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Designing and Evaluating a Collaborative Knowledge Management Framework for Leaf Disease Detection

Computer Systems Science & Engineering 2022年第8期42卷 751-777页

作者： Komal Bashir Mariam Rehman Afnan Bashir faria kanwal Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Department of Information Technology Government CollegeFaisalabad54000Pakistan Engage Research Lab School of Law and SocietyUniversity of the Sunshine CoastSippy DownsQLD 4556Australia

Knowledge Management(KM)has become a dynamic concept for inquiry in *** 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 *** research aims to propose a Collaborative Knowledge Management(CKM)Framework that bridges gaps and overcomes weaknesses in existing *** 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 *** aKMS served as an effective system for rice leaf disease detection and *** aimed to enhance CKM through knowledge sharing,lessons learned,feedback on problem resolutions,help from co-workers,collaboration,and helping *** were collected from 300 rice crop farmers by questionnaires based on *** Olfman model was used to estimate the effectiveness of *** tests were performed including frequency measures of variables,Cronbach’s alpha reliability,and Pearson’s *** research provided a KMS depicting KM and collaborative *** disease detection module was evaluated using the precision and recall method and found to be 94.16%*** system could replace the work of extension agents,making it a cost and time-effective initiative for farmer betterment.

关键词： Collaborative knowledge management framework jennex olfman km success model knowledge management rice disease detection

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Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

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Intelligent Automation & Soft Computing 2023年第6期36卷 2607-2622页

作者： Aqsa Aslam Saima Farhan Momina Abdul Khaliq Fatima Anjum Ayesha Afzaal faria kanwal Lahore College for Women University Lahore54000Pakistan

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful *** increase in retinal dis-eases is alarming as it may lead to permanent blindness if left ***-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time *** researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary *** paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus *** this research,the data has been collected and combined from three distinct *** images are preprocessed for enhancing the *** layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal *** is observed that the results are reliant on the number of *** binary classification(healthy ***),up to 100%accuracy has been *** 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained *** 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

关键词： Classification convolutional neural network fundus images medical image diagnosis retinal diseases

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