research paper on microcontroller applications

• STM Journals
• Special Issues
• Conferences
• Editorial Board Members
• Reviewers Board Members
• Advisory Panel
• Indexing Bodies
• For Authors
• For Reviewers
• For Editors
• For Advisory Board
• Special Issue Guidelines
• Peer-Review Policy
• Manuscript Submission Guidelines
• Publication Ethics and Virtue
• Article Processing Charge
• Editorial Policy
• Advertising Policy
• STM Website and Link Policy
• Distribution and dessemination of Research
• Informed consent Policy

"Connect with colleagues and showcase your academic achievements."

"Unleashing the potential of your words"

 "Explore a vast collection of books and broaden your horizons."

 "Empower yourself with the knowledge and skills needed to succeed."

"Collaborate with like-minded professionals and share your knowledge."

"Learn from experts and engage with a community of learners."

• ICDR Group of Companies

• Training Programs

Journal of Microcontroller Engineering and Applications

ISSN: 2455-197X

Journal Menu

Editors overview.

Dr. Hetal N. Patel

Institutional Profile Link : http://www. . . View Full Profile

STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd. A-118, 1st Floor, Sector-63, Noida, U.P. India, Pin – 201301 E-mail: [email protected] (Tel) (+91) 0120- 4781 200 (Mob) (+91) 9810078958, +919667725932

Design of a SmartMesh IP Network with Wireless Motes and GUI Control

Design and Analysis of PV-based Micro-inverter using INC MPPT Controller and Fuzzy Logic Controller

DTMF Based Home Automation Without Using Microcontroller

About the Journal

Journal of Microcontroller Engineering and Applications (jomea) : 2455-197X(e) is a peer-reviewed hybrid open-access journal launched in 2014 focused on the rapid publication of fundamental research papers on all areas of Microprocessor Engineering & Applications. View Full Focus and Scope…

Journal Particulars

For Subscriber Access

Special Issue

WEBSITE DISCLAIMER

Last updated: 2022-06-15

The information provided by STM Journals (“Company”, “we”, “our”, “us”) on https://journals.stmjournals.com / (the “Site”) is for general informational purposes only. All information on the Site is provided in good faith, however, we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the Site.

UNDER NO CIRCUMSTANCE SHALL WE HAVE ANY LIABILITY TO YOU FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF THE SITE OR RELIANCE ON ANY INFORMATION PROVIDED ON THE SITE. YOUR USE OF THE SITE AND YOUR RELIANCE ON ANY INFORMATION ON THE SITE IS SOLELY AT YOUR OWN RISK.

EXTERNAL LINKS DISCLAIMER

The Site may contain (or you may be sent through the Site) links to other websites or content belonging to or originating from third parties or links to websites and features. Such external links are not investigated, monitored, or checked for accuracy, adequacy, validity, reliability, availability, or completeness by us.

WE DO NOT WARRANT, ENDORSE, GUARANTEE, OR ASSUME RESPONSIBILITY FOR THE ACCURACY OR RELIABILITY OF ANY INFORMATION OFFERED BY THIRD-PARTY WEBSITES LINKED THROUGH THE SITE OR ANY WEBSITE OR FEATURE LINKED IN ANY BANNER OR OTHER ADVERTISING. WE WILL NOT BE A PARTY TO OR IN ANY WAY BE RESPONSIBLE FOR MONITORING ANY TRANSACTION BETWEEN YOU AND THIRD-PARTY PROVIDERS OF PRODUCTS OR SERVICES.

PROFESSIONAL DISCLAIMER

The Site can not and does not contain medical advice. The information is provided for general informational and educational purposes only and is not a substitute for professional medical advice. Accordingly, before taking any actions based on such information, we encourage you to consult with the appropriate professionals. We do not provide any kind of medical advice.

Content published on https://journals.stmjournals.com / is intended to be used and must be used for informational purposes only. It is very important to do your analysis before making any decision based on your circumstances. You should take independent medical advice from a professional or independently research and verify any information that you find on our Website and wish to rely upon.

THE USE OR RELIANCE OF ANY INFORMATION CONTAINED ON THIS SITE IS SOLELY AT YOUR OWN RISK.

AFFILIATES DISCLAIMER

The Site may contain links to affiliate websites, and we may receive an affiliate commission for any purchases or actions made by you on the affiliate websites using such links.

TESTIMONIALS DISCLAIMER

The Site may contain testimonials by users of our products and/or services. These testimonials reflect the real-life experiences and opinions of such users. However, the experiences are personal to those particular users, and may not necessarily be representative of all users of our products and/or services. We do not claim, and you should not assume that all users will have the same experiences.

YOUR RESULTS MAY VARY.

The testimonials on the Site are submitted in various forms such as text, audio, and/or video, and are reviewed by us before being posted. They appear on the Site verbatim as given by the users, except for the correction of grammar or typing errors. Some testimonials may have been shortened for the sake of brevity, where the full testimonial contained extraneous information not relevant to the general public.

The views and opinions contained in the testimonials belong solely to the individual user and do not reflect our views and opinions.

ERRORS AND OMISSIONS DISCLAIMER

While we have made every attempt to ensure that the information contained in this site has been obtained from reliable sources, STM Journals is not responsible for any errors or omissions or the results obtained from the use of this information. All information on this site is provided “as is”, with no guarantee of completeness, accuracy, timeliness, or of the results obtained from the use of this information, and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability, and fitness for a particular purpose.

In no event will STM Journals, its related partnerships or corporations, or the partners, agents, or employees thereof be liable to you or anyone else for any decision made or action taken in reliance on the information in this Site or for any consequential, special or similar damages, even if advised of the possibility of such damages.

