Noha Alnazzawi

Hypertension and diabetes are major risk factors for heart disease, which remains among the leading causes of morbidity and mortality worldwide. Heart disease includes heart failure, myocardial infarction, stroke, and atherosclerosis. The identification and monitoring of these risk factors are crucial for early intervention and effective management. Machine learning techniques have the potential to improve the management and prevention of heart disease by enabling the automatic detection of risk factors, which in turn can help doctors personalize treatment and facilitate preventive interventions. In this study, heart disease risk factors were automatically extracted using a combination of multimodal data: both unstructured clinical narratives (e.g., the PrevComp corpus) and structured datasets (e.g., the UCI heart disease dataset) were used to predict the presence or absence of heart disease. The classification model is based on the Light Gradient Boosting Machine (LightGBM), a state-of-the-art implementation of the gradient boosting framework that employs tree-based learning algorithms. The developed classification model demonstrated a robust predictive accuracy of 83% for the presence/absence of heart disease, supporting its potential to accurately identify high-risk patients and improve clinical outcomes.

The Consumer Internet of Things (CIoT) is rapidly transforming multiple aspects of daily life. To optimize routine operations, ensure the privacy of valuable consumer data, and enhance decision-making, CIoT devices require the adaptation of distributed machine learning frameworks such as Split Federated Learning (SFL). SFL combines the key strengths of Federated Learning (FL) and Split Learning (SL) to promote resource and communication efficiency. However, SFL architectures are susceptible to several potential security threats, such as model poisoning and data tampering, which may compromise the integrity and performance of the entire CIoT network. To address these significant security issues, this article proposes a lightweight and efficient SFL architecture integrated with Hash-based Message Authentication Code (HMAC). In the proposed framework, HMAC ensures the authenticity and integrity of model updates by generating and verifying cryptographic signatures using three well-known SHA3 hash variants. To analyze the effectiveness of the designed security framework, a comprehensive evaluation model was developed to simulate and examine the behavior of the SFL architecture under model poisoning and tampering attacks. The designed model exhibited a higher attack detection accuracy of 98.19% across different client configurations. The system demonstrated good computational efficiency with an average attack detection time of 12.09 ms. Experimental outcomes confirm the improved attack detection performance, scalability, and reduced latency of our architecture, making it a promising approach to secure SFL-enabled CIoT applications.

The Social Internet of Things (SIoT) enables autonomous nodes to form socially aware communities and collaborate based on trust and interaction history. However, ensuring fair communication and equitable resource allocation remains a challenge due to selfish or selectively cooperative node behavior. This work presents a socially adaptive framework that integrates Virtual Currency (VC), Reputation Scores (RS), and Social Preference (SP) within a community-based incentive model. Once communities are established, Community Heads (CHs) are elected to monitor node behavior, allocate VC based on RS, and enforce cooperation through localized control mechanisms. Nodes earn VC by participating in data relaying tasks. Conversely, nodes that avoid cooperation and fail to accumulate VC may become temporarily ineligible to initiate their own transmissions. In such cases, if a strong social bond exists, a destination node may assist the underfunded source by contributing to the route cost via an adjustment factor. This socially driven support mechanism enhances communication resilience under resource-limited conditions. The framework also supports a recovery mechanism that allows previously selfish nodes to rejoin the network by improving their behavior and regaining RS. Simulations conducted on a MATLAB-based SIoT testbed evaluate the framework against several recent and efficient cooperation models. Compared to the best-performing existing scheme, the proposed mechanism achieves notable improvements in packet delivery ratio (3.9% higher), energy efficiency (0.95% lower consumption), network throughput (11% higher), end-to-end delay (2% lower), and overhead control (9.4% lower). These results confirm that the proposed framework enhances communication efficiency, promotes sustainable cooperation, and mitigates fragmentation caused by non-cooperative behavior in SIoT environments.

The Internet of Things (IoT) and unmanned aerial vehicles (UAVs) continue to advance the low-carbon smart agriculture technologies for next-generation consumer electronics and unlock more informed agricultural practices. Reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated notable achievements in resolving complex problems, including resource allocation, energy efficiency, anomaly detection, and bandwidth utilization for multimodal tasks. This research explores multimodal data analysis and resource optimization using FRL for agricultural consumer electronics. The proposed framework employs IoT devices to monitor temperature, humidity, soil temperature, and soil moisture in real time, while UAVs provide aerial imagery for soil moisture, crop growth, and pest identification across three fields. This framework supports distributed learning, which trains local RL models on each node and combines them into the global model. The proposed FRL model demonstrated significant enhancements, including a 17% reduction in energy consumption for IoT devices and a 15% reduction for UAVs compared to non-FRL methods. This research emphasizes the effectiveness of FRL in integrating IoT and UAV for efficient resource allocation, energy efficiency, and reduced carbon emissions for low-carbon agricultural consumer electronics.

