Knowledge Management(KM)has become a dynamic concept for inquiry in research.The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related...Knowledge Management(KM)has become a dynamic concept for inquiry in research.The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline,several KM frameworks have been designed to serve this purpose.This research aims to propose a Collaborative Knowledge Management(CKM)Framework that bridges gaps and overcomes weaknesses in existing frameworks.The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan.A software LCWU aKMS was developed which serves as a practical implementation of the concepts behind the proposed CKMF framework.LCWU aKMS served as an effective system for rice leaf disease detection and identification.It aimed to enhance CKM through knowledge sharing,lessons learned,feedback on problem resolutions,help from co-workers,collaboration,and helping communities.Data were collected from 300 rice crop farmers by questionnaires based on hypotheses.Jennex Olfman model was used to estimate the effectiveness of CKMF.Various tests were performed including frequency measures of variables,Cronbach’s alpha reliability,and Pearson’s correlation.The research provided a KMS depicting KM and collaborative features.The disease detection module was evaluated using the precision and recall method and found to be 94.16%accurate.The system could replace the work of extension agents,making it a cost and time-effective initiative for farmer betterment.展开更多
The paper is dealing with the design and development of a knowledge management （KM） system in radiology dedicated for learning and training to support radiologists in senology in the detection of breast cancer. The ...The paper is dealing with the design and development of a knowledge management （KM） system in radiology dedicated for learning and training to support radiologists in senology in the detection of breast cancer. The work has been performed in cooperation with the department of radiology of Gustave Roussy Institute in Paris （Villejuif）. It provides a case-based reasoning system to support diagnosis by finding similar cases to the case at hand. The outcome of the research effort is both conceptual and practical. It has also a methodological dimension. Conceptually, the paper contributes to the breast cancer diagnosis domain by defining an ontology and a conceptual model for representing cases which are generic solutions reusable in many different settings. It contributes to the case-based reasoning （CBR） field in two ways （1） by defining a case representation model, and （2） a retrieval algorithm which exploits the case representation model structure to find similar cases by aggregating similar cases. Last but not least, the research has a methodological contribution by which the retrieval algorithm is embedded in a broader process perspective including the capture of the actual case, the case-based reasoning related to this case and the support to decision-making by adapting the retrieved case. An interesting aspect of the process model is its intentional dimension which makes possible the representation of different ways to achieve the result. The research will be illustrated by a case study based on real clinical cases （n = 40） of patients provided by Dr. Corinne Balleyguier.展开更多
The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed block...The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed blocking and other such consequences. The problem is exacerbated by the growth in size, complexity and the number of chronic diseases in the NHS. In addition, there is an increase in demand for high quality care, processes and planning. Effective Discharge Planning (DP) requires practitioners to have appropriate, patient personalised and updated knowledge in order to be able to make informed and holistic decisions about a patients’ discharge. This paper examines the role of Knowledge Management (KM) in both sharing knowledge and using tacit knowledge to create appropriate patient discharge pathways. The paper details the factors resulting in inadequate DP, and demonstrates the use of Internet of Things (IoT) and Machine2Machine (M2M) as candidate technologies and possible solutions which can help reduce the problem. The use of devices that a patient can take home and devices which are perused in the hospital generate information, which can serve useful when presented to the right person at the right time, thus harvesting knowledge. The knowledge when fed back can support practitioners in making holistic decisions with regards to a patients’ discharge.展开更多
To accelerate the digital transformation of small and medium-sized manufacturing enterprises(SMEs),this study delves into the primary challenges encountered in adopting knowledge management(KM)within these organizatio...To accelerate the digital transformation of small and medium-sized manufacturing enterprises(SMEs),this study delves into the primary challenges encountered in adopting knowledge management(KM)within these organizations and identifies the essential methods for successful implementation.The objective is to provide practical recommendations for the effective adoption of KM.This research suggests that enterprises should promote knowledge management through three key approaches:enhancing employees’cognitive understanding,standardizing knowledge systems,and tailoring business scenarios to meet diverse needs.These findings offer valuable insights into the digital transformation of SMEs in the manufacturing sector,ultimately helping these businesses to remain competitive and innovative in a rapidly changing market.By addressing the specific needs and challenges faced by SMEs,this study aims to contribute to a more comprehensive understanding of how knowledge management can be leveraged to drive digital transformation and improve overall business performance.展开更多