GUEST CONTRIBUTORS DISCLAIMER

This Site may include content from guest contributors and any views or opinions expressed in such posts are personal and do not represent those of STM Journals or any of its staff or affiliates unless explicitly stated.

LOGOS AND TRADEMARKS DISCLAIMER

All logos and trademarks of third parties referenced on https://journals.stmjournals.com / are the trademarks and logos of their respective owners. Any inclusion of such trademarks or logos does not imply or constitute any approval, endorsement, or sponsorship of STM Journals by such owners.

Should you have any feedback, comments, requests for technical support, or other inquiries, please contact us by email: [email protected] .

• < Previous

Home > ENGR > ENE > ENEGS > 27

School of Engineering Education Graduate Student Series

Microcontrollers for mechanical engineers: from assembly language to controller implementation.

Noah Salzman , Purdue University Peter H. Meckl , Purdue University Follow

2013 ASEE Annual Conference, Atlanta, Georgia.

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2013 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference.

https://peer.asee.org/22290

This paper describes the evolution of a graduate and advanced undergraduate mechanical engineering course on microcontrollers and electromechanical control systems. The course begins with developing an understanding of the architecture of the microcontroller, and low-level programming in assembly language. It then proceeds to working with various functions of the microcontroller, including serial communications, interrupts, analog to digital conversion, and digital to analog conversion. Finally, the students learn how to characterize first and second order systems, and develop and implement their own controllers for a variety of electromechanical systems. The course takes the uncommon approach of teaching assembly language programming to mechanical engineering students, with the students using assembly language programming for approximately half of the course and the remainder using the C programming language. The authors believe that this approach helps students develop a better understanding of the architecture of the microcontroller and low-level routines found in embedded control applications. The course provides a bridge between traditional mechatronics courses that focus on electronics and interfacing, and lab-based control courses that use turnkey data acquisition systems and graphical programming tools such as Simulink or LabVIEW. The course has existed for over two decades, using a variety of microprocessor and microcontroller platforms. After evaluating numerous alternatives, the course was recently updated to use a 32-bit ARM Cortex-M3 microcontroller evaluation board from STMicroelectronics paired with custom interfacing circuitry. This platform was chosen not only for more modern microcontroller technology, but also for the availability of free development tools and very inexpensive evaluation boards. This allows the students to write and test their programs outside of scheduled lab times, along with the ability to cost-effectively utilize microcontrollers in future projects.

Date of this Version

Since December 18, 2015

Included in

Engineering Education Commons

Advanced Search

• Notify me via email or RSS
• Purdue Libraries
• Purdue University Press Open Access Collections

Links for Authors

• Policies and Help Documentation
• Submit Research
• Collections
• Disciplines

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

Introduction to microcontrollers. I

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Internet of things technology, research, and challenges: a survey

• Published: 02 May 2024

Cite this article

• Amit Kumar Vishwakarma 1 ,
• Soni Chaurasia 2 ,
• Kamal Kumar 3 ,
• Yatindra Nath Singh 4 &
• Renu Chaurasia 5  

The world of digitization is growing exponentially; data optimization, security of a network, and energy efficiency are becoming more prominent. The Internet of Things (IoT) is the core technology of modern society. This paper is based on a survey of recent and past technologies used for IoT optimization models, such as IoT with Blockchain, IoT with WSN, IoT with ML, and IoT with big data analysis. Suppose anyone wants to start core research on IoT technologies, research opportunities, challenges, and solutions. In that case, this paper will help me understand all the basics, such as security, interoperability, standards, scalability, complexity, data management, and quality of service (QoS). This paper also discusses some recent technologies and the challenges in implementation. Finally, this paper discusses research possibilities in basic and applied IoT Domains.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data Availability

Available on request.

Ghorbani HR, Ahmadzadegan MH (2017) Security challenges in internet of things: survey. In: 2017 IEEE conference on wireless sensors (ICWiSe)

Brock DL (2013) The electronic product code (EPC) a naming scheme for physical objects. http://www.autoidlabs.org/ uploads/media/MIT-AUTOID-WH-002.pdf

IoT Analytics (2014) Why the internet of things is called internet of things: definition, history, disambiguation. https://iot-analytics.com/internet-of-things-definition/

Internet of Things (2005) International telecommunication union (ITU), Geneva. https://www.itu.int/net/wsis/tunis/newsroom/stats/The-Internet-of-Things-2005.pdf

Internet of Things (2010) https://en.oxforddictionaries.com/definition/us/ internetofthings

Manyika Chui M, Bisson P, Woetzel J, Dobbs R, Bughin J, Aharon D (2015) Unlocking the potential of the internet of things. https://www.mckinsey.com/~/media/McKinsey

Vermesan O, Friess P, Guillemin P, Gusmeroli S, Sundmaeker H, Bassi A, Jubert IS, Mazura M, Harrison M, others (2011) Internet of things strategic research roadmap

Artik cloud (2017) https://developer.artik.cloud/documentation/getting-started/index.html

Fusion Connect (2014) https://autodeskfusionconnect.com/iot-devices

(2016) https://docs.aws.amazon.com/iot/latest/developerguide/what-is-aws-iot.html

Guth J, Breitenbücher U, Falkenthal M, Leymann F, Reinfurt L (2016) Comparison of IoT platform architectures: A field study based on a reference architecture. In: 2016 Cloudification of the internet of things (CIoT)

Balani, Naveen and Hathi, Rajeev, Enterprise IoT: A Definitive Handbook. In: CreateSpace Independent Publishing Platform, 2015

GE Predix (2017) https://docs.predix.io/en-US/platform

Soliman M, Abiodun T, Hamouda T, Zhou J, Lung CH, (2013) Smart home: integrating internet of things with web services and cloud computing. In: 2013 IEEE 5th International conference on cloud computing technology and science

