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نتیجه جستجو - decision-making

تعداد مقالات یافته شده: 485
ردیف عنوان نوع
1 تجزیه و تحلیل پوششی داده مبتنی بر نسبت: یک رویکرد تعاملی برای شناسایی معیار
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 40
در دنیای واقعی ما با موارد زیادی مواجه هستیم که در آن نسبت داده های ورودی/خروجی برای مدیران بسیار مهم است، بنابراین در این رابطه نمی توان از مدل های سنتی تحلیل پوششی داده (DEA) برای ارزیابی کارایی واحدهای تصمیم گیری (DMU) استفاده کرد، و باید از مدل های DEA بر اساس داده های نسبت بهره برد. برای بدست آوردن معیار مربوطه برای هر واحد تصمیم‌گیری ناکارآمد، باید ورودی‌ها و خروجی‌ها را به ترتیب کاهش و افزایش دهیم و به یک پیش‌بینی واحد و منسجم تصمیم‌گیرنده در مرز کارایی برسیم. در این مقاله ما یک مدل برنامه‌ریزی خطی چندهدفه (MOLP) (multi-objective linear programming) را برای ارزیابی کارایی بر اساس تعریف مجموعه امکان تولید در حضور داده‌های نسبت و به دست آوردن معیار مربوطه برای هر واحد تصمیم‌گیری DMU ارائه می‌کنیم. ما از روش تعاملی زایونتس و والنیوس (Z-W) برای حل مدل MOLP ارائه شده استفاده می‌کنیم. با استفاده از تنظیم هدف توسط مدیر از بین راه حل های حاصل از مسئله MOLP، بهترین راه حل را با توجه به ترجیحات مدیران به عنوان معیار انتخاب می کنیم و در پایان نتایج تحقیق را ارائه می کنیم.
واژگان کلیدی: کارایی | DEA-R | معیار | برنامه ریزی چند هدفه | روش تعاملی
مقاله ترجمه شده
2 پیاده سازی یک راه حل حسابداری هزینه هوش تجاری در یک محیط مراقبت های بهداشتی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 12
محیط سیستم سلامت در پرتغال یک نگرانی دائمی برای جامعه ما است. با توجه به این موضوع، مانند هر بخش دیگری، منطقه بیمارستان دارای ساختار پیچیده ای است که حجم زیادی از اطلاعات را در خود جای داده است که فرآیند تصمیم گیری را دشوار می کند. با این کار، نیاز به بهبود مدیریت خدمات و منابع موسسات بهداشتی وجود دارد. با در نظر گرفتن این موضوع، راه حل شامل تبدیل سیستم فعلی با کمک سیستم های اطلاعاتی برای پیاده سازی می شود. بنابراین، ایده پیاده‌سازی سیستم‌های اطلاعاتی که از هوش تجاری در بیمارستان‌ها استفاده می‌کنند، مطرح می‌شود، تمرکز این پروژه کمک به مدیران در تحلیل حسابداری تحلیلی است. با مشارکت Centro Hospitalar Universitário do Porto، تصمیم گرفته شد تا استفاده از هوش تجاری را با هدف پیاده سازی یک راه حل تکمیلی برای طرح حسابداری بهای تمام شده موجود، با هدف بهبود کارایی و ارائه ابزارهای جدید مدیریت به مدیران مورد بررسی قرار دهیم.
کلمات کلیدی: حسابداری بهای تمام شده | هوش تجاری | مراقبت های بهداشتی
مقاله ترجمه شده
3 Quantum Pythagorean Fuzzy Evidence Theory: A Negation of Quantum Mass Function View
نظریه شواهد فازی کوانتومی فیثاغورث: نفی عملکرد جرم کوانتومی-2022
Dempster–Shafer (D-S) evidence theory is an effective methodology to handle unknown and imprecise information because it can assign probability into the power set. However, the process of obtaining information is a complex task, which can consider the rational, conscious, objective evaluation of utility with behavioral effects. Besides, in most cases, information can be obtained from different angles at the same time. The quantum model of mass function (QM) uses amplitude and phase angle to easily express those properties of information that can extend D-S evidence theory to the unit circle in a complex plane. Moreover, everything in nature will have its opposite, which is a kind of universality. The Bayes theorem is essentially the process of negation. However, in most cases, decisions can be made by only fully considering the known information without considering the other side of the information. Hence, considering the negation of information is a question to be investigated deeply, which can analyze information from the other point. This article proposes negation of QM by using the subtraction of vectors in the unit circle, which can degenerate into negation proposed by Yager in standard probability theory and negation proposed by Yin et al. in D-S evidence theory. Negation can provide us more information to consider the problem from both positive and negative aspects. In this article, negation can be understood information, which does not belong to event A, that is to say, negation can be regarded as nonmembership by using the fuzzy terms. Based on the above discussion, this article proposes the quantum pythagorean fuzzy evidence theory (QPFET), which is the novel work to consider QPFET from the point of negation. Besides, there are some numerical examplesto explainthe proposed method. In order to explore the applications of QPFET, this article discusses the possibility ofthe VIsˇekriterijumskoKompromisno Rangiranje method underQPFETto handle multicriteria decision-makingthat enables us to capture 2-D data, considering not only amplitude but also phase angle.
