Arman Process Journal (APJ)

Abstract Today, modern logistics is moving towards the web-based services. Nowadays, e-commerce is growing rapidly in the digital economy and customers expect to be able to make purchases quickly and easily. This technology has evolved over the years and is still evolving, so that many businesses have decided to enter their business in this space. On the other side, the widespread growth of the Internet has created a wider platform, called the Internet of Things (IoT), which seeks to create a powerful infrastructure for collecting, processing and transmitting distributed information in the shortest possible time. The impact of the IoT on the payment industry and e-commerce is no secret, to both buyers and sellers. Although examining the technical aspects of the Internet of Things for the development of e-commerce is sometimes a vague issue and has not been fully clarified. In this regard and in order to achieve this goal, in this article we intend to examine the role of the IoT in the development of e-commerce functions and the technical aspects of the problem.

Abstract In this article, the challenges of software development in the agile method were examined by focusing on non-functional requirements. Non-functional requirements are an important research area that mainly occurs due to the frequency of project failures caused by ignoring the qualitative characteristics related to user values. In the investigations carried out, the increase in development cost and time, the decrease in system flexibility, the increase in complexity and the problems of maintenance and subsequent development, and finally the increase in security risks and software errors, including the damage of neglecting non-functional requirements, have been identified. Effective coordination and cooperation between development groups and other stakeholders have also been identified. It is very important. By following the best practices and non-functional requirements management methods, you can have a significant improvement in the software development process in an agile way with higher accuracy and quality. The study field of software engineering is considered as one of the attractive and practical fields. Undoubtedly, the research conducted in this field will expand day by day and will provide better results. Among the proposed fields for further relevant studies in this field, we can mention the examination of agile management models and methods and improvement of development processes.

Abstract Background and Objectives: Cardiovascular diseases have been identified as one of the most prevalent global health issues, and delays in treatment can lead to increased mortality among patients. The primary objective of this study has been to enhance the identification of heart disease patients using a machine learning classification system.
Methods: In this research, machine learning classification systems with rule-based learning techniques have been employed. These techniques are built upon two fundamental principles, reinforcement learning, and genetic algorithms. The Mishgan style has been selected as the optimization method, and a dataset of heart disease patients from the Afshar Research Center has been utilized for the training and learning of the system.
Findings: Following the training of the system, a set of valuable rules has been generated and utilized in the testing phase for predicting heart disease patients. The experimental results indicate that using the Mishgan-style machine learning classification system has improved the identification of heart disease patients, resulting in an 88% increase in prediction accuracy. In other words, this approach enables a more comprehensive identification of heart disease patients.
Conclusion Considering the study's outcomes, the use of the Mishgan-style machine learning classification system as an optimal approach has enhanced the identification of heart disease patients and increased prediction accuracy. This method can contribute significantly to timely treatment of heart disease patients and the reduction of morbidity and mortality associated with these diseases.

Abstract The instantaneous increase in users and their need for internet services caused that, in a short time, the companies that provided this type of service faced problems such as the inability to respond quickly to users and the increase in their costs. Therefore, many of these companies, with a lot of investments in research fields, thought of effective and cost-effective ways to serve a high volume of users, and in this way, new technology and an efficiency system called cloud computing were created. With the increase in users using cloud computing services and therefore the increase in the number of requests, in order to achieve the mentioned benefits, there is a need to establish appropriate mechanisms for load balancing, work scheduling and virtualization. Sazi is in cloud computing. This load can include memory capacity, network load or delay. Load balancing is the process of distributing load among different nodes of a distributed system in order to improve the utilization of resources and response time, while it is a situation in which some nodes have a heavy load while the node Others avoid being unemployed or having very little work to do. Considering the necessity and importance of load balancing in cloud computing, in this article, a comprehensive review of static, dynamic and nature-inspired algorithms for load balancing in a cloud space to handle the response time of data centers and their overall performance is given. We pay by analyzing the load balancing algorithms. We show that the ant colony algorithm, the genetic algorithm and the particle swarm optimization algorithm with optimal allocation of tasks can play a more effective role in balancing the load in the cloud space. Also, the results show that CloudSim software has been used the most in simulating load balancing algorithms in the cloud space.

Abstract Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short of providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems are centralized, manual, and highly disintegrated, which make them vulnerable to manipulation and the single point of failure problem. Emerging technologies such as the Internet of Things (IoT), fog computing, cloud computing, and block chain can play a vital role in boosting the operational efficiency of the oil and gas industry. In this paper, we explore the potential opportunities and applications of Artificial Intelligence technology in managing the exploration, production, and supply chain and logistics operations in the Natural Gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. This research highlights the use cases of AI in decentralized Block chain with smart contracts, the company’s trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. Results spotlight the use of AI in decision accuracy for the developed smart contract-based Natural Gas Industry, thereby qualitatively limiting the threshold level of costs, energy and other control functions in procurement, production and distribution.

Abstract Nowadays, cyber-physical systems can be used for many new technological applications such as traffic monitoring, battlefield surveillance, and monitoring based on sensors and distribution networks. These systems intelligently monitor and monitor the state of the surrounding environment and react with an intelligent response to the events of the surrounding environment. Wireless sensor network is one of the important applications of cyber-physical systems, in which it starts collecting information from the surrounding environment by using a large number of sensor nodes in a wide environment. Tracking moving targets is a fundamental and challenging issue in this field. In this article, we investigate the tracking of targets on the wireless sensor networks, then we will review and present the issues and problems of tracking in this area. for this purpose, we will first have a comprehensive definition of cyber physical systems as well as the wireless sensor network, then we will continue to review We will track targets in the sensor network and we will examine the methods of tracking in the network and related quality parameters.