Write in a clean editor, then format for Applied AI Letters in one click — DocuGuru applies the official Wiley template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Applied AI Letters is a peer-reviewed journal published by Wiley, covering Explainable Artificial Intelligence (XAI), Diverse Scientific and Economic Studies, Human auditory perception and evaluation.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Applied AI Letters 12 (3): 45–58.
Formats any DOI in Applied AI Letters style. No sign-up. |
| Publishes research in | Explainable Artificial Intelligence (XAI) Diverse Scientific and Economic Studies Human auditory perception and evaluation Educational Robotics and Engineering Topic Modeling |
| ISSN | 2689-5595 |
| Citation impact (2-yr) | 1.56 |
| h-index | 16 |
| i10-index | 29 |
| Total citations | 980 |
| Article processing charge | $2,100 |
| Open access | Yes |
| Top institutions publishing here | Makerere University |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Despite recent dramatic successes, Natural Language Processing (NLP) is not ready to address a variety of real-world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and large computational resource requirements, makes domain-specific application of even the most successful NLP techniques difficult. This paper proposes…
Abstract Low‐cost air quality monitoring networks can potentially increase the availability of high‐resolution monitoring to inform analytic and evidence‐informed approaches to better manage air quality. This is particularly relevant in low and middle‐income settings where access to traditional reference‐grade monitoring networks remains a challenge. However, low‐cost air quality sensors are impacted by ambient conditions which…
Abstract The DARPA Explainable Artificial Intelligence (AI) (XAI) Program focused on generating explanations for AI programs that use machine learning techniques. This article highlights progress during the DARPA Program (2017‐2021) relative to research since the 1970s in the field of intelligent tutoring systems (ITSs). ITS researchers learned a great deal about explanation that is directly…
Abstract Automatic video understanding is becoming more important for applications where real‐time performance is crucial and compute is limited: for example, automated video tagging, robot perception, activity recognition for mobile devices. Yet, accurate solutions so far have been computationally intensive. We propose efficient models for videos—Tiny Video Networks—which are video architectures, automatically designed to comply…
Abstract Here are presented technical notes and tips on developing graph generative models for molecular design. Although this work stems from the development of GraphINVENT, a Python platform for iterative molecular generation using graph neural networks, this work is relevant to researchers studying other architectures for graph‐based molecular design. In this work, technical details that…
