Arman Process Journal (APJ)

Abstract Statistics deals with the collection, analysis, and use of data to solve problems. All people, both in specialized fields and in everyday life, come across information in the form of numbers or data through contact with the press, radio, television and other mass media. So some understanding of statistics will be useful for everyone. Because engineers, scientists, and administrators are constantly involved in data collection and analysis, statistical knowledge is essential for these disciplines. Experimental and observational studies The general purpose of a statistical research project is to investigate random events, and in particular to draw conclusions about the effect of changes in the value of indicators or independent variables on a response or dependent variable. There are two main methods of random statistical studies: experimental studies and observational studies. In both types of studies, the effect of changes in a non-dependent variable (or variables) on the behavior of dependent variables is observed. The difference between the two methods is in how the study is actually conducted.
In human subject research, a survey is a list of questions that aims to extract specific data from a specific group of people. Surveys may be conducted by telephone, post, Internet, as well as on street corners or shopping malls. Statistics are used to gather or gain knowledge in areas such as social research and demography. Once the data have been collected, either by a specific sampling method or by recording responses to behaviors in an experimental set (experimental design) or by repeatedly observing a process over time (time series), graphical or numerical summaries can be made. Use of descriptive statistics achieved.

Abstract Memory management is one of the critical and major functions of the modern operating systems, that manages the main memory operations and shifts processes between main memory and disk during the application execution. Memory management manages them all, whether or not different memory locations are allocated to a process. This process determines the amount of memory that must be allocated to operating system processes. Memory management approches decide which process to access memory at a time and track the amount of memory allocated or free up and update the corresponding states. In this study, we have examined the major dimensions of the memory management issue from the perspective of the operating system administrators, the validation of quality requirements and also how to use the quality dimensions in the memory management of the modern operating systems, due to its great importance in this field for ensuring efficiency and performance.

Abstract Statistics is a broad science that studies ways of collecting, summarizing and producing conclusions from data. This science is used for a wide range of academic sciences from physics and social sciences to anthropology as well as business, governance and industry. The difficulty of understanding statistics is one of the most important obstacles that prevent researchers from applying research results in their work, which limits the opportunity to perform evidence-based research. As the emphasis on evidence-based practice increases, more pressure is placed on researchers to describe the research of others and to contribute to their own research. This article examines and emphasizes why technology researchers should understand simple statistical concepts both to use the research works of others and to carry out their own research works. In this article, the types of indicators and statistical methods used in applied statistical research are reviewed and described. This article reviews relevant sources and provides a list of important points that should be reviewed and confirmed before applying appropriate statistical methods to a data set and preparing for the implementation of a research.

Abstract E-commerce is one of the advanced dimensions of the development of communication and information technology in the social, political and economic fields. The results of studies of the actions of developed countries in this field focus on the fact that managerial and citizenship behaviors and interactions between elements of society in this phenomenon do not follow its traditional state. Also, policymakers and managers consider e-commerce not as a goal but as a tool to benefit from new methods of interaction between influential elements of society in creating purposeful, fast and flexible governance tailored to the dynamic needs of citizens. This article first describes the concept of e-commerce and mentions the important points about the challenges and prospects ahead. In the following, the appropriate infrastructure for the use of this technology is expressed and then, according to the literature and the available evidence, the challenges and opportunities facing the implementation of this technology are discussed and finally, according to the aggregate information, practical suggestions for the successful implementation of this technology in the Iran have been presented.

Abstract In recent years, organizations have faced various challenges such as increasing competition in operational areas. In recent years, we have encountered an emerging phenomenon called Internet of Things technology, whose wide applications have been investigated in industries, trade, smart services, healthcare, etc., however, the qualitative aspects of the problem remains unknown. It is expected that services based on the Internet of Things will increase the quality level of such services, in addition to reducing costs. However, the multiplicity of protocols, the amount of sending packets in the network, the sensitivity of packets to delay and data loss, etc., have various levels of service quality and have different evaluation indicators that require deep analysis and investigation. The purpose of this research is to identify and prioritize the technological applications of the Internet of Things and related service quality management by using the structural interpretation approach and examining the required indicators in this regard. The results of this study can be used in the development of models and frameworks to improve service quality management in the Internet of Things network.