Google Cloud (2016) https://cloud.google.com/solutions/iot-overview

Familiar B (2015) IoT and microservices. In: Microservices, IoT, and Azure. Apress, Berkeley, CA. In: Internet of Things;Web services:Azure IOT

Microsoft IoT platform (2015) https://docs.microsoft.com/en-us/rest/api/iothub/?redirectedfrom=MSDN

High R (2012) The era of cognitive systems: an inside look at ibm watson and how it works. In: Internet of Things;Web services:Azure IOT

IBM Watson IoT (2017) https://www.ibm.com/internet-of-things

Deering S, Hinden R (2017) Internet Protocol, Version 6 (IPv6) Specification. https://tools.ietf.org/html/rfc8200

WInter Ed T, Thubert P, Brandt A, Hui J, Kelsey R, Levis P, Pister K, Struik R (2012) ipv6 routing protocol for low-power and lossy networks. https://tools.ietf.org/html/rfc6550

Saputro N, Akkaya K, Uludag S (2012) A survey of routing protocols for smart grid communications. http://www.sciencedirect.com/science/article/pii/S1389128612001429 , vol 56

Yi P, Iwayemi A, Zhou C (2011) Building automation networks for smart grids. In: International journals of digital multimedia broadcasting

Fairhurst G Jones T (2018) Transport features of the user datagram protocol (UDP) and lightweight UDP (UDP-Lite). https://www.rfceditor.org/info/rfc8304

Palattella MR, Accettura N, Vilajosana X, Watteyne T, Grieco LA, Boggia G, Dohler M (2013) Standardized protocol stack for the internet of (Important) things. In: IEEE communications surveys tutorials, vol 15

Karagiannis V, Chatzimisios P, Vazquez-Gallego F, Alonso-Zarate J (2015) A survey on application layer protocols for the internet of things. Transaction on IoT and Cloud Computing

Banks A, Gupta R (2014) MQTT Version 3.1.1. Edited by Andrew Banks and Rahul Gupta. OASIS Committee Specification Draft 02 / Public Review Draft 02. http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/csprd02/mqtt-v3.1.1-csprd02.html

Bormann C, Castellani AP, Shelby Z (2012) CoAP: An Application Protocol for Billions of Tiny Internet Nodes. IEEE Internet Computing

Shelby Z, Hartke K, Bormann C (2014) The constrained application protocol (CoAP). https://tools.ietf.org/html/rfc7252

Johansson P, Kazantzidis M, Kapoor R, Gerla M (2001) Bluetooth: an enabler for personal area networking. IEEE Network

Kirsche M, Klauck R (2012) Unify to bridge gaps: Bringing XMPP into the Internet of Things. In: 2012 IEEE international conference on pervasive computing and communications workshops

Naik N, Jenkins P (2016) Web protocols and challenges of Web latency in the Web of Things. In: 2016 Eighth international conference on ubiquitous and future networks (ICUFN)

Han Dm, Lim Jh (2010) Smart home energy management system using IEEE 802.15.4 and zigbee. IEEE Transactions on Consumer Electronics

Eriksson J, Balakrishnan H, Madden S (2008) Cabernet: vehicular content delivery using wifi. https://doi.org/10.1145/1409944.1409968

Ratasuk R, Vejlgaard B, Mangalvedhe N, Ghosh A (2016) NB-IoT system for M2M communication. In: 2016 IEEE Wireless communications and networking conference

ANDRIES MI, BOGDAN I, NICOLAESCU SV, SCRIPCARIU L (2007) WiMAX features and applications. http://www.agir.ro/buletine/687.pdf

Kucharzewski L, Kotulski Z (2014) WiMAX networks architecture and ata security. Annales UMCS Informatica AI X

Adams JT (2006) An introduction to IEEE STD 802.15.4. In: 2006 IEEE aerospace conference

Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. journal = Computer Networks. http://www.sciencedirect.com/science/article/pii/S1389128610001568 , vol.54

Mainetti L, Patrono L Vilei A (2011) Evolution of wireless sensor networks towards the Internet of Things: A survey. In: SoftCOM 2011, 19th international conference on software, telecommunications and computer networks

Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: Vision, applications and research challenges. http://www.sciencedirect.com/science/article/pii/S1570870512000674 , vol 10, pp 1497–1516

Xu LD, He W, Li S (2014) Internet of things in industries: a survey. In: IEEE Transactions on industrial informatics, vol 10

Botta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. http://www.sciencedirect.com/science/article/pii/S0167739X15003015 , vol 56

Seyedzadegan M, Othman M (2013) IEEE 802.16: WiMAX Overview, WiMAX Architecture. http://www.ijcte.org/papers/796-Z1030.pdf

Abdulzahra AM, Al-Qurabat AK, Abdulzahra SA (2023) Optimizing energy consumption in WSN-based IoT using unequal clustering and sleep scheduling methods. Internet of Things 22:100765

Chaurasia S, Kumar K (2023) ACRA:Adaptive Meta-heuristic Based Clustering and Routing Algorithm for IoT-Assisted Wireless Sensor Network. Peer to Peer Networking and Application. Springer

Chaurasia S, Kumar K (2023) MBASE: Meta-heuristic Based optimized location allocation algorithm for baSE station in IoT assist wireless sensor networks. Multimedia Tools and Applications, pp 1–33

Senthil GA, Raaza A, Kumar N (2022) Internet of things energy efficient cluster-based routing using hybrid particle swarm optimization for wireless sensor network. Wirel Pers Commun 122.3: 2603-2619

Prasanth A, Jayachitra S (2020) A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer Peer Netw Appl 13:1905–1920

