Image watermarking is the process of securely embedding a higher amount of information in the host object. These processes ensure authentication, image integration, and content verification. Several existing methods face complicated problems, such as security issues, robustness, and data leakage. Therefore, researchers developed specific methods for different applications. However, the performance of the currently obtained method was lower due to their low resistances. Therefore, to overcome this issue, we employed a novel technique, a fuzzy equilibrium optimization (FEO) approach, for embedding water image encryption. Initially, the raw image undergoes fuzzification to determine the critical point; thus, the intensity of the radial line selects a region of interest (ROI). Finally, the watermarking images are converted into a time-frequency domain via discrete wavelet transform (DWT), where the sub-band is converted based on value of magnitude. The proposed technique is analyzed using three medical image datasets, namely magnetic resonance imaging (MRI), ultrasound (US), and computed tomography (CT) datasets. However, all pixels in each sub-band are replaced to form a fully encrypted image, guaranteeing a watermarked reliable, secure, non-breakable format. Singular values are obtained for the encrypted watermarking image to provide high robustness to the watermarked image. After validation, the proposed fuzzy equilibrium optimization technique achieved higher robustness and security against different types of attacks. Moreover, the proposed FEO technique achieved a value of peak signal to noise ratio (PSNR) about 42.5 dB higher than other compared techniques.

Due to the dynamic nature of the business environment, organisations regularly face rapid changes for which they need to adapt their Enterprise Systems (ESs). This makes ES adaptability essential. We identify ES adaptability as a conceptual notion that bridges the strategic and the technical domains. We provide a conceptual and empirical analysis that captures the current state of ESs from a top management perspective and shows the challenges of and potential for ES adaptability. Results show that having an adaptable ES is critical for an organisation to manage uncertainties and continue providing services that meet the expectations of their customers. However, the evidence from this study suggests that many in top management may have not fully understood the meaning of ES adaptability and as a result did not assess the current state of their implemented ES accurately. Therefore, they perceived their current ESs as adaptable in essence, when, in fact, they were less adaptable in practice. The study findings, limitations and possible future research are discussed.

The COVID-19 pandemic impacted many if not all aspects of our lives, including the way we handle money. This paper takes mobile payments as an example to show how COVID-19 affected the United Arab Emirates. Many researchers rushed into writing their first impressions in response to the pandemic to produce early study results. This paper uses these early electronic articles and blogs as valuable data sources for a first-level analysis. It attempts to assess the impact of COVID-19 on mobile payment services in the post-pandemic world from the demand and supply perspective in the UAE. The previous literature has been reviewed to understand the scope of the services present in the UAE before the onset of the pandemic. Further, an online questionnaire with open- and closed-ended questions was used to gather data from 125 vendors capable of receiving mobile payments. Regression analysis with two time periods has been included to develop a better perception of mobile payment services before and after the spread of the coronavirus. It was found that COVID-19 has created a suitable environment for mobile payments, with significant positive relationship in both periods. The reasons for using mobile payments during the pandemic were to apply social distancing and to avoid physical touch, a requirement in the COVID-19 period, resulting in a move towards creating a positive attitude towards a cashless society.

With the increasing usage of direct-to-consumer healthcare devices, it is paramount for physicians to identify the potential usage for patient management. The complexity of sensor signal patterns in healthcare, in addition to the variability of physiological data, presents essential challenges in developing specific diagnostic and monitoring tools. The Explainable Artificial Intelligence (XAI) role in improving the interpretability of machine learning (ML) methods for sensor signal analysis, mainly in applications like electrocardiography (ECG), wearable health tracking, pressure sensors, and wearable inertial sensors. An integration of cloud-based DL methods with wearable devices is advanced as a smart healthcare system to help automate cardiac classification using consumer electronics. This paper proposes an Explainable Artificial Intelligence for Customer Healthcare Electronics Using Feature Selection and Attention Mechanism (XAICHE-FSAM) model. The aim is to develop novel XAI techniques tailored for enhancing transparency, reliability, and decision-making in customer electronics used in healthcare applications. Initially, the data pre-processing stage applies dual levels, such as denoising-based adaptive wavelet thresholding (AWT) and robust scaling-based Z-score normalization method to normalize the input data. Furthermore, various filter techniques like Chi-square, Fisher’s Score, Correlation Coefficient, and Information Gain (IG) are employed for the feature selection (FS) process. For the classification process, the attention-based long short-term memory technique is utilized. Finally, the XAI method Local Interpretable Model-agnostic Explanations (LIME) is used to interpret and explain the AI decisions of the model. The performance validation of the XAICHE-FSAM methodology is examined under the ECG Arrhythmia Classification dataset. The comparison study of the XAICHE-FSAM methodology portrayed a superior accuracy value of 99.06% over existing methods.