مطالب مرتبط با کلیدواژه

artificial intelligence (AI)


۱.

Cloud Computing Technology Algorithms Capabilities in Managing and Processing Big Data in Business Organizations: MapReduce, Hadoop, Parallel Programming(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۲, Issue ۳, ۲۰۲۰ 100 - 113

کلیدواژه‌ها: Cloud Computing Data Data Warehouse Big Data artificial intelligence (AI) Business Organizations

حوزه‌های تخصصی:
تعداد بازدید : ۳۸۰ تعداد دانلود : ۲۲۳
The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hosted by various Companies on their servers or so-called cloud computing that have become an excellent opportunity to provide services efficiently and at low cost, but managing big data presents a definite challenge in the cloud space beginning with the processes of extracting, processing data, storing data and analyze it. Through this study, we dealt with the concept of cloud computing and its capabilities in business organizations. We also interpreted the notion of big data and its distinct characteristics and sources. Finally, the relationship between cloud computing with big data was also explained (extraction, storage, analysis).
۲.

Interactive Form-Generation in High-Performance Architecture Theory(مقاله علمی وزارت علوم)

نویسنده:
منبع: Architecture and Urban Development, Volume ۱۰, Issue ۲ - Serial Number ۳۶, Spring ۲۰۲۰ 37 - 48

کلیدواژه‌ها: artificial intelligence (AI) high-performance architecture performance-based design mathematical-algorithmic thinking contemporary architecture

حوزه‌های تخصصی:
تعداد بازدید : ۵۲۷ تعداد دانلود : ۲۴۷
Architecture as a designerly way of thinking and knowing is to interact with its environment. The manuscript is to speculate “interactive form-generation” based on high-performance architecture theory, and discuss the precursors and the potentials. The research aims to explore and determine the roots, aspects of interactive architecture as a part of performance-based design in contemporary architecture. The research question opens a designerly perspective as an umbrella term that can include many streams of architectural paradigms. Emancipatory new-interpretivism is the research philosophy which is employed alongside deductive reasoning, logical argumentation research paradigm, descriptive research method and cross-sectional study. Based on methodology of the manuscript, the research is to addresses related phenomena, their relationship and interaction. The results of the research show that interactive architecture is an umbrella to address a wide-range of architectural emerging streams such as: 1- The evolutionary architectural trends include kinetics, responsive, smart, responsive and intelligent environment. 2- The emerging phenomena in the field such as leading sci-tech approach toward architectural design process including cybernetics, artificial intelligence (AI), virtual reality (VR), conditional generative adversarial network (cGAN), and agent based modeling. The conclusion of the research indicates that the interactive architecture branched off from high-performance architecture theory. The conclusion emphasizes on: 1- Designerly flexibility: better space efficiency, flexibility, intelligence, and smartness; 2- Energy efficient form-generation: using less energy and offering more thermal-visual comfort; 3- Mathematical-algorithmic thinking: the integration of internet of things (IoT), robotics, and kinetics. 4- Futurism: a platform for outlining future architecture and architecture of the future.
۳.

Effectiveness of AI-Driven Knowledge Management System in Improving the Performance of Banking Sector in Jordan(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 182 - 200

کلیدواژه‌ها: artificial intelligence (AI) Banking sector Customer Satisfaction Jordan Knowledge Management service quality

حوزه‌های تخصصی:
تعداد بازدید : ۴۴۸ تعداد دانلود : ۲۴۸
The present research examines the benefits of implementing knowledge management (KM) principles in the Jordanian banking sector to enhance performance. The study emphasizes the significance of Artificial Intelligence (AI) and how Jordanian banks utilize it to improve the quality of customer service they provide. This study targets managers at all levels and focuses on the Jordanian banking sector as its research environment. A questionnaire is created to gather information from a random sample to achieve the research's objectives. The study involves a sample of 250 managers. Additionally, the research adopts a descriptive methodology, and SPSS is used to analyze the data. The statistical findings provide robust evidence for the importance of performance expectations, social influence, and perceived risk in influencing consumer intentions. Marketers and decision-makers within the banking industry can leverage these insights to shape their long-term strategies for effectively utilizing and maximizing AI technology in the banking sector. Furthermore, by providing policymakers and practitioners of Jordanian commercial banks with insight into the variables influencing user satisfaction, the findings will help these complex institutions operate more effectively.
۴.

eXtensible Business Reporting Language Data Assurance Challenges and Strategic Approaches: A Study in the Malaysian Business Reporting System Context(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۳, ۲۰۲۴ 173 - 191