Article   Google Scholar  

Vaiyapuri T, et al (2022) A novel hybrid optimization for cluster-based routing protocol in information-centric wireless sensor networks for IoT based mobile edge computing. Wirel Pers Commun 127.1: 39-62

Dhiman G, Sharma R (2022) SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer. Complex Intell Syst 8(5):3779–3787

Seyfollahi A, Taami T, Ghaffari A (2023) Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things. Microprocess Microsyst 96:104747

Donta PK et al (2023) iCoCoA: intelligent congestion control algorithm for CoAP using deep reinforcement learning. J Ambient Intell Humaniz Comput 14(3):2951–2966

Rosati R et al (2023) From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in industry 4.0. J Intell Manuf 34.1:107–121

Babar M et al (2022) An optimized IoT-enabled big data analytics architecture for edge-cloud computing. IEEE Internet Things J 10(5):3995–4005

Article   MathSciNet   Google Scholar  

Lv Z, Singh AK (2021) Big data analysis of internet of things system. ACM Trans Internet Technol 21(2):1–15

Qiu Y, Zhu X, Jing L (2021) Fitness monitoring system based on internet of things and big data analysis. IEEE Access 9:8054–8068

Rahman A et al (2021) Smartblock-sdn: An optimized blockchain-sdn framework for resource management in iot. IEEE Access 9:28361–28376

Zhao Y et al (2023) A lightweight model-based evolutionary consensus protocol in blockchain as a service for IoT. IEEE Transactions on Services Computing

Saba T et al (2023) Blockchain-enabled intelligent iot protocol for high-performance and secured big financial data transaction. IEEE Transactions on Computational Social Systems

Abed S, Reem J, Bassam JM (2023) A review on blockchain and IoT integration from energy, security and hardware perspectives. Wirel Pers Commun 129(3):2079–2122

Javanmardi S et al (2023) An SDN perspective IoT-Fog security: A survey. Comput Netw 229:109732

Qayyum A et al (2023) Secure and trustworthy artificial intelligence-extended reality (AI-XR) for metaverses. ACM Computing Surveys

Rawat P, Chauhan S (2021) Clustering protocols in wireless sensor network: A survey, classification, issues, and future directions. Comput Sci Rev 40:100396

Albouq SS et al (2023) A survey of interoperability challenges and solutions for dealing with them in IoT environment. IEEE Access 10:36416–36428

Rana B, Singh Y, Singh PK (2021) A systematic survey on internet of things: Energy efficiency and interoperability perspective. Trans Emerg Telecommun Technol 32(8):e4166

Sasaki Y (2021) A survey on IoT big data analytic systems: current and future. IEEE Internet of Things Journal 9(2):1024–1036

Alfandi O et al (2021) A survey on boosting IoT security and privacy through blockchain: Exploration, requirements, and open issues. Cluster Comput 24(1):37–55

Bian Jet al. Machine learning in real-time internet of things (iot) systems: A survey. IEEE Internet of Things J 9(11): 8364–8386

Donta PK et al (2022) Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digital Commun Netw 8(5):727–744

Download references

No funding was received to carry out this work.

Author information

Authors and affiliations.

Management science and technology, Khalifa University, Abu Dhabi, UAE

Amit Kumar Vishwakarma

Computer science & Engineering, SGT University, Gurugram, India

Soni Chaurasia

Department of Information Technology, IGDTUW, New Delhi, India

Kamal Kumar

Electrical Engineering, IIT Kanpur, Kanpur, India

Yatindra Nath Singh

Computer science & Engineering, AIT, Rooma, Kanpur, India

Renu Chaurasia

You can also search for this author in PubMed   Google Scholar

Contributions

Equally contributed.

Corresponding author

Correspondence to Soni Chaurasia .

Ethics declarations

Conflicts of interest.

No conflict of interest.

Consent to Publish

As per journal policy.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Vishwakarma, A.K., Chaurasia, S., Kumar, K. et al. Internet of things technology, research, and challenges: a survey. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19278-6

Download citation

Received : 18 October 2023

Revised : 13 March 2024

Accepted : 18 April 2024

Published : 02 May 2024

DOI : https://doi.org/10.1007/s11042-024-19278-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Semantic intelligence
• IoT protocol
• IoT application
• Research possibilities
• IoT Platforms
• IoT optimization models
• Find a journal
• Publish with us
• Track your research

• MyUHart MyUHart Blackboard Self-Service Hawkmail Compass UNotes UHartHub
• Healthy Hawks
• Self-Service

Daniel Jimenez Gil Receives Belle K. Ribicoff Prize

Gil had a fairly clear view of his professional aspiration when he began his studies in the University of Hartford’s College of Engineering, Technology, and Architec­ture. But through his work on a number of projects, he realized that his interests extended beyond civil engi­neering to include programming and other aspects of computer science.

Gil's trajectory through the semesters has included work as a research assistant; internships at top engineering firms; several teaching and mentoring experiences on campus; and a number of student awards, including one from the Connecticut Society of Civil Engineers and another from the Association of General Contractors of Connecticut.

“I have found Daniel to be an outstanding student who is intelligent, hardworking, highly motivated, and with a pleasant personality,” says Clara Fang, professor and chair of the civil, environmental, and biomedical engineering department. “He has displayed a remarkable ability to dive into intricate subjects independently, using a wide array of resources, including academic literature and online refer­ences. His ability to grasp complex concepts and apply them effectively was evident throughout all his projects.”

Fang, who worked with Gil on many of those projects, co-authored a research paper with him on AI bridge tech­nology, which Gil had the chance to present in front of the Transportation Research Board in Washington, D.C.

