Gotowe bibliografie tematyczne / Deep learning technology

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Gotowa bibliografia na temat „Deep learning technology”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Deep learning technology"

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING TECHNOLOGY IN DISEASE DIAGNOSIS." NATURE AND SCIENCE 04, no. 05 (2020): 4–11. http://dx.doi.org/10.36719/2707-1146/05/4-11.

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The rapid development of deep learning technology provides new methods and ideas for assisting physicians in high-precision disease diagnosis. This article reviews the principles and features of deep learning models commonly used in medical disease diagnosis, namely convolutional neural networks, deep belief networks, restricted Boltzmann machines, and recurrent neural network models. Based on several typical diseases, the application of deep learning technology in the field of disease diagnosis is introduced; finally, the future development direction is proposed based on the limitations of cu

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Naylor, Amanda, and Janet Gibbs. "Deep Learning." International Journal of Mobile and Blended Learning 10, no. 1 (2018): 62–77. http://dx.doi.org/10.4018/ijmbl.2018010105.

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This article presents results from an international collaboration between college students and pre-service teachers in Norway and the UK. This research is part of a large, international project exploring and developing the interrelationship between mobile technology and teachers' perceptions of teaching and learning. Data was collected for this study through an on-line survey of 37 pre-service teachers followed by six semi-structured, in-depth interviews. The data analysis revealed the themes of collaboration, authenticity and professional learning through the use of mobile technology in the d

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Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

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Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intellige

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Hao, Xing, and Guigang Zhang. "Deep learning." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630018. http://dx.doi.org/10.1142/s2425038416300184.

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Artificial intelligence is one of the most beautiful dreams of mankind. Although computer technology has made considerable progress, so far, there is no computer showing intelligence like human beings. The emergence of deep learning gives people a glimmer of hope. So, what is learning deep? Why is it so important? How does it work? And what are the existing achievements and difficulties? This paper provides an overview of deep learning which will answer these questions.

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Baimakhanova, A. S., K. M. Berkimbayev, A. K. Zhumadillayeva, and E. T. Abdrashova. "Technology of using deep learning algorithms." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 89, no. 3 (2023): 35–45. http://dx.doi.org/10.47533/2023.1606-146x.30.

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Deep learning is a branch of machine learning (machine learning-ML). Deep learning methods utilize high-level model abstraction of nonlinear transformations in large databases. In other areas, the implementation of deep learning architectures has contributed significantly to the development of artificial intelligence. This paper presents recent research on newly applied deep learning algorithms. Convolutional Neural Networks are used in deep learning. Database Management System PostgreSQL object-relational database. The implementation resulted in achieving the set goals and objectives. The met

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Lee, Suk-Ho, and Dae-Ki Kang. "Deep Learning Technology and Image Sensing." Sensors 24, no. 16 (2024): 5130. http://dx.doi.org/10.3390/s24165130.

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Huu Huy, NGO, BUI Van Tung, LE Hung Linh, and NGUYEN Duy Minh. "Garbage classification using deep learning technology." Vinh University Journal of Science 52, no. 1A (2023): 35–44. http://dx.doi.org/10.56824/vujs.2022a048.

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Garbage classification has always been an important issue in environmental protection, resource recycling, and social livelihood. However, garbage classification takes a lot of time and effort. Moreover, garbage classification directly affects the health of workers. Currently, due to the development of artificial intelligence, advanced garbage classification robots are being used more and more in recycling factories. With the sufficient support of robots integrated with artificial intelligence technology, garbage will be more and more quickly processed and accurately classified. Therefore, thi

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Yuan, Bin. "Deep learning technology for face recognition." Thermal Science 29, no. 3 Part A (2025): 2007–14. https://doi.org/10.2298/tsci2503007y.

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In China, the rapid development of public transportation network construction has been accompanied by a high incidence of traffic accidents caused by sleep-deprived driving. The monitoring of drivers' sleep-deprived driving and the sending out of early warnings has been identified as a field of research with both important theoretical and practical value. This article proposes a fatigue detection algorithm based on facial recognition information fusion. The algorithm extracts facial feature information and head features from the driver's face and fuses them into facial recognition information

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Wu, Chong. "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology." International Journal of Machine Learning and Computing 11, no. 3 (2021): 182–87. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1033.

