Widespread digitalization has led to almost all utilities and services thrive on an online medium. A real-time, personalized, and trend grasping recommendation system is necessary to enhance user experience and boost business on E-commerce platforms. We propose the Multimodel Contextual Reinforcement Learning (MMCR) constituting three novel features for real-time and customized recommendations. The first feature is user-item interactive state embedding which uses not only item information but also assigns weightage to this information according to its usage history. It gives higher importance to the newly clicked items by the users than the older ones. Second, we devised Contextual Cluster Exploration (CCE) strategy. This strategy enhances the item-choice recommendations by consistently reducing the randomness during exploration. The third novelty is an item-based multi-agent framework that can tackle the case of sparsely chosen items. Generally, such items are disregarded in a single agent model as the more popular items take supremacy. Our technique ensures that the user-item history per item is learned separately; thus, no item is neglected. MMCR has shown an average of 5% increase in CTR rate. Moreover, CCE exploration gives a considerably higher score than state-of-the-art exploration strategies. Thorough experimentation demonstrates that our proposed strategy has shown significantly improved results over various state-of-the-art strategies.

Abstract Low‐light image enhancement is a challenging field in image processing. Retinex‐based methods perform well for low‐light images. However, reflectance and illumination estimation is an ill‐posed problem. This paper presents a new framework for the simultaneous estimation of reflectance and illumination for low‐light image enhancement. The algorithm estimates multiple instances of illumination and reflectance and blends them to estimate the final components. The proposed approach uses multi‐scale fusion for illumination estimation and naive fusion for reflectance estimation. Extensive experimentation and analysis with a large set of low‐light images validates the performance of the proposed approach. The comparison shows the superiority of the proposed approach over most of the existing low‐light image enhancement methods. The proposed method provides colour constancy in low‐light image enhancement and preserves the naturalness of the image.

Operational maturity of biological control systems have fuelled the inspiration for a large number of mathematical and logical models for control, automation and optimisation. The human brain represents the most sophisticated control architecture known to us and is a central motivation for several research attempts across various domains. In the present work, we introduce an algorithm for mathematical optimisation that derives its intuition from the hierarchical and distributed operations of the human motor system. The system comprises global leaders, local leaders and an effector population that adapt dynamically to attain global optimisation via a feedback mechanism coupled with the structural hierarchy. The hierarchical system operation is distributed into local control for movement and global controllers that facilitate gross motion and decision making. We present our algorithm as a variant of the classical Differential Evolution algorithm, introducing a hierarchical crossover operation. The discussed approach is tested exhaustively on standard test functions as well as the CEC 2017 benchmark. Our algorithm significantly outperforms various standard algorithms as well as their popular variants as discussed in the results.

This paper presents a fuzzy system for edge detection, using smallest univalue segment assimilating nucleus (USAN) principle and bacterial foraging algorithm (BFA). The proposed algorithm fuzzifies the USAN area obtained from the original image, using a USAN area histogram-based Gaussian membership function. A parametric fuzzy intensification operator (FINT) is proposed to enhance the weak edge information, which results in another fuzzy set. The fuzzy measures, i.e., fuzzy edge quality factor and sharpness factor, are defined on fuzzy sets. The BFA is used to optimize the parameters involved in the fuzzy membership function and the FINT. The fuzzy edge map is obtained using optimized parameters. The adaptive thresholding is used to defuzzify the fuzzy edge map to obtain a binary edge map. The experimental results are analyzed qualitatively and quantitatively. The quantitative measures, i.e., Pratt's figure of merit, Cohen' Kappa, Shannon's entropy, and edge strength similarity-based edge quality metric, are used. The quantitative results are statistically analyzed using t-test. The proposed algorithm outperforms many of the traditional and state-of-the-art edge detectors.

This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed as the fuzzy dissimilarity histogram (FDH). A cumulative distribution function (CDF) is formed with normalized values of FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement (FCCE) algorithm. The proposed algorithms are compared with conventional and state-of-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.

This study presents a new contrast‐enhancement approach called entropy‐based dynamic sub‐histogram equalisation. The proposed algorithm performs a recursive division of the histogram based on the entropy of the sub‐histograms. Each sub‐histogram is divided recursively into two sub‐histograms with equal entropy. A stopping criterion is proposed to achieve an optimum number of sub‐histograms. A new dynamic range is allocated to each sub‐histogram based on the entropy and number of used and missing intensity levels in the sub‐histogram. The final contrast‐enhanced image is obtained by equalising each sub‐histogram independently. The proposed algorithm is compared with conventional as well as state‐of‐the‐art contrast‐enhancement algorithms. The quantitative results for a large image data set are statistically analysed using a paired t ‐test. The quantitative and visual assessment shows that the proposed algorithm outperforms most of the existing contrast‐enhancement algorithms. The proposed algorithm results in natural‐looking, good contrast images with almost no artefacts.