The electrocardiogram (ECG) is one of the most extensively employed signals used in diagnosis and prediction cardiovascular diseases (CVDs). ECG can capture heart's rhythmic irregularities, commonly known as arrhythmias. A careful study crucial for precise diagnoses patients' acute chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model classification into eight classes; namely, normal beat, premature ventricular contraction paced...

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet vehicles (IoV) to enhance intelligent transportation capabilities. By updating twins and offloading services devices (ECDs), insufficiency in vehicles' computational resources can be complemented. However, owing intensity DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates quality (QoS). To address this problem,...

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, developing efficient collaborative decision-making algorithms a swarm UAVs limit their autonomy, robustness, reliability. Thus, growing focus been witnessed on to allow coordinate communicate autonomously cooperative completion...

The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly algorithms mainly use the restructure abnormal areas and detect by calculating errors between original image reconstructed image. However, traditional CNNs struggle extract global context information, resulting in poor performance. Thus, masked Swin Transformer Unet (MSTUnet)...

Diabetic Retinopathy (DR) is one of the major causes visual impairment and blindness across world. It usually found in patients who suffer from diabetes for a long period. The focus this work to derive optimal representation retinal images that further helps improve performance DR recognition models. To extract representation, features extracted multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations used train Deep Neural Network...

The development of vehicular Internet Things (IoT) applications, such as E-Transport, Augmented Reality, and Virtual Reality are growing progressively. mobility aware services network-based security fundamental requirements these applications. However, multi-side offloading enabling blockchain cost-efficient scheduling in heterogeneous fog cloud nodes network become a challenging task. study formulates this problem convex optimization problem, where all constraints the set. goal is to...

Industrial Internet of Things (IIoT), which consists massive IoT devices and industrial infrastructures such as wireless access points to acquire intelligent services, has been devoted a critical physical information platform realizing the fourth revolution, otherwise known Industry 4.0. Currently, new paradigm called mobile edge computing (MEC) brought an opportunity accelerate development IIoT. It powerful capabilities can be well-used provide low-latency services execute some computation...

In location-based social networks (LBSNs), extensive user check-in data incorporating preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group (PPCM). proposed three reasons: 1) because influence training deep learning model, taken into account. To protect privacy...

Mobile edge computing (MEC) provides a novel paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), latency-sensitive tasks are offloaded RSUs for execution, reducing transmission latency compared with cloud solutions. However, repetitive executions same whose outputs dependent on inputs lead extra system latency, an alternative is cache required services in advance. The service satisfied by jointly...

Recent developments in the Internet of Vehicles (IoV) enabled myriad emergence a plethora data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables vehicles utilize resources servers reduce data transfer burden stress. Although utilization VEC is favourable support for IoV vehicle mobility other factors further complicate challenge designing implementing such...

As autonomous vehicles and the other supporting infrastructure (e.g., smart cities intelligent transportation systems) become more commonplace, Internet of Vehicles (IoV) is getting increasingly prevalent. There have been attempts to utilize Digital Twins (DTs) facilitate design, evaluation, deployment IoV-based systems, for example by high-fidelity modeling, real-time monitoring, advanced predictive capabilities. However, literature review undertaken in this paper suggests that integrating...

Mobile edge computing is one of the key enabling technologies smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks to service providers. However, it challenging make optimal decisions computation resource allocation while ensuring privacy information security MDs. Consequently, we consider a new vision digital twin (DT) empowered networks, where optimization problem formulated as two-stage incentive mechanism....

Using the powerful communication capability of 6G, various in-vehicle services in Internet Vehicles (IoV) can be offered with low delay, which provide users a high-quality driving experience. Edge computing 6G-enabled IoV utilizes edge servers distributed at road, enabling rapid responses to delay-sensitive tasks. However, how execute computation offloading effectively remains challenge. In this paper, Computation Offloading method Demand prediction and Reinforcement learning, named CODR, is...

To cope with the ongoing changing demands of internet, `in-network caching' has been presented as an application solution for two decades. With advent information-centric network (ICN) architecture, becomes a level solution. Some unique features ICNs, e.g., rapidly cache states, higher request arrival rates, smaller sizes, and other factors, impose diverse requirements on content eviction policies. In particular, policies should be fast lightweight. this paper, we propose replication...

The rapid development and deployment of Internet Things (IoT) devices in modern networks Industry 4.0 have attracted substantial interest from cybersecurity researchers. In this study, we propose a software-defined framework that improves network intrusion detection systems by using manufacturer usage description (MUD) to enhance the behavioral monitoring IoT networks. We aim explore whether Industrial (IIoT) typically serve common role cyber–physical systems, their communications...

In recent years, wireless communication has experienced a massive shift from single service (i.e., voice) to an interconnected web of networks. Although many techniques have been developed improving the offered services mobile users, still demand for high-quality cannot be reached. Therefore, this paper proposes joint non-orthogonal multiple access (NOMA)-enabled optimization framework small-cell network (SCNet) by utilizing concepts multi-objective problem. particular, transmit power base...