust in the performance of these algorithms. This gap emphasizes the need to guarantee safety by capturing performance boundaries, as these algorithms are integrated. Additionally, multiple similar algorithms might need to be executed sequentially or concurrently to accomplish a mission or provide guidance for safety-critical operations. The selection among algorithm functionalities is a complex and critical activity that needs to be systematically designed and analyzed before actual implementation. Toward this end, we discuss our proposed process, which includes formally modeling abstractions of the algorithms in an architectural framework, then identifying the key performance parameters, followed by verification of the composition of these algorithms with formal contracts based on assumptions and guarantees. Finally, to reduce the gap between design and implementation, an automated translation from the architectural model to source code has been developed, which is a Java-based outline of the implementation. We demonstrate our compositional approach in assuring the behavior of an autonomous aerial system via a collision avoidance case study with advanced algorithms to handle critical emerging situations.

Supplemental Digital Content is available in the text.Rosai-Dorfman disease (RDD) is an uncommon disorder, characterized by an atypical expansion of histiocytes which classically shows emperipolesis and immunoreactivity with S-100 protein. RDD affects the lymph nodes as well as extranodal sites; however, RDD of the breast is exceptionally rare. Herein, we describe the histopathologic features of 22 cases of RDD occurring in the breast, with an emphasis on the differential diagnosis. All cases were notable for an exuberant lymphocytic infiltrate with and without germinal center formation, and the majority (19/22) showed numerous plasma cells: 5 to 132/high-power field (HPF). IgG and IgG4 immunohistochemical stains were available for 13 cases; in no instance were criteria for IgG4-related sclerosing disease met, though in a single case the IgG4/IgG ratio was increased to 25%. Sclerosis was present in the majority of cases (18/22), and was frequently prominent. RDD cells showing emperipolesis were present in all cases (22/22), and ranged from rare (<1/50 HPF) to numerous (>50/50 HPF). Two of the cases in our series were initially misdiagnosed as inflammatory myofibroblastic tumor and plasma cell mastitis with granulomatous inflammation. As emperipolesis can be indistinct, the presence of stromal fibrosis and a prominent lymphoplasmacytic inflammatory infiltrate should prompt a careful search for the characteristic histiocytes, which can be aided by the use of S-100 immunohistochemistry.

• A novel intuitionistic hesitant fuzzy sets based image enhancement scheme is presented for low illuminated dronogram where the hesitant score is used as a new way to measure image uncertainty. • The proposed method initially separates a dronogram into the foreground/background areas based on a global threshold, and determine the membership functions by intuitionistic hesitant fuzzification approach via hyperbolic operations in membership modification. • Finally, a highly informative and improved dronogram is obtained by defuzzification. • Results of application on a drone database indicate the efficacy of the proposed method. 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. [ABSTRACT FROM AUTHOR]

Video shot boundary detection (SBD) or video cut detection is one of the fundamental processes of video-processing with respect to semantic understanding, contextual information accumulation, labeling, content-based information retrieval and many more applications, such as video surveillance and monitoring. In this work, the authors have proposed a generative-model based framework for detecting shot boundaries in between the frames of a video segment. To generate a model of shot-boundaries, the authors have applied the concepts of Support Vector Machine to estimate the distance between any two images, and then, have generated a Gaussian Mixture Model from the estimated distances. Next, a Bayesian Estimation process checks the presence of boundaries in between the images by exploiting the Gaussian Mixture-based boundary model. Further, the authors have used the principles of Compressive Sensing to reduce the overhead of boundary detection process without losing of important information.