کلیدواژه‌ها: XBRL Data Assurance Challenges Malaysian Business Reporting System (MBRS) Stakeholder Insights artificial intelligence (AI)

حوزه‌های تخصصی:
تعداد بازدید : ۳۰۲ تعداد دانلود : ۱۹۲
The eXtensible Business Reporting Language (XBRL) functions as an independent, open platform that facilitates efficient information transmission over the Internet, improving business information utilization. Despite its widespread adoption and numerous benefits, unresolved assurance issues undermine its effectiveness, revealing a significant research gap. This study explores the complex landscape of XBRL data assurance challenges within the Malaysian Business Reporting System (MBRS). Utilizing a qualitative case study methodology, the research highlights key challenges in XBRL data assurance and presents strategic, innovative solutions. Through semi-structured interviews and document analysis, insights from diverse stakeholders are captured, revealing the development of artificial intelligence-enhanced audit software aimed at improving the quality of XBRL filings in Malaysia. Despite its potential, awareness of this advanced software among preparers remains disappointingly low. This research serves as a valuable resource for practitioners and researchers, offering an in-depth analysis of XBRL data assurance challenges and pioneering solutions, thereby making a significant contribution to this critical field.
۵.

The Dual Nature of AI; A Double-Edged Sword Amid Digital Transformation; an Ethical Analysis(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Ethics and Society, Vol ۶, No ۲, (۶-۲۰۲۴) 1 - 8

کلیدواژه‌ها: ethics artificial intelligence (AI) Digital Transformation

تعداد بازدید : ۲۵۵ تعداد دانلود : ۱۳۴
Introduction: AI technologies have led to transformative changes in various industries across medicine and healthcare, environmental assessments, smart cities, smart surveillance and security etc. In this article, the dual nature of AI has been investigated. Material and Methods: It is a review article that described the ethical concerns of societies in AI. Conclusion: Despite of the many amazing prospective applications, there are ethical and dual use issues of AI which necessitates enhanced governance and vigilance. Addressing these issues requires adherence to the basic ethical principles. Herein, we present the various concerning dimensions of the AI and discuss them under the lens of ethical concepts.
۶.

The Evolution of Disinformation from Fake News Propaganda to AI-driven Narratives as Deepfake

نویسنده:
منبع: Cyberspace Studies,Volume ۹, Issue ۱, January ۲۰۲۵ 229 - 250

کلیدواژه‌ها: artificial intelligence (AI) fake news election interference deepfakes disinformation misinformation social media

حوزه‌های تخصصی:
تعداد بازدید : ۲۰۱ تعداد دانلود : ۱۲۹
Background: Misinformation has undergone significant transformations over the past few decades, evolving from relatively simple text-based fake news articles to highly sophisticate AI-driven content such as deepfakes and other forms of manipulated media.Aims: This paper traces the historical development of misinformation, its increasing reliance on Artificial Intelligence (AI), and the potential future trajectories of disinformation as AI technologies advance.Methodology: We begin by examining the shift from traditional text-based disinformation campaigns, often propagated via social media platforms, to more immersive and persuasive forms of AI-generated media.Discussions: We discuss how AI techniques, such as Generative Adversarial Networks and Natural Language Processing, have revolutionized the landscape of false information, allowing for the automation of misinformation production and its widespread dissemination at an unprecedented scale. Furthermore, this paper investigates the role of social media algorithms in amplifying disinformation, demonstrating how these platforms, originally designed to prioritize user engagement, inadvertently aid in the spread of false information by promoting sensationalized or emotionally charged content. Through an in-depth analysis of case studies, including the COVID-19 pandemic and the 2020 U.S. elections, this paper highlights the dangers posed by AI-generated misinformation, particularly deepfakes, which are becoming increasingly difficult to detect, even by advanced AI systems. The implications of this shift for democratic processes, public trust, and societal cohesion are profound. This paper also explores the ethical dilemmas posed by AI-driven misinformation and presents potential solutions through the lens of AI-enhanced detection technologies and policy interventions. Lastly, this paper emphasizes the urgent need for interdisciplinary cooperation between policymakers, technologists, and media organizations to mitigate the harmful impacts of AI-driven misinformation while preserving the integrity of information in the digital age.Conclusions: By exploring both technological and regulatory approaches, a comprehensive framework for understanding the evolving threat of AI-driven disinformation is essential and pathways for future research in this critical area is suggested.
۷.