While working with a club called Engineers Without Borders, Gil discovered his love of—and talent for—programming. He began to teach himself computer sci­ence and has ventured into areas such as microcontroller programming and training statistical models.

Gil has been accepted into a program in the civil engineering department at the University of Illinois Urbana-Champaign, where he will research the application of AI and robotics into structural engineering.

Microsoft Research Blog

Microsoft at asplos 2024: advancing hardware and software for high-scale, secure, and efficient modern applications.

Published April 29, 2024

By Rodrigo Fonseca , Sr Principal Research Manager Madan Musuvathi , Partner Research Manager

Share this page

• Share on Facebook
• Share on Twitter
• Share on LinkedIn
• Share on Reddit
• Subscribe to our RSS feed

Modern computer systems and applications, with unprecedented scale, complexity, and security needs, require careful co-design and co-evolution of hardware and software. The ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (opens in new tab) , is the main forum where researchers bridge the gap between architecture, programming languages, and operating systems to advance the state of the art.

ASPLOS 2024 is taking place in San Diego between April 27 and May 1, and Microsoft researchers and collaborators have a strong presence, with members of our team taking on key roles in organizing the event. This includes participation in the program and external review committees and leadership as the program co-chair.

We are pleased to share that eight papers from Microsoft researchers and their collaborators have been accepted to the conference, spanning a broad spectrum of topics. In the field of AI and deep learning, subjects include power and frequency management for GPUs and LLMs, the use of Process-in-Memory for deep learning, and instrumentation frameworks. Regarding infrastructure, topics include memory safety with CHERI, I/O prefetching in modern storage, and smart oversubscription of burstable virtual machines. This post highlights some of this work.

Spotlight: AI-POWERED EXPERIENCE

Microsoft research copilot experience

Discover more about research at Microsoft through our AI-powered experience

Paper highlights

Characterizing power management opportunities for llms in the cloud.

The rising popularity of LLMs and generative AI has led to an unprecedented demand for GPUs. However, the availability of power is a key limiting factor in expanding a GPU fleet. This paper characterizes the power usage in LLM clusters, examines the power consumption patterns across multiple LLMs, and identifies the differences between inference and training power consumption patterns. This investigation reveals that the average and peak power consumption in inference clusters is not very high, and that there is substantial headroom for power oversubscription. Consequently, the authors propose POLCA: a framework for power oversubscription that is robust, reliable, and readily deployable for GPU clusters. It can deploy 30% more servers in the same GPU clusters for inference tasks, with minimal performance degradation.

PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization

PIM-DL is the first deep learning framework specifically designed for off-the-shelf processing-in-memory (PIM) systems, capable of offloading most computations in neural networks. Its goal is to surmount the computational limitations of PIM hardware by replacing traditional compute-heavy matrix multiplication operations with Lookup Tables (LUTs). PIM-DL first enables neural networks to operate efficiently on PIM architectures, significantly reducing the need for complex arithmetic operations. PIM-DL demonstrates significant speed improvements, achieving up to ~37x faster performance than traditional GEMM-based systems and showing competitive speedups against CPUs and GPUs.

Cornucopia Reloaded: Load Barriers for CHERI Heap Temporal Safety

Memory safety bugs have persistently plagued software for over 50 years and underpin some 70% of common vulnerabilities and exposures (CVEs) every year. The CHERI capability architecture (opens in new tab) is an emerging technology (opens in new tab) (especially through Arm’s Morello (opens in new tab) and Microsoft’s CHERIoT (opens in new tab) platforms) for spatial memory safety and software compartmentalization. In this paper, the authors demonstrate the viability of object-granularity heap temporal safety built atop CHERI with considerably lower overheads than prior work.

AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines

Burstable virtual machines (BVMs) are a type of virtual machine in the cloud that allows temporary increases in resource allocation. This paper shows how to oversubscribe BVMs. It first studies the characteristics of BVMs on Microsoft Azure and explains why traditional approaches based on using a fixed oversubscription ratio or based on the Central Limit Theorem do not work well for BVMs: they lead to either low utilization or high server capacity violation rates. Based on the lessons learned from the workload study, the authors developed a new approach, called AUDIBLE, using a nonparametric statistical model. This makes the approach lightweight and workload independent. This study shows that AUDIBLE achieves high system utilization while enforcing stringent requirements on server capacity violations.

Complete list of accepted publications by Microsoft researchers

Amanda: Unified Instrumentation Framework for Deep Neural Networks Yue Guan, Yuxian Qiu, and Jingwen Leng; Fan Yang , Microsoft Research; Shuo Yu, Shanghai Jiao Tong University; Yunxin Liu, Tsinghua University; Yu Feng and Yuhao Zhu, University of Rochester; Lidong Zhou , Microsoft Research; Yun Liang, Peking University; Chen Zhang, Chao Li, and Minyi Guo, Shanghai Jiao Tong University

AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines Seyedali Jokar Jandaghi and Kaveh Mahdaviani, University of Toronto; Amirhossein Mirhosseini, University of Michigan; Sameh Elnikety , Microsoft Research; Cristiana Amza and Bianca Schroeder, University of Toronto, Cristiana Amza and Bianca Schroeder, University of Toronto

Characterizing Power Management Opportunities for LLMs in the Cloud (opens in new tab) Pratyush Patel, Microsoft Azure and University of Washington; Esha Choukse (opens in new tab) , Chaojie Zhang (opens in new tab) , and Íñigo Goiri (opens in new tab) , Azure Research; Brijesh Warrier (opens in new tab) , Nithish Mahalingam,  Ricardo Bianchini (opens in new tab) , Microsoft AzureResearch

