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The rapid integration of artificial intelligence (AI) into healthcare has enhanced diagnostic accuracy; however, patient engagement and satisfaction remain significant challenges that hinder the widespread acceptance and effectiveness of AI-driven clinical tools. This study introduces CareAssist-GPT, a novel AI-assisted diagnostic model designed to improve both diagnostic accuracy and the patient experience through real-time, understandable, and empathetic communication. CareAssist-GPT combines high-resolution X-ray images, real-time physiological vital signs, and clinical notes within a unified predictive framework using deep learning. Feature extraction is performed using convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based NLP modules. Model performance was evaluated in terms of accuracy, precision, recall, specificity, and response time, alongside patient satisfaction through a structured user feedback survey. CareAssist-GPT achieved a diagnostic accuracy of 95.8%, improving by 2.4% over conventional models. It reported high precision (94.3%), recall (93.8%), and specificity (92.7%), with an AUC-ROC of 0.97. The system responded within 500 ms—23.1% faster than existing tools—and achieved a patient satisfaction score of 9.3 out of 10, demonstrating its real-time usability and communicative effectiveness. CareAssist-GPT significantly enhances the diagnostic process by improving accuracy and fostering patient trust through transparent, real-time explanations. These findings position it as a promising patient-centered AI solution capable of transforming healthcare delivery by bridging the gap between advanced diagnostics and human-centered communication.

In this study, an extension of the generalized Lindley distribution using the Marshall-Olkin method and its own sub-models is presented. This new model for modelling survival and lifetime data is flexible. Several statistical properties and characterizations of the subject distribution along with its reliability analysis are presented. Statistical inference for the new family such as the Maximum likelihood estimators and the asymptotic variance covariance matrix of the unknown parameters are discussed. A simulation study is considered to compare the efficiency of the different estimators based on mean square error criterion. Finally, a real data set is analyzed to show the flexibility of our proposed model compared with the fit attained by some other competitive distributions.

Drones have limited computing and storage resources, which makes them unable to perform complex tactical tasks; drones working in clusters can be employed for such complex tactical task completion. But, the synergy between these drones working in clusters is mandatory. Otherwise, the adversary can easily target them, disturb their routine work, and even disturb the whole system. Only a robust and lightweight authentication protocol can handle both information fusion and collaboration and coordination of drones working in clusters efficiently and effectively and is one of the essential components of the Internet of Drones (IoD) environment as it is deployed for quick and intelligent decisions. In this regard, the already available security mechanisms for IoD deployment drones have either design flaws or failed to provide security against session key disclosure, spoofing, man in the middle (MITM), and denial of service (DoS) attacks. Therefore, this article presents a protocol based on elliptic curve cryptography (ECC), one-way hash, concatenation, and exclusive-OR (XOR) operations to establish a secure communication session among drones which makes them a powerful system to perform complex tactical tasks and information fusion intelligently. The security analysis of the proposed protocol has formally been scrutinized via BAN logic and ProVerif and informally via realistic discussion and illustrations. The results obtained from the analysis sections demonstrate that the proposed protocol is robust in security and is 63.87% more efficient in terms of communication cost compared to its competitors, and 66.46% better in terms of computational cost.

In this study, we introduce a new compounded model called the complementary Bell–Weibull model and use it to address the problem of a group acceptance sampling plan predicted on a truncated life test. The median lifespan is used as a quality index to obtain the design constraints, namely sample size and approval number, under a predefined consumerś risk and test termination period. Additionally, two real data applications are presented, and unknown parameters are estimated using the maximum likelihood approach.

Designing fair and efficient blockchain reward mechanisms requires going beyond raw execution time to account for behavioral variability. We present a simulation framework for evaluating BCRPs using entropy as a systems-level indicator of reward fairness and stability. Three strategies are assessed on simulated miner profiles with log-normal execution times, Laplace-distributed noise, and tercile-based complexity classes: a classical execution-time baseline, “Mining ” (penalizing miner noise and task complexity), and “Adaptive ” (Mining with exponential time decay). Reward distributions are summarized via KDE and ECDF and scored using Shannon, Rényi , Tsallis , and normalized Shannon entropies computed on discretized rewards ( bins). An interactive Shiny application accompanies the method for reproducible exploration without programming. Across simulations, Adaptive yields the most behavior-sensitive and equitable allocations, achieving the lowest entropy on all four metrics. Quantitatively, relative to the Traditional baseline, Adaptive reduces entropy by (Shannon: ), (Rényi- : ), (Tsallis- : ), and (Normalized: ); Mining achieves intermediate improvements of and respectively. These results provide an evidence-based, deployable framework for evaluating reward fairness in decentralized systems.

A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination of generalised exponential distributions can be used to characterise the density of a new family, which is critical in assessing some of the family’s properties. The statistical features of this family are determined, including exact formulations for the quantile function, ordinary and incomplete moments, generating function, and order statistics. The model parameters are estimated using the maximum likelihood method. Further, one of the unique models has been systematically studied. Along with conventional skewness measures, MacGillivray skewness is also used to quantify the skewness measure. The new probability distribution also enables us to determine certain critical risk indicators, both numerically and graphically. We use a simulated assessment of the suggested distribution, as well as apply three real-world data sets in modelling the proposed model, in order to ensure its authenticity and superiority.