文摘Knowledge Management(KM)has become a dynamic concept for inquiry in research.The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline,several KM frameworks have been designed to serve this purpose.This research aims to propose a Collaborative Knowledge Management(CKM)Framework that bridges gaps and overcomes weaknesses in existing frameworks.The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan.A software LCWU aKMS was developed which serves as a practical implementation of the concepts behind the proposed CKMF framework.LCWU aKMS served as an effective system for rice leaf disease detection and identification.It aimed to enhance CKM through knowledge sharing,lessons learned,feedback on problem resolutions,help from co-workers,collaboration,and helping communities.Data were collected from 300 rice crop farmers by questionnaires based on hypotheses.Jennex Olfman model was used to estimate the effectiveness of CKMF.Various tests were performed including frequency measures of variables,Cronbach’s alpha reliability,and Pearson’s correlation.The research provided a KMS depicting KM and collaborative features.The disease detection module was evaluated using the precision and recall method and found to be 94.16%accurate.The system could replace the work of extension agents,making it a cost and time-effective initiative for farmer betterment.
文摘The paper is dealing with the design and development of a knowledge management （KM） system in radiology dedicated for learning and training to support radiologists in senology in the detection of breast cancer. The work has been performed in cooperation with the department of radiology of Gustave Roussy Institute in Paris （Villejuif）. It provides a case-based reasoning system to support diagnosis by finding similar cases to the case at hand. The outcome of the research effort is both conceptual and practical. It has also a methodological dimension. Conceptually, the paper contributes to the breast cancer diagnosis domain by defining an ontology and a conceptual model for representing cases which are generic solutions reusable in many different settings. It contributes to the case-based reasoning （CBR） field in two ways （1） by defining a case representation model, and （2） a retrieval algorithm which exploits the case representation model structure to find similar cases by aggregating similar cases. Last but not least, the research has a methodological contribution by which the retrieval algorithm is embedded in a broader process perspective including the capture of the actual case, the case-based reasoning related to this case and the support to decision-making by adapting the retrieved case. An interesting aspect of the process model is its intentional dimension which makes possible the representation of different ways to achieve the result. The research will be illustrated by a case study based on real clinical cases （n = 40） of patients provided by Dr. Corinne Balleyguier.
文摘The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed blocking and other such consequences. The problem is exacerbated by the growth in size, complexity and the number of chronic diseases in the NHS. In addition, there is an increase in demand for high quality care, processes and planning. Effective Discharge Planning (DP) requires practitioners to have appropriate, patient personalised and updated knowledge in order to be able to make informed and holistic decisions about a patients’ discharge. This paper examines the role of Knowledge Management (KM) in both sharing knowledge and using tacit knowledge to create appropriate patient discharge pathways. The paper details the factors resulting in inadequate DP, and demonstrates the use of Internet of Things (IoT) and Machine2Machine (M2M) as candidate technologies and possible solutions which can help reduce the problem. The use of devices that a patient can take home and devices which are perused in the hospital generate information, which can serve useful when presented to the right person at the right time, thus harvesting knowledge. The knowledge when fed back can support practitioners in making holistic decisions with regards to a patients’ discharge.
文摘To accelerate the digital transformation of small and medium-sized manufacturing enterprises(SMEs),this study delves into the primary challenges encountered in adopting knowledge management(KM)within these organizations and identifies the essential methods for successful implementation.The objective is to provide practical recommendations for the effective adoption of KM.This research suggests that enterprises should promote knowledge management through three key approaches:enhancing employees’cognitive understanding,standardizing knowledge systems,and tailoring business scenarios to meet diverse needs.These findings offer valuable insights into the digital transformation of SMEs in the manufacturing sector,ultimately helping these businesses to remain competitive and innovative in a rapidly changing market.By addressing the specific needs and challenges faced by SMEs,this study aims to contribute to a more comprehensive understanding of how knowledge management can be leveraged to drive digital transformation and improve overall business performance.