Cornucopia Reloaded: Load Barriers for CHERI Heap Temporal Safety Nathaniel Wesley Filardo , University of Cambridge and Microsoft Research; Brett F. Gutstein, Jonathan Woodruff, Jessica Clarke, and Peter Rugg, University of Cambridge; Brooks Davis, SRI International; Mark Johnston, University of Cambridge; Robert Norton , Microsoft Research; David Chisnall, SCI Semiconductor; Simon W. Moore, University of Cambridge; Peter G. Neumann, SRI International; Robert N. M. Watson, University of Cambridge

CrossPrefetch: Accelerating I/O Prefetching for Modern Storage Shaleen Garg and Jian Zhang, Rutgers University; Rekha Pitchumani, Samsung; Manish Parashar, University of Utah; Bing Xie , Microsoft; Sudarsun Kannan, Rutgers University

Kimbap: A Node-Property Map System for Distributed Graph Analytics Hochan Lee, University of Texas at Austin; Roshan Dathathri, Microsoft Research; Keshav Pingali, University of Texas at Austin

PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization Cong Li and Zhe Zhou, Peking University; Yang Wang , Microsoft Research; Fan Yang, Nankai University; Ting Cao and Mao Yang , Microsoft Research; Yun Liang and Guangyu Sun, Peking University

Predict; Don’t React for Enabling Efficient Fine-Grain DVFS in GPUs Srikant Bharadwaj , Microsoft Research; Shomit Das, Qualcomm; Kaushik Mazumdar and Bradford M. Beckmann, AMD; Stephen Kosonocky, Uhnder

Conference organizers from Microsoft

Program co-chair, madan musuvathi, submission chairs.

Jubi Taneja Olli Saarikivi

Program Committee

Abhinav Jangda (opens in new tab) Aditya Kanade (opens in new tab) Ashish Panwar (opens in new tab) Jacob Nelson (opens in new tab) Jay Lorch (opens in new tab) Jilong Xue (opens in new tab) Paolo Costa (opens in new tab) Rodrigo Fonseca (opens in new tab) Shan Lu (opens in new tab) Suman Nath (opens in new tab) Tim Harris (opens in new tab)

External Review Committee

Career opportunities.

Microsoft welcomes talented individuals across various roles at Microsoft Research, Azure Research, and other departments. We are always pushing the boundaries of computer systems to improve the scale, efficiency, and security of all our offerings. You can review our open research-related positions here .

Related publications

Predict; don’t react for enabling efficient fine-grain dvfs in gpus, amanda: unified instrumentation framework for deep neural networks, crossprefetch: accelerating i/o prefetching for modern storage, kimbap: a node-property map system for distributed graph analytics, meet the authors.

Rodrigo Fonseca

Sr Principal Research Manager

Partner Research Manager

Continue reading

Research Focus: Week of April 15, 2024

Research at Microsoft 2023: A year of groundbreaking AI advances and discoveries

PwR: Using representations for AI-powered software development

Research Focus: Week of November 22, 2023

Research areas.

• Follow on Twitter
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram

Share this page:

This paper is in the following e-collection/theme issue:

Published on 2.5.2024 in Vol 26 (2024)

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

Authors of this article:

• Khaled El Emam 1, 2 , BEng, PhD   ; 
• Tiffany I Leung 3, 4 , MD, MPH   ; 
• Bradley Malin 5 , BA, MSc, PhD   ; 
• William Klement 2 , PhD   ; 
• Gunther Eysenbach 3, 6 , MD, MPH  

1 School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada

2 Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada

3 JMIR Publications, Inc, Toronto, ON, Canada

4 Department of Internal Medicine (adjunct), Southern Illinois University School of Medicine, Springfield, IL, United States

5 Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, United States

6 School of Health Information Science, University of Victoria, Victoria, BC, Canada

Corresponding Author:

Khaled El Emam, BEng, PhD

School of Epidemiology and Public Health

University of Ottawa

401 Smyth Road

Ottawa, ON, K1H 8L1

Phone: 1 6137377600

Email: [email protected]

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors’ application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

Introduction

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased over time. The cross-journal JMIR Publications e-collection “Machine Learning” includes nearly 1300 articles as of April 1, 2024 [ 1 ], and there are additional sections in other journals, which collate articles related to the field (eg, “Machine Learning from Dermatological Images” [ 2 ] in JMIR Dermatology ). From 2015 to 2022, the number of published articles with “artificial intelligence” (AI) or “machine learning” in the title and abstract in JMIR Publications journals increased from 22 to 298 (13.5-fold growth), and there are already 312 articles in 2023 (14-fold growth). For JMIR Medical Informatics , the number of articles increased from 10 to 160 (16-fold growth) until 2022. This is consistent with the growth in the research and application of medical AI in general where a similar PubMed search (with the keyword “medicine”) revealed a 22-fold growth (from 640 to 14,147 articles) between 2015 and 2022, and there are already 11,272 matching articles in 2023.

Many papers reporting the use of ML models in medicine have used a large clinical data set to make diagnostic or prognostic predictions [ 3 - 6 ]. However, the use of data from electronic health records and other resources is often not without pitfalls as these data are typically collected and optimized for other purposes (eg, medical billing) [ 7 ].

Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting [ 8 ]. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for instance, avoid instances of missing information. For example, in the experiences of the editors-in-chief of JMIR AI , missing information is especially notable because for manuscripts reporting on ML models, which are submitted to JMIR AI , this can delay the overall review interval by adding more revision cycles.

According to the EQUATOR (Enhancing the Quality and Transparency of Health Research) network, a reporting guideline is “a simple, structured tool for health researchers to use while writing manuscripts. A reporting guideline provides a minimum list of information needed to ensure a manuscript can be, for example: understood by a reader, replicated by a researcher, used by a doctor to make a clinical decision, and included in a systematic review” [ 9 ]. These can be presented in the form of a checklist, flow diagram, or structured text.