Multiplication is a fundamental mathematical operation that finds extensive applications across various disciplines, particularly in computation-intensive and error-resilient applications, such as image processing. As hardware circuits become more complex, there is a growing demand for approximation circuit methods. Implementation of approximate multipliers has the potential to yield substantial reductions in hardware costs while maintaining acceptable performance levels. Most current designs for approximate multipliers are optimized for ASIC-based circuits, which may not produce similar performance improvements when adapted for FPGA-based circuits. Additionally, many of these existing multiplier designs are limited to unsigned numbers. This paper proposes a novel approach for designing signed approximate multipliers tailored specifically for FPGAs. Two efficient architectures are introduced that efficiently utilize key FPGA components, such as LUTs and Carry4 primitives, by designing the optimal LUT-Carry4 netlists. A Pareto-based analysis is also performed to balance trade-offs and achieve a low mean error distance (MED). Simulation results confirm that the proposed architectures offer superior performance compared to existing signed approximate multipliers, delivering improved power efficiency, reduced resource usage, shorter critical path delay (CPD), and enhanced computational accuracy. The practical applicability of these approximate multipliers is further validated through their use in image processing applications.

For the first time, ten frequentist estimation methods are considered on stress-strength reliability R = P(Y < X) when X and Y are two independent Weibull distributions with the same shape parameter. The start point to estimate the parameter R is the maximum likelihood method. Other than the maximum likelihood method, a nine frequentist estimation methods are used to estimate R, namely: least square, weighted least square, percentile, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, method of Cramér-von Mises, Anderson-Darling and Right-tail Anderson-Darling. We also consider two parametric bootstrap confidence intervals of R. We compare the efficiency of the different proposed estimators by conducting an extensive Mont Carlo simulation study. The performance and the finite sample properties of the different estimators are compared in terms of relative biases and relative mean squared errors. The Mont Carlo simulation study revels that the percentile and maximum product of spacing methods are highly competitive with the other methods for small and large sample sizes. To show the applicability and the importance of the proposed estimators, we analyze one real data set.

In many cases, the first signs of a DDoS attack are not abnormal spikes in traffic but rather a distortion of the distribution of traffic at the flow level. This paper proposes a structural framework to measure the effects of such distortions on availability and proposes a composite measure called ASI to quantify the effect of availability stress on traffic. Using a real-world DDoS traffic dataset, the traffic is separated into attack and benign traffic, and the distribution of fifteen features at the flow level is analyzed. For each feature, several complementary stress components are analyzed, including central displacement, dispersion inflation, and heavy-tail amplification. These components are combined to compute the ASI to measure the effect of availability stress on traffic. The statistical significance of the distributional differences in the features is tested using the Mann–Whitney rank-sum test with false discovery rate corrections for multiple testing. The results indicate that the features of packet lengths and backward inter-arrival timings are most affected by availability stress, with several features showing extremely low p-values (< 10⁻ 3 ⁰). However, the results also indicate that while statistical separation of traffic is possible, structural availability stress is not necessarily implied. Instead, structural availability degradation is a result of the combined effect of timing irregularity and traffic activity bursts. The proposed framework is flexible and uses changes in the distribution of traffic features to measure availability stress and is applicable to both simulated and real-world traffic. The results indicate the structural traffic distortion approach can potentially identify early signs of availability degradation in availability-critical systems.

Background: Accurate and timely segmentation of ischemic stroke lesions from diffusion-weighted imaging (DWI) is crucial for diagnosis and treatment planning. Manual segmentation is labor-intensive, time-consuming, and prone to inter-observer variability. This study aims to develop and validate a novel deep learning framework that overcomes the common trade-off between high segmentation accuracy and the computational efficiency required for practical clinical use. Methods: We developed FasterNet and Attention-Gated UNet (FA-UNet), a hybrid U-Net-based architecture. The model’s design features two key innovations: a computationally efficient FasterNet block at the bottleneck to capture global lesion context and multi-scale attention gates (MSAGs) on the skip connections to adaptively refine features and suppress noise. The model was trained and validated on the public Ischemic Stroke Lesion Segmentation (ISLES) 2022 dataset (n = 250 patients) and its performance was assessed on an independent, private test set of 600 DWI scans from 80 patients. FA-UNet’s performance was benchmarked against several state-of-the-art U-Net variants using the Dice coefficient, Intersection over Union (IoU), sensitivity, and precision as primary outcome measures. Results: On the independent test set (n = 80), the proposed FA-UNet model achieved a Dice coefficient of 0.8676 and an IoU of 0.7584. This performance surpassed all benchmarked architectures, including U-Net, U-Net3plus, and CMU-Net. Compared with the next best performing model, this represents a relative improvement of approximately 1.64% in the Dice score and 1.42% in IoU. Conclusion: The FA-UNet architecture establishes a new state-of-the-art performance benchmark for automated ischemic stroke segmentation. By effectively balancing high accuracy with computational efficiency, it offers a robust, reliable, and clinically viable tool.