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نتیجه جستجو - Knowledge discovery

تعداد مقالات یافته شده: 55
ردیف عنوان نوع
1 Dynamic resilience for biological wastewater treatment processes: Interpreting data for process management and the potential for knowledge discovery
انعطاف پذیری پویا برای فرآیندهای تصفیه بیولوژیکی فاضلاب: تفسیر داده ها برای مدیریت فرآیند و پتانسیل برای کشف دانش-2021
Climate change, population growth and increasing regulation are causing wastewater treatment plants to become increasingly stressed, especially in countries like the UK, where many of these systems date back to the early part of the 20th century. Understanding resilience dynamics for these ageing wastewater assets represents a funda- mental step in classifying multi-dimensional water stressors toward preventing severe pollution incidents. This paper explores the potential of a novel dynamic resilience approach to assess and predict the dynamic resilience of biological wastewater treatment based on the separation of stressor events (cause) and process stress (effect) to consider the deviation from reference conditions. The approach presented provides a fundamental link between (1) conventional activated sludge modelling methodologies, (2) actual biological wastewater process instrument data (potential for knowledge discovery) and (3) the characterisation of dynamic resilience in wastewater treatment processes. Results first present the dynamic resilience approach by modelling simulated shock flow conditions on an activated sludge plant, then incorporates ten years of wastewater process instrument data to demonstrate the actual dynamic resilience. The aim is to represent the “dynamic resilience” as self-ordering windows, a visual knowledge base (three dimensional, heat map), which operational staff can easily interpret. The outcomes presented suggest that such an approach is feasible and has the potential for real-time identifi- cation of conditions that result in pollution incidents based on actual historical process instrument data (knowledge discovery). Also, the methods presented could be extended to develop an improved understanding of wastewater system resilience under a range of future stressor scenarios.
keywords: انعطاف پذیری پویا | مدل سازی تاثیر فرآیند | استرس فرایند | مدل سازی پویا | مدل سازی فاضلاب | Dynamic resilience | Process impact modelling | Process stress | Dynamic modelling | Wastewater modelling
مقاله انگلیسی
2 Transition from building information modeling (BIM) to integrated digital delivery (IDD) in sustainable building management: A knowledge discovery approach based review
انتقال از مدل سازی اطلاعات ساختمان (BIM) به تحویل دیجیتال یکپارچه (IDD) در مدیریت پایدار ساختمان: بررسی روش کشف دانش مبتنی بر بررسی-2021
Over the past decade, building information modeling (BIM) and sustainability have attracted increased interest, with a concomitant rise in the number of related publications. However, research efforts made in BIM for sustainable building management, especially using in all four Integrated Digital Delivery (IDD) phases are minimal. Therefore, this study features a combined scientometric analysis and IDD thematic discussion examining 471 scholarly bibliographies accessed from the Web of Science (WoS) database. The purpose of this study is to statistically classify BIM-sustainability publications from 2007 to 2019 in order to understand the research status, key themes, trends, and future challenges to be addressed in the field of sustainable BIM. The most influential scholars, institutes, regions/countries, articles, and journals have been identified. Moreover, clustering analyses identified topics that sustainable BIM research tended to gravitate toward, such as cloud approaches, data sharing, life cycle energy efficiency, and informetric analysis. The top 100 most cited documents from WoS were also manually classified into four quadrants of IDD, namely design, fabrication, construction, and asset delivery and management. Ten BIMsustainability phenomena were observed throughout the life cycle of IDD. Finally, key research gaps and important areas for future research in this field were identified. The clearly delineated clusters and themes provide a practical overview and a deeper understanding of the current research progress of IDD for building sustainability, highlighting the challenges and research gaps for future research.
keywords: مدلسازی اطلاعات ساختمان | بررسی انتقادی | سبوس سبز | تحویل دیجیتال مجتمع (IDD) | تجزیه و تحلیل علم سنجی | پایداری | Building information modeling | Critical review | Green BIM | Integrated digital delivery (IDD) | Scientometric analysis | Sustainability
مقاله انگلیسی
3 Generalized fuzzy logic based performance prediction in data mining
پیش بینی عملکرد مبتنی بر منطق فازی تعمیم یافته در داده کاوی-2020
In recent days, the single and multiple economies depend upon the human capital to build a valuable service. The individual employee level is important to process and maintain the whole organization. Consequently, performance management is needed at each employee level and the business level to implement a system in order to measure the employee performance and provide growth based on the performance. In data mining applications, the knowledge discovery of interest in Human Resources Management (HRM) is applicable. To extract the knowledge significant data mining classification techniques were used. The scope of this work compares the predictive analyzing of theC4.5 algorithm, Naive Bayes and Fuzzy logics are made by comparing its accuracy. This paper proposed a framework to help human resource to monitor the employee performance. The exact accuracy of the proposed framework found to be more efficient in terms of the accurately predicting the outcome of the employee.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research – 2019.
