Kamalrulnizam Abu Bakar

BACKGROUND Human Digital Twins (HDTs) represent a revolutionary leap in the field of cardiac medicine. They have been made possible by the fusion of personal physiological information with the application of artificial intelligence concepts in modeling cardiac activity. However, studies have shown that the field of AI-driven cardiovascular HDT studies has become fragmented, lacking a holistic framework for research methodology. OBJECTIVE This review aims to consolidate global evidence on AI-driven cardiovascular HDTs by systematically classifying the 73 included full-text studies into seven application domains, mapping regional contributions and highlighting regional leading roles alongside notable equity gaps, identifying the most frequently employed modelling architectures, and compiling benchmark datasets with evaluation metrics to guide reproducible modelling. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews, literature published between 2018 and November 2025 was retrieved from a database (IEEE Xplore, ScienceDirect, SpringerLink, ACM DL, and Web of Science). The search was conducted using word combinations such as “AI models”, Human Digital Twins”, and “Cardiovascular Medicine”. Only those studies that reported Machine learning and Deep learning for Cardiovascular HDTs were included. As such, the review included 73 full-text studies that met the inclusion criteria after duplicate removal and multi-stage screening, and these were analysed through qualitative and quantitative synthesis. RESULTS This review synthesizes the studies that reported the use of AI models for various applications of HDTs in cardiovascular disease care. The findings show a marked rise in publications after 2021, with a strong concentration of studies in 2024 and 2025, reflecting the field’s rapid acceleration. Most studies were centered on patient-specific modeling, representing 40.5%, while risk prediction and physiological monitoring were also major areas of application. Regionally, Asia dominated the research landscape with 29 studies (44.59%), driven primarily by India with 13 studies (44.83%) and China with 11 studies (37.93%). Regarding modeling approaches, Convolutional Neural Networks (CNNs) accounted for 27.54%, while Recurrent Neural Networks and LSTM models accounted for 8.70%. Multimodal data integration was evident in 26 studies (35.14%), with imaging modalities used in 30 studies (40.54%). Across the included studies, performance evaluation relied heavily on accuracy-based metrics, with 23 studies (31.08%) reporting Accuracy, ROC-AUC, or F1-Score as key indicators. However, external validation was limited to fewer than 10% of the studies, underscoring a continued translational gap between technical validation and real-world clinical assessment. CONCLUSIONS This review clearly identifies the great promise of artificial intelligence and machine learning (AI/ML) algorithms for addressing challenges in patient-specific modeling, risk evaluation, and cardiovascular diagnostics. The review also discusses various concerns associated with the standardization of benchmarks and evaluation measures, which are critical to the use of cardiovascular HDTs.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community's attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques' design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research.

Software-Defined Networking (SDN) is a trending architecture that separates controller and forwarding planes. This improves network agility and efficiency. The proliferation of the Internet of Things devices has increased traffic flow volume and its heterogeneity in contemporary networks. Since SDN is a flow-driven network, it requires the corresponding rule for each flow in the flowtable. However, the traffic heterogeneity complicates the rules update operation due to varied quality of service requirements and en-route behavior. Some flows are delay-sensitive while others are long-lived with a propensity to consume network buffers, thereby inflicting congestion and delays on the network. The delay-sensitive flows must be routed through a path with minimal delay, while congestion-susceptible flows are guided along a route with adequate capacity. Although several efforts were introduced over the years to efficiently route flows based on different QoS parameters, the current path selection techniques consider either link or switch operation during decisions. Incorporating composite path metrics with flow classification during path selection decisions has not been adequately considered. This paper proposes a technique based on composite metrics with flow classification to differentiate congestion-prone flows and reroute them along appropriate paths to avoid congestion and loss. The technique is integrated into the SDN controller to guide the selection of paths suitable to each traffic class. Compared to other works, the proposed approach improved the path load ratio by 25%, throughput by 35.6%, and packet delivery ratio by 31.7%.

Smart environments offer various services, including smart cities, e-healthcare, transportation, and wearable devices, generating multiple traffic flows with different Quality of Service (QoS) demands. Achieving the desired QoS with security in this heterogeneous environment can be challenging due to traffic flows and device management, unoptimized routing with resource awareness, and security threats. Software Defined Networks (SDN) can help manage these devices through centralized SDN controllers and address these challenges. Various schemes have been proposed to integrate SDN with emerging technologies for better resource utilization and security. Software Defined Wireless Body Area Networks (SDWBAN) and Software Defined Internet of Things (SDIoT) are the recently introduced frameworks to overcome these challenges. This study surveys the existing SDWBAN and SDIoT routing and security challenges. The paper discusses each solution in detail and analyses its weaknesses. It covers SDWBAN frameworks for efficient management of WBAN networks, management of IoT devices, and proposed security mechanisms for IoT and data security in WBAN. The survey provides insights into the state-of-the-art in SDWBAN and SDIoT routing with resource awareness and security threats. Finally, this study highlights potential areas for future research.

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.