IndexTerms— Dempster–Shafer(D-S) evidencetheory | negation | pythagorean fuzzy sets (PFSs) | quantum mass function | quantum pythagorean fuzzy evidence theory (QPFET).
مقاله انگلیسی
4 یک مدل ریاضی چند منظوره برای زنجیره تامین داروسازی با توجه به تراکم دارو در کارخانه‌ها
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 47
مدیریت زنجیره تامین ( SCM ) , به روش یکی از مسائل مهم در جنبه مدیریتی , نقش مهمی در مقابله با مسایل انسانی و مشکلات ایفا می‌کند . به دلیل برخی محدودیت‌ها ( به عنوان مثال , ظرفیت تولید و ظرفیت ذخیره‌سازی ) و خواسته ها( به عنوان مثال , کاهش هزینه و افزایش درآمد ) , مدیران زنجیره تامین همیشه به دنبال بهترین پاسخ به مقدار و نوع ارتباط بین سطوح مختلف SCM هستند . در تحقیقات آتی , یک زنجیره تامین دارو ( PSC ) با سه تابع هدف توسعه‌یافته , با هدف به حداقل رساندن هزینه‌های کلی , خواسته‌های برآورده نشده , و کاهش زمان انتظار در ورودی کارخانه . در تحقیقات آتی , موضوع کلی و تحقیقات در مدل‌سازی PSC و حل مساله مورد بحث قرار گرفته‌اند . سپس یک مدل برنامه‌ریزی غیرخطی با تحقیقات قبلی برای حل کاستی‌های موجود پیشنهاد شده‌است.
همچنین روش‌های تصمیم‌گیری چند هدفه برای انطباق با اهداف متناقض مدل به طور همزمان استفاده می‌شوند . سپس نرم‌افزار تجاری GAMS برای حل مشکل اندازه‌های مختلف به کار می‌رود . در نهایت ، تحلیل حساسیت گسترده و ارزیابی نتایج مورد بحث قرار می‌گیرد و پیشنهادهای توسعه آتی ارایه می‌شوند.
واژه های کاربردی : زنجیره تامین دارو | فسادپذیری | زمان‌بندی | فهرست | نظریه کیوینگ
مقاله ترجمه شده
5 Smart City Data Science: Towards data-driven smart cities with open research issues
علم داده شهر هوشمند: به سوی شهرهای هوشمند مبتنی بر داده با مسائل تحقیقاتی باز-2022
Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting useful knowledge or actionable insights from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ‘‘Smart City Data Science’’, where city data collected from various sources such as sensors, Internet-connected devices, or other external sources, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, artificial intelligence, particularly, machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world city services. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, industry professionals, as well as policy-makers of a country, particularly, from the technological point of view.
keywords: شهرهای هوشمند | علم داده | فراگیری ماشین | اینترنت اشیا | تصمیم گیری داده محور | خدمات هوشمند | امنیت سایبری | Smartcities | Datascience | Machinelearning | InternetofThings | Data-drivendecisionmaking | Intelligentservices | Cybersecurity
مقاله انگلیسی
6 A novel machine learning pipeline to detect malicious anomalies for the Internet of Things
پایپ لاین یادگیری ماشینی جدید برای شناسایی ناهنجاری های مخرب برای اینترنت اشیا-2022
Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The anomalies are considered as samples that do not follow a normal pattern and significantly differ from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data generated from IoT devices. There has been wide-scale research done on anomaly detection problems in general, i.e., finding the samples in data that differ significantly from the expected values. However, there has been limited work done to figure out the inherent cause of the anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed, the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data manipulation by an attacker has not been explored in detail.
