Software isn't the Real World. - Steve Cook; John Daniels (J. Object Oriented Program., 1994)

Lets get Formal. - Steve Cook; John Daniels (J. Object Oriented Program., 1994)

Object Communication. - Steve Cook; John Daniels (J. Object Oriented Program., 1994)

Managing Object Oriented Projects - Panel Session. - Laura Hill; Kenneth S. Rubin; John Daniels; Charles Berman; James Coplien; Douglas Johnson (OOPSLA, 1995)

Domains for Flexibility, Symplicity, and Reuse. - Steve Cook; John Daniels (J. Object Oriented Program., 1995)

Object-oriented project management (panel). - Laura Hill; Douglas Johnson; Kenneth S. Rubin; Charles Berman; James Coplien; John Daniels (OOPSLA Addendum, 1995)

Getting value for money from software architecture: a goldfish bowl. - John Daniels (OOPSLA Addendum, 1998)

Cracking the software paradox (panel session). - John Daniels; Jim Amsden; Larry L. Constantine; David E. Delano; Martin L. Griss; Ivar Jacobson; Else-Marie Östling; Rebecca Wirfs-Brock (OOPSLA Addendum, 2000)

Object principles (poster session): back to basics. - Jutta Eckstein; John Daniels (OOPSLA Addendum, 2000)

Component Contracts. - John Daniels (TOOLS (33), 2000)

Component-Based Design: A Complete Worked Example. - John Daniels (TOOLS (33), 2000)

Modeling with a Sense of Purpose. - John Daniels (IEEE Softw., 2002)

Programming the world with sun SPOTs. - Randall B. Smith; Bernard Horan; John Daniels; Dave Cleal (OOPSLA Companion, 2006)

JavaTM on the bare metal of wireless sensor devices: the squawk Java virtual machine. - Doug Simon; Cristina Cifuentes; Dave Cleal; John Daniels; Derek White (VEE, 2006)

A Portable Low-Power Platform for Ambulatory Closed Loop Control of Blood Glucose in Type 1 Diabetes. - Mohamed Fayez El-Sharkawy; John Daniels; Peter Pesl; Monika Reddy; Nick Oliver; Pau Herrero; Pantelis Georgiou (ISCAS, 2018)

Convolutional Recurrent Neural Networks for Blood Glucose Prediction. - Kezhi Li; John Daniels; Pau Herrero Viñas; Chengyuan Liu; Pantelis Georgiou (CoRR, 2018)

A Data-Driven Detection System for Predicting Stress Levels from Autonomic Signals. - John Daniels; Pantelis Georgiou (BioCAS, 2019)

Personalised Glucose Prediction via Deep Multitask Networks. - John Daniels; Pau Herrero; Pantelis Georgiou (KDH@ECAI, 2020)

Convolutional Recurrent Neural Networks for Glucose Prediction. - Kezhi Li; John Daniels; Chengyuan Liu; Pau Herrero; Pantelis Georgiou (IEEE J. Biomed. Health Informatics, 2020)

Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI. - Lei Kuang; Taiyu Zhu; Kezhi Li; John Daniels; Pau Herrero; Pantelis Georgiou (BioCAS, 2021)

A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems. - John Daniels; Pau Herrero; Pantelis Georgiou (Sensors, 2022)

A Multitask Learning Approach to Personalized Blood Glucose Prediction. - John Daniels; Pau Herrero; Pantelis Georgiou (IEEE J. Biomed. Health Informatics, 2022)

IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing. - Taiyu Zhu; Lei Kuang; John Daniels; Pau Herrero; Kezhi Li; Pantelis Georgiou (IEEE Internet Things J., 2023)

Predicting deterioration in dengue using a low cost wearable for continuous clinical monitoring. - Damien K. Ming; John Daniels; Ho Q. Chanh; Stefan Karolcík; Bernard Hernandez; Vasileios Manginas; Van Hao Nguyen; Quang Huy Nguyen; Tu Qui Phan; Thi Hue Tai Luong; Huynh Trung Trieu; Alison Helen Holmes; et al. (npj Digit. Medicine, 2024)

Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review. - Bernard Hernandez; Damien K. Ming; Timothy M. Rawson; William J. Bolton; Richard Wilson 004; Vasikasin V.; John Daniels; Jesus Rodriguez-Manzano; Davies F. J.; Pantelis Georgiou; Alison H. Holmes (Artif. Intell. Medicine, 2025)