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In this work, artificial intelligence methods are designed and adopted for evaluating various risk levels of thyroid hormone and blood pressure in humans. Fuzzy Logic (FL) method is firstly exploited to provide the risk levels. Additionally, a machine learning was proposed using the Adaptive Neuron- Fuzzy Inference System (ANFIS) to learn and assess the risk levels by fusing a multiple-layer Neural Network (NN) with the FL. The data are collected for standard risk levels from real medical centers. The results lead to well ANFIS design based on the FL, which can generate such interesting outcomes for predicting risk levels for thyroid hormone and blood pressure. Both proposed methods of the FL and ANFIS can be exploited for medical applications.

Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI research resulted in diverse scholars possessing significant challenges in designing proper evaluation methods. This paper proposes a novel framework of a three-layered top-down approach on how to arrive at an optimal explainer, accenting the persistent need for consensus in XAI evaluation. This paper also investigates a critical comparative evaluation of explanations in both model agnostic and specific explainers including LIME, SHAP, Anchors, and TabNet, aiming to enhance the adaptability of XAI in a tabular domain. The results demonstrate that TabNet achieved the highest classification recall followed by TabPFN, and XGBoost. Additionally, this paper develops an optimal approach by introducing a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations by quantifying the extent to which a model’s capabilities diminish when eliminating topmost features. This addresses a conspicuous gap in the lack of consensus among researchers regarding how global feature importance impacts classification loss, thereby undermining the trust and correctness of such applications. Finally, a practical use case on medical tabular data is provided to concretely illustrate the findings.

Palmprint biometrics is the best method of identifying an individual with a unique palmprint for every person. The present paper formulates a new methodology towards the identification of 3D palmprints using the Modified Four- Patch Local Binary Pattern (MFPLBP). It improves upon the conventional Four-Patch Local Binary Pattern (FPLBP) by integrating the adaptive weight with the improved texture extraction. Both approaches are created to support the intricate surface information of 3D palmprints. The MFPLBP can exactly capture local variations and is noise and illumination invariant. There are extensive experiments done in this paper and establish that MFPLBP outperforms traditional LBP methods and other stateof- the-art methods in recognition rates. The experiments establish that MFPLBP is a efficient and effective method of making use of 3D palmprints in real-world biometric verification.

Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR).

Positron emission tomography (PET) imaging requires high‐quality yet rapid reconstruction to ensure clinical effectiveness, as these reconstructions enable timely and accurate diagnosis, guide treatment decisions, and reduce the risk of delayed interventions in critical clinical settings. This study introduces a deep learning‐based method that employs conditional generative adversarial networks (cGANs) for direct sinogram‐to‐image PET reconstruction. A dual approach was used: simulation experiments with Zubal phantoms, which provided a controlled and reproducible environment to test the reconstruction accuracy and robustness, and validation with real patient datasets, ensuring the method’s applicability and effectiveness in clinical settings. The primary objective was to evaluate the ability of the cGAN‐based method to enhance image quality, reduce noise, and improve reconstruction speed compared to conventional algorithms, such as maximum likelihood expectation maximization (MLEM) and total variation (TV). The methodology involved training a U‐net‐based generator and a whole‐image discriminator iteratively to reconstruct PET images with superior resolution and accuracy. Key outcome measures included bias, variance, structural similarity index (SSIM), and relative root mean square error (rRMSE), as these metrics effectively quantify image fidelity, noise levels, and structural accuracy, which are critical for evaluating the clinical reliability and precision of reconstructed PET images. The results showed that the proposed method achieved significant improvements in image clarity, noise suppression, and computational efficiency, outperforming the traditional techniques. These findings highlight the potential of cGAN‐based reconstruction in improving diagnostic accuracy and clinical workflow.

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies.

Valve control for patients feeding saline is a sensitive issue. Such a valve is used to control the amount of important fluid entering the body. In this paper, we present an intelligent method based on fuzzy logic (FL) to deliver drugs and nutrients to patients through intravenous catheters. The proposed method is called intelligent saline controlling valve (ISCV) and relies on three inputs and two outputs. Specifically, the fluid’s drip, pressure, and the number of available bubbles are the three-valve inputs that control the two outputs which are the opening and closing. In other words, the ISCV controls the feeding saline for medical purposes. The obtained results show the success of the proposed method.

World recently faced big challenges with the pandemic of coronavirus disease 2019 (COVID-19). Governments suffer from the problem of appropriately identifying the risk factor of this virus and establishing their safety procedures accordingly. This paper concentrates on designing a coronavirus risk factor (CRF) by the power of Sugeno fuzzy logic (SFL). The main advantage of the CRF is that it can provides a quick and suitable risk evaluation. According to the degree of severity, three essential parameters are considered: number of infected cases, number of people in intensive care units (ICU) and number of deaths. All of these parameters are provided per population. Such interesting and promising outcomes are attained, where the total effect is found equal to 95.3%.

DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual’s parents. The model’s flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.

Up to now, it has been noted that there is no reference in relation to reproduce face images from multi-spectral hand images. Because vein patterns require substantial efforts to acquire, a system security can be increased further. [...]a feature fusion between multi-spectral images to combine the features, where Haar wavelet fusion based on the mean rule was used. [...]to convert the image from 8-bit grayscale to a binary image a threshold в was executed in (1): ... for the binary image B(x,y) and a structuring element h(a,b), the erosion © and dilation 0 are denoted as [16]: ... small white objects should be removed from the binary image, while holding the very large area. [...]these top-hat features are added to the hand image according to (8) [18]: ... then, an unsharp filter is applied to enhance the Im0T details of the edges.