Abstract Nowadays, modern technologies provide a certain level of digitalization of business processes. Due to the influence of information and communication technology in all areas of social life and business processes, it is possible to implement production decisions based on the needs and requirements of the customer. This trend is mainly due to advances in information and communication technology and the Internet. Thanks to the Internet and a phenomenon called the Internet of Things (IOT), higher profits and improved quality of life in society can be achieved. This article discusses the use of internet of things solutions to streamline smart business services and equip services to support management decisions in smart businesses. The main purpose of this article is to evaluate the increasing progress of this technology that is affecting the whole world now, so IoT can not be talked about as a topic in the future. The main findings of the article can be summarized through the statement that, in the near future, all businesses will try to implement the concept of the Internet of Things in their processes and thus become smart businesses. Thanks to the Internet of Things, businesses can intelligently and quickly process data and distribute results to those responsible for making decisions in the smart organization.

Abstract Nowadays, the information and communication technology has changed the way people, organizations and governments work and has transformed the functions of economic interactions. Despite the fact that information technology has assumed important roles as a fundamental tool for creating value in smart economic interactions, but Currently, Iran is facing many infrastructural problems and many efforts have been made to solve these problems and achieve ideals. This research was written with the aim of investigating the functions of information technology in smart economic systems and related interactions in Iran and the challenges ahead. The present research was compiled by the researcher in a library and note-taking manner. At the end, the findings of the research are summarized and solutions are suggested to overcome the current situation. The findings of the research show that in order to face the challenges examined in the research, it is necessary to develop the necessary infrastructure, support government institutions, create culture, hold training courses and create the necessary legal and business platforms in this area.

Abstract Most adaptive computing systems use reconfigurable hardware in the form of field programmable gate arrays (FPGA). In order for these systems to be fielded in harsh environments where high availability and reliability are a requirement, the programs running on FPGAs must be hardware fault tolerant, as this is the case during the lifetime of the system. may occur In this paper, we present new fault tolerance techniques for FPGA logic blocks, which are developed as part of the Self-Standing Test Areas (STAR) approach for test and diagnosis, and online configuration (we tolerate over 100 logic faults through the actual implementation on an FPGA containing a 20 x 20 array of logic blocks). A key feature is the reuse of incomplete logic blocks to increase the number of effective spares and extend mission length. To increase fault tolerance, we not only use faulty non-faulty sections or logic blocks with minor faults, but also use faulted sections of faulty logic blocks in non-faulty modes. By using and reusing faulty resources, our multilevel approach extends the number of tolerable faults beyond the number of available spare logic resources. Unlike many row, column, and piecewise methods, our multi-level approach can tolerate faults that are evenly distributed over the logic area, while also clustering faults in the same local area. Meanwhile, system operations are not interrupted for fault detection or computational fault-transitor configurations. Our fault tolerance techniques are implemented using ORAC2 series FPGAs that specify incremental dynamic runtime configuration

Abstract Considering the increasing importance of hardware testing in the field of multimedia compression, this article examines different methods and approaches for this problem. These tests are performed in order to ensure the accuracy and efficiency of multimedia compression, as well as increase resistance to errors. Also, in this article, the importance of using linear transformations in multimedia compression has been investigated, and hardware testing methods for evaluating the quality and performance of multimedia compression have been investigated. These tests include random error tests, special error tests, and sampling tests, which aim to ensure that multimedia information is robust to errors. In another part of this research, a fault-tolerant scheme at the system level is proposed for systems where a linear transformation is combined with quantization. Using the concept of acceptable degradation, our scheme categorizes hardware defects into acceptable and unacceptable defects. Analytical techniques are also proposed that allow us to estimate the effect of defects on compression performance and to propose methods for creating acceptable degradation thresholds and corresponding test algorithms for DCT-based systems. In general, the results show that using linear transformations and performing appropriate hardware tests can help improve the quality and efficiency of multimedia compression and increase resistance to errors, and the main achievement includes increasing the compression speed, reducing the size of files, The quality of image and sound has been improved, as well as the resistance against errors has been increased, and it is also possible to effectively recover information in the event of an error.