Hyperspectral images contain large spectral information with an abundance of redundancy and a curse of dimensionality. Due to the absence of prior knowledge or availability of ground-truth data, clustering of these images becomes a herculean task. Hence, unsupervised cluster detection methods are more beneficial for utilising hyperspectral images in real-life scenarios. In this paper, six multilevel quantum inspired metaheuristics are proposed viz., Qubit Genetic Algorithm, Qutrit Genetic Algorithm, Qubit Multi-exemplar Particle Swarm Optimization Algorithm, Qutrit Multi-exemplar Particle Swarm Optimization Algorithm, Qubit Artificial Humming Bird Algorithm, and Qutrit Artificial Humming Bird Algorithm, for determining the optimal number of clusters in hyperspectral images automatically. Binary and ternary quantum versions of the algorithms are developed to enhance their exploration and exploitation capabilities. Simple algorithms for implementing quantum rotation gates are developed to bring diversity in the population without resorting to look-up tables. One of the main features of quantum gates is that they are reversible in nature. This property has been utilized for implementing quantum disaster operations. The application of a dynamic number of exemplars also enhances the performance of the Multi-exemplar Particle Swarm Optimization Algorithm. The six proposed algorithms are compared to the classical Genetic Algorithm, Multi-exemplar Particle Swarm Optimization Algorithm, and Artificial Humming Bird Algorithm. All the nine algorithms are applied on three hyperspectral image datasets viz., Pavia University, Indian Pines, and Xuzhou HYSPEX datasets. Statistical tests like mean, standard deviation, Kruskal Wallis test, and Tukey’s Post Hoc test are performed on all the nine algorithms to establish their efficiencies. Three cluster validity indices viz., Xie-Beni Index, Object-based Validation with densities, and Correlation Based Cluster Validity Index are used as the fitness function. The F, F’, and Qscores are used to compare the clustered images. The proposed algorithms are found to perform better in most of the cases when compared to their classical counterparts. It is also observed that the qutritversions of the algorithms are found to converge faster. They also provide the optimal number of clusters almost equivalent to the number of classes identified in the ground-truth image.

The classical techniques used for noisy object extraction recline that some a priori information concerning the noise characteristics is required during the extraction process. The proposed quantum version parallel self-organizing neural network (QVPSONN) architecture uses the quantum characteristics like superposition, coherence, decoherence, entanglement, etc. of quantum principle for its operation. The extraction of object achieved is better as well as the extraction time is reduced. QVPSONN architecture uses the phase shifting property of qubits to extract the objects from the noisy environment. At first the pure color input image is separated into three color components into the source layer. Then, the three color components are fed to the three parallel architectures of QMLSONNs for processing which are finally fused in the sink layer. Each of the processing layers of QVPSONN comprises qubit-based neurons. The weights between the network layers are demonstrated by single qubit rotation gates. Quantum measurement is applied for processing the information at the output layers, whereby the quantum states are destroyed using the decoherence property. When the system becomes stable, the sink layer fuses it and generates the output. Results of application on a synthetic image and a real-life reveal its efficacy.

Artificial Neural Network model and isotherm study were done to predict the removal efficiency of phenol. An inexpensive adsorbent was developed from orange peel ash (OPA) for effective uptake of phenol from aqueous solution. The influence of different experimental parameters (initial concentration, pH, adsorbents dose, contact time, stirring rate and temperature) on phenol uptake efficiency was evaluated. Phenol was adsorbed by the OPA up to maximum of 97.34 %. Adsorption of phenol on OPA correlated well with the Langmuir isotherm model, implying monolayer coverage of phenol onto the surface of the adsorbent. The maximum adsorption capacity was found to be 3.55 mg g−1at 303 K. Pseudo-second-order kinetic model provided a better correlation for the experimental data. Moreover, the activation energy of the adsorption process (Ea) was found to be −18.001 kJ mol−1indicating physorption nature of phenol onto OPA. A negative enthalpy (∆H°) value indicated that the adsorption process was exothermic. Again multi-layer Neural Network model was in very good agreement with the experimental results.

Studies show that supply chain structure is a key factor affecting information sharing. Business-to-business (B2B) e-hubs have fundamentally changed many companies’ supply chain structure, from a one-to-many to a many-to-many configuration. Traditional supply chains typically center around one company, which interacts with multiple suppliers or customers, forming a one-to-many structure. B2B e-hubs, on the contrary, usually connect many buyers and sellers together, without being dominated by a single company, thus forming a many-to-many configuration. Information sharing in traditional supply chains has been studied extensively, but little attention has been paid to the same in B2B e-hubs. In this study, the authors identified and examined five information sharing strategies in B2B e-hubs. Agent performances under different information sharing strategies were measured and analyzed using an agent-based e-hub model and practical implications were discussed.

This study demonstrated potential of genetic programming (GP) as a base classifier algorithm in building ensembles in the context of large-scale data classification. An ensemble built upon base classifiers that were trained with GP was found to significantly outperform its counterparts built upon base classifiers that were trained with decision tree and logistic regression. The superiority of GP ensemble was partly attributed to the higher diversity, both in terms of the functional form of as well as with respect to the variables defining the models, among the base classifiers upon which it was built on. Implications of GP as a useful tool in other data mining problems, such as feature selection, were also discussed. [ABSTRACT FROM AUTHOR]