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Gaurav, Singh, Kumar Shubham, Vijayan Surya, Perumal Thinagaran, and Sathiyanarayanan Mithileysh. "CYBER BULLYING DETECTION USING MACHINE LEARNING AND DEEP LEARNING." International Journal For Technological Research In Engineering 9, no. 7 (2022): 11–17. https://doi.org/10.5281/zenodo.6392440.

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The use of information and technology to bully a person online is referred to as cyberbullying. Individuals use Information and Communication Technology (ICT) to ridicule, embarrass, taunt, defame, intimidate, and criticise a person without making a direct eye contact. With the rise of social media, bullies have created a “virtual playground” in Facebook, Instagram, WhatsApp, Twitter and YouTube by targeting specific set of individuals or groups. It is necessary to deploy models and mechanisms in place for bullying contents, where the content is automatically detected and resolved,

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Rozprawy doktorskie na temat "Deep learning technology"

Fourie, Aidan. ""Online Platform for Deep Learning Education"." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31381.

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My thesis is going to focus on the development of a standalone, web based, machine learning educational platform. This platform will have a specific focus on neural networks. This tool will have the primary intention to provide a theoretical background to the mathematics of neural networks and thereafter to allow users to train their own networks on regression problems of their own creation. This is so as to provide the user with both theoretical, and first-hand, experience in the applications and functions of artificial intelligence. The primary success metric of this project will be how info

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Backstad, Sebastian. "Federated Averaging Deep Q-NetworkA Distributed Deep Reinforcement Learning Algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149637.

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In the telecom sector, there is a huge amount of rich data generated every day. This trend will increase with the launch of 5G networks. Telco companies are interested in analyzing their data to shape and improve their core businesses. However, there can be a number of limiting factors that prevents them from logging data to central data centers for analysis. Some examples include data privacy, data transfer, network latency etc. In this work, we present a distributed Deep Reinforcement Learning (DRL) method called Federated Averaging Deep Q-Network (FADQN), that employs a distributed hierarc

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Lindström, Marcus, and Jahangir Jazayeri. "Deep reinforcement learning i distribuerad optimering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230707.

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Reinforcement learning has recently become a promising area of machine learning with significant achievements in the subject. Recent successes include surpassing human experts on Atari games and also AlphaGo becoming the first computer ranked on the highest professional level in the game Go, to mention a few. This project aims to apply Policy Gradient Methods (PGM) in a multi agent environment. PGM are widely regarded as being applicable to more problems than for instance Deep Q-Learning but have a tendency to converge upon local optimums. In this report we aim to explore if an optimal policy

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Ljunggren, Henrik. "Exploring the capabilities of deep learning in seasurveillance : Using deep learning to classify motion trajectories from AIS data." Thesis, KTH, Mekatronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217526.

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In this master thesis deep learning is proven to be applicable in the field of seasurveillance. Commercial ships using the AIS system have to report the type of thevessel such as fishing ship or cargo ship. A problem with AIS data is that it can beeasily manipulated and therefore deliberately or accidentally incorrect. This thesis will focus on detecting false ship types. To detect a false ship type 19 different methods were tested on the 1100 hour long AIS data set. Three of these methods were baseline methods using a more conventional approach to the sea surveillanceproblem. The testing show

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Örnberg, Oscar, and Jonas Nylund. "Incrementally Expanding Environment in Deep Reinforcement Learning." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230754.

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Tillräckligt snabb konvergens till optimala strategier är ett mål inom maskininlärning och speciellt förstärkande inlärning. Realtidslösningar till komplexa inlärningsproblem behövs för att expandera fältet till nya områden där maskininlärning tidigare varit en omöjlighet. I denna rapport introducerar vi en ny metod för att träna djupa Q-learning agenter i en miljö vars storlek är skalbar, i hopp om att förkorta inlärningstiden. I denna metod börjar agenten i en mycket liten miljö där den snabbt kan utforska olika situationer på en liten skala och lära sig att hantera dem. Miljön expanderar se

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Andersson, Gustav. "Classification of Heart Sounds with Deep Learning." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149699.

