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نتیجه جستجو - Decision support sys

تعداد مقالات یافته شده: 78
ردیف عنوان نوع
1 Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستم‌های تولید هوشمند با استفاده از تجزیه و تحلیل پیش‌بینی‌کننده-2022
Smart manufacturing systems (SMS) are one of the most important applications in the Industry 4.0 era, offering numerous advantages over traditional production systems and rapidly being used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT), Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more. SMS is an innovative and popular manufacturing setup that produces increasingly intelligent production systems; yet, designers must adapt to business tastes and requirements. This study employs an analytical and descriptive research technique to identify and assess functional and non-functional, technological, economic, social, and performance evaluation components that are essential to SMS evaluation. A predictive analytics framework, which is a key component of many decision support systems, is used to assess corporate needs as well as proposed and prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system
مقاله انگلیسی
2 An easy-to-explain decision support framework for forensic analysis of dynamic signatures
یک چارچوب پشتیبانی تصمیم آسان برای تجزیه و تحلیل پزشکی قانونی امضاهای پویا-2021
Forensic handwriting examination is often criticized for its lack of objective standards and rigorous scientific validation. On the other hand, cutting-edge techniques for biometric handwriting and signature verification are often perceived as perfect black boxes and are not used by forensic handwriting examiners in their work environment. This paper presents an easy-to-explain yet effective framework to support semi-automatic signature verification in forensic settings. The proposed approach is based on measuring similarities between signatures by applying Dynamic Time Warping on easy-to-derive dynamic features. The goal is to provide forensic handwriting examiners with a decision support tool for making reproducible and less questionable inferences, while being both intuitive and easy to explain. The method is tested on a newly proposed dataset that also takes into account the so-called disguised sig- natures which are of extreme importance in this scenario.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Dynamic signatures | Forensic handwriting examination | Behavioral biometrics | Decision support system | Disguised signatures
مقاله انگلیسی
3 Telemedicine DSS-AI Multi Level Platform for Monoclonal Gammopathy Assistance
پلت فرم چند سطحه از راه دور پزشکی DSS-AI برای کمک به گاموپاتی مونوکلونال-2020
The proposed work describes preliminary results of a research project based on the realization of a Decision Support System -DSS- platform embedding medical and artificial intelligence -AI- algorithms. Specifically the telemedicine platform is suitable for the optimization of assistance processes of patients affected by Monoclonal Gammopaty. The results are related to the whole design of the platform implementing a DSS based on a multi-level decision making process. Starting from the main architecture specifications, is formulated a flowchart based on different alerting levels of patient risk including artificial intelligence - AI- decision supporting facilities. Finally, the perspectives of the performed research are discussed.
Keywords: Telemedicine | Digital Assistance | Decision Support System | Artificial Intelligence | Monoclonal Gammopathy
مقاله انگلیسی
4 The Importance of the Social License to Operate at the Investment and Operations Stage of Coal Mining Projects: Application using a Decision Support System
اهمیت پروانه اجتماعی برای فعالیت در مرحله سرمایه گذاری و عملیات پروژه های استخراج زغال سنگ: درخواست با استفاده از سیستم پشتیبانی تصمیم-2020
The Social License to Operate (SLO) and the Value Chain business model are basic elements that need to be considered both at the planning and operation stages of mining operations and in particular in coal mining projects. If a coal mining enterprise loses its SLO, it may face risks in operations, which may lead to value chain risks. One of the causes of enterprise failure as related to coal mining operations is the inability to reliably assess/ manage risk holistically and the inability to understand that lack of SLO is a critical risk. Although financial risks are typically assessed for mining projects, lack of SLO risk should also be taken into account starting as early as the bankable feasibility study. Furthermore, as it is difficult to establish a proactive decision-making policy for SLO risk in coal mining operations, the Operational Risk Management (ORM) methodology is probably a good tool to apply towards that goal. For this reason, a Mining Operational Risk Management Model (MORMM) was developed to incorporate risk probabilities and risk severities evaluated by experts. The final risk assessment is coded using Risk Assessment Codes (RACs). A hypothetical scenario was developed utilizing the MORMM model in order to illustrate how risks can be managed during the SLO granting process. This scenario describes a hypothetical coal mining project evaluated by virtual risk evaluators under specific hazard categories. Risk evaluation involves the assessment of risk probability and risk severity. Through this scenario this paper presents ways: (i) to establish a baseline ORM process that will be applicable to any coal mining operation environment, and (ii) to provide a theoretical example to demonstrate how the method can be applied to coal mining operations. The resulting RACs can provide critical information to decision makers regarding the rejection, acceptance or re-engineering of the mining business plan.
