The ability of any retrieval system to extract features by using its feature descriptor is the primary criterion to measure its efficiency. In this paper a novel technique for feature extraction of biomedical images is presented. The mooted system uses the Radial Associated Laguerre Moments (RALMs) as a feature descriptor to obtain features from two types of medical images: computer tomography (CT) and magnetic resonance images (MRI). RALMs represent one sort of discrete orthogonal moments. RALMs extract the features from images using orthogonal moments to retrieve images from a database. Our approach is extensively assessed with noise-free and noisy images from three different benchmark databases: Emphysema-CT, NEMA CT, and NEMA MRI. The first two databases are used for CT image retrieval, while the third is for MR image retrieval. The proposed approach was tested against the state-of-the art local feature descriptors: Local Binary Pattern (LBP), and local diagonal extrema pattern (LDEP). It was also evaluated against orthogonal Fourier-Mellin moments (OFMMs) as a global descriptor. The comparison shows a significant improvement in favor of the proposed approach in terms of three different performance metrics: ARP, ARR, and F_score. The proposed approach was also compared against the convolutional neural network (CNN) as a deep learning based method over the NEMA-MRI dataset. The RALMs based approach showed a significant improvement when compared against two state-of-the-art medical image retrieval approaches: Histogram of Compressed Scattering Coefficients (HCSCs) and a local bit-plane decoding-based AlexNet descriptor (LBpDAD), the study has done over the TCIA-CT dataset. The proposed approach was also tested with big well-known dataset from the international skin imaging collaboration (ISIC) 2018.

The development of Internet of Healthcare Things (IoHT) technology leads to high upgrades in monitor signal systems concerning healthcare. Battery life-saving is one of the biggest issues facing healthcare monitoring signal systems. The most important approach to achieve this goal is to decrease the signal size (i.e., data or signal compression); thus, we need to make data compression. In this study, we introduced a novel approach to decrease the size of the transmitted Phonocardiography (FPCG) bio-medical signal by a set of Fractional Order Chebychev Discrete Orthogonal Moments with the Modified Gram-Schmidt Method (FrCMs- MGSM). The efficiency of the proposed approach has been estimated over a benchmark dataset called Fetal PCG, using many sound metrics: QS, CR, SNR, PRD, and PSNR. The efficiency of the proposed approach was evident when compared to recently published approaches. The empirical findings show that the suggested strategy is highly effective regarding compression time (0.99) and computing efficiency (37.64). The outcomes also demonstrate a (4388 ms) reduction in wearable device wake-up time. Thanks to the suggested compression algorithm, which significantly reduces energy consumption.

Orthogonal generalized Laguerre moments of fractional orders (FrGLMs) are signal and image descriptors. The utilization of the FrGLMs in the analysis of big-size signals encounters three challenges. First, calculating the high-order moments is a time-consuming process. Second, accumulating numerical errors leads to numerical instability and degrades the reconstructed signals’ quality. Third, the QR decomposition technique is needed to preserve the orthogonality of the higher-order moments. In this paper, the authors derived a new recurrence formula for calculating the FrGLMs, significantly reducing the computational CPU times. We used the Schwarz–Rutishauser algorithm as an alternative to the QR decomposition technique. The proposed method for computing FrGLMs for big-size signals is accurate, simple, and fast. The proposed algorithm has been tested using the MIT-BIH arrhythmia benchmark dataset. The results show the proposed method’s superiority over existing methods in terms of processing time and reconstruction capability. Concerning the reconstructed capability, it has achieved superiority with average values of 25.3233 and 15.6507 with the two metrics PSNR and MSE, respectively. Concerning the elapsed reconstruction time, it also achieved high superiority with an efficiency gain of 0.8. The proposed method is suitable for utilization in the Internet of Healthcare Things.

The feature selection (FS) procedure is a critical preprocessing step in data mining and machine learning, aiming to enhance model performance by eliminating redundant features and reducing dimensionality. The Energy Valley Optimizer (EVO), inspired by particle physics concepts of stability and decay, offers a novel metaheuristic approach. This study introduces an enhanced binary version of EVO, termed Improved Binarization in the Energy Valley Optimizer with Fractional Chebyshev Transformation (IBEVO-FC), and specifically designed for feature selection challenges. IBEVO-FC incorporates several key advancements over the original EVO. Firstly, it employs a novel fractional Chebyshev transformation function to effectively map the continuous search space of EVO to the binary domain required for feature selection, leveraging the unique properties of fractional orthogonal polynomials for improved binarization. Secondly, the Laplace crossover method is integrated into the initialization phase to improve population diversity and local search capabilities. Thirdly, a random replacement strategy is applied to enhance exploitation and mitigate premature convergence. The efficacy of IBEVO-FC is rigorously evaluated on 26 benchmark datasets from the UCI Repository and compared against 7 contemporary wrapper-based feature selection algorithms. Statistical analysis confirms the competitive performance of the proposed IBEVO-FC method in terms of classification accuracy and feature subset size.

Advancements in Internet of Things (IoT) technologies have had a profound impact on interconnected devices, leading to exponentially growing networks of billions of intelligent devices. However, this growth has exposed Internet of Things (IoT) systems to cybersecurity vulnerabilities. These vulnerabilities are primarily caused by the inherent limitations of these devices, such as finite battery resources and the requirement for ubiquitous connectivity. The rapid evolution of deep learning (DL) technologies has led to their widespread use in critical application domains, thereby highlighting the need to integrate DL methodologies to improve IoT security systems beyond the basic secure communication protocols. This is essential for creating intelligent security frameworks that can effectively address the increasingly complex cybersecurity threats faced by IoT networks. This study proposes a hybrid methodology that combines fractional discrete Tchebichef moment analysis with deep learning for the prevention of IoT attacks. The effectiveness of our proposed technique for detecting IoT threats was evaluated using the UNSW-NB15 and Bot-IoT datasets, featuring illustrative cases of common IoT attack scenarios, such as DDoS, identity spoofing, network reconnaissance, and unauthorized data access. The empirical results validate the superior classification capabilities of the proposed methodology in IoT cybersecurity threat assessments compared with existing solutions. This study leveraged the synergistic integration of discrete Tchebichef moments and deep convolutional networks to facilitate comprehensive attack detection and prevention in IoT ecosystems.

Biomedical color (BMC) images are being used on a wide scale by physicians, where their diagnosis would be more accurate. Hence, it is recommended to develop new approaches that are able to represent and retrieve the BMC images efficiently. This work proposes two methods to represent BMC images: Quaternion Associated Laguerre. Moments (Q_ALMs), and Quaternion Chebyshev Moments (Q_CMs). Q_ALMs and Q_CMs are derived by extending the ALMs and CMs to the quaternion field. ALMs and CMs represent discrete orthogonal moments, and they are defined using the Associated Laguerre Polynomials (ALPs) and Chebychev Polynomials, respectively. Hospitals and medical institutes everywhere in the world create and store a large variety of datasets of BMC images during the routine clinical practices; hence, the mastery to retrieve the BMC images correctly is crucial for precise diagnoses and also for the researchers in medical sciences. So that in this study, we also introduced two image retrieval systems for BMC images based on the Q_CMs and Q_ALMs approaches. Our approaches extensively assessed with two standard benchmark datasets: LGG Segmentation dataset for brain magnetic resonance MR images and NEMA-CT for the computed tomography (CT) images. The performance of the proposed retrieval systems is assessed through three performance metrics: Average retrieval precision (ARP), average retrieval rate (ARR), and F_score. Results have shown the outperformance of Q_CMs over Q_ALMs in both the cases of representing and retrieval of BMC images.