A new case-deletion strategy for case-base maintenance based on K-means Clustering Algorithm applied to medical data

2021 ◽  
pp. 1-19
Author(s):  
Akila Djebbar ◽  
Hayet Farida Merouani ◽  
Hayet Djellali
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Case Based Reasoning ◽  
Medical Systems ◽  
Medical Databases ◽  
Case Deletion ◽  
Systems Research ◽  
System Maintenance ◽  
Case Base ◽  
And Performance

Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the K-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the K-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving competence.Furthermore, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging.

scholarly journals An Empirical Study of Knowledge Tradeoffs in Case-Based Reasoning

2018 ◽  
Author(s):  
Devi Ganesan ◽  
Sutanu Chakraborti
Keyword(s):  
Empirical Study ◽  
Domain Knowledge ◽  
Size Reduction ◽  
Case Based Reasoning ◽  
Practical Standpoint ◽  
Case Base ◽  
Real World Datasets ◽  
And Performance ◽  
The Impact ◽  
Case Based

Case-Based Reasoning provides a framework for integrating domain knowledge with data in the form of four knowledge containers namely Case base, Vocabulary, Similarity and Adaptation. It is a known fact in Case-Based Reasoning community that knowledge can be interchanged between the containers. However, the explicit interplay between them, and how this interchange is affected by the knowledge richness of the underlying domain is not yet fully understood. We attempt to bridge this gap by proposing footprint size reduction as a measure for quantifying knowledge tradeoffs between containers. The proposed measure is empirically evaluated on synthetic as well as real world datasets. From a practical standpoint, footprint size reduction provides a unified way of estimating the impact of a given piece of knowledge in any knowledge container, and can also suggest ways of characterizing the nature of domains ranging from ill-defined to well-defined ones. Our study also makes evident the need for maintenance approaches that go beyond case base and competence to include other containers and performance objectives.

scholarly journals Strategies to Improve the Performance and Energy Efficiency of Stencil Computations for NVIDIA GPUs

2018 ◽  
Author(s):  
Pablo José Pavan ◽  
Matheus da Silva Serpa ◽  
Víctor Martínez ◽  
Edson Luiz Padoin ◽  
Jairo Panetta ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Locality ◽  
Large Scale Systems ◽  
Systems Research ◽  
Gpu Algorithms ◽  
Read Only Memory ◽  
And Performance ◽  
Gpu Architecture

Energy and performance of parallel systems are an increasing concern for new large-scale systems. Research has been developed in response to this challenge aiming the manufacture of more energy efficient systems. In this context, we improved the performance and achieved energy efficiency by the development of three different strategies which use the GPU memory subsystem (global-, shared-, and read-only- memory). We also develop two optimizations to use data locality and use of registers of GPU architecture. Our developed optimizations were applied to GPU algorithms for stencil applications achieve a performance improvement of up to 201:5% in K80 and 264:6% in P 100 when used shared memory and read-only cache respectively over the naive version. The computational results have shown that the combination of use read-only memory, the Z-axis internalization of stencil application and reuse of specific architecture registers allow increasing the energy efficiency of up to 255:6% in K80 and 314:8% in P 100.

Large-Scale Text Clustering Based on Improved K-Means Algorithm in the Storm Platform

2014 ◽  
Vol 543-547 ◽  
pp. 1913-1916
Author(s):  
Sheng Hang Wu ◽  
Zhe Wang ◽  
Ming Yuan He ◽  
Huai Lin Dong
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Distributed Processing ◽  
Text Clustering ◽  
Research Field ◽  
Data Intensive Computing ◽  
Operation Mechanism ◽  
Data Intensive ◽  
And Performance ◽  
Algorithm Base

With the web information dramatically increases, Distributed processing of mass data through a cluster have been the focus of research field. An efficient distributed algorithm is the determinant of the scalability and performance in data analyses. This dissertation firstly studies the operation mechanism of Storm, which is a simplified distributed and real-time computation platform. Based on the Storm platform, an improved K-Means algorithm which could be used for data intensive computing is designed and implemented. Finally, the experience results show that the K-Means clustering algorithm base on Storm platform could obtain a higher performance in experience and improve the effectiveness and accuracy in large-scale text clustering.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

A Bridge Too Far?

2018 ◽  
Author(s):  
Charlotte P. Lee ◽  
Kjeld Schmidt
Keyword(s):  
Information Systems ◽  
Science And Technology Studies ◽  
Large Scale ◽  
Cooperative Work ◽  
Computer Supported Cooperative Work ◽  
Practical Action ◽  
Systems Research ◽  
Information Infrastructures ◽  
Information Systems Research ◽  
Critical Investigation

The study of computing infrastructures has grown significantly due to the rapid proliferation and ubiquity of large-scale IT-based installations. At the same time, recognition has also grown of the usefulness of such studies as a means for understanding computing infrastructures as material complements of practical action. Subsequently the concept of “infrastructure” (or “information infrastructures,” “cyberinfrastructures,” and “infrastructuring”) has gained increasing importance in the area of Computer-Supported Cooperative Work (CSCW) as well as in neighboring areas such as Information Systems research (IS) and Science and Technology Studies (STS). However, as such studies have unfolded, the very concept of “infrastructure” is being applied in different discourses, for different purposes, in myriad different senses. Consequently, the concept of “infrastructure” has become increasingly muddled and needs clarification. The chapter presents a critical investigation of the vicissitudes of the concept of “infrastructure” over the last 35 years.

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals Review of Battery Management Systems (BMS) Development and Industrial Standards

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Hossam A. Gabbar ◽  
Ahmed M. Othman ◽  
Muhammad R. Abdussami
Keyword(s):  
Energy Storage ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Management System ◽  
Battery Management ◽  
Electrical Power System ◽  
Safety Aspects ◽  
And Performance

The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented.

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