Keywords: Social License to Operate | Operational risk management | coal mining | Value Chain
مقاله انگلیسی
5 Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI
اعتماد به نفس و پذیرش مبتنی بر هوش مصنوعی ایجاد کنید سیستم های پشتیبانی تصمیم - هوش مصنوعی قابل توضیح و مسئولیت پذیر-2020
Artificial Intelligence has known an incredible development since 2012. It was due to the impressive improvement of sensors, data quality and quantity, storage and computing capacity, etc. The promises AI offered led many scientific domains to implement AI-based decision support tool. However, despite numerous amazing results, very serious failures have raised Human mistrust, fear and scorn against AI. In Industries, staff members cannot afford to use tools that might fail them. This is especially true for Transportation operators where security and safety are at risk. Then, the question that arises is how to build Human confidence and acceptance of AI-based decision support system. In this paper, we combine different points of view to propose a structured overview of Transparency, Explicability and Interpretability, with new definitions arising as a consequence. Then we discuss the need for understandable information from the AI system, to legitimate or refute the tool’s proposal. To conclude we offer ethical reflexions and ideas to develop confidence in AI.
Keywords: explainable AI | liable AI | decision support system | confidence | technology
مقاله انگلیسی
6 An analytic infrastructure for harvesting big data to enhance supply chain performance
یک زیرساخت تحلیلی برای برداشت داده های بزرگ به منظور افزایش عملکرد زنجیره تأمین-2020
Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this pa- per, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infras- tructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based opera- tional decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s op- erations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development.
Keywords: Decision support systems | Big data | Analytic infrastructure | Competence set | Deduction graph
مقاله انگلیسی
7 A decision support system using hybrid AI based on multi-image quality model and its application in color design
یک سیستم پشتیبانی تصمیم گیری با استفاده از هوش مصنوعی ترکیبی مبتنی بر مدل کیفیت چند تصویر و کاربرد آن در طراحی رنگ-2020
The product-color image conveys consumers’ color demands through emotion cognition. In this paper, a decision support system is proposed based on the hybrid artificial intelligence algorithm. The proposed system explores the internal correlation between the color image and demand of users. In the proposed system, an artificial neural network based on the radial basis function is employed. The network model is trained with an improved particle swarm optimization combined with the weight-adaptive strategy and chaos theory. The proposed model predicts the multi-uses’ color images. Then, the decision colors are extracted from the predicted colors by K-harmonic means clustering. The experimental results show that the proposed color decision support system is promising in designing the color scheme and providing theoretical guidance for the product-color design.
Keywords: Decision support system | Artificial intelligence | Product design | Multi-users’ images
مقاله انگلیسی
8 Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information
شفافیت و پاسخگویی در پشتیبانی تصمیم گیری هوش مصنوعی : توضیح و تجسم شبکه های عصبی کانولوشن برای اطلاعات متن-2020
Proliferating applications of deep learning, along with the prevalence of large-scale text datasets, have revolutionized the natural language processing (NLP) field, thereby driving the recent explosive growth. Nevertheless, it is argued that state-of-the-art studies focus excessively on producing quantitative performances superior to existing models, by playing “the Kaggle game.” Hence, the field requires more effort in solving new problems and proposing novel approaches and architectures. We claim that one of the promising and constructive efforts would be to design transparent and accountable artificial intelligence (AI) systems for text analytics. By doing so, we can enhance the applicability and problem-solving capacity of the system for realworld decision support. It is widely accepted that deep learning models demonstrate remarkable performances compared to existing algorithms. However, they are often criticized for being less interpretable, i.e., the “black box.” In such cases, users tend to hesitate to utilize them for decision-making, especially in crucial tasks. Such complexity obstructs transparency and accountability of the overall system, potentially debilitating the deployment of decision support systems powered by AI. Furthermore, recent regulations are emphasizing fairness and transparency in algorithms to a greater extent, turning explanations more compulsory than voluntary. Thus, to enhance the transparency and accountability of the decision support system and preserve the capacity to model complex text data at the same time, we propose the Explaining and Visualizing Convolutional neural networks for Text information (EVCT) framework. By adopting and ameliorating cutting-edge methods in NLP and image processing, the EVCT framework provides a human-interpretable solution to the problem of text classification while minimizing information loss. Experimental results with large-scale, real-world datasets show that EVCT performs comparably to benchmark models, including widely used deep learning models. In addition, we provide instances of human-interpretable and relevant visualized explanations obtained from applying EVCT to the dataset and possible applications for real-world decision support.
