Toward Automated Knowledge Discovery in Case-Based Reasoning by Sherri Weitl-Harms, John Hastings, Jay Powell

“…Automated Case Elicitation (ACE) enables case-based reasoning (CBR) systems to automatically acquire knowledge through real-time exploration and interaction with environments. …”
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Exploring the application of knowledge transfer to sports video data by Shahrokh Heidari, Gibran Zazueta, Riki Mitchell, David Arturo Soriano Valdez, Mitchell Rogers, Mitchell Rogers, Jiaxuan Wang, Ruigeng Wang, Marcel Noronha, Alfonso Gastelum Strozzi, Mengjie Zhang, Patrice Jean Delmas, Patrice Jean Delmas

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Medical language model specialized in extracting cardiac knowledge by Hansle Gwon, Jiahn Seo, Seohyun Park, Young-Hak Kim, Tae Joon Jun

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Unleashing the Potential of Knowledge Distillation for IoT Traffic Classification by Mahmoud Abbasi, Amin Shahraki, Javier Prieto, Angelica Gonzalez Arrieta, Juan M. Corchado

“…This makes it a suitable alternative for resource-constrained scenarios like mobile or IoT traffic classification. We find that the knowledge distillation technique effectively transfers knowledge from the teacher model to the student model, even with reduced training data. …”
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On the role of knowledge graphs in AI-based scientific discovery by Mathieu d’Aquin

“…However, for machine learning models to be truly used as a tool for scientific advancement, we have to find ways for the knowledge implicitly gained by these models from their training to be integrated with the explicitly represented knowledge captured through knowledge graphs. …”
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Crowd counting at the edge using weighted knowledge distillation by Muhammad Asif Khan, Hamid Menouar, Ridha Hamila, Adnan Abu-Dayya

“…Knowledge distillation enables lightweight models to emulate deeper models by distilling the knowledge learned by the deeper model during the training process. …”
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Augmentation of Semantic Processes for Deep Learning Applications by Maximilian Hoffmann, Lukas Malburg, Ralph Bergmann

“…One important factor that hinders the adoption of DL in certain areas is the availability of sufficiently large training datasets, particularly affecting domains where process models are mainly defined manually with a high knowledge-acquisition effort. …”
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deepTFBS: Improving within‐ and Cross‐Species Prediction of Transcription Factor Binding Using Deep Multi‐Task and Transfer Learning by Jingjing Zhai, Yuzhou Zhang, Chujun Zhang, Xiaotong Yin, Minggui Song, Chenglong Tang, Pengjun Ding, Zenglin Li, Chuang Ma

“…Taking advantages of multi‐task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large‐scale TF binding profiles to enhance the prediction of TFBSs under small‐sample training and cross‐species prediction tasks. …”
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A Coaching-Based Training for Underrepresented Mentors in STEM by Molly E. Tuck, Kaylee A. Palomino, Julie A. Bradley, Margaret Mohr-Schroeder, Luke H. Bradley

“…For underrepresented groups in STEM, such training positions mentors as knowledge facilitators, helping bridge gaps in mentorship experiences and bolstering confidence in their roles, thereby contributing to a more inclusive and effective learning ecosystem. …”
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A Knowledge-enhanced Generative Summary Model for Audit News by ZHU Siwen, ZHANG Yangsen, WANG Xuesong, SUN Longyuan, XU Ruiyi, JIA Qilong

“…Finally, the generative summary model is used to summarize the high-quality news texts with background knowledge and obtain the summary results. At the same time, a set of audit news dataset is constructed for targeted training to improve the model effect. …”
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Deeper knowledge of entrepreneurs in decision-making. Innovating through neuroentrepreneurship by Juan Camilo Serna Zuluaga, David Juárez-Varón, Ana Mengual-Recuerda, Vincenzo Corvello

“…The use of GSR and EEG provides a deep understanding of how experience influences decision-making and stress management, offering a basis for developing specific support and training programs for entrepreneurs at different stages of business evolution. …”
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Blind Interleaver Recognition Using Deep Learning Techniques by Nayim Ahamed, Swaminathan R., B. Naveen

“…While information about the encoder and interleaver is typically available in cooperative scenarios, non-cooperative military communication systems often lack such knowledge. This paper explores the application of deep learning to recognize four different interleavers such as block, convolutional, helical, and random especially in non-cooperative environments. …”
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Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification by Wasen Yahya Melhem, Asad Abdi, Farid Meziane

“…On the other hand, transfer learning leverages pre-trained knowledge but may be biased towards the pre-training domain. …”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…Deep learning for flux upscaling holds great promise, while remedies for its vulnerability to training data distribution changes still need consideration by the community.…”
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Comparative Study of Deep Learning-Based Sentiment Classification by Seungwan Seo, Czangyeob Kim, Haedong Kim, Kyounghyun Mo, Pilsung Kang

“…However, one practical issue occurring in deep-learning-based sentiment classification is that the best model structure depends on the characteristics of the dataset on which the deep learning model is trained; moreover, it is manually determined based on the domain knowledge of an expert or selected from a grid search of possible candidates. …”
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Predicting the Performance of Students Using Deep Ensemble Learning by Bo Tang, Senlin Li, Changhua Zhao

“…The proposed method employs an optimization strategy to concurrently configure and train the deep neural networks within our ensemble system. …”
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“Nip and Tuck”: Training the Next Generation of Aesthetic Plastic Surgeons by Lok Ka Cheung, MBChB, MRCS, Vasilios Skepastianos, MUDR, MSc, Epameinodas Kostopoulos, MUDR, MSc, PhD, Georgios Skepastianos, MUDR, MSc, PhD

“…The demand for aesthetic surgery continues to increase, and it is therefore essential to ensure that the next generation of plastic surgeons are adequately trained. We propose a safe method in aesthetic training in abdominoplasty and facelift, utilizing free deep inferior epigastric perforator (DIEP) flap and parotidectomy for training aesthetic procedures. …”
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Using N-Version Architectures for Railway Segmentation with Deep Neural Networks by Philipp Jaß, Carsten Thomas

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Correlation-Based Knowledge Distillation in Exemplar-Free Class-Incremental Learning by Zijian Gao, Bo Liu, Kele Xu, Xinjun Mao, Huaimin Wang

“…Class-incremental learning (CIL) aims to learn a family of classes incrementally with data available in order rather than training all data at once. One main drawback of CIL is that standard deep neural networks suffer from catastrophic forgetting (CF), especially when the model only has access to data from the current incremental step. …”
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Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping by Jun-Lin Yu, Cong Zhou, Xiang-Li Ning, Jun Mou, Fan-Bo Meng, Jing-Wei Wu, Yi-Ting Chen, Biao-Dan Tang, Xiang-Gen Liu, Guo-Bo Li

“…Despite the availability of many pharmacophore tools, the adoption of deep learning for pharmacophore-guided drug discovery remains relatively rare. …”
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