In this Editorial, we discuss the general JMIR Publications policy regarding authors’ application of reporting guidelines. We then focus specifically on the reporting of ML studies in JMIR Publications journals.

JMIR Publications Policy on the Use of Reporting Guidelines

Accumulating evidence suggests that when authors apply reporting guidelines and reporting checklists in health research, they can be beneficial for authors, readers, and the discipline overall by enabling the replication or reproduction of studies. Recent evidence suggests that asking reviewers to use reporting checklists, instead of authors, offers no added benefits regarding reporting quality [ 10 ]. However, Botos [ 11 ] reported a positive association between reviewer ratings of adherence to reporting guidelines and favorable editorial decisions, while Stevanovic et al [ 12 ] reported a significant positive correlation between adherence to reporting guidelines and citations and between adherence to reporting guidelines and publication in higher-impact-factor journals.

JMIR Publications’ editorial policy recommends that authors adhere to applicable study design and reporting guidelines when preparing manuscripts for submission [ 13 ]. Authors should note that most reporting guidelines are strongly recommended, particularly because they can improve the quality, completeness, and organization of the presented work. At this time, JMIR Publications requires reporting checklists to be completed and supplied as multimedia appendices for randomized controlled trials without [ 14 - 16 ] or those with eHealth or mobile health components [ 17 ], systematic and scoping literature reviews across the portfolio, and Implementation Reports in JMIR Medical Informatics [ 18 ]. Although some medical journals have mandated the use of certain reporting guidelines and checklists, JMIR Publications recognizes that authors may have concerns about the additional burden that the formalized use of checklists may bring to the submission process. As such, JMIR Publications has chosen to begin recommending the use of ML reporting guidelines and will evaluate their benefits and gather feedback on implementation costs before considering more stringent requirements.

Reporting on ML Models

Regarding the reporting of prognostic and diagnostic ML studies, multiple directly relevant checklists have been developed. Klement and El Emam [ 19 ] have consolidated these guidelines and checklists into a single set that we refer to as the Consolidated Reporting of Machine Learning Studies (CREMLS) checklist. CREMLS serves as a reporting checklist for journals publishing research describing the development, evaluation, and application of ML models, including all JMIR Publications journals, which have officially adopted these guidelines. CREMLS was developed by identifying existing relevant reporting guidelines and checklists. The initial item list was identified through a structured literature review and expert curation, and then the quality of the methods used for their development was assessed to narrow them down to a high-quality subset. This high-quality item subset was further filtered to reveal those that meet specific inclusion and exclusion criteria. The resultant items were converted to guidelines and a checklist that was reviewed by the editorial board of JMIR AI , followed by a preliminary application to assess articles published in JMIR AI . The final checklist offers present-day best practices for high-quality reporting of studies using ML models.

Examples of the application of the CREMLS checklist are presented in Table 1 . In doing so, we identified 7 articles published in JMIR Publications journals, which exemplify each checklist item. Note that not all of the items are relevant to each article, and some articles are particularly good examples of how to operationalize a checklist item.

a ML: machine learning.

b AUC: area under the curve.

c SMOTE: synthetic minority oversampling technique.

d SHAP: Shapely additive explanations.

We strongly advise authors who seek to submit their manuscripts describing the development, evaluation, and application of ML models to the Journal of Medical Internet Research , JMIR AI , JMIR Medical Informatics , or other JMIR Publications journals to adhere to the CREMLS guidelines and checklist to ensure that they have considered and addressed all relevant details for their work before initiating their submission and review process. More complete and high-quality reporting benefits the authors by accelerating the review cycle and reducing the burden on reviewers. Hence, the need exists for reporting guidelines and checklists for papers describing prognostic and diagnostic ML studies. This is expected to assist, for example, in reducing missing documentation on hyperparameters for an ML model and to clarify how data leakage was avoided. We have observed that peer reviewers have, in practice, been asking authors to improve reporting on the same topics covered in the CREMLS checklist. This is not a surprise given that peer reviewers are experts in the field and would note important information that is missing. Nevertheless, we would encourage reviewers to use the checklist regularly to ensure completeness and consistency.

The CREMLS checklist’s scope is limited to ML models using structured data that are trained and evaluated in silico and in shadow mode. This provides a significant opportunity to expand on the CREMLS to different data modalities and additional phases of model deployment. Should such extended reporting guidelines and checklists be developed, they may be considered for recommendation for submissions to JMIR Publications journals, incorporating lessons learned from the initial checklist for studies reporting the use of ML models.

There is evidence that the completeness of reporting of research studies is beneficial to the authors and the broader scientific community. For prognostic and diagnostic ML studies, many reporting guidelines have been developed, and these have been consolidated into CREMLS, capturing the combined value of the source guidelines and checklists in one place. In this Editorial, we extend journal policy and recommend that authors follow these guidelines when submitting articles to journals in the JMIR Publications portfolio. This will improve the reproducibility of research studies using ML methods, accelerate review cycles, and improve the quality of published papers overall. Given the rapid growth of studies developing, evaluating, and applying ML models, it is important to establish reporting standards early.

Authors' Contributions

KEE and BM conceptualized this study and drafted, reviewed, and edited the manuscript. TIL and GE reviewed and edited the manuscript. WK prepared the literature summary and reviewed the manuscript.

Conflicts of Interest

KEE and BM are co–editors-in-chief of JMIR AI . KEE is the cofounder of Replica Analytics, an Aetion company, and has financial interests in the company. TIL is the scientific editorial director at JMIR Publications, Inc. GE is the executive editor and publisher at JMIR Publications, Inc, receives a salary, and owns equity.

