Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability.

The transceiver design for multi-hop multiple-input multiple-output (MIMO) relay is very challenging, and for a large scale network, it is not economical to send the signal through all possible links. Instead, we can find the best path from source-to-destination that gives the highest end-to-end signal-to-noise ratio (SNR). In this paper, we provide a linear minimum mean squared error (MMSE) based multi-hop multi-terminal MIMO non-regenerative half-duplex amplify-and-forward (AF) parallel relay design for a wireless sensor network (WSN) in an underground mines. The transceiver design of such a network becomes very complex. We can simplify a complex multi-terminal parallel relay system into a series of links using selection relaying, where transmission from the source to the relay, relay to relay, and finally relay to the destination will take place using the best relay that provides the best link performance among others. The best relay selection using the traditional technique in our case is not easy, and we need a strategy to find the best path from a large number of hidden paths. We first find the set of simplified series multi-hop MIMO best relays from source to destination using the optimum path selection technique found in the literature. Then we develop a joint optimum design of the source precoder, the relay amplifier, and the receiver matrices using the full channel diagonalizing technique followed by the Lagrange strong duality principle with known channel state information (CSI). Finally, simulation results show an excellent agreement with numerical analysis demonstrating the effectiveness of the proposed framework.

Cardiovascular illness is frequently the cause of my-ocardial infarction (MI), also referred to as a heart attack, which happens when the blood supply to a portion of the heart muscle is interrupted or diminished. This event can range from being asymptomatic to causing significant declines in cardiovascular function and premature mortality. An electrocardiogram (ECG) is vital for diagnosing and assessing MI, as it measures the heart's electrical activity and can detect various cardiac conditions. In this study, we developed a Residual Network (ResNet)-based model for MI detection using ECG signals. The model was trained on the PTB-XL dataset, which includes 21,837 annotated ECG signals, and tested on converted ECG signals from the Mendeley Data ECG Images dataset. Our model achieved high precision, recall, and F1-scores, demonstrating robust performance with an accuracy of 98.95 % on the validation set and 98.63 % on the test set. In addition, we developed ECGInsight, a web application that lets users take or upload a picture of an ECG report. The photo is converted to ECG signals, which are then processed by ResNet to produce predictions with confidence levels. This study underscores the potential of Deep Learning (DL) models in automated MI detection and highlights the practical utility of the ECGInsight web application in making MI detection accessible to users, ultimately contributing to advancements in cardiovascular healthcare.

Arabic Natural Language Processing (NLP) presents unique challenges due to the complexity of the language, including variations in dialects, rich morphology, and context-dependent semantics. These linguistic intricacies make accurate classification a challenging task. This study presents the development and evaluation of an Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model trained on a publicly available dataset named UltimateArabic dataset, which comprises 10 diverse classes. The aim is to perform multiclass classification and to address the imbalance class problem, using AraVec, of Arabic text data in the field of NLP. The model achieved an overall accuracy of 98% on augmented data, showcasing its proficiency in categorizing a wide range of Arabic texts including medical, technology, art, culture, diversity, society, religion, sports, politics, and economy. The AraBERT model exhibits strong classification performance compared to non-augmented data across the majority of categories, with an encouraging raise on macro-average precision of 4%, recall of 4%, and F1-score of 4%. This study underscores the potential of AraBERT along with AraVec for diverse applications in imbalanced Arabic text classification tasks and highlights the need for continued research to address the complexities of multiclass classification in Arabic NLP.

The self-organizing method of distributed Wireless Sensor Networks (WSN) has an energy-efficient feature that can detect and assign channels to each node at different frequencies to avoid inter-node collision. The fading and interference of the channel may disconnect the wireless linkage between connected nodes. Most of the published work on this topic to date use digital filters to eliminate noise and interference for the wireless sensor network topology. However, estimation using a digital filter may not be the perfect candidate for communication in an underground mine, where node-to-node transmission is greatly affected by multipath fading. We propose a power control -based decision criteria for node transmission in case of deep fading to save energy. The and the node can also automatically re-establishment connection by adjusting its transmission power. The goal of the auto power control mechanism is to ensure the target Mean Squared Error performance in case of a random fading channel. The main objective of this work is to use a Random Walk -based Kalman Filtering scheme with Diffusion Least Mean Square (LMS) algorithm that can reduce the multi-path fading effect more accurately for successful signal transmission in in an underground environment. Our simulation results show that the proposed method outperforms the Mean Squared Error performance at low power consumption over the existing schemes.

