Artificial Neural Networks for Visual Learning

Topic Information

Dear Colleagues,

This Topic focuses on Artificial Neural Networks for visual learning, which aims to highlight recent advancements in the field of visual analysis using deep learning and neural networks. Visual analysis, including image and video processing systems, is closely related to various fields, such as Internet of Things, automatic navigation, intelligent robots and smart healthcare, etc. However, image and video analysis can be time-consuming, costly, and prone to human error. With the emergence of artificial neural networks, many of these challenges can be addressed by automating tasks involved in visual learning and analysis. Therefore, this Topic “Artificial Neural Networks for Visual Learning” aims to bring together the leading researchers and developers from both academia and industry to discuss and present their latest research and innovations on the theory, algorithms, and system technologies that can substantially improve existing artificial neural networks for visual learning. We encourage prospective authors to submit related distinguished research papers on this subject, including new theoretical methods, innovative applications, and system prototypes.

Dr. Lei Chen
Prof. Dr. Xianye Ben
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.6	5.4	2008	17.6 Days	CHF 1800	Submit
AppliedMath appliedmath	1.4	1.4	2021	20.4 Days	CHF 1200	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Journal of Superintelligence superintelligence	-	-	2026	15.0 days *	CHF 1000	Submit
Machine Learning and Knowledge Extraction make	8.4	12.7	2019	18.7 Days	CHF 1800	Submit
Symmetry symmetry	2.2	5.2	2009	16.3 Days	CHF 2400	Submit

* Median value for all MDPI journals in the first half of 2026.

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Published Papers (2 papers)

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All Algorithms AppliedMath Electronics Journal of Superintelligence Symmetry MAKE Information

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32 pages, 26739 KB  

Open AccessReview

Artificial Intelligence Applications in Biomass Pyrolysis: A Systematic Literature Review

by Vilmar Steffen, Maiquiel Schmidt de Oliveira and Maressa Fontana Mezoni

J. Superintelligence 2026, 1(1), 4; https://doi.org/10.3390/superintelligence1010004 - 14 Jul 2026

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Abstract

The integration of artificial intelligence (AI) techniques into biomass pyrolysis research has attracted increasing attention in recent years; however, the existing literature remains fragmented across diverse methodological approaches and application domains. This study presents a systematic literature review of AI applications in biomass [...] Read more.

The integration of artificial intelligence (AI) techniques into biomass pyrolysis research has attracted increasing attention in recent years; however, the existing literature remains fragmented across diverse methodological approaches and application domains. This study presents a systematic literature review of AI applications in biomass pyrolysis, combining bibliometric and qualitative analyses to map the current state of the art, identify prevailing research trends, and highlight existing knowledge gaps. Following a structured search conducted in the Scopus database, 33 peer-reviewed journal articles published in English between 2003 and 2026 were selected according to predefined eligibility criteria. The final portfolio was prioritized using an adapted version of the Normalized Index for Ranking Papers (NIRP 2.0), while the review procedure followed, whenever applicable, the recommendations of PRISMA, PRISMA for Abstracts, and PRISMA-S guidelines. The ranking methodology incorporated four scientometric indicators: Field-Weighted Citation Impact, average citations per year, SNIP, and CiteScore. The bibliometric analysis revealed a significant intensification of research activity during the last five years, with China, India, and Pakistan emerging as the most productive countries in the field. Machine learning techniques, particularly ensemble learning methods such as Extreme Gradient Boosting, Random Forest, and Gradient Boosting Decision Trees, were identified as the dominant approaches, especially in applications related to product yield prediction (biochar, bio-oil, and gas), kinetic and thermodynamic modeling, co-pyrolysis optimization, and process parameter estimation. Recent studies have also demonstrated growing interest in explainable artificial intelligence methods aimed at improving model interpretability and supporting physical understanding of pyrolysis systems. Despite the promising predictive and optimization capabilities demonstrated by AI-based models, important challenges remain, including limited dataset sizes, data heterogeneity, inconsistent terminology, reduced model generalizability, and the absence of physically informed constraints in many machine learning frameworks. The findings of this review indicate that future advances in the field will strongly depend on the development of standardized and publicly accessible databases, harmonized reporting protocols, and the integration of physics-informed artificial intelligence approaches capable of providing reliable, interpretable, and transferable predictions for biomass pyrolysis processes. Full article

(This article belongs to the Topic Artificial Neural Networks for Visual Learning)

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30 pages, 11249 KB  

Open AccessArticle

Alignment-Aware 3D Point Cloud Anomaly Detection with Adversarial Normalizing Flows

by Andrés Jiménez-García, Jonnatan Arias-Garcia, Hernán F. Garcia, Julian Gil-Gonzalez and David Cárdenas-Peña

Mach. Learn. Knowl. Extr. 2026, 8(7), 206; https://doi.org/10.3390/make8070206 - 13 Jul 2026

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Abstract

Detecting localized morphological anomalies in three-dimensional point clouds is difficult because geometric deviations are entangled with rigid pose variation, residual registration error, sampling noise, and normal inter-subject variability. This challenge is particularly relevant in translational neuroimaging, where abnormal shape changes may be subtle [...] Read more.

Detecting localized morphological anomalies in three-dimensional point clouds is difficult because geometric deviations are entangled with rigid pose variation, residual registration error, sampling noise, and normal inter-subject variability. This challenge is particularly relevant in translational neuroimaging, where abnormal shape changes may be subtle and abnormal annotations are scarce. We propose an unsupervised framework that formulates 3D anomaly detection as a two-stage factorization problem, termed AdvFlow3D-AD. First, Fast Global Registration, followed by multi-scale Iterative Closest Point refinement, establishes a common geometric reference frame and reduces rigid-body nuisance variation. Second, an adversarially regularized normalizing flow models the residual distribution of aligned normal coordinates, enabling localized anomaly scores based on distance from the learned normal latent support. Percentile calibration on normal data then defines interpretable point-level and object-level operating points without requiring abnormal samples during training. We evaluate AdvFlow3D-AD on the Real3D-AD and Anomaly ShapeNet3D datasets, achieving a point-level area under the receiver operating characteristic curve (AUROC) of 0.747 on Real3D-AD and an object-level AUROC of 0.816 on Anomaly ShapeNet3D. We further present an exploratory neurodevelopmental brain-shape case study involving pediatric perinatal-asphyxia cases. The resulting anomaly maps showed qualitative spatial correspondence with anatomically plausible hippocampal and cerebellar regions under neuroradiological review. These results suggest that separating geometric nuisance variation from residual morphology can support interpretable anomaly localization when abnormal labels are limited. Full article

(This article belongs to the Topic Artificial Neural Networks for Visual Learning)

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