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The concept of transfer learning has received a great deal of concern and interest throughout the last decade. Selecting an ideal representational framework for instances of various domains to minimize the divergence among source and target domains is a fundamental research challenge in representative transfer learning. The domain adaptation approach is designed to learn more robust or higher-level features, required in transfer learning. This paper presents a novel transfer learning framework that employs a marginal probability-based domain adaptation methodology followed by a deep autoencoder. The proposed frame adapts the source and target domain by plummeting distribution deviation between the features of both domains. Further, we adopt the deep neural network process to transfer learning and suggest a supervised learning algorithm based on encoding and decoding layer architecture. Moreover, we have proposed two different variants of the transfer learning techniques for classification, which are termed as (i) Domain adapted transfer learning with deep autoencoder-1 (D-TLDA-1) using the linear regression and (ii) Domain adapted transfer learning with deep autoencoder-2 (D-TLDA-2) using softmax regression. Simulations have been conducted with two popular real-world datasets: ImageNet datasets for image classification problem and 20_Newsgroups datasets for text classification problem. Experimental findings have established and the resulting improvements in accuracy measure of classification shows the supremacy of the proposed D-TLDA framework over prominent state-of-the-art machine learning and transfer learning approaches. [ABSTRACT FROM AUTHOR]

The vendor managed inventory (VMI) is the most commonly known marketing and distribution methodology among the service provider and the retailer in a supply chain environment. The VMI framework's critical feature is to satisfy the demand of the products or items by the supplier rapidly generated from the retailers with the number of orders (order quantities). Due to the business's changing conditions, necessities of the particular product, production cycle, and manufacturing expenses, the VMI system's demand and order quantity are highly uncertain. Therefore, the total cost of a VMI system possesses higher-order uncertainties for a real-world scenario. This paper proposes a novel interval type-2 fuzzy vendor managed inventory (IT2FVMI) system, in which demand and order quantity are represented by the interval type-2 fuzzy numbers. The proposed IT2FVMI model aims to minimize the overall cost for the single vendor-retailer business, merchandise of multi-product, and a centralized warehouse of retailers' inventory managed by the vendor. Since the proposed model is an NP-hard, a particle swarm optimization (PSO) based solution approach is developed for solving it appropriately. Moreover, we have also formulated the classical/crisp VMI model and type-1 fuzzy vendor managed inventory (T1FVMI) model via considering demand and order quantity as the deterministic and the type-1 fuzzy numbers, respectively. The proposed solution technique is capable of solving both crisp VMI and T1FVMI problems. An appropriate real-world application is considered to conduct experimental simulations for the five test problems that displayed the various situations, and the minimum costs of the models are obtained. All the three models are thoroughly analyzed, and from comparison, it is demonstrated that the proposed IT2FVMI model outperforms both T1FVMI and crisp VMI models by providing a more trustworthy solution in terms of minimum cost, statistical analysis, and significantly faster convergence. [ABSTRACT FROM AUTHOR]

Distance measures are among the most studied phenomena for deciding the degree of belongingness between the two distinct objects. This paper presents a new distance measure among intuitionistic fuzzy sets (IFSs) using the generalized difference sequence spaces within p-summable intuitionistic fuzzy bounded variation (IFBV). The IFBV is a procedure used to approximate the arc-length of an intuitionistic fuzzy-valued function (IFVF) over the IFS, i.e. distance function of all data points of IFS distributed over the geometrical structure of IFBV with power p. The topological uniqueness in the proposed distance measure made it distinguishable, independent from the reflective relationship, and free from the reflective symmetry. All distance measure characteristics are satisfied, and various situations showing the inclusive relations within distance metrics are drawn. Moreover, the proposed distance measure is applied for multi-attribute decision-making approaches, namely the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and GRA (Grey Relational Analysis) techniques. An improved Intuitionistic Fuzzy TOPSIS (IFTOPSIS) and Trapezoidal Intuitionistic Fuzzy GRA (TrIFGRA) techniques are developed to demonstrate the superiority of proposed distance measure with several established ones. With suitable examples, we thoroughly illustrate the functioning of the IFTOPSIS and TrIFGRA decision-making procedures. Extensive comparisons with other popular distance measures are performed to check the effectiveness of our proposed distance measure. [ABSTRACT FROM AUTHOR]

Benzocaine is a widely used topical oropharyngeal anesthetic and has been reported to cause methemoglobinemia. We discuss benzocaine-induced methemoglobinemia and review the causes, presentation, and management of this serious complication. Treatment with methylene blue will result in reversal of methemoglobinemia and clinical recovery in most cases but needs to be used at appropriate doses in carefully selected individuals. Physicians who perform procedures involving the application of benzocaine for topical anesthesia need to rapidly identify and treat methemoglobinemia to avoid significant associated morbidity and mortality. [ABSTRACT FROM AUTHOR]

Primary CNS lymphoma (PCNSL) is a rare B cell variant non-Hodgkins lymphoma that is confined to the brain, leptomeninges, spinal cord and eyes. Its incidence is increasing, primarily due to increase in the number of organ transplantations being undertaken. The majority of the PTLD (post-transplant lymphoproliferative disorder) is seen in kidney transplant recipients simply because they constitute a larger group of transplant recipients each year as compared to other solid organ transplantations. Primary infection of previously infected EBV seronegative patients and immunosuppression are found to be the main etiologic factors in the development of PTLD-PCNSL. There are no clear guidelines on treatment regimens, and it should be individualized according to patient comorbidities. We report a case of PCNS lymphoproliferative disorder in a kidney transplant recipient, which underwent complete remission with decreasing immunosuppression. The patient could not undergo chemotherapy/radiotherapy due to underlying comorbidities. We highlight the available treatment modalities for PTLD-PCNSL. [ABSTRACT FROM AUTHOR]