This paper provides the complete details of current challenges and solutions in the cybersecurity of cyber-physical systems (CPS) within the context of the IIoT and its integration with edge computing (IIoT–edge computing). We systematically collected and analyzed the relevant literature from the past five years, applying a rigorous methodology to identify key sources. Our study highlights the prevalent IIoT layer attacks, common intrusion methods, and critical threats facing IIoT–edge computing environments. Additionally, we examine various types of cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy of primary security mechanisms for CPS within IIoT–edge computing is developed, followed by a comparative analysis of our approach against existing research. The findings underscore the widespread vulnerabilities across the IIoT architecture, particularly in relation to DoS, ransomware, malware, and MITM attacks. The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. Finally, the paper outlines potential future research directions aimed at advancing cybersecurity in this rapidly evolving domain.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs’ learning capabilities to model the network’s dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system’s ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system’s performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

The regular frequent pattern mining (RFPM) approaches are aimed to discover the itemsets with significant frequency and regular occurrence behavior in a dataset. However, these approaches mainly suffer from the following two issues: (1) setting the frequency threshold parameter for the discovery of regular frequent patterns technique is not an easy task because of its dependency on the characteristics of a dataset, and (2) RFPM approaches are designed to mine patterns from the static datasets and are not able to mine dynamic datasets. This paper aims to solve these two issues by proposing a novel top-K identical frequent regular patterns mining (TKIFRPM) approach to function on online datasets. The TKIFRPM maintains a novel synopsis data structure with item support index tables (ISI-tables) to keep summarized information about online committed transactions and dataset updates. The mining operation can discover top-K regular frequent patterns from online data stored in the ISI-tables. The TKIFRPM explores the search space in recursive depth-first order and applies a novel progressive node’s sub-tree pruning strategy to rapidly eliminate a complete infrequent sub-tree from the search space. The TKIFRPM is compared with the MTKPP approach, and it found that it outperforms its counterpart in terms of runtime and memory usage to produce designated topmost-K frequent regular pattern mining on the datasets following incremental updates.

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.

Online transactions have been reported to be hindered by privacy concerns. Although information privacy presents a threat to e-commerce adoption, cultural differences between nations can additionally impede this trend while raising people’s concerns about the privacy of their personal information. By removing geographic and time restrictions, the rise of e-commerce has completely changed how businesses interact with their clients. As a result, this research looked into how national culture affected the relationship between e-commerce adoption and information privacy in the United Arab Emirates. We suggested that, regardless of a society’s technological and economic infrastructure, privacy concerns and e-commerce adoption are influenced by cultural values. Our research model, which was validated using online survey questionnaires, was created employing Hofstede’s cultural dimensions. Self-administered questionnaires were used in a quantitative strategy. A sample of 249 consumers was chosen, and partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. Our analysis revealed that key factors in people’s intention to transact online include their degree of perceptions of Internet safety, acceptance of e-commerce, privacy concerns, and personal interests. Additionally, the results show that gender has a positive effect as a mediator between the factors: “Privacy Concerns, Personal Interest, Safety Perceptions, and Transaction Willingness”. These results show how culture affects the adoption of e-commerce. Nevertheless, surprisingly, according to the research’s findings, privacy concerns were not indicative of cultural values, indicating that the idea of information privacy is more sophisticated than what a society’s culture represents.

In this study, we present the results of an assessment of an initiative that seeks to transcend the application of digital information in the higher education sector by recommending an integrative approach that quantifies both the flow of digital information and tutors’ quality impacts concerning technology acceptance model (TAM) constructs and the perceived experience of digital information in education (DIE). There is a mounting evidence that the educational institutions that prioritize the perceived experience and the quality of the tutors do not, generally, take into account the limited exposure to digital information and technologies. Data gathered from a survey of 485 college students were used to evaluate the model and hypotheses. The findings show that users’ perceptions of the value of DIE may depend on several extrinsic conditions that improve their experiences of learning and teaching. The user’s traits, such as technological preparedness, are vital in determining perceived ease of use. In some cultures, the superior quality of the tutor may further increase perceptions of the technology’s perceived usefulness. The intention to adopt technology may also be highly influenced by other variables such as information flow. Therefore, academic institutions must reevaluate the usefulness of digital information technology as a tool for improving educational sections. This research limited its focus to educational environments in which DIE has a significant impact on the teaching and learning setting. Future works may concentrate on health or monetary organizations.