Low-delay high-gain optimal multi-hop routing path is crucial to guarantee both the latency and reliability requirements for infotainment services in the high mobility internet of vehicles (IoVs) subject to queue stability. The high mobility in multi-hop IoVs reduces reliability and energy efficiency, and becomes bottleneck for the optimal route solution using classical optimization methods. To a great extent, deep reinforcement learning (DRL)-based method is not applicable in IoVs environment because of the continuously changing topology and space complexity, which grows exponentially with the number of state variables as well as the relaying hops. Usually, in multi-hop scenario, network reliability and latency are affected by mobility as well as average hop count, which limit the vehicle-to-vehicle (V2V) link connectivity. To cope with this problem, in this paper, we formulate a minimum hop count delay-sensitive buffer-aided optimization problem in a dynamic complex multi-hop vehicular topology using a digital twin-enabled dynamic coordination graph (DCG). Particularly, for the first time, a DCG-based multi-agent deep deterministic policy gradient (DCG-MADDPG) decentralized algorithm is proposed that combines the advantage of DCG and MADDPG to model continuously changing topology and find the optimal routing solutions by cooperative learning in the aforementioned communications. The proposed DCG-MADDPG coordinated learning trains each agent towards highly reliable and low latency optimal decision-making path solutions while maintaining queue stability and convergence on the way to a desired state. Experimental results reveal that the proposed coordinated learning algorithm outperforms the existing learning in terms of energy consumption and latency at less computational complexity.

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention.

This paper investigates the computation offloading problem in a high mobility internet of vehicles (IoVs) environment, aiming to guarantee latency, energy consumption, and payment cost requirements. Both moving and parked vehicles are utilized as fog nodes. Vehicles in high mobility environments need collaborative interactions in a decentralized manner for better network performances, where agent action space grows exponentially with the number of vehicles. The vehicular mobility introduces additional dynamicity in the network, and the learning agent requires a joint cooperative behavior for establishing convergence. The traditional deep reinforcement learning (DRL)-based offloading in IoV ignores other agent’s actions during the training process as an independent learner, which makes a lack of robustness against the high mobility environment. To overcome it, we develop a cooperative three-layer, more generic decentralized vehicle-assisted multi-access edge computing (VMEC) network, where vehicles in associated RSU and neighbor RSUs are in the bottom fog layer, MEC servers are in the middle cloudlet layer, and cloud in the top layer. Then multi-agent DRL-based Hungarian algorithm (MADRLHA) in the bipartite graph maximum matching problem is applied to solve dynamic task offloading in VMEC. Extensive experimental results and comprehensive comparisons are conducted to illustrate the superiority of our proposed method.

In this paper, we consider a Fog-IoT network, comprising multiple terminal nodes (TNs) as well as fog nodes (FNs), in which each TN first determines the proper FN to be associated with different quality of service (QoS) and quality of transmission (QoT) constraints, such as energy efficiency, processing delay, and bandwidth requirements; then, it sends its tasks to the associated FN. The main objective of this paper is to maximize the transaction throughput of FNs, i.e., the number of tasks that FNs can process, while there exists a throughput fairness as well as an energy consumption fairness among all FNs in the network. To this end, two algorithms, namely TF-TRADE and QL-TRADE, are proposed. The TF-TRADE aims to ensure throughput fairness and at the same time optimize the total transaction throughput of the network. The QL-TRADE not only follows the same objective, but also tries to balance the energy consumption among all FNs. Analysis and simulation results reveal that the proposed algorithms could alleviate the network performance in terms of the transaction throughput as well as the energy consumption balance among all FNs.