乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T09:05:14Z","timestamp":1742807114447},"reference-count":80,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1016\/j.eswa.2023.120955","type":"journal-article","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T23:21:56Z","timestamp":1688858516000},"page":"120955","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["Towards improving prediction accuracy and user-level explainability using deep learning and knowledge graphs: A study on cassava disease"],"prefix":"10.1016","volume":"233","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3905-7878","authenticated-orcid":false,"given":"Tek Raj","family":"Chhetri","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2298-9353","authenticated-orcid":false,"given":"Armin","family":"Hohenegger","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1391-7104","authenticated-orcid":false,"given":"Anna","family":"Fensel","sequence":"additional","affiliation":[]},{"given":"Mariam Aramide","family":"Kasali","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7712-4202","authenticated-orcid":false,"given":"Asiru Afeez","family":"Adekunle","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2023.120955_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas","year":"2021","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2023.120955_b2","series-title":"Digital-for-development: Enabling transformation, inclusion and sustainability through ICTs","first-page":"320","article-title":"An ensemble model based on learning vector quantization algorithms for early detection of cassava diseases using spectral data","author":"Ahishakiye","year":"2023"},{"issue":"7","key":"10.1016\/j.eswa.2023.120955_b3","doi-asserted-by":"crossref","first-page":"915","DOI":"10.22434\/IFAMR2016.0151","article-title":"Determinants of food security and technical efficiency of cassava farmers in Ondo State, Nigeria","volume":"21","author":"Ajayi","year":"2018","journal-title":"International Food and Agribusiness Management Review"},{"key":"10.1016\/j.eswa.2023.120955_b4","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.neucom.2022.12.010","article-title":"GEnI: A framework for the generation of explanations and insights of knowledge graph embedding predictions","volume":"521","author":"Amador-Dom\u00ednguez","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2023.120955_b5","series-title":"Plant disease ontology","author":"American Phytopathological Society","year":"2016"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b6","doi-asserted-by":"crossref","DOI":"10.15837\/ijccc.2022.2.4356","article-title":"Cassava leaf disease identification and detection using deep learning approach","volume":"17","author":"Anitha","year":"2022","journal-title":"International Journal of Computers Communications & Control"},{"issue":"11","key":"10.1016\/j.eswa.2023.120955_b7","doi-asserted-by":"crossref","first-page":"3632","DOI":"10.3390\/s21113632","article-title":"SEED-g: Simulated EEG data generator for testing connectivity algorithms","volume":"21","author":"Anzolin","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2023.120955_b8","first-page":"480","article-title":"Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks","volume":"51","author":"Ashwinkumar","year":"2022","journal-title":"Materials Today: Proceedings"},{"key":"10.1016\/j.eswa.2023.120955_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2020.101182","article-title":"Plant leaf disease classification using EfficientNet deep learning model","volume":"61","author":"Atila","year":"2021","journal-title":"Ecological Informatics"},{"key":"10.1016\/j.eswa.2023.120955_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107119","article-title":"Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming","volume":"198","author":"Ayoub Shaikh","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2023.120955_b11","series-title":"Proceedings of the 13th international conference on intelligent systems: Theories and applications","article-title":"A novel interpretable model for solar radiation prediction based on adaptive fuzzy clustering and linguistic hedges","author":"Bahani","year":"2020"},{"issue":"3","key":"10.1016\/j.eswa.2023.120955_b12","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1177\/0890334419848734","article-title":"Conducting online surveys","volume":"35","author":"Ball","year":"2019","journal-title":"Journal of Human