The effect of a 6-week AI-generated core stability training program on balance and flatfoot in blind female students

نویسنده:
منبع: Humanistic Approach to Sport and Exercise Studies, Volume ۴, Issue ۲ ُSpring ۲۰۲۴ 83-91

کلیدواژه‌ها: artificial intelligence (AI) balance flatfoot visually impaired

حوزه‌های تخصصی:
تعداد بازدید : ۱۱۶ تعداد دانلود : ۷۹
Over the past decade, there has been a rapid increase in the study of using artificial intelligence (AI) to improve the quality of life of individuals with disabilities. Therefore, the study aims to investigate the effect of a 6-week AI-generated core stability training program on balance and flatfoot in blind female students. This quasi-experimental study selected 30 female students aged 9-12 years with flatfoot in Tehran City, dividing them into two groups: one for experimental (N = 15) and another for control (N = 15). The experimental groups had six weeks of AI-based intervention with three sessions per week. During this period, the control group engaged in the routine activities of the physical education class. The navicular drop index and Y balance test were done as pre-posttest, respectively. The Covariance (ANCOVA) was used for inferential statistics. Data analysis was conducted at a significance level of 95% with an alpha level less than or equal to 0.05. The findings showed that there was a significant difference between the two groups in the scores of the Y balance test (p<0.035) and the navicular drop test (p<0.001), even when the pre-test effect was taken into account (covariate). By leveraging AI to design tailored exercise regimens, practitioners can enhance postural control and musculoskeletal health in visually impaired individuals. These results underscore the potential of AI-assisted rehabilitation strategies in special education settings, highlighting the need for further researc
۹.

AI-Driven Automation for Transforming the Future of Software Development(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 87 - 118

کلیدواژه‌ها: AI-driven automation Software development artificial intelligence (AI) continuous integration (CI) continuous delivery (CD) automated testing code generation debugging Machine Learning (ML) Software Engineering

حوزه‌های تخصصی:
تعداد بازدید : ۱۱ تعداد دانلود : ۸
Background : Artificial Intelligence (AI) has recently emerged as a transformative innovation within the software industry, disrupting conventional approaches to application development by automating tasks, refining code, and enhancing resource efficiency. Prior research indicates the effectiveness of AI-powered tools across various domains. However, contemporary studies lack a detailed analysis of the diverse sectors utilizing AI tools for software development. Objective : This article aims to identify the potential benefits and impacts of AI in software development, specifically regarding time-to-market, productivity, code quality, bug-fixing rates, resource flexibility, and developer satisfaction. The goal is to present fact-based information about AI’s impact on multiple industries and scopes of work. Methods : A mixed-methods research design was employed to analyze quantitative data from 40 projects across healthcare, financial services, retail, technology, and e-commerce industries. Data were collected using various project management tools, automated testing environments, and online questionnaires addressed to developers. The study incorporated a comparative evaluation of AI-based projects and traditional projects, with statistical analysis. Results : AI-driven software development projects demonstrated a mean reduction in time-to-market by 34.6%, an improvement in code quality by 70%, and a mean reduction in bug-fixing time by 57.7%. Productivity per sprint increased by over 70%, resource flexibility was higher (90.2% in AI projects vs. 67.8% in traditional projects), and developers reported higher satisfaction levels. These findings reinforce the concept that AI significantly enhances workflow and the achievement of optimal results. Conclusion : AI substantially improves both the speed and quality of software development. Further research should expand to explore the experiences of different sectors, the application of AI-driven tools, their differentiation, and usage, as well as the ethical considerations to promote sustainable and innovative software engineering solutions.
۱۰.

Artificial Intelligence in Healthcare: Revolutionizing Diagnostics with Predictive Algorithms(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 149 - 176

کلیدواژه‌ها: artificial intelligence (AI) Healthcare Predictive Algorithms Diagnostics Personalized Medicine early detection Diagnostic Accuracy Medical Errors Patient Outcomes Clinical Applications

حوزه‌های تخصصی:
تعداد بازدید : ۹ تعداد دانلود : ۸
ABSTRACT Background: Artificial Intelligence (AI) has rapidly integrated into healthcare, proving indispensable in diagnostic processes. Event-predicting equations in medicine offer solutions to longstanding issues related to early diagnosis and personalized patient care. Objective: This article aims to explore best practices in objective and quantitative diagnostic predictions using AI and predictive algorithms. It seeks to revolutionize healthcare diagnostics by enhancing effectiveness and reducing diagnostic error rates. Methods: This study involves a literature review of the past five years, focusing on recent innovations in AI for healthcare diagnostics. The review includes fields such as oncology, cardiology, and others to evaluate the efficacy of prediction algorithms in practice. Results: The findings indicate that machine learning-based computer-aided diagnosis models significantly improve diagnostic accuracy by detecting diseases at early stages and personalizing treatment programs. The integration of these algorithms has led to reduced diagnostic errors and improved patient experiences across various medical fields. Conclusion: AI predictive algorithms represent the future of diagnostic medicine. Their adoption is set to personalize and advance patient treatment, enhance health outcomes, and improve the efficiency of healthcare systems. However, comprehensive research and precise implementation are essential to fully harness the potential of AI in diagnostics.
۱۱.