Abstract With the increasing development of technology and the expansion of the use of the Internet, botnets are considered as one of the most important security threats in the digital space. Botnets are networks of infected devices controlled by attackers and used for various purposes such as sending spam, DDoS attacks, and stealing sensitive information. Considering the increasing trend of using botnets, it is very important to detect and prevent their activity. The spread of communication, resource sharing, curiosity, earning money, gathering information and gaining resource capacity are motivations for creating botnets. In addition to these, political, economic and military motives should also be added. Our method has the ability to detect known and unknown botnets that use this method. Our goal in this paper is to present an innovative method to detect botnets using failed response analysis and neural network. In this method, botnets are detected based on failed responses or NXDomain in each host. This feature increases the accuracy of detection in small and medium networks. This method has been tested in networks infected with Konfiker and Kraken botnets and the information obtained from it has been analyzed using neural networks. The evaluation results show the good performance of this method in botnet detection.

Abstract Background and Objectives: Multiple sclerosis is a brain disease where early diagnosis is crucial for treatment. One of the ways to diagnose this disease is by observing lesions caused by it in MRI scans. Most previous approaches have issues such as low diagnostic accuracy, a high number of features, time-consuming analysis, and a lack of certainty in achieving optimal answers.
Methods: In this article, for the first time, a feature vector set is formed by aggregating results from image MRI texture descriptors such as wavelet transform, chaotic features (fractal), and local binary patterns. The presentation of a selected feature set using a differential evolutionary algorithm has not been utilized in this area of identification before, so our proposed technique is based on this approach. Additionally, the proposed classifier will be an improved model combining three types of neural networks. Moreover, improvements in accuracy, sensitivity, and the ability to verify the correctness of the classification results are also considered innovative aspects.
Findings: The data used in this article was obtained from two datasets. After K-fold cross-validation, the experimental accuracy for both image datasets were found to be 95% and 97%, respectively, which represents a 2% improvement over a method that used wavelet transform along with principal component analysis and support vector machines, while also addressing the uncertainty issue.
Conclusion: Our integrated algorithm introduced greater diagnostic accuracy compared to previous methods and takes into account the accuracy factor that was not considered in past approaches. This algorithm not only reduces processing time but also enables simultaneous processing from different channels, aligning better with the opinions of specialized doctors. Despite the lack of a simultaneous separation-processing technique, the rates of false positives and negatives in identifying MS disease are very low. For future work, recommendations include noise removal in the preprocessing stage, combining feature selection techniques to increase accuracy, and using parallel processing as the primary tool in separation software.

Abstract Background and Objectives: Wind turbines as one of the means of producing electrical energy from renewable and clean energies have been the focus of many researchers. The discussion of turbine control in order to produce more power and its economical use against fossil fuels has challenged different control methods.

Methods: In the current research, the purpose of using intelligent fuzzy controllers is to improve the output power and stabilize it when necessary due to its robustness. For this purpose, the induction generator with two-way feeding and variable wind was modeled first, then phase controllers will be designed to separately control active and reactive powers, reduce interference and uncertainty effects. that we used the particle swarm algorithm and the best rules and fuzzy parameters of the intelligent fuzzy system based on deep learning to create the rule and interference system to better performance.

Findings: The comparison of the simulation results of intelligent fuzzy and PI controllers shows the better performance and efficiency of the fuzzy controller in terms of more stability, steady state error and less settling time than the PI controller used in the system. The performance accuracy of the fuzzy controller based on deep learning due to rule extraction and optimal PSO design using random forest algorithm for this system is suitable according to the obtained outputs and the system is controlled in less than 0.4 seconds.