Keywords: Employee performance prediction | Data mining | Naive Bayes | Fuzzy logics | Decision tree | C4.5algorithm
مقاله انگلیسی
4 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
5 Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
فدراسیون دانش: یک چارچوب متحد و سلسله مراتبی حفظ حریم خصوصی هوش مصنوعی-2020
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many missioncritical and data-sensitive scenarios, such as finance, government, and health. In the meantime, tremendous datasets are scattered in isolated silos in various industries, organizations, different units of an organization, or different branches of an international organization. These valuable data resources are well underused. To advance AI theories and applications, we propose a comprehensive framework (called Knowledge Federation - KF) to address these challenges by enabling AI while preserving data privacy and ownership. Beyond the concepts of federated learning and secure multi-party computation, KF consists of four levels of federation: (1) information level, low-level statistics and computation of data, meeting the requirements of simple queries, searching and simplistic operators; (2) model level, supporting training, learning, and inference; (3) cognition level, enabling abstract feature representation at various levels of abstractions and contexts; (4) knowledge level, fusing knowledge discovery, representation, and reasoning. We further clarify the relationship and differentiation between knowledge federation and other related research areas. We have developed a reference implementation of KF, called iBond Platform, to offer a productionquality KF platform to enable industrial applications in finance, insurance, marketing, and government. The iBond platform will also help establish the KF community and a comprehensive ecosystem and usher in a novel paradigm shift towards secure, privacy-preserving and responsible AI. As far as we know, knowledge federation is the first hierarchical and unified framework for secure multi-party computing (statistics, queries, searching, and low-level operations) and learning (training, representation, discovery, inference, and reasoning).
Index Terms: Knowledge Federation |Knowledge | Federated Learning | Secure Multi-party Computation | Secure Multi-party Learning
مقاله انگلیسی
6 Improving symbolic data visualization for pattern recognition and knowledge discovery
بهبود تجسم داده های نمادین برای تشخیص الگو و کشف دانش-2020
This paper examines the visualization of symbolic data and considers the challenges rising from its complex structure. Symbolic data is usually aggregated from large data sets and used to hide entry specific details and to transform huge amounts of data (like big data) into analyzable quantities. It is also used to offer an overview in places where general trends are more important than individual details. Symbolic data comes in many forms like intervals, histograms, categories and modal multi-valued objects. Symbolic data can also be considered as a distribution. Currently, the de facto visualization approach for symbolic data is zoomstars which has many limitations. The biggest limitation is that the default distributions (histograms) are not supported in 2D as additional dimension is required. This paper proposes several new improvements for zoomstars which would enable it to visualize histograms in 2D by using a quantile or an equivalent interval approach. In addition, several improvements for categorical and modal variables are proposed for a clearer indication of presented categories. Recommendations for different approaches to zoomstars are offered depending on the data type and the desired goal. Furthermore, an alternative approach that allows visualizing the whole data set in comprehensive table-like graph, called shape encoding, is proposed. These visualizations and their usefulness are verified with three symbolic data sets in exploratory data mining phase to identify trends, similar objects and important features, detecting outliers and discrepancies in the data.
Keywords: Data visualization | Symbolic data | Zoomstar | Shape encoding | Exploratory data analysis
مقاله انگلیسی
7 Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction
کاوش مجموعه موارد تکراری حفظ حریم خصوصی: حداکثر سازی تسهیل داده ها براساس بازسازی بانک اطلاعاتی-2019
The process of frequent itemset mining (FIM) within large-scale databases plays a significant part in many knowledge discovery tasks, where, however, potential privacy breaches are possible. Privacy preserving frequent itemset mining (PPFIM) has thus drawn increasing attention recently, where the ultimate goal is to hide sensitive frequent itemsets (SFIs) so as to leave no confidential knowledge uncovered in the resulting database. Nevertheless, the vast majority of the proposed methods for PPFIM were merely based on database per- turbation, which may result in a significant loss of data utility in order to conceal all SFIs. To alleviate this issue, this paper proposes a database reconstruction-based algorithm for PPFIM (DR-PPFIM) that can not only achieve a high degree of privacy but also afford a rea- sonable data utility. In DR-PPFIM, all SFIs with related frequent itemsets are first identified for removing in the pre-sanitize process by implementing a devised sanitize method. With the remained frequent itemsets, a novel database reconstruction scheme is proposed to re- construct an appropriate database, where the concepts of inverse frequent itemset mining (IFIM) and database extension are efficiently integrated. In this way, all SFIs are able to be hidden under the same mining threshold while maximizing the data utility of the synthetic database as much as possible. Moreover, we also develop a further hiding strategy in DRPPFIM to further decrease the significance of SFIs with the purpose of reducing the risk of disclosing confidential knowledge. Extensive comparative experiments are conducted on real databases to demonstrate the superiority of DR-PPFIM in terms of maximizing the utility of data and resisting potential threats.