Integrating IoT, Artificial Intelligence, and Blockchain Technologies for the Development of Smart Networks(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 341 - 367

کلیدواژه‌ها: Internet of Things (IoT) artificial intelligence (AI) Blockchain Smart Networks Data Integrity Network Optimization Decentralization Scalability Energy Efficiency smart cities

حوزه‌های تخصصی:
تعداد بازدید : ۱۰ تعداد دانلود : ۷
Background: IoT Smart networks are the latest creation of smart technology where Internet of Things, Artificial Intelligence, and Blockchain technologies have merged. Such technologies have the possibility of increasing performance, security and the degree of expansion in different fields like smart city, health and manufacturing. As it is, there are several issues that organisations continued to encounter when implementing both these systems in order to address diversified network requirements. Objective: The study aims to define how IoT, AI, and Blockchain technologies can be integrated to develop smart networks and how their integration will address the issues of performance, data integrity, and resource utilization in smart networks. Methods: The solution consisted of three components: IoT for instant data gathering, AI for modeling and efficient traffic control, Blockchain for secure data storage. Analyses of various objectives such as data throughput, latency, energy consumption, and security were conducted for smart city applications through simulations. Results: The linked matrix obtained a 45% increase in data transfer rate, a 40% cut in response time and a 50% enhancement of power utilization compared to other systems. Purchases made using blockchain were correct to the last digit, achieved with a success rate of 99.9%, and there were no cases of hacking. AI algorithms minimized congestion levels of the network by 55%, and IoT devices remained available 98% of the time. Conclusion: The incorporation of the IoT, AI and Blockchain enhances the effectiveness and assures the stability of smart networks greatly. From these findings, there is a significant potential for broad utility thus the need for research on the scale, integration, and testing of these in practice.
۱۲.

Next-Gen Machine Learning Models: Pushing the Boundaries of AI(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 435 - 464

کلیدواژه‌ها: Next-gen machine learning artificial intelligence (AI) transformer models reinforcement learning (RL) neural architecture search (NAS) Quantum Computing model interpretability cross-domain tasks Automation Scalability

حوزه‌های تخصصی:
تعداد بازدید : ۱۲ تعداد دانلود : ۷
Background: Machine learning (ML) has developed significantly over the years, changing several industries through the use of automation and Big Data. By building better next-generation machine learning models, AI’s future has the potential of improving on existing problematic methods such as scalability, interpretability, and generalization. Objective: This article examines about how new generation of ML models are developed and used to explain about the capabilities of AI in different fields. In particular, it is focused on changes in structural models, certain methods of training them, and the application of brand-new technologies as quantum computing. Methods: A review of the state of the art and several case studies were carried out with regard to the latest work being done on different types of ML algorithms such as transformer models, reinforcement learning, and Neural Architecture Search. Moreover, the given models were tested in experiments concerning the applicability of these models in tasks including image recognition, natural language processing, and in autonomous systems. Results: The next-gen models, thereby outperformed the traditional models in terms of accuracy, computational speed, and flexibility. The identified benefits were decreased training time, better interpretability, and better performance with multi-modal and cross-domain tasks. Conclusion: These new generation of ML models are the game changers in AI development solving previous challenges while providing opportunities across numerous sectors. In this vein, further research in this field is needed to achieve AI’s solving of problems.
۱۳.

Synergizing 5G and Artificial Intelligence: Catalyzing the Evolution of Industry 4.0(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 495 - 524

کلیدواژه‌ها: 5G artificial intelligence (AI) Industry 4.0 IoT Machine Learning robotics Automation Smart Manufacturing real-time data predictive analytics