Lactation"},{"key":"10.1016\/j.eswa.2023.120955_b13","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.aiia.2021.05.002","article-title":"Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network","volume":"5","author":"Bedi","year":"2021","journal-title":"Artificial Intelligence in Agriculture"},{"issue":"9","key":"10.1016\/j.eswa.2023.120955_b14","first-page":"1","article-title":"Logic and learning (Dagstuhl Seminar 19361)","volume":"9","author":"Benedikt","year":"2020","journal-title":"Dagstuhl Reports"},{"key":"10.1016\/j.eswa.2023.120955_b15","series-title":"Yolov4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b16","doi-asserted-by":"crossref","first-page":"634","DOI":"10.3390\/s23020634","article-title":"Survey of explainable AI techniques in healthcare","volume":"23","author":"Chaddad","year":"2023","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.eswa.2023.120955_b17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s12911-022-01817-6","article-title":"Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan","volume":"22","author":"Chan","year":"2022","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"6","key":"10.1016\/j.eswa.2023.120955_b18","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1049\/ipr2.12402","article-title":"Improved cross entropy loss for noisy labels in vision leaf disease classification","volume":"16","author":"Chen","year":"2022","journal-title":"IET Image Processing"},{"key":"10.1016\/j.eswa.2023.120955_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107901","article-title":"Identifying crop diseases using attention embedded MobileNet-V2 model","volume":"113","author":"Chen","year":"2021","journal-title":"Applied Soft Computing"},{"issue":"1","key":"10.1016\/j.eswa.2023.120955_b20","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3390\/bdcc6010026","article-title":"A combined system metrics approach to cloud service reliability using artificial intelligence","volume":"6","author":"Chhetri","year":"2022","journal-title":"Big Data and Cognitive Computing"},{"key":"10.1016\/j.eswa.2023.120955_b21","series-title":"Code: Towards an explainable artificial intelligence using deep learning and knowledge graphs: A study on cassava disease","author":"Chhetri","year":"2022"},{"issue":"3","key":"10.1016\/j.eswa.2023.120955_b22","doi-asserted-by":"crossref","first-page":"985","DOI":"10.3390\/s22030985","article-title":"Knowledge graph based hard drive failure prediction","volume":"22","author":"Chhetri","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2023.120955_b23","series-title":"Rice ontology","author":"Detras","year":"2016"},{"key":"10.1016\/j.eswa.2023.120955_b24","doi-asserted-by":"crossref","DOI":"10.1002\/eng2.12651","article-title":"Classification of cassava leaf diseases using deep Gaussian transfer learning model","author":"Emmanuel","year":"2023","journal-title":"Engineering Reports"},{"key":"10.1016\/j.eswa.2023.120955_b25","series-title":"2017 IEEE\/ACIS 16th international conference on computer and information science","first-page":"221","article-title":"Simulation tools for cloud computing: A survey and comparative study","author":"Fakhfakh","year":"2017"},{"key":"10.1016\/j.eswa.2023.120955_b26","doi-asserted-by":"crossref","unstructured":"Feng, L., Shu, S., Lin, Z., Lv, F., Li, L., & An, B. (2021). Can cross entropy loss be robust to label noise?. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence (pp. 2206\u20132212).","DOI":"10.24963\/ijcai.2020\/305"},{"key":"10.1016\/j.eswa.2023.120955_b27","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2023.120955_b28","series-title":"Climate change fans spread of pests and threatens plants and crops, new FAO study","author":"Food and Agriculture Organization of the United Nations","year":"2021"},{"issue":"1","key":"10.1016\/j.eswa.2023.120955_b29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MIC.2020.3031769","article-title":"Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?","volume":"25","author":"Gaur","year":"2021","journal-title":"IEEE Internet Computing"},{"issue":"4","key":"10.1016\/j.eswa.2023.120955_b30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MIC.2022.3179759","article-title":"Knowledge-infused learning: A sweet spot in neuro-symbolic AI","volume":"26","author":"Gaur","year":"2022","journal-title":"IEEE Internet Computing"},{"key":"10.1016\/j.eswa.2023.120955_b31","series-title":"Dataset: Cassava plant disease Merged 2019\u20132020","author":"Gohil","year":"2021"},{"key":"10.1016\/j.eswa.2023.120955_b32","doi-asserted-by":"crossref","unstructured":"Greff, K., Belletti, F., Beyer, L., Doersch, C., Du, Y., Duckworth, D., Fleet, D. J., Gnanapragasam, D., Golemo, F., Herrmann, C., Kipf, T., Kundu, A., Lagun, D., Laradji, I., Liu, H.