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نتیجه جستجو - Case-based

تعداد مقالات یافته شده: 21
ردیف عنوان نوع
1 آموزش آسیب شناسی از راه دور تحت همه گیری COVID-19: برداشت های دانشجویان پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12
زمینه: همه‌گیری COVID-19 آموزش سنتی را مجبور کرده است که دوباره ساختار یافته و به صورت آنلاین ارائه شود. هدف: تجزیه و تحلیل ادراک دانشجویان پزشکی در مورد مزایا و مشکلات آموزش از راه دور پاتولوژی در طول همه گیری COVID-19.
طراحی: یک مطالعه مقطعی با یک نظرسنجی آنلاین برای دانشجویان سال سوم و چهارم فارغ‌التحصیلی پزشکی، که در آموزش از راه دور پاتولوژی در طول همه‌گیری COVID-19 شرکت کردند، انجام شد. روش‌های تدریس آنلاین شامل فعالیت‌های همزمان با سخنرانی‌های تعاملی زنده، بحث‌های مبتنی بر مورد و فعالیت‌های ناهمزمان با سخنرانی‌های ضبط‌شده، آموزش‌ها و متون موجود در پلت فرم آموزش آنلاین است. ادراک دانشجویان در مورد آموزش از راه دور آسیب شناسی از طریق نظرسنجی آنلاین مورد ارزیابی قرار گرفت.
یافته‌ها: 90 دانشجو (47%) از 190 شرکت‌کننده پرسشنامه را تکمیل کردند که 45 نفر مرد و 52 نفر در سال سوم فارغ‌التحصیلی پزشکی بودند. شرایط درک شده ای که یادگیری آسیب شناسی را تسهیل می کرد شامل استفاده از پلت فرم آموزش آنلاین و انعطاف پذیری زمانی برای مطالعه بود. دانشجویان سخنرانی های زنده تعاملی را برتر از سخنرانی های سنتی سنتی می دانستند. شرایط درک شده ای که مانع اجرای آموزش آنلاین شد، شامل دشواری جداسازی مطالعه از فعالیت های خانگی، بی انگیزگی و بدتر شدن کیفیت زندگی به دلیل دوری فیزیکی از همکاران و اساتید بود. به طور کلی، آموزش از راه دور آسیب شناسی توسط 80٪ از دانشجویان ارزش مثبت داشت.
نتیجه‌گیری: ابزارهای آنلاین اجازه می‌دهند تا محتوای پاتولوژی با موفقیت در طول همه‌گیری COVID-19 به دانش‌آموزان ارائه شود. این تجربه می تواند الگویی برای فعالیت های آموزشی آتی آسیب شناسی در آموزش علوم بهداشت باشد.
کلید واژه ها: پاتولوژی | آموزش از راه دور | کووید -19 | آموزش پزشکی
مقاله ترجمه شده
2 Internal benchmarking to assess the cost efficiency of a broiler production system combining data envelopment analysis and throughput accounting
محک گذاری داخلی برای ارزیابی کارایی هزینه سیستم تولید جوجه های گوشتی با ترکیب تحلیل پوششی داده ها و حسابداری توان عملیاتی-2021
Economic efficiency assessments based on Data Envelopment Analysis are scarce compared to technical effi- ciency studies, even in for-profit firms. Some aspects justify this scarcity, such as the difficulty to estimate ac- curate prices, given their variability over time. In many situations, external benchmarking is hindered due to organizations’ unique nature and the barriers to sharing information considered critical to competitiveness. The use of internal benchmarking can overcome some of these difficulties. This study conducted an internal benchmarking analysis of a broiler production system, focusing on cost efficiency. We conducted longitudinal case-based research over six years (2014–2019). The concepts of throughput accounting of the Theory of Con- straints were applied to structure the DEA model (inputs, prices, and output). The Critical Incident Technique was used to explore the effects of interventions on the production system’s cost efficiency. The results show that the broiler production system could reduce 32% of the total cost per unit of production if the balance of inputs suggested by the DEA evaluation was used. This work contributes to the literature by showing the potential of internal benchmarking to explore the evolution of cost efficiency over time. From a practical perspective, this study is important for managers by showing how to measure the impact of management actions on performance, providing valuable information to guide continuous improvement.