حوزه‌های تخصصی:
تعداد بازدید : ۱۰ تعداد دانلود : ۶
  Background: The marriage of 5G and Artificial Intelligence (AI) has been brought forward as a key enabler of Industry 4.0 and smart city applications. These technologies solve the problem of latency, scalability, and energy use, providing technology support for real-time decision-making and efficient organization of work. Nevertheless, studies regarding their individual and collective effects in a plethora of industrial and urban contexts are still limited. Objective: The objective of this research is to assess the performance, energy saving, and expansibility of 5G and AI synergies in manufacturing, logistics, healthcare, and smart city applications and highlight their challenges and potential for further exploration. Methods: An experimental data collection, mathematical modeling and comparative analysis approach was employed. Performance indicators including latency, possible and actual throughput, power usage, and predicting achievement were measured in real pilot tests implemented in dense networks and IoT contexts. Available data were compared with other similar studies to gain an understanding of the results. Results: The conjoin with 5G and AI suggested potential optimization of process; the latency has been decreased to more than 90%, its predictive maintenance was sharpened, and its power consumption was decrease to 75%. The feasibility of extending scalability and system reliability of the protocol was confirmed in dense IoT environments, with further potential for emission reduction. Conclusion: The study identifies the use of 5G in Industry 4.0 with AI in addressing dynamic issues but potential drawback includes scalability and security. More studies should be conducted on the novel hybrid architectures and 6G integration concerning more extensive areas.
۱۴.

Artificial Intelligence and Machine Learning in Telecommunications Revolutionizing Customer Experience and Enhancing Service Delivery(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 609 - 636

کلیدواژه‌ها: artificial intelligence (AI) Machine Learning (ML) telecommunications Customer Experience (CX) Service delivery Network Optimization predictive analytics Resource Allocation Bandwidth Utilization Predictive maintenance

حوزه‌های تخصصی:
تعداد بازدید : ۷ تعداد دانلود : ۷
Background: The telecommunications industry is at the crossroad of change seemingly precipitated by the use of Artificial Intelligence (AI) and Machine Learning (ML). These technologies have yielded new features like network automation, prescriptive analytics, and contextual-consumer engagement, solving traditional dilemmas in service delivery and operationalization. Objective: The current article seeks to understand how AI and ML has positively affected customer experience and service provision in the telecommunication industry. The research objectives focus on how to increase KPIs to service latencies, network reliability, and customer retention while at the same time establishing the problems associated with big data large-scale implementation. Methods: Samples were gathered using systematic reviews of the current literature, meta-analysis of case studies, and assessment of industry datasets. This concerned artificial intelligence enabled operations such as dynamic resource management, real-time customer emotions analysis and real-time fault detection. Regression analysis and time series models were used in order for measuring performance indices. Results: AI and ML integration led to multifaceted advancements: a decrease of average service latency by 55%, reduction of network downtime by 70%, and an increase of maintenance predictions accuracy by 35%. The customer retention rate which had improved to 25% was also credited to better personalization of the services as well as having proper service management. AI-equipped resource allocation also raised efficiency in bandwidth utilization by 60%. Conclusion: AI and ML are positively disrupting telecommunications as they deliver remarkable enhancements in the caliber of services and client satisfaction. With all the challenges in data governance and interoperability, it is clear that their adoption promises a great chance in enhancing the current standards within the telecommunications field and creating the basis for the development of a more sophisticated environment.
۱۵.

Leveraging AI for Predictive Maintenance with Minimizing Downtime in Telecommunications Networks(مقاله علمی وزارت علوم)

نویسنده:
منبع: پژوهشنامه پردازش و مدیریت اطلاعات دوره ۴۰ تابستان ۱۴۰۴ ویژه نامه انگلیسی ۴ (پیاپی ۱۲۵) 1117 - 1147

کلیدواژه‌ها: Predictive maintenance artificial intelligence (AI) Machine Learning Telecom Networks Downtime Reduction Network Reliability deep learning Failure Prediction Operational Efficiency Network Optimization

حوزه‌های تخصصی:
تعداد بازدید : ۱۲ تعداد دانلود : ۲۵
Background: Telecommunications networks are exposed to numerous issues concerning equipment and that causes network outage, which proves very expensive. Basic maintenance methodologies like reactive or even scheduled preventive maintenance cannot cope up with the increasing trends in the facilities of telecom companies. Objective: The article examines how AI is applied to support predictive maintenance so that telecommunication networks can perform as intended with reduced downtime. Methods: The review of existing AI algorithms is presented, focusing on the ML models and deep learning methods. Network operations and maintenance logs are analyzed for data to assess the capabilities of the AI models in terms of prediction. It identifies and analyses such quantifiable parameters as the failure rate prediction accuracy and the response time cut. Results: Computerisation of the forecast maintenance revealed a corresponding decrease in equipment failure incidences and generally reduced time lost due to unscheduled stops. Through the improved network performance, the response to potential threats was quicker than before and services became more reliable and inexpensive to offer. Conclusion: To reduce network outages, reduce network vulnerability, and maximize the efficiency of telecommunications operations, the use of AI-based predictive maintenance can be viewed as a prospect. As technology advances, newer versions of AI algorithms will provide improved predictive strength and incorporation into the telecommunications system.