In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression‐based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

Photoplethysmogram (PPG) and its second derivative of the photoplethysmogram (SDPTG) are simple and low cost optical techniques for detecting and tracking blood volume changes. The PPG waveform and its SDPTG have been used by many scholars to obtain valuable information about heart and cardiovascular system. Since PPG and SDPTG reflect blood volume changes, much work has been done on its application as a diagnostic tool for screening arterial structure and its related diseases and disorders. In this article, we first provide a short review of the effects of atherosclerosis in losing arterial elasticity. Secondly, we introduce the PPG waveform and discuss in details the analysis methods and applications of its SDPTG waveform. Finally, we demonstrate links between elastic properties of arteries, atherosclerosis, PPG and SDPTG. The main focus of the review is on the analysis methods and applications of SDPTG. Key words: Photoplethysmogram (PPG), second derivative of the photoplethysmogram (SDPTG), atherosclerosis, aging, arterial stiffness, distensibility, morphological changes, endothelial function.

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

Heart disease remains a leading cause of mortality worldwide, prompting healthcare researchers to leverage analytical tools for comprehensive data analysis. This study focuses on exploring crucial parameters and employing deep learning (DL) techniques to enhance understanding and prediction of cardiovascular disease (CVD) risk factors. Utilizing SPSS and Weka tools, a cross-sectional and correlational design was employed to analyze extensive medical datasets. Binomial regression analysis revealed significant associations between age (𝑝 = 0.004) and body mass index (𝑝 = 0.002) with CVD development, highlighting their importance as risk factors. Leveraging Weka's DL algorithms, a predictive model was constructed to classify CVD causes. Particularly, convolutional neural networks (CNN) showcased remarkable accuracy, reaching 98.64%. The findings underscore the elevated risk of CVD among university students and employees in Saudi Arabia, emphasizing the need for heightened awareness and preventive measures, including dietary improvements and increased physical activity. This study underscores the importance of further research to enhance CVD risk perception among students and individuals in similar settings.

Individuals and businesses are frequently seen engaging in a fraud scheme, which results in the loss of funds, rights, and assets. This paper aims to provide an empirical analysis and study of a supervised learning technique, decision trees (DT), on a credit card transaction dataset as a benchmark. The proposed approach can be employed to reduce FPs and FNs through supervised machine learning (ML) technique. The fraud detection system also uses historical data to construct a training set and then analyses it to identify fraudulent activity. In this study, suspicious activity is detected using the decision tree classifier. The obtained results showed that FPs and FNs in an online transaction or activity can be reduced to a large extent using DT classification. This method achieved a promising result in terms of performance measures such as precision (99.7%), accuracy (92.25%), recall (81.49%), and F1_score (86.47%) respectively. As a result, the use of ML classifiers can reduce FPs and FNs, increasing customer satisfaction in an online transaction.

In recent years, the extensive growth of the Internet, coupled with the integration of sensors and wireless sensor networks into critical areas like healthcare and military defense, has led to a significant expansion in the use of artificial intelligence and the Internet of Things applications. Due to the importance and sensitivity of the data in these fields, it is crucial to use an intrusion detection system (IDS), which applies specific algorithms to analyze and process the data from the network in order to find any suspicious activities or behaviors on the system and improve system security. A network frequently carries enormous amounts of data, particularly in military applications where data must flow continuously. In this research, an enhanced IDS using the scatter search algorithm is proposed. A population of randomly generated initial solutions is used to produce a diverse group of selected and high-performing solutions, which serve as a reference set to steer the search process. This reference set is then adopted as a feature selection method based on a support vector machine. The efficiency of the suggested method was examined utilizing the NSL-KDD dataset, and the results were compared with the Gazelle Optimization Algorithm, Algorithmic Optimization Algorithm, Gray Wolf Optimizer, Adjusted Gray Wolf Optimizer, and Particle Swarm Optimization. The main performance metrics utilized to test the efficiency of the suggested method include accuracy, detection efficiency, false-positive rate, and feature count. The results illustrated that the suggested method has obtained a high intrusion detection accuracy in the IDS system of 99% and decreased false alarm rates (0.02) and selected only 17 features from the initial dataset, which contained 41 features, demonstrating the effectiveness of the suggested method. Received: 6 October 2024 | Revised: 14 January 2025 | Accepted: 13 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Feras E. AbuAladas: Conceptualization, Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Rasha Israwah: Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing. Ghaith Jaradat: Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Yousef Qawqzeh: Software, Investigation, Writing – original draft, Writing – review & editing.