Keywords: Convolutional neural network | Machine learning interpretability | Class activation mapping | Explainable artificial intelligence
مقاله انگلیسی
9 AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings
حکمرانی هوش مصنوعی در بخش عمومی: سه داستان از مرزهای تصمیم گیری خودکار در تنظیمات دموکراتیک-2020
The rush to understand new socio-economic contexts created by the wide adoption of AI is justified by its far-ranging consequences, spanning almost every walk of life. Yet, the public sector’s predicament is a tragic double bind: its obligations to protect citizens from potential algorithmic harms are at odds with the temptation to increase its own efficiency - or in other words - to govern algorithms, while governing by algorithms. Whether such dual role is even possible, has been a matter of debate, the challenge stemming from algorithms’ intrinsic properties, that make them distinct from other digital solutions, long embraced by the governments, create externalities that rule-based programming lacks. As the pressures to deploy automated decision making systems in the public sector become prevalent, this paper aims to examine how the use of AI in the public sector in relation to existing data governance regimes and national regulatory practices can be intensifying existing power asymmetries. To this end, investigating the legal and policy instruments associated with the use of AI for strenghtening the immigration process control system in Canada; “optimising” the employment services” in Poland, and personalising the digital service experience in Finland, the paper advocates for the need of a common framework to evaluate the potential impact of the use of AI in the public sector. In this regard, it discusses the specific effects of automated decision support systems on public services and the growing expectations for governments to play a more prevalent role in the digital society and to ensure that the potential of technology is harnessed, while negative effects are controlled and possibly avoided. This is of particular importance in light of the current COVID-19 emergency crisis where AI and the underpinning regulatory framework of data ecosystems, have become crucial policy issues as more and more innovations are based on large scale data collections from digital devices, and the real-time accessibility of information and services, contact and relationships between institutions and citizens could strengthen – or undermine - trust in governance systems and democracy.
Keywords: Artificial intelligence | Public sector innovation | Automated decision making | Algorithmic accountability
مقاله انگلیسی
10 یک سیستم پشتیبانی تصمیم‌ گیری با استفاده از هوش مصنوعی مبتنی بر مدل کیفیت چند تصویر و کاربرد آن در طراحی رنگ
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 31
تصویر رنگ محصول نیازهای رنگ مصرف کنندگان را از طریق شناخت احساسات منتقل می‌کند . در این مقاله ، یک سیستم پشتیبانی تصمیم‌گیری براساس الگوریتم هوش مصنوعی ترکیبی پیشنهاد شده‌است . سیستم پیشنهادی به بررسی همبستگی داخلی بین تصویر رنگی و نیاز کاربران می‌پردازد . در سیستم پیشنهادی ، یک شبکه عصبی مصنوعی براساس تابع پایه شعاعی بکارگرفته می شود. مدل شبکه با بهینه‌سازی انبوه ذرات در ترکیب با استراتژی انطباقی و تئوری هرج و مرج آموزش داده می‌شود . مدل پیشنهادی تصاویر رنگی "uses " را پیش‌بینی می‌کند . سپس ، رنگ‌ها از رنگ‌های پیش‌بینی‌شده توسط خوشه‌بندی K - هارمونیک استخراج می‌شوند . نتایج تجربی نشان می‌دهد که سیستم پشتیبان تصمیم رنگ پیشنهادی در طراحی الگوی رنگ و ارائه راهنمایی تیوری برای تولید - طراحی رنگ امیدوار کننده است .
واژگان کاربردی: سیستم پشتیبانی از تصمیم‌گیری | هوش مصنوعی | طراحی محصول | تصاویر چند کاربردی
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