• Machine Learning. JMIR Medical Informatics. URL: https://medinform.jmir.org/themes/500-machine-learning [accessed 2024-04-01]
• Machine Learning from Digital Images in Dermatology. JMIR Dermatology. URL: https://derma.jmir.org/themes/922-machine-learning-from-digital-images-in-dermatology [accessed 2023-09-22]
• Lee S, Kang WS, Kim DW, Seo SH, Kim J, Jeong ST, et al. An artificial intelligence model for predicting trauma mortality among emergency department patients in South Korea: retrospective cohort study. J Med Internet Res. Aug 29, 2023;25:e49283. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Deng Y, Ma Y, Fu J, Wang X, Yu C, Lv J, et al. Combinatorial use of machine learning and logistic regression for predicting carotid plaque risk among 5.4 million adults with fatty liver disease receiving health check-ups: population-based cross-sectional study. JMIR Public Health Surveill. Sep 07, 2023;9:e47095. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M. Machine learning for the prediction of procedural case durations developed using a large multicenter database: algorithm development and validation study. JMIR AI. Sep 8, 2023;2:e44909. [ CrossRef ]
• Williams DD, Ferro D, Mullaney C, Skrabonja L, Barnes MS, Patton SR, et al. An "All-Data-on-Hand" deep learning model to predict hospitalization for diabetic ketoacidosis in youth with type 1 diabetes: development and validation study. JMIR Diabetes. Jul 18, 2023;8:e47592. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Maletzky A, Böck C, Tschoellitsch T, Roland T, Ludwig H, Thumfart S, et al. Lifting hospital electronic health record data treasures: challenges and opportunities. JMIR Med Inform. Oct 21, 2022;10(10):e38557. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Emam KE, Klement W, Malin B. Reporting and methodological observations on prognostic and diagnostic machine learning studies. JMIR AI. 2023.:e47995. [ FREE Full text ]
• What is a reporting guideline? Enhancing the QUAlity and Transparency Of health Research. URL: https://www.equator-network.org/about-us/what-is-a-reporting-guideline/ [accessed 2023-09-22]
• Speich B, Mann E, Schönenberger CM, Mellor K, Griessbach AN, Dhiman P, et al. Reminding peer reviewers of reporting guideline items to improve completeness in published articles: primary results of 2 randomized trials. JAMA Netw Open. Jun 01, 2023;6(6):e2317651. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Botos J. Reported use of reporting guidelines among authors, editorial outcomes, and reviewer ratings related to adherence to guidelines and clarity of presentation. Res Integr Peer Rev. Sep 27, 2018;3(1):7. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Stevanovic A, Schmitz S, Rossaint R, Schürholz T, Coburn M. CONSORT item reporting quality in the top ten ranked journals of critical care medicine in 2011: a retrospective analysis. PLoS One. May 28, 2015;10(5):e0128061. [ FREE Full text ] [ CrossRef ] [ Medline ]
• What reporting guidelines should I follow for my article? JMIR Publications Knowledge Base and Help Center. URL: https:/​/support.​jmir.org/​hc/​en-us/​articles/​115001575267-What-reporting-guidelines-should-I-follow-for-my-article [accessed 2024-01-30]
• Schulz KF, Altman D, Moher D, CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. Trials. Mar 24, 2010;11:32. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schulz KF, Altman D, Moher D, CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Ann Intern Med. Jun 01, 2010;152(11):726-732. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. Mar 23, 2010;340(mar23 1):c869-c869. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Eysenbach G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res. Dec 31, 2011;13(4):e126. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Perrin Franck C, Babington-Ashaye A, Dietrich D, Bediang G, Veltsos P, Gupta PP, et al. iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations. J Med Internet Res. May 10, 2023;25:e46694. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Klement W, El Emam K. Consolidated reporting guidelines for prognostic and diagnostic machine learning modeling studies: development and validation. J Med Internet Res. Aug 31, 2023;25:e48763. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lee C, Jo B, Woo H, Im Y, Park RW, Park C. Chronic disease prediction using the common data model: development study. JMIR AI. Dec 22, 2022;1(1):e41030. [ FREE Full text ] [ CrossRef ]
• Zhang X, Xue Y, Su X, Chen S, Liu K, Chen W, et al. A transfer learning approach to correct the temporal performance drift of clinical prediction models: retrospective cohort study. JMIR Med Inform. Nov 09, 2022;10(11):e38053. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Steiger E, Kroll LE. Patient embeddings from diagnosis codes for health care prediction tasks: Pat2Vec machine learning framework. JMIR AI. Apr 21, 2023;2:e40755. [ FREE Full text ] [ CrossRef ]
• Sang S, Sun R, Coquet J, Carmichael H, Seto T, Hernandez-Boussard T. Learning from past respiratory infections to predict COVID-19 outcomes: retrospective study. J Med Internet Res. Feb 22, 2021;23(2):e23026. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kang HYJ, Batbaatar E, Choi D, Choi KS, Ko M, Ryu KS. Synthetic tabular data based on generative adversarial networks in health care: generation and validation using the divide-and-conquer strategy. JMIR Med Inform. Nov 24, 2023;11:e47859. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wilimitis D, Walsh CG. Practical considerations and applied examples of cross-validation for model development and evaluation in health care: tutorial. JMIR AI. Dec 18, 2023;2:e49023. [ FREE Full text ] [ CrossRef ]

Abbreviations

Edited by T Leung; This is a non–peer-reviewed article. submitted 04.04.24; accepted 04.04.24; published 02.05.24.

©Khaled El Emam, Tiffany I Leung, Bradley Malin, William Klement, Gunther Eysenbach. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.