Arman Process Journal (APJ)

Abstract Cloud computing has emerged as a paradigm that transcends traditional distributed computing systems, such as Grid and Cluster systems, offering the capability to handle dynamic requests and diverse user requirements. As the number of users grows, there is a pressing need to deploy effective mechanisms for load balancing and task scheduling. Load balancing is essential for evenly distributing workloads across physical servers, preventing resource congestion, and enhancing overall system performance. Furthermore, considering users' service requests and the necessity for service providers to deliver accurate and timely responses, coupled with the limited resources available in the cloud environment, efficient task scheduling becomes imperative. This paper proposes an approach for optimizing virtual machine (VM) migration by combining Genetic Algorithms and Ant Colony Optimization for resource scheduling operations. Additionally, it employs K-Means clustering and fuzzy logic to quantify the dependencies between VMs and physical machines during migration, thereby maintaining load balance. The proposed model is evaluated and compared against three existing load balancing algorithms within the CloudSim simulation environment. The evaluation results demonstrate that our proposed model achieves a 4.5% reduction in task completion time, a 4.9% increase in the deadline success rate, and a 3.9% improvement in task diversity. Furthermore, computational complexity is reduced by 8.3%, VM migration efficiency is improved by 2.5%, and decision-making delay is significantly decreased by 9.5%. Additionally, the model achieves substantial energy savings of 30-35%.

Abstract Background and Objectives: With the advancement of vehicular networks and the increasing demand for accurate and timely data, challenges such as data retrieval delays and security concerns have garnered significant attention. Traditional cloud-based storage methods are unable to meet temporal and security requirements due to the considerable distance between vehicles and servers. Although edge computing offers a solution for reducing latency, it requires improvements due to limitations in storage and distributed management. Previous research has primarily focused on one aspect of optimization (reducing delay or enhancing security), with less attention paid to combining these two objectives. This paper aims to propose an innovative hybrid optimization model using deep learning and blockchain that considers both security and delay reduction. This is achieved by optimizing caching locations, storage, retrieval processes, and storing critical information on the blockchain, ensuring a scalable and flexible model adaptable to traffic changes and user demands.

Methods: The study population and sample include mobile vehicular networks (VANETs), considering edge servers and vehicular nodes. An LSTM model was used to predict traffic patterns and data popularity, while blockchain with a Proof of Authority (PoA) consensus mechanism and smart contracts was employed for secure data storage. Performance was evaluated based on delay, security, and scalability metrics, and compared with existing methods such as Tabu Search, CCS-AGP, and Random Caching in terms of delay and security.

Findings: The proposed model significantly reduced delay (by 10% to 30% compared to baseline methods). The use of blockchain introduced only an 8% additional delay while elevating security to a "very high" level. The system demonstrated stability and scalability under increasing numbers of nodes and data volume. Simulation results indicated that the combination of deep learning and blockchain achieves an optimal balance between performance and security.

Conclusion: The proposed model, integrating deep learning and blockchain, not only reduces delay but also ensures data security and integrity. This framework can serve as a foundation for developing intelligent systems in domains such as the Internet of Things (IoT), smart cities, and next-generation transportation.

Abstract One of the most important applications of the Internet of Things in the field of health is remote monitoring of patients. This technology allows doctors to check the health status of patients in real time, which is especially vital for people suffering from or prone to heart diseases. Prediction of cardiovascular diseases is known to be a complex challenge that faces low accuracy in existing models. In this research, a new recommender system for predicting cardiovascular diseases is proposed that uses a convolutional neural network to analyze physiological data of patients. Physiological data from patients are collected remotely through four biological sensors including ECG sensor, blood pressure sensor, heart rate sensor and blood sugar sensor. These data are then processed by an Arduino controller and the convolutional neural network model is used to predict cardiovascular disease. With outstanding capabilities in extracting local features and without the need for complex time sequence analysis, this model can effectively use fixed numerical data such as blood pressure, heart rate, and blood sugar to diagnose heart diseases. The experimental results showed that the convolutional neural network was able to effectively extract local and non-temporal features of the data and help the model achieve a prediction accuracy of 98.90%.