-T. D., Meyer, H., Miao, Y., Nowrouzezahrai, D., Oztireli, C., .... Tagliasacchi, A. (2022). Kubric: A Scalable Dataset Generator. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 3749\u20133761).","DOI":"10.1109\/CVPR52688.2022.00373"},{"key":"10.1016\/j.eswa.2023.120955_b33","series-title":"Plant protection ontology","author":"Halabi","year":"2009"},{"issue":"10","key":"10.1016\/j.eswa.2023.120955_b34","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MC.2021.3092610","article-title":"Toward human\u2013AI interfaces to support explainability and causability in medical AI","volume":"54","author":"Holzinger","year":"2021","journal-title":"Computer"},{"issue":"79","key":"10.1016\/j.eswa.2023.120955_b35","first-page":"1","article-title":"SWRL: A semantic web rule language combining OWL and RuleML","volume":"21","author":"Horrocks","year":"2004","journal-title":"W3C Member Submission"},{"key":"10.1016\/j.eswa.2023.120955_b36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.websem.2018.06.003","article-title":"SOSA: A lightweight ontology for sensors, observations, samples, and actuators","volume":"56","author":"Janowicz","year":"2019","journal-title":"Journal of Web Semantics"},{"key":"10.1016\/j.eswa.2023.120955_b37","series-title":"Proceedings of the 10th international conference on advances in information technology","article-title":"An ontology-based approach to plant disease identification system","author":"Jearanaiwongkul","year":"2018"},{"issue":"10","key":"10.1016\/j.eswa.2023.120955_b38","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","article-title":"Drug discovery with explainable artificial intelligence","volume":"2","author":"Jim\u00e9nez-Luna","year":"2020","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.eswa.2023.120955_b39","series-title":"Mobile computing and sustainable informatics","first-page":"567","article-title":"Predictive maintenance for remote field IoT devices\u2014A deep learning and cloud-based approach","author":"Kannammal","year":"2023"},{"key":"10.1016\/j.eswa.2023.120955_b40","series-title":"Proceedings of the international conference on paradigms of computing, communication and data sciences","first-page":"183","article-title":"Cassava leaf disease detection using ensembling of EfficientNet, SEResNeXt, ViT, DeIT and MobileNetV3 models","author":"Kumar","year":"2023"},{"key":"10.1016\/j.eswa.2023.120955_b41","doi-asserted-by":"crossref","first-page":"187814","DOI":"10.1109\/ACCESS.2020.3031477","article-title":"Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools","volume":"8","author":"Kuzlu","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2023.120955_b42","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.compag.2018.06.049","article-title":"Agricultural recommendation system for crop protection","volume":"152","author":"Lacasta","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"issue":"4","key":"10.1016\/j.eswa.2023.120955_b43","doi-asserted-by":"crossref","first-page":"42","DOI":"10.4018\/JITR.2017100103","article-title":"An ontology-based decision support system for the diagnosis of plant diseases","volume":"10","author":"Lagos-Ortiz","year":"2017","journal-title":"Journal of Information Technology Research (JITR)"},{"key":"10.1016\/j.eswa.2023.120955_b44","series-title":"Sgdr: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016"},{"issue":"1","key":"10.1016\/j.eswa.2023.120955_b45","doi-asserted-by":"crossref","first-page":"13","DOI":"10.17221\/112\/2017-RAE","article-title":"Temperature and relative humidity effect on equilibrium moisture content of cassava pulp","volume":"65","author":"Luampon","year":"2019","journal-title":"Research in Agricultural Engineering"},{"key":"10.1016\/j.eswa.2023.120955_b46","series-title":"Proceedings of the 25th international conference on artificial intelligence and statistics","first-page":"4499","article-title":"CF-GNNExplainer: Counterfactual explanations for graph neural networks","volume":"vol. 151","author":"Lucic","year":"2022"},{"key":"10.1016\/j.eswa.2023.120955_b47","series-title":"Advances in neural information processing systems, vol. 30","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.eswa.2023.120955_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2022.100169","article-title":"Explainable artificial intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities","volume":"9","author":"Machlev","year":"2022","journal-title":"Energy