Conclusion: Our integrated and hybrid algorithm shows the good performance due to accuracy and precision parameters, applying the deep learning in order to select the effective parameters on system design for rule extraction in fuzzy and create the decision making in PSO leads the novel way to approach the results.

Abstract In today's competitive world, information has emerged as one of the important production factors. As a result, the effort to extract information from data has attracted the attention of many people involved in the information industry and related fields. The large volume of data is constantly growing in all fields and the vast difference in data production process has increased the complexity of information management and extraction. Recently, several strategies and techniques have been used to collect, store, organize and efficiently manage existing data and achieve meaningful results, and data mining is one of the recent developments in the direction of data management technologies. The term data mining refers to the semi-automatic process of analyzing large databases and data warehouses in order to find useful and applicabale patterns. In this research, we are going to examine the operationalization process of data mining and do a practical analysis of this issue. In addition, we will research about the important applications and tools of this field.

Abstract The convergence of the Internet of Things (IoT) and artificial intelligence (AI), as one of the most central trends in digital transformation in the last decade, has created a new landscape in various industries, including manufacturing, transportation, energy, and smart cities. Despite the revolutionary potential of these technologies, their effective adoption and implementation face numerous challenges. The aim of this study is to provide a comprehensive framework for analyzing the factors affecting the adoption of artificial intelligence in IoT systems and to provide a proposed perspective to overcome existing barriers. This study, with an analytical-descriptive approach and using a systematic literature review, examines 46 authoritative articles published between 2018 and 2025. The theoretical framework of the study is based on the combined technology-organization-environment (TOE) model and the technology acceptance model (TAM). The literature analysis shows that the key applications of artificial intelligence in IoT include predictive maintenance, anomaly detection, real-time monitoring, and automated control. Facilitating factors include 5G infrastructure, organizational readiness, and skilled workforce, and major barriers include cybersecurity challenges, data quality, integration with legacy systems, and model scalability limitations. The proposed approach emphasizes the implementation of a hybrid architecture based on edge computing, federated learning, and software-defined networking (SDN) to overcome the existing challenges. This framework can serve as a roadmap for researchers and industry players on the path to AI-IoT-based digital transformation.

Abstract Today, e-commerce has become a way of doing business in the modern world. Basically, e-commerce can not grow enough without security. To achieve a dynamic e-commerce, we must implement security in it within the framework of principles, so that we can use it as a sustainable sample of business. Security issues, unauthorized users, viruses and the like are terrifying for companies at any level of internet connection. Most companies focus on their hardware and software to deal with these problems. Understanding security threats and risks, especially the dangers of e-commerce, can be a great help in designing and building a secure infrastructure. This article discusses various ways to reduce security threats, especially in cases where the greatest threat is posed by unauthorized users, viruses and other forms of network intrusion; Finally, security approaches, recommendations and solutions to deal with these threats are provided.

Abstract Cloud computing is the evolution of information technology and a dominant business model for providing IT resources. Using cloud computing, individuals and organizations can gain access to the requested network through a shared set of managed and scalable IT resources such as servers, storage space, and applications. Recently, academics have paid close attention to cloud computing. There are basically three basic models of cloud computing services: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). In terms of storage and resource collection, there are clear differences between them. There are three models and what they can offer to a business, but they can also interact with each other to form a comprehensive model of cloud computing. Performance isolation is a major challenge in providing cloud services and performance management is essential to achieving quality goals in the cloud. Although many studies have examined this issue, there is a lack of analysis on the dimensions, challenges and opportunities of management. This study examines the dimensions and functions of performance isolation in basic cloud services. At the end of the research, some recommendations have been described, for more effective use of this feature in cloud computing environments.