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Health care is becoming more and more digitalized and examinations of patients from a distance are closer to reality than fiction. One of these examinations would be to automatically classify a patient-recorded audiosegment of its heartbeats as healthy or pathological. This thesis examines how it can be achieved by examining different kinds of neural networks; convolutional neural networks (CNN) and long short-term memory networks (LSTM). The theory of artificial neural networks is explained. With this foundation, the feed forward CNN and the recurrent LSTM-network have their methods described

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Gao, Yuan. "Surround Vision Object Detection Using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231929.

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The thesis first develops an object detection framework for front view camera images in surround vision data set. And with the goal of reducing as much annotated data as possible, various domain adaptation methods are applied to train other camera images based on the pretraining of a baseline model. Relevant data analysis work is performed to reveal useful information in object distribution over all cameras. Regularization techniques involving dropout, weight decay, data augmentation are attempted to lower the complexity of training model. Also, the experiments of ratio reduction are carried o

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Oxenstierna, Johan. "Warehouse Vehicle Routing using Deep Reinforcement Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396853.

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In this study a Deep Reinforcement Learning algorithm, MCTS-CNN, is applied on the Vehicle Routing Problem (VRP) in warehouses. Results in a simulated environment show that a Convolutional Neural Network (CNN) can be pre-trained on VRP transition state features and then effectively used post-training within Monte Carlo Tree Search (MCTS). When pre-training works well enough better results on warehouse VRP’s were often obtained than by a state of the art VRP Two-Phase algorithm. Although there are a number of issues that render current deployment pre-mature in two real warehouse environments MC

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Johns, Rasmus Johns. "Intelligent Formation Control using Deep Reinforcement Learning." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-152687.

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In this thesis, deep reinforcement learning is applied to the problem of formation control to enhance performance. The current state-of-the-art formation control algorithms are often not adaptive and require a high degree of expertise to tune. By introducing reinforcement learning in combination with a behavior-based formation control algorithm, simply tuning a reward function can change the entire dynamics of a group. In the experiments, a group of three agents moved to a goal which had its direct path blocked by obstacles. The degree of randomness in the environment varied: in some experimen

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Geirsson, Gunnlaugur. "Deep learning exotic derivatives." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-430410.

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Monte Carlo methods in derivative pricing are computationally expensive, in particular for evaluating models partial derivatives with regard to inputs. This research proposes the use of deep learning to approximate such valuation models for highly exotic derivatives, using automatic differentiation to evaluate input sensitivities. Deep learning models are trained to approximate Phoenix Autocall valuation using a proprietary model used by Svenska Handelsbanken AB. Models are trained on large datasets of low-accuracy (10^4 simulations) Monte Carlo data, successfully learning the true model with

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Książki na temat "Deep learning technology"

author, Prous Jaime, Bras, Michel, writer of foreword, Cuchi, Albert, writer of foreword, McDonah Eamonn translator, and Hormaeche Virginia translator, eds. Deep skin: Learning architecture + technology. Fundació Alumilux, 2018.

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V, Subramaniyaswamy, G. Revathy, Logesh Ravi, N. Thillaiarasu, and Naresh Kshetri. Deep Learning and Blockchain Technology for Smart and Sustainable Cities. Auerbach Publications, 2025. https://doi.org/10.1201/9781003476047.

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Feichtinger, Paul Christoph. Deep Learning: AI Technology. Independently Published, 2019.

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Lee, Sukho, and Dae-Ki Kang, eds. Deep Learning Technology and Image Sensing. MDPI, 2024. https://doi.org/10.3390/books978-3-7258-2814-2.

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Zefs Guide to Deep Learning. Zefs Publishing, 2022.

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Hassanien, Aboul Ella, Vaclav Snasel, B. K. Tripathy, Siddhartha Bhattacharyya, and Satadal Saha. Deep Learning: Research and Applications. de Gruyter GmbH, Walter, 2020.