Keywords: Privacy preserving data mining | Frequent itemset | Database reconstruction | Inverse frequent itemset mining | Database extension
مقاله انگلیسی
8 Classifying longevity profiles through longitudinal data mining
طبقه بندی پروفایل های طول عمر از طریق داده کاوی طولی-2019
Populational studies of human ageing often generate longitudinal datasets with high dimensionality. In order to discover knowledge in such datasets, the traditional knowledge discovery in database task needs to be adapted. In this article, we present a full knowledge discovery process that was performed on a lon- gitudinal dataset, mentioning the singularities of this process. We investigated the English Longitudinal Study of Ageing’s (ELSA’s) database, employing both semi-supervised and supervised learning techniques to determine and describe the profiles of individuals annotated with the class labels “short-lived”and “long-lived”who participated in the study. We report on the data preprocessing, the clustering task of finding the best sets of representatives of the profiles of each class, and the use of supervised learning to describe these profiles and perform a longitudinal classification on the dataset to investigate how consis- tently the unlabelled records would fit into the classes. The results show that several aspects are used to discriminate the individuals between the longevity profiles. Those aspects include economic, social and health-related attributes. The findings have pointed towards a need to further investigate the relation- ships between the different aspects, especially those related to physical health and wellbeing, and how they affect the lifespan of an individual. Furthermore, our methodology and the adopted procedures can be applied to any other data mining applications for longitudinal studies of ageing.
Keywords: Machine learning | Longitudinal data | Cluster analysis | Ageing studies
مقاله انگلیسی
9 Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization
داده کاوی مبتنی بر خوشه بندی و تجزیه و تحلیل قاعده انجمن برای کشف دانش در بهینه سازی توپولوژی چند رده-2019
Optimum design problems, including structural optimization problems, often include multiple objec- tives. A multiobjective optimization problem usually provides a number of optimal solutions, called non- dominated solutions or Pareto-optimal solutions. In multiobjective topology optimization scenarios, deci- sion makers face the challenging task of choosing the most effective solution that meets their needs; se- rial comparisons among a set of Pareto-optimal solution are cumbersome, as are trial-and-error attempts to find an appropriate solution among a host of alternatives. On the other hand, the recent integration of data mining techniques in multiobjective optimization methods can provide decision makers with impor- tant, highly pertinent, and useful knowledge. In this paper, we propose a data mining technique for knowledge discovery in multiobjective topol- ogy optimization. The proposed method sequentially applies clustering and association rule analysis to a Pareto-optimal solution set. First, clustering is applied in the design space and the result is then vi- sualized in the objective space. After clustering, detailed features in each cluster are analyzed based on the concept of association rule analysis, so that characteristic substructures can be extracted from each cluster of solutions. In four numerical examples, we demonstrate that the proposed method provides per- tinent knowledge that aids comprehension of the key substructures responsible for one or more desired performances, thereby giving decision makers a useful tool for discovery of particularly effective design solutions.
Keywords: Data mining | Optimum design | Clustering | Association analysis | Topology optimization
مقاله انگلیسی
10 BIM-oriented data mining for thermal performance of prefabricated buildings
داده کاوی BIM گرا برای عملکرد حرارتی ساختمانهای پیش ساخته-2019
The use of energy efficiency procedures is a typical practice in building construction process that creates a huge amount of data regarding the building. This is particularly valid in structures which include complex collaborations, for example, ventilation, sunlight-based increases, inner additions, and warm mass. This paper proposes a new approach for automating building construction when improving their energy efficiency, aiming to foresee comfort levels based on Heating, Ventilating, Air Conditioning (HVAC), constructive systems performance, environmental conditions, and occupant behavior. More specifically, it presents a research work about thermal performance of prefabricated construction systems developed by an Argentine enterprise called Astori, using two Knowledge Discovery in Databases (KDD) processes to extract knowledge. In this context, Building Information Modeling (BIM) will give data to support the calculation to outline goal levels of a sustainable building performance concerning classification systems. The data were collected from a project in Uruguay referring to the construction systems and the energy efficiency of the building. The data mining tool SPMF was used to test the performance of classification and its use in prediction. Particularly, FP-Growth Algorithm and Clustering methodologies were used to analyze a combination of ambient conditions, in order to compare them using Revit© software. The results generated by these methods can be generalized for a set of buildings, according to the objective to be achieved concerning the thermal building performance
Keywords: Data mining | Association rules | Clustering | Building information | Green buildings
مقاله انگلیسی
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