Abstract Nowadays, due to the large amount of information and the need of rapid processing, the need for computers with up-to-date and efficient operating systems with high processing power is felt. During the last decades, progress in the field of information technology has made available fast distribution systems. Distributed systems are one of the best solutions to achieve the above goal. By placing computer systems on the platform of the distribution network, it is possible to create a high processing power, whose complexity and organization are hidden from the eyes of users and in addition, it brings us the availability of resources and scalability. Securing the core of distributed operating systems is a necessity that can help improve the security of information technology in distributed network environments. In order to fundamentally implement this process, it is necessary to create an expert security team. Also, in addition to the technical review and the implementation of protection settings and access controls, there is a need to develop new methods and tools to study the sequence of certain or possible protection operations in the core of distributed operating systems. In this article, we are going to examine the security aspects in the core of distributed operating systems.

Abstract Based on the research and efforts of statistical scientists and even non-statistical fields, different methods have been developed to conduct statistical research, analyze findings and describe related results. Different methods of statistical tests are different approaches based on statistics that are widely used to conduct scientific research methods in different sciences. Usually, based on the theory and method of research, they are divided into two categories: "qualitative research" and "quantitative research". Usually in qualitative research, based on one or more observations, we try to describe and diagnose the cause-and-effect relationship and attribute the considered characteristic to the society. This is usually done by sampling and using statistical inference and inductive method, and the result of this research leads to a specific hypothesis or theory. Whereas in quantitative methods, we first intend to prove or disprove a theory, and by sampling from the statistical population, we reject or confirm this theory. This is done with the help of deductive inference. In order to better understand the problem and create an analytical schema, in this article, a review of basic statistical research methods and a comparison of their characteristics is presented.

Abstract Cloud computing is an emerging model of business computing. It distributes computing tasks in shared resource pools, which consist of many computers, so that various applications can access the cloud as they need. In computing, resource contention refers to a conflict over a shared resource between several components. Resource contention often negatively affects the performance of components competing for the resource. It is a common concern in cloud computing. Unmanaged resource contention in cloud computing environments can causes problems such as performance interference, service quality degradation and consequently service agreements violations. Performance management is an indispensable remedy solution for the mentioned challenges. Monolithic analysis and management of the performance from the cloud services perspective and different entities has not been studied in previous researches and most of the previous studies focus on algorithms and methods for specific applications and mostly, lack sufficient descriptions about management aspects of the performance. Due to the importance of this issue, this paper aims to make an in-depth investigation of this problem. In this paper, we propose a novel approach to monolithic management of the performance and its general requirements for clouds. The proposed approach enables service providers to realize different optimization methods and manage the performance for their offers and products. The experimental results demonstrate the effectiveness, flexibility and practicality of the proposed approach for effective performance management in different cloud service centers.

Abstract Currently, the significant growth of the number of users of online services and distributed systems generate a large amount of database information at the level of data warehouses and big data. Data warehouses are large collections of business data that help organizations and businesses make more accurate and intelligent decisions. Big data is also a very large collection of data collected from multiple sources. These data can be the results of evaluating the performance of an organization or the interactions of its audience in social networks. Due to the increasing importance of big data functions and data warehouses, the issue of maintaining data security is the biggest threat these systems face. Basically, dealing with the category of information security and security of internet networks, data warehouses and services based on big data, requires special attention of the organization to the position of information security and all-round security of these systems, and this category should be considered at the macro level and from the perspective of benefits and benefits. it looked The existence of security weaknesses in these systems, the lack of proper training and justification of users regardless of their point of view regarding the position and importance of security, the absence of necessary instructions to prevent security defects, the absence of specific and codified policies in order to properly and timely deal with Security flaws will lead to issues that harm all users and the operational capabilities of these systems and actually expose the information infrastructure of organizations to serious damage and threats. In this article, we are going to research the security aspects of data warehouses and big data and examine effective and logical solutions in this area.