keywords: بهره وری اقتصادی | بهره وری هزینه | معیار سنج داخلی | تحلیل پوششی داده ها | تولید جوجه های گوشتی | حسابداری | Economic efficiency | Cost efficiency | Internal benchmarking | Data envelopment analysis | Broiler production | Throughput accounting
مقاله انگلیسی
3 A knowledge-based risk management tool for construction projects using case-based reasoning
یک ابزار مدیریت ریسک مبتنی بر دانش برای پروژه های ساختمانی با استفاده از استدلال مبتنی بر مورد-2021
Construction projects are often deemed as complex and high-risk endeavours, mostly because of their vulnera- bility to external conditions as well as project-related uncertainties. Risk management (RM) is a critical success factor for companies operating in the construction industry. RM is a knowledge-intensive process that requires effective management of risk-related knowledge. Although some research has already been conducted to develop tools to support knowledge-based RM processes, most of these tools ignore some critical features, such as live knowledge capture, web-based platform for knowledge sharing and effective case retrieval for learning from past projects. Moreover, several RM phases, such as risk identification, analysis, response and monitoring are not usually integrated. Thus, this study aims to bridge these gaps by developing a knowledge-based RM tool (namely, CBRisk) via case-based reasoning (CBR). CBRisk has been developed as a web-based tool that supports the cyclic RM process and utilises an effective case retrieval method considering a comprehensive list of project similarity features in the form of fuzzy linguistic variables. Finally, the developed tool was evaluated and validated by conducting black-box testing and expert review meeting. Results demonstrated that CBRisk has a considerable potential to enhance the effectiveness of RM in construction projects and may be used in other project-based industries with minimal modifications.
keywords: هوش مصنوعی | فراگیری ماشین | مدیریت ریسک مبتنی بر دانش | مدیریت ریسک | مدیریت دانش | استدلال مبتنی بر مورد | ابزار مبتنی بر وب | Artificial intelligence | Machine learning | Knowledge-based risk management | Risk management | Knowledge management | Case-based reasoning | Web-based tool
مقاله انگلیسی
4 Integrating case-based analysis and fuzzy optimization for selecting project risk response actions
تلفیق تجزیه و تحلیل مبتنی بر مورد و بهینه سازی فازی برای انتخاب اقدامات واکنش ریسک پروژه-2020
This article proposes a method based on case-based analysis and fuzzy optimization to provide decision support in project risk response. The main steps of the method are: (1) the formulation of alternative risk response actions (RRAs) based on case-based analysis, and (2) the determination of the optimal set of RRAs using a fuzzy optimization model. Based on the method, project managers (PMs) can find out alternative RRAs and further determine an optimal set of RRAs. Some managerial suggestion and implication are drawn from the results of the article. First, to perform better risk response in future, it is suggested that organizations should always capture a long-term perspective, with an awareness of keeping documents of all handled historical projects. Second, since each RRA obtained from alternative historical cases nee Keywords: Project risk management | Case-based | Risk response action (RRA) | Fuzzy optimization
مقاله انگلیسی
5 DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning
DECAF: استنتاج سیاست های مبتنی بر مورد عمیق برای انتقال دانش در یادگیری تقویتی-2020
Having the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-based Reasoning (CBR) there exists a general methodology that provides a framework for knowledge transfer which has been underrepresented in the RL literature so far. We for- mulate a terminology for the CBR framework targeted towards RL researchers with the goal of facilitating communication between the respective research communities. Based on this framework, we propose the Deep Case-based Policy Inference (DECAF) algorithm to accelerate learning by building a library of cases and reusing them if they are similar to a new task when training a new policy. DECAF guides the train- ing by dynamically selecting and blending policies according to their usefulness for the current target task, reusing previously learned policies for a more effective exploration but still enabling the adaptation to particularities of the new task. We show an empirical evaluation in the Atari game playing domain depicting the benefits of our algorithm with regards to sample efficiency, robustness against negative transfer, and performance increase when compared to state-of-the-art methods.