and AI"},{"key":"10.1016\/j.eswa.2023.120955_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.118473","article-title":"An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting","volume":"309","author":"Mitrentsis","year":"2022","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2023.120955_b50","series-title":"Advances in neural information processing systems, vol. 32","article-title":"When does label smoothing help?","author":"M\u00fcller","year":"2019"},{"key":"10.1016\/j.eswa.2023.120955_b51","series-title":"iCassava 2019 fine-grained visual categorization challenge","author":"Mwebaze","year":"2019"},{"issue":"1","key":"10.1016\/j.eswa.2023.120955_b52","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13007-019-0479-8","article-title":"Plant disease identification using explainable 3D deep learning on hyperspectral images","volume":"15","author":"Nagasubramanian","year":"2019","journal-title":"Plant Methods"},{"key":"10.1016\/j.eswa.2023.120955_b53","series-title":"Ontology development 101: A guide to creating your first ontology","author":"Noy","year":"2001"},{"key":"10.1016\/j.eswa.2023.120955_b54","series-title":"Advanced network technologies and intelligent computing","first-page":"101","article-title":"Deep learning for the classification of cassava leaf diseases in unbalanced field data set","author":"Paiva-Peredo","year":"2023"},{"key":"10.1016\/j.eswa.2023.120955_b55","series-title":"Python machine learning","author":"Raschka","year":"2017"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b56","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.12862","article-title":"Attention deep learning-based large-scale learning classifier for cassava leaf disease classification","volume":"39","author":"Ravi","year":"2022","journal-title":"Expert Systems"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b57","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/03772063.2018.1537814","article-title":"A new approach for VM failure prediction using stochastic model in cloud","volume":"67","author":"Rawat","year":"2021","journal-title":"IETE Journal of Research"},{"key":"10.1016\/j.eswa.2023.120955_b58","series-title":"2022 16th International conference on open source systems and technologies","first-page":"1","article-title":"Diagnosis of cassava leaf diseases and classification using deep learning techniques","author":"Riaz","year":"2022"},{"key":"10.1016\/j.eswa.2023.120955_b59","series-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"1135","article-title":"\u201cWhy should I trust you?\u201d: Explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.eswa.2023.120955_b60","series-title":"Technologies and innovation","first-page":"18","article-title":"CropPestO: An ontology model for identifying and managing plant pests and diseases","author":"Rodr\u00edguez-Garc\u00eda","year":"2020"},{"issue":"8","key":"10.1016\/j.eswa.2023.120955_b61","doi-asserted-by":"crossref","DOI":"10.3390\/electronics10080905","article-title":"Knowledge-based system for crop pests and diseases recognition","volume":"10","author":"Rodr\u00edguez-Garc\u00eda","year":"2021","journal-title":"Electronics"},{"issue":"3","key":"10.1016\/j.eswa.2023.120955_b62","doi-asserted-by":"crossref","first-page":"413","DOI":"10.3390\/ai2030026","article-title":"A deep learning enabled multi-class plant disease detection model based on computer vision","volume":"2","author":"Roy","year":"2021","journal-title":"AI"},{"key":"10.1016\/j.eswa.2023.120955_b63","series-title":"Mobile computing and sustainable informatics","first-page":"169","article-title":"Plant disease detection using transfer learning with DL model","author":"Sahu","year":"2022"},{"key":"10.1016\/j.eswa.2023.120955_b64","doi-asserted-by":"crossref","unstructured":"Sammani, F., Mukherjee, T., & Deligiannis, N. (2022). NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 8322\u20138332).","DOI":"10.1109\/CVPR52688.2022.00814"},{"key":"10.1016\/j.eswa.2023.120955_b65","series-title":"2022 22nd IEEE international symposium on cluster, cloud and internet computing","first-page":"933","article-title":"Shapley value as an aid to biomedical machine learning: a heart disease dataset analysis","author":"Scapin","year":"2022"},{"key":"10.1016\/j.eswa.2023.120955_b66","doi-asserted-by":"crossref","DOI":"10.1016\/j.scienta.2021.110723","article-title":"How sustainable is organic management in cassava? Evidences from yield, soil quality, energetics and economics in the humid tropics of South India","volume":"293","author":"Seena