Abstract Nowadays, the Internet of Things (IoT) has emerged as an effective and innovative technology for developing the infrastructure of many hardware and related software applications. Moreover, blockchain technology has emerged as the backbone for the development of IoT-based applications. The use of blockchain in the Internet of Things as a reliable and safe system can help improve the security and quality of the Internet of Things network and in the long run lead to energy savings and improve the efficiency of these systems. However, security challenges, including distributed service breach attacks, have revealed a fundamental fault line within the blockchain-based IoT network. Therefore, according to the necessity of the problem, in this article, we intend to first examine the types of security challenges and denial of service attacks in Internet of Things networks based on block chains and then examine and propose solutions for identifying, managing and dealing with these attacks. Certainly, the correct use of such security approaches can be effective toward securing Internet of Things environments and create more quality and reliable services and increase users' acceptance of these services. 

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 Introduction: The use of Information Technology (IT) in business model innovation and entrepreneurship in Qazvin province, especially for startups, presents significant opportunities. Technology startups can create new business models by leveraging innovative solutions, leading to increased productivity and economic growth in the region. Given the importance of innovation and the role of IT in facilitating this process, it is essential to examine the existing challenges and opportunities. Providing appropriate solutions can pave the way for the development of entrepreneurship and innovation in Qazvin province. Promoting a culture of innovation and supporting technology-driven startups will contribute to job creation, increased competitiveness, and improved quality of life in the region. The research method is applied and descriptive in nature. The statistical population includes employees and managers of research and development, production, marketing, and sales units of startups in Qazvin province.
Methodology: This research is applied in terms of purpose and quantitative in terms of variables. It is also cross-sectional, examining the status of variables at a specific point in time. In terms of research design, it is descriptive. The sample size was estimated to be approximately 109 individuals using Cochran's formula, selected through simple random sampling. A questionnaire was used for data collection. The data were analyzed using Smart PLS software, and the validity of the questionnaire was assessed through construct validity and reliability using Cronbach's alpha coefficient.
Findings: The analysis of the findings showed a significant relationship between innovative management and entrepreneurial intention, with business model innovation contributing to this relationship. Business model innovation plays a mediating role in the impact of innovative management on entrepreneurial intention. Regarding the formulation of research hypotheses, the main hypothesis test confirmed that "innovative management has a significant impact on entrepreneurial intention through business model innovation," and business model innovation strengthens this relationship. Business model innovation mediates the effect of innovative management on entrepreneurial intention, and innovative management significantly influences entrepreneurial intention through business model innovation.

Abstract These days, eCommerce has become widely known in the digitalized world, and many businesses have applied this to their business to approach more customers. Extensive literature review establishes that Advancement in E-commerce Infrastructure strengthens the relationship of good management and customer retention. However, the most important part which many companies did not recognize is eCommerce delivery and complexities of the related models. The eCommerce delivery strategy is really important to help the company keep and nurture its loyal customers. Ecommerce delivery refers to all of the services necessary to transfer items ordered online from a retailer to the customer’s delivery location. With a suitable partner, Ecommerce delivery may be reasonable, economical, and quick. In this article, the author tries to review the types of e-commerce models in a simple, eloquent and fluent way.

Abstract Cloud computing systems, which provide IT-related capabilities as Internet services to multiple customers, make the payment model based on the use of computing services provided to users through distribution networks. In cloud computing, a set of interconnected virtual computers is considered as one or more integrated computing resources, based on service level agreements and these agreements are established during the negotiations between service providers and consumers. Cloud computing seeks to enable a new generation of data centers by providing services in dynamic networked virtual machines, so that users can access applications from anywhere in the world. Cloud computing is revolutionizing the IT industry because of its efficiency, availability, low cost and other benefits. Today, many technology-based web service providers are moving towards adopting this technology and applying cloud computing approaches, which will lead to a significant increase in the use of various cloud services. Although much progress has been made in the field of cloud computing, many of the challenges in this area are still not well understood. This article examines cloud computing in terms of historical evolution, basic concepts, technology, reference architecture and various challenges.