In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement.
keywords: IoT | Anomaly detection | Ensemble learning | Predictive analytics
مقاله انگلیسی
7 A Novel Hypercube-Based Heuristic for Quantum Boolean Circuit Synthesis
یک رویکرد مبتنی بر هایپرمکعب جدید برای سنتز مدار بولی کوانتومی-2022
Quantum computation has extraordinary capabilities for solving complicated problems. As quantum computations are reversible by nature, reversible circuits are important for the development of quantum computation techniques. Designing an effective and efficient method for synthesizing reversible circuits to reduce costs and stabilize circuit efficiency is crucial. The traditional synthesis methods of solving reversible circuits focus on the conversion efficiency rather than discussing the properties of the reversible function. Thus, this paper aims to propose a novel synthesis method that directly and efficiently optimizes reversible circuit synthesis with the properties of the reversible circuit. The proposed method converts the reversible function into a hypercube, allowing visual observations of the overall circuit. Two new indicators, the adjacent Hamming distance (AHD) and total cycle distance (TCD), aid in effective decision-making, generating shorter circuits. Furthermore, we use the generalized Toffoli gate set, which without requiring any additional ancilla bits and has applications in error correction and fault tolerance. The experimental results show that our method can find better solutions than traditional methods, significantly reducing the gate count, while the hypercube assists in synthesizing the reversible circuit.
Index Terms— Quantum computing | quantum Boolean circuits | reversible circuits | synthesis algorithm | hypercube | generalized Toffoli gate.
مقاله انگلیسی
8 A Quantum-Like Model for Predicting Human Decisions in the Entangled Social Systems
یک مدل کوانتومی برای پیش‌بینی تصمیمات انسانی در سیستم‌های اجتماعی درهم تنیده-2022
Human-centered systems of systems, such as social networks, the Internet of Things, or healthcare systems are growingly becoming significant facets of modern life. Realistic models of human behavior in such systems play an essential role in their accurate modeling and prediction. Nevertheless, human behavior under uncertainty often violates the predictions by the conventional probabilistic models. Recently, quantum-like decision theories have shown a considerable potential to explain the contradictions in human behavior by applying quantum probabilities. But providing a quantum-like decision theory that could predict rather than describe the current state of human behavior is still one of the unsolved challenges. The fundamental contribution of this work is introducing the concept of entanglement from quantum information theory to Bayesian networks (BNs). This concept leads to an entangled quantum-like BN (QBN), in which each human is a part of the entire society. Accordingly, society’s effect on the dynamic evolution of the decision-making process, which is less often considered in decision theories, is modeled by entanglement measures. To reach this aim, we introduce a quantum-like witness and find the relationship between this witness and the famous concurrence entanglement measure. The proposed predictive entangled QBN (PEQBN) is evaluated on 22 experimental tasks. Results confirm that PEQBN provides more realistic predictions of human decisions under uncertainty when compared with classical BNs and three recent quantum-like approaches.
Index Terms: Bayesian networks (BNs) | entanglement | human behavior | quantum physics | quantum-like decision making | social systems.
مقاله انگلیسی
9 A Review and Conceptual Analysis of Cancer Pain Self-Management
بررسی و تجزیه و تحلیل مفهومی از خود مدیریت سرطان-2021
Objectives: In this concept analysis article, we will clarify the concept “self-management of cancer pain” by identifying related antecedents, attributes, and consequences to further refine the conceptual and operational definitions of the concept. Design: A review was conducted.
Review/Analysis Methods: The Walker and Avant method was used for this concept analysis. Data sources: CINAHL, PubMed, and PsycInfo were searched systemically.A total of eight studies on “selfmanagement of cancer pain or self-care of cancer pain” published between 2004 and 2019 were identified.
Results: Attributes for self-management of cancer pain include self-efficacy, integration of methods for pain relief into daily life, decision-making related to pain management, process for solving pain-related issues, and initiation of interactions with healthcare professionals. Antecedents include knowledge regarding pain assessment and management, cognitive abilities, motivation, undergoing pain treatment, patient education and counseling, social support, and accountability from all parties involved. Consequences include pain control, improved quality of life, and increased opioid intake.
Conclusions: Self-management of cancer pain was reported to be a self-regulation process with the aim to encourage patients to use skills attained through development of self-efficacy, so they can actively participate in their pain management. This outcome may enhance their quality of life by decreasing their pain, depression, and anxiety and increasing the availability of social support.
مقاله انگلیسی
10 Digital Livestock Farming
دامداری دیجیتال-2021
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
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