Radhakrishnan","year":"2022","journal-title":"Scientia Horticulturae"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b67","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.inpa.2021.06.001","article-title":"ResTS: Residual deep interpretable architecture for plant disease detection","volume":"9","author":"Shah","year":"2022","journal-title":"Information Processing in Agriculture"},{"key":"10.1016\/j.eswa.2023.120955_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.cor.2020.104926","article-title":"A systematic literature review on machine learning applications for sustainable agriculture supply chain performance","volume":"119","author":"Sharma","year":"2020","journal-title":"Computers & Operations Research"},{"key":"10.1016\/j.eswa.2023.120955_b69","series-title":"Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing","first-page":"6092","article-title":"Incorporating domain knowledge into medical NLI using knowledge graphs","author":"Sharma","year":"2019"},{"key":"10.1016\/j.eswa.2023.120955_b70","series-title":"Proceedings of the 38th international conference on machine learning","first-page":"10096","article-title":"EfficientNetV2: Smaller models and faster training","volume":"vol. 139","author":"Tan","year":"2021"},{"key":"10.1016\/j.eswa.2023.120955_b71","series-title":"EfficientNetV2: Smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.eswa.2023.120955_b72","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","article-title":"A comparative study of fine-tuning deep learning models for plant disease identification","volume":"161","author":"Too","year":"2019","journal-title":"Computers and Electronics in Agriculture"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b73","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TSTE.2021.3092137","article-title":"Interpretable probabilistic forecasting of imbalances in renewable-dominated electricity systems","volume":"13","author":"Toubeau","year":"2022","journal-title":"IEEE Transactions on Sustainable Energy"},{"key":"10.1016\/j.eswa.2023.120955_b74","series-title":"The sustainable development goals report 2021","author":"UN","year":"2021"},{"key":"10.1016\/j.eswa.2023.120955_b75","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. In Proceedings of the IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.634"},{"key":"10.1016\/j.eswa.2023.120955_b76","series-title":"Artificial intelligence in HCI","first-page":"107","article-title":"What does it mean to explain? A user-centered study on AI explainability","author":"Yang","year":"2021"},{"issue":"2","key":"10.1016\/j.eswa.2023.120955_b77","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.103245","article-title":"Generating knowledge aware explanation for natural language inference","volume":"60","author":"Yang","year":"2023","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.eswa.2023.120955_b78","series-title":"Advances in neural information processing systems, vol. 32","article-title":"GNNExplainer: Generating explanations for graph neural networks","author":"Ying","year":"2019"},{"key":"10.1016\/j.eswa.2023.120955_b79","series-title":"Volo: Vision outlooker for visual recognition","author":"Yuan","year":"2021"},{"key":"10.1016\/j.eswa.2023.120955_b80","series-title":"2020 IEEE 4th conference on energy internet and energy system integration","first-page":"711","article-title":"Explainable AI in deep reinforcement learning models: A SHAP method applied in power system emergency control","author":"Zhang","year":"2020"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417423014574?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417423014574?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T19:18:27Z","timestamp":1702149507000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417423014574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":80,"alternative-id":["S0957417423014574"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2023.120955","relation":{"is-supplemented-by":[{"id-type":"uri","id":"https:\/\/github.com\/Research-Tek\/xai-cassava-agriculture\/blob\/master\/survey_data\/survey_data_cleaned_corrected.csv","asserted-by":"subject"}]},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2023,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Towards improving prediction accuracy and user-level explainability using deep learning and knowledge graphs: A study on cassava disease","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2023.120955","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"120955"}}