Arman Process Journal (APJ)

Abstract Ambiguity in technology projects is one of the major challenges of modern management and directly affects decision-making, communication, teamwork, and innovation. Using a systematic review approach, this study examines research published between 2010 and 2024 and identifies four main types of ambiguity: requirements ambiguity, technological ambiguity, market ambiguity, and organizational ambiguity. Based on the findings, a three-stage framework for managing ambiguity is proposed, consisting of “recognition and acceptance,” “analysis and prioritization,” and “action and iteration.” In addition, the ADAPT model is introduced as a five-step approach that helps transform uncertain situations into opportunities for innovation. The results indicate that success in technology projects does not depend on eliminating ambiguity, but on the ability to adapt, engage in continuous learning, and make effective decisions under uncertain conditions. The study emphasizes that teams and organizations capable of embracing ambiguity and converting it into a driver of innovation demonstrate stronger and more resilient performance in dynamic and unpredictable technological environments.

Abstract Background and Objectives: Website clicks that redirect Android phone users to malicious ‎websites with fake virus warnings or phishing attacks are increasing exponentially. Although a ‎Uniform Resource Locator (URL) blacklist is considered as a suitable countermeasure for such ‎attacks, it is difficult to efficiently identify malicious websites. To the best of our knowledge, no ‎research has focused on detecting attacks that redirect Android phone users to malicious ‎websites. Therefore, we propose a redirection detection method that focuses on the URL bar ‎change interval of the Android-based Google Chrome browser.‎
Methods: The proposed method, which can be easily installed as an Android application, uses ‎the Android Accessibility Service to detect unwanted redirects to malicious websites without ‎collecting information about these websites in advance. This paper describes the details of the ‎design, implementation, and evaluation results of the proposed application on a real Android ‎device. We set threshold values for the number of times the URL bar changes and the elapsed ‎time to detect redirects to malicious websites for the proposed method.‎
Finding: Based on the results, we investigated the causes of false positive detections of ‎redirects to safe websites and proposed solutions to manage them. We also present threshold ‎values that can minimize the false positive and negative rates, as well as the detection accuracy ‎of the proposed method based on these threshold values. In addition, we present evaluation ‎results based on access reports of real users participating in the WarpDrive project experiment, ‎which show that the proposed method minimizes false positives and successfully detects most ‎redirects to malicious websites.‎

Abstract The rapid growth of big data, coupled with advancements in data science and artificial intelligence, has significantly accelerated the potential for developing digital twins. A digital twin is a continuously updated virtual copy that enables the analysis, simulation, and prediction of a real-world object or process. Recently, applications of digital twins have seen substantial expansion across both academic communities and diverse governmental and military industries, and the healthcare sector is no exception. The concept of the digital twin for health promises a transformation in medical systems, encompassing service management and delivery, disease treatment and prevention, health maintenance, and ultimately, the enhancement of human life. By harnessing the ability to aggregate and analyze vast datasets from multiple sources, digital twins can facilitate personalized treatment pathways tailored to individual patient characteristics, medical history, and physiological data. This enables predictive analytics, preventative interventions, and the early identification of health risks and diseases through machine learning algorithms. Furthermore, digital twins can optimize clinical operations by analyzing treatment processes and resource allocation, leading to simplified and expedited treatment protocols. This review outlines the current applications of digital twins within the healthcare sector, delineates their core components in medicine, and examines the present landscape of open research opportunities. We demonstrate how the integration of diverse enabling technologies and tools—such as artificial intelligence, large language models, and mechanistic modeling—paves the way for overcoming limitations and fostering broader clinical adoption and implementation of digital twins. This review also aims to assist data scientists, clinicians, and policymakers in developing future medical digital twins and bridging the gap between this emerging paradigm's theoretical promise and practical realization.

Abstract Quantom computation is a new approach based on the principles of quantum mechanics to perform computations .Quantum computation uses unique behaviors of quantum physics to solve problems that are too complex for classical calculations .Theoretically , the connected inverters can use the interference between wave quantum states such as themselves for computations that may take millions of years .The potential use of these calculations is widespread and is used in areas such as cryptography , finance and drug discovery .With the implementation of quantum computing , several industries can be transformed . Although quantum computing can create a large transformation in the encryption and security system, they can be a threat to the privacy and digital information in the world .The reason for this is that quantum computers can easily break the toughest modern code .In this article, we intend to analyze and examine the new achievements of quantum computing in the field of information and communication technology, challenges and prospects.                                              

Abstract Rapid development of cloud computing technologies and a significant increase in demand for scalable software systems has forced organizations to review their distributed systems architecture .traditional monolithic architecture , despite its initial efficiency , is faced with serious challenges in the field of maintenance , scalability , and independent deployment because of the strong connection between components .this paper reviews new approaches to improve distribution systems with focus on microservice architecture .in this research , a comprehensive four phase framework ( discovery and analysis , service identification , design and implementation , and implementation ) is presented that guides the migration process from integrated architecture to microservice systematically .the proposed framework combines static and dynamic analyses of support system , using graph - based learning techniques to identify the boundaries of service , and use architecture patterns , enables organizations to experience successful migration by maintaining service quality and reducing operational risks .evaluating the framework on a real financial system ( UVT ) shows that the application of the proposed approach reduces the response time by 42 % , reduces the error rate to less from 0.5 % and improves the error recovery time by 65 % .this research presents practical guidelines and evaluation of existing methodologies , a way for researchers and software engineers in the field of migration to microservice architecture.

Abstract The explosive growth of Internet of Things (IoT) devices in home environments, coupled with the inherent limitations of these devices in terms of computing power, memory, and energy consumption, has increased the level of cyberattacks to an unprecedented level. Traditional home networks lack adequate security mechanisms to deal with emerging IoT-specific threats. This paper presents a comprehensive and multi-layered solution to enhance security in IoT-based home networks. The proposed approach, titled "SecHome-IoT", is composed of three main layers: (1) a deep learning-based anomaly detection layer (automated preprocessing and 1D convolutional neural network with long-term short-term memory), (2) a secure virtualization layer based on lightweight microservices (using hardware containers and critical path isolation), and (3) Dynamic and adaptive policy management layer (using adaptive-neural fuzzy inference system). Best hardware-software simulation on real-world datasets CICIDS2017, Bot-IoT and UNSW-NB15 along with implementation on Raspberry Pi platform and OpenWrt smart switches shows that the proposed method is superior to the previous ones (such as IoT-IDSA and Deep-STM) with an attack detection rate of 98.6% and a false positive rate of 1.4%. It has an improvement of 25% in security and performance metrics. This paper provides a roadmap for the security of IoT home networks by providing a comprehensive threat analysis, real-world and similar implementations.