Keywords: Deep Reinforcement Learning | Case-based Reasoning | Transfer Learning | Knowledge discovery | Knowledge management | Neural networks
مقاله انگلیسی
6 Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship
استفاده از تحلیل مقایسه ای کیفی مجموعه فازی برای درک دقیق تر از کارآفرینی-2020
Entrepreneurship theory has largely been developed and tested using symmetrical correlational methods, effectively describing the sample-average respondent and subsuming individual differences. Such methods necessarily limit investigation of asymmetries that are evident in entrepreneurship, and provide only a single explanation that belies the multiple pathways to entrepreneurship observed in practice. This paper employs a case-based approach—fuzzy-set Qualitative Comparative Analysis (fsQCA)—to identify configurations of antecedent attributes of individuals in groups within samples, thereby revealing asymmetries and multiple entrepreneurial pathways that are otherwise hidden in the data. We explain the application of fsQCA to reveal these common issues in entrepreneurship; demonstrate how fsQCA complements correlational methods and offers finer-grained understanding of individual entrepreneurial behavior; and offer a comprehensive research agenda to build new entrepreneurship theory.
Keywords: Fuzzy set | Entrepreneurship | Asymmetric data | Outliers | Configurations
مقاله انگلیسی
7 No luck for moral luck
بدون شانس برای شانس اخلاقی-2019
Moral philosophers and psychologists often assume that people judge morally lucky and morally unlucky agents differently, an assumption that stands at the heart of the Puzzle of Moral Luck. We examine whether the asymmetry is found for reflective intuitions regarding wrongness, blame, permissibility, and punishment judg- ments, whether people’s concrete, case-based judgments align with their explicit, abstract principles regarding moral luck, and what psychological mechanisms might drive the effect. Our experiments produce three findings: First, in within-subjects experiments favorable to reflective deliberation, the vast majority of people judge a lucky and an unlucky agent as equally blameworthy, and their actions as equally wrong and permissible. The philosophical Puzzle of Moral Luck, and the challenge to the very possibility of systematic ethics it is frequently taken to engender, thus simply do not arise. Second, punishment judgments are significantly more outcome- dependent than wrongness, blame, and permissibility judgments. While this constitutes evidence in favor of current Dual Process Theories of moral judgment, the latter need to be qualified: punishment and blame judgments do not seem to be driven by the same process, as is commonly argued in the literature. Third, in between-subjects experiments, outcome has an effect on all four types of moral judgments. This effect is mediated by negligence ascriptions and can ultimately be explained as due to differing probability ascriptions across cases.
Keywords: Moral luck | Moral judgment | Outcome effect | Dual process theory of moral judgment | Hindsight bias
مقاله انگلیسی
8 Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms
حل مسئله برنامه ریزی حرکت با استفاده از تجربه یادگیری از طریق استدلال مبتنی بر مورد و الگوریتم های یادگیری ماشین-2019
This article presents two novel methodologies for solving the motion planning problem through retained experience. Both approaches employ AI’s case-based reasoning (CBR) technique. Case-based reasoning is an expert system development methodology which reuses past solutions to solve new problems. The first approach uses CBR to retain K similar cases to solve the motion planning problem by merging those solutions into a set. Afterwards, it picks from this set based on a heuristic function to assemble a final solution. Regarding the second approach, it employs the retained K similar cases differently. It uses those solution to build a graph which can be queried using traditional graph search algorithms. Results prove the success of such approaches concerning solution quality and success rate compared to different experience-based algorithms. Such utilization for CBR systems develops new research directions for building systems that can solve NP problems based on retained experiences exclusively.
Keywords: Sampling-based algorithms | Experience-based algorithms | Case-based reasoning Artificial intelligence | Motion planning
مقاله انگلیسی
9 Legal ontologies over time: A systematic mapping study
هستی شناسی های قانونی با گذشت زمان: یک مطالعه نگاشت سیستماتیک-2019
Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge repre- sentation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal prac- titioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as pur- pose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a sys- tematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law.
Keywords: Legal ontology| Systematic mapping study | Legal expert system | Legal theory | Semantic web
مقاله انگلیسی
10 Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes
نگهداری موردی از یک سیستم توصیه گر انسولین بولوس CBR شخصی و سازگار برای دیابت نوع 1-2019
People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system’s response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.
Keywords: Case-based reasoning | Insulin recommender system | Case-base maintenance | Attribute weight learning | Patient empowerment | Diabetes
مقاله انگلیسی
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