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Hassanien, Aboul Ella, Vaclav Snasel, B. K. Tripathy, Siddhartha Bhattacharyya, and Satadal Saha. Deep Learning: Research and Applications. de Gruyter GmbH, Walter, 2020.

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Deep Learning: Research and Applications. de Gruyter GmbH, Walter, 2020.

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Hackworth, Gregorio. Deep Learning Revolutionize : Transform Our Technology and Ultimately Our Civilization: Introduction to Deep Learning Medium. Independently Published, 2021.

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Deep Learning in Biometrics. Taylor & Francis Group, 2023.

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Części książek na temat "Deep learning technology"

Wang, Rujing, Lin Jiao, and Kang Liu. "Deep Learning Technology." In Deep Learning for Agricultural Visual Perception. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4973-1_2.

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Bian, Xihui. "Deep Learning Methods." In Chemometric Methods in Analytical Spectroscopy Technology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1625-0_18.

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Siu, Sai Cheong. "Deep Learning and Translation Technology." In Routledge Encyclopedia of Translation Technology, 2nd ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003168348-50.

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Seeram, Euclid, and Vijay Kanade. "Principles of Deep Learning." In Artificial Intelligence in Medical Imaging Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64049-0_4.

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Mousa, Yehia, Radwa Taha, Ranpreet Kaur, and Shereen Afifi. "Melanoma Classification Using Deep Learning." In Image and Video Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0376-0_20.

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Liang, Xuefeng, Longshan Yao, and XingYu Liu. "Noisy Label Learning in Deep Learning." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14903-0_10.

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Kumar, Ashish, Prince Sarren, and Raja. "Deep Learning-Based Multi-object Tracking." In Object Tracking Technology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3288-7_8.

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Islam, Tariqul, Dm Mehedi Hasan Abid, Tanvir Rahman, Zahura Zaman, Kausar Mia, and Ramim Hossain. "Transfer Learning in Deep Reinforcement Learning." In Proceedings of Seventh International Congress on Information and Communication Technology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1607-6_13.

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Rustamov, Raif M., and Leonidas J. Guibas. "Wavelets on Graphs via Deep Learning." In Signals and Communication Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_5.

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Jorwal, Sunil, Ankit, Aman Tibrewal, Kumar Saurav, and Smriti Agarwal. "Malaria Parasite Detection Using Deep Learning." In Signals and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47942-7_33.

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Streszczenia konferencji na temat "Deep learning technology"

Chen, Chen, and Di Zhang. "Image Recognition Technology Based on Deep Learning." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862024.

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Wei, Dan, Ran Li, and Wenjing Lu. "Data encryption technology based on adversarial neural network." In International Conference on Machine Vision and Deep Learning (MVDL 2025), edited by Chengzhong Xu and Dickson K. W. Chiu. SPIE, 2025. https://doi.org/10.1117/12.3071699.

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Dake, Delali Kwasi, Esther Gyimah, and Godwin Kudjo Bada. "A Deep Learning Model to Predict Information Technology Students Learning Style in Adaptive Learning Systems." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933094.

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Wang, Wanhao, Qinyu Huang, Lang Ling, Tiejun Ma, Zheng Wang, and Tao Jin. "A Deep Learning-Dominated Stacked Machine Learning and Deep Learning Method for Electricity Theft Detection." In 2024 3rd Asia Power and Electrical Technology Conference (APET). IEEE, 2024. https://doi.org/10.1109/apet63768.2024.10882617.

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Kate, Priya, and Utkarsha Pacharney. "5G Technology and Impact on Modern Engineering." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933066.

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K, Nivetha, Harini M, Harshinni V, Nivetha N, and Tamilselvan P. "Secure Photo Sharing Across Platforms Using Blockchain Technology." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933359.

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Rahman, Hasanur, Ashfakur Rahman, Ashraful Islam, Shormila Akter Raki, and Md Abu Naser Mojumder. "BDeedNet: A Deep Learning Framework for Bengali Deed Summarization." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11021891.

