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  Contextual multi-arm Bandit (CMAB) is a popular framework for sequential decision-making problems where an agent must repeatedly choose among multiple actions, each with an unknown reward distribution...

In the era of artificial cognizance, context-aware decision-making problems have attracted significant attention. Contextual bandit addresses these problems by solving the exploration versus exploitation dilemma faced to provide customized solutions as per the user’s liking. However, a high level of accountability is required, and there is a need to understand the underlying mechanism of the black box nature of the contextual bandit algorithms proposed in the literature. To overcome these shortcomings, an explainable AI (XAI) based FuzzyBandit model is proposed, which maximizes the cumulative reward by optimizing the decision at each trial based on the rewards received in previous observations and, at the same time, generates explanations for the decision made. The proposed model uses an adaptive neuro-fuzzy inference system (ANFIS) to address the vague nature of arm selection in contextual bandits and uses a feedback mechanism to adjust its parameters based on the relevance and diversity of the features to maximize reward generation. The FuzzyBandit model has also been empirically compared with the existing seven most popular art of literature models on four benchmark datasets over nine criteria, namely recall, specificity, precision, prevalence, F1 score, Matthews Correlation Coefficient (MCC), Fowlkes–Mallows index (FM), Critical Success Index (CSI) and accuracy.

Aeration is a cost-effective and efficient method for increasing the available oxygen or dissolved oxygen content in water bodies, which is crucial for the existence of aquatic life. However, conventional techniques for estimating aeration in different hydraulic structures are time-consuming and incorrect ways to approximate aeration. Therefore, new, computationally more efficient, and more accurate methods are required. In this article, three machine learning models are presented: (1) ELM (extreme learning machine) model, (2) online sequential extreme learning machine model, and (3) I-ELM (incremental extreme learning machine) model. These models assess the air conditioning capacity of the three variants of Piano Key Weirs (PKWs), denoted as A, B, and C, about Cd, Cs, and Cu, which are the three most important parameters for aeration efficiency at different temperatures. The model performance is evaluated and compared based on mean squared error, root-mean-square error, correlation coefficient, mean absolute error, and Nash–Sutcliffe efficiency. This research concludes that I-ELM is the best-performing model for complete available data that are time invariant.

A Piano Key Weir (PKW) is a nonlinear (labyrinth-type) weir with a small spillway footprint and a large discharge carrying capacity. It (PKW) enables water bodies to continue functioning at elevated supply levels while causing no damage to dam structures, resulting in increased storage. PKW's geometrical structure is extremely complex, and geometrical aspects have a significant impact on its efficiency and on energy dissipation. Among them relative width ratio (Wi/Wo) (i.e., inlet to outlet key width ratio) is a critical parameter that affects the PKW's discharge efficiency, and energy dissipation across the weir significantly. This study predicts the PKW's inlet to the outlet key ratio and understands the resulting hydraulic behaviours based on a Fuzzy Neural Network (FNN). The dataset used in this study was collected experimentally, which adds to the study's authenticity because it is not a conventional dataset. The model's performance is evaluated by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE); both values are 0.0305 and 0.0222, respectively. According to the dataset, these scores tell the model's reliability as it is in the ideal range. The FNN approach can be applied in a variety of fields to predict or solve different problems erent problems.

CAPTCHAs are computer generated tests in the form of images, audios and object recognition that world can communicate easily and computer systems cannot. Internet sites present users with captchas to set apart human users from false computer programs, often referred to as bots. Their purpose is to obstruct attackers from performing automatic registration, online polling and other such actions. IRCTC, being the website to reserve tickets for Indian railways, one of the biggest railway network, has also employed both image and audio captchas for security purposes. However, the audio captchas used on the website are not effective in distinguishing between humans and bots. Most of the visual CAPTCHAs and some audio CAPTCHAs on different websites have been cracked using various methods of machine learning and we propound an identical idea to examine the security of audio CAPTCHAs on IRCTC website. In this paper, we show that our bot is able to break the IRCTC audio captchas with a success rate of 98%, 96.04% and 80.3% using three different models. Along with breaking the captcha, another python script written by us was able to automate the process of ticket booking. Thus, combining all of it into a single package could result in a system which would login and reserve tickets only by a single click. Travel brokers can easily use such a system for easy and fast booking of tatkal tickets which would lead to commercializing this activity for deriving huge profit from needy travelers.

Visual Tracking is a complex problem due to unconstrained appearance variations and dynamic environment. Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work. To this end, we propose a robust object tracking framework based on Unified Graph Fusion (UGF) of multi-cue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multi-cue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric.

With the advancement in modern imaging techniques like CT scan, MRI, PET scan etc., a vast amount of data is generated every day in the field of healthcare. Big data contains hidden information, which necessitates the development of intelligent systems to analyze it and extract relevant information, allowing for accurate and cost-effective decisions in the medical field. By utilizing the untapped potential of the big data available in the medical field, very precise models can be developed for the medical diagnosis of retinal diseases. Optical coherence tomography (OCT) is a non-invasive imaging test that captures different, distinctive layers of the retina and optic nerve in a living eye to map and measure their thickness, that helps diagnose various retinal disorders. With the advancement of the application of deep learning-based techniques in the field of medical sciences, the use of convolutional neural network (CNN) based approaches for disease detection is gaining popularity. While the manual examination of 3D OCT images for the diagnosis of retinal disorders requires extensive time and expert intervention, the use of CNNs provides an effective automated option that provides results with higher accuracy while also reducing the time involved in the overall process. In this paper, we have implemented the aforementioned idea by proposing OCT-CNN, a CNN architecture, that automatically classifies retinal OCT images and identifies potential disorders in a living eye. Several techniques have been employed to enhance the performance of the proposed approach, including digital enhancement of the images, dropout regularization, adaptive learning rates, and early stopping of training to attain optimal performance. The performance of the proposed OCT-CNN is evaluated on the UCSD dataset against several popular deep CNN architectures and existing state-of-the-art approaches to automatic retinal OCT classification. The proposed OCT-CNN attains the best performance on all evaluated metrics, pushing the classification accuracies to 99.28% on CNV, 99.9% on DME, 99.38% on DRUSEN, and 100% on NORMAL images, indicating its superiority over existing state-of-the-art techniques.