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Dang, Qian, Wenbo Shang, Fazheng Luo, Pengdong Lu, Guobin Lin, and Xiaolin Gui. "Insulator Defect Detection Technology Based on Deep Learning." In 2024 4th International Conference on Energy Engineering and Power Systems (EEPS). IEEE, 2024. https://doi.org/10.1109/eeps63402.2024.10804456.

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Zhang, Haoran, Guang Yang, Hongyun Yu, Lingzhong Meng, and Baolin Yan. "Deep Learning Model Risk Identification and Control Technology." In 2024 11th International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2024. https://doi.org/10.1109/dsa63982.2024.00014.

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Wang, Sibo. "Deep Learning-Based Channel State Information Estimation Technology." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11084101.

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Raporty organizacyjne na temat "Deep learning technology"

Fullan, Michael, and Joanne Quinn. How Do Disruptive Innovators Prepare Today's Students to Be Tomorrow's Workforce?: Deep Learning: Transforming Systems to Prepare Tomorrow’s Citizens. Inter-American Development Bank, 2020. http://dx.doi.org/10.18235/0002959.

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Disruptive innovators take advantage of unique opportunities. Prior to COVID-19 progress in Latin America and the Caribbean for integrating technology, learning, and system change has been exceedingly slow. In this paper we first offer a general framework for transforming education. The framework focuses on the provision of technology, innovative ideas in learning and well-being, and what we call systemness which are favorable change factors at the local, middle/regional, and policy levels. We then take up the matter of system reform in Latin America and the Caribbean noting problems and poten

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Wortman, Amanda, and Nick Schiner. Access to Powerful Technology as a Catalyst for Career Pathway Engagement. Digital Promise, 2024. http://dx.doi.org/10.51388/20.500.12265/217.

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This paper explores the opportunities and affordances presented when historically and systemically excluded (HSE) youth gain access to high-powered technology and tools in their school environment through an innovative program. Many research studies have shown the challenges HSE youth face in accessing high-value occupations, particularly in the fields of Science, Technology, Engineering, and Mathematics (STEM). Despite the increasing demand for STEM jobs, pathways from schools to careers in these fields often lack effectiveness, especially among HSE students. The Reinvent the Classroom initia

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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Cheng, DingXin. Development of the Roadway Pothole Management Program. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2306.

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Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pot

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Tellei, Adeeshia, King Sam, Megan Cook, et al. Omesubel a Nautilus - Ocean Exploration Vocabulary in Palauan Language. Ocean Exploration Trust, 2024. http://dx.doi.org/10.62878/bgj821.

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This learning resource includes a glossary of ocean exploration and technology terms in Palauan language along with their definitions and translations into English. It was developed in support of Lebuu’s Voyage I & II — the 2024 deep ocean exploration expeditions aboard E/V Nauutilus led by Ocean Exploration Trust, Palau International Coral Reef Center, and NOAA Ocean Exploration in the Palau National Marine Sanctuary. Its contents are certified by the Palau Language Commission. The Lebuu’s Voyage expeditions were funded by NOAA Ocean Exploration via the Ocean Exploration Cooperative Insti

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Salter, R., Quyen Dong, Cody Coleman, et al. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past succ

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wang, peng, and xiaobo wu. The Implementation Strategy of the "Four Single" Model in Digital Textbooks for Deep Learning: Taking the "Biology and Environment" Unit of the Second Volume of the Fourth Grade of Primary School Science in Hunan Science & Technology Press as an Example. New Course Rivew, 2023. http://dx.doi.org/10.58911/bdic.2023.10.001.

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Waters, Donald. Meeting the Climate Emergency: University Information Infrastructure for Researching Wicked Problems. Coalition for Networked Information, 2025. https://doi.org/10.56561/lcoa2799.

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Commissioned by the Coalition for Networked Information, this report examines the role of research universities in addressing complex societal challenges. It focuses on climate change, which is best characterized as a “wicked” problem. Such problems are difficult to define and lack clear solutions in part because they involve multiple stakeholders who sometimes have sharply differing interests and perspectives. Given this complexity, understanding climate change is not just a matter for researchers in the STEM fields of science, technology, engineering, and medicine. It requires an all-hands-o

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