Synthetic Aperture Radar (SAR) images for ship classification often face the problem of low resolution. Techniques like super-resolution (SR) can help to enhance the images for better ship classification. In this paper, we compared traditional interpolation techniques (bilinear, bicubic, Lanczos, nearest-neighbor) with deep learning SR methods (EDSR, RCAN, CARN) at 2x and 4x resolutions to analyze their effect in terms of image quality and classification performance. The image quality was assessed using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The findings indicate that while 2x resolution images typically achieved higher image quality scores, the 4x images often performed equally well or better in classification tasks. We utilized two versions of VGG: SR techniques yielded similar scores with a simple VGG, whereas, in the multi-scale VGG (MSVGG), traditional interpolation methods outperformed deep learning methods. Experiments confirm that super-resolved images reach high scores in terms of classical image quality metrics. However, this does not always translate directly into improved performance in SAR ship classification. This highlights the need to select SR techniques by jointly evaluating image quality metrics and classification performance.

Abstract In recent times, with the exception of sporadic cases, the trend in computer vision is to achieve minor improvements compared to considerable increases in complexity. To reverse this trend, we propose a novel method to boost image classification performances without increasing complexity. To this end, we revisited ensembling, a powerful approach, often not used properly due to its more complex nature and the training time, so as to make it feasible through a specific design choice. First, we trained two EfficientNet‐b0 end‐to‐end models (known to be the architecture with the best overall accuracy/complexity trade‐off for image classification) on disjoint subsets of data (i.e., bagging). Then, we made an efficient adaptive ensemble by performing fine‐tuning of a trainable combination layer. In this way, we were able to outperform the state‐of‐the‐art by an average of 0.5% on the accuracy, with restrained complexity both in terms of the number of parameters (by 5–60 times), and the FLoating point Operations Per Second FLOPS by 10–100 times on several major benchmark datasets.

Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models.However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection.This study investigates the application of AI techniques to address these issues within natural science.We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data.Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on openaccess platforms such as iNaturalist.By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.

From ancient times olive tree cultivation has been one of the most crucial agricultural activities for Mediterranean countries.In recent years, the role of Artificial Intelligence in agriculture is increasing: its use ranges from monitoring of cultivated soil, to irrigation management, to yield prediction, to autonomous agricultural robots, to weed and pest classification and management, for example, by taking pictures using a standard smartphone or an unmanned aerial vehicle , and all this eases human work and makes it even more accessible.In this work, a method is proposed for olive disease classification, based on an adaptive ensemble of two EfficientNet-b0 models, that improves the state-of-the-art accuracy on a publicly available dataset by 1.6-2.6%.Both in terms of the number of parameters and the number of operations, our method reduces complexity roughly by 50% and 80%, respectively, that is a level not seen in at least a decade.Due to its efficiency, this method is also embeddable into a smartphone application for real-time processing.

Artificial neural networks (ANN) face challenges in the biomedical and health care sectors due to the elastic nature of biomedical data. This data requires a knowledge-centric approach rather than a purely data-centric one. Fuzzy systems efficiently handle the vagueness in medical big data, emulating human perception. These systems provide precise analysis for various medical situations, neutralizing uncertainties like varying disease patterns. They also support ranking populations based on health attributes, aiding in early prognosis and preventive medicine. This special issue is dedicated to focus on the recent advancements and applications of fuzzy systems within the area of healthcare data analysis. It has provided a platform for researchers to share innovative techniques andmethodologiesmore effectively. Through this issue,we aspire to stimulate discussions, foster collaborations and inspire further innovations in leveraging fuzzy systems for more nuanced, human-like interpretations of complex biomedical datasets. As technology evolves, healthcare and diagnostics keeps changing continously. Taking a look at the array of innovative methods, we observe a clear inclination towards deep learning and computational intelligence in diagnostics. For instance, the application of Computational intelligence for analysing CT images for lung cancer detection and the XlmNet, which uses an Extreme Learning Machine Algorithm for classifying lung cancer from histopathological images, both focus on early-stage detection of lung diseases. Their reliance on intricate computational techniques demonstrates a move towards more precise and early diagnostic procedures. On the other hand, we have algorithms like the Residual neural network-assisted one-class classification, specifically tailored for melanoma recognition in imbalanced datasets. It’s evident that there’s a conscious effort to tackle class imbalance issues, which have long been a hurdle in medical image analysis. Mental health and wellbeing are not left behind either. The “Smart Analysis of Anxiety People and Their Activities” and the “Classification Analysis of Burnout People’s Brain Images” both emphasize the growing role of technology in understanding and diagnosing psychological health issues. Similarly, kidney diseases, retinal issues, skin lesions, and other specific conditions are being targeted with specialized models like the Explainable Deep Learning Model for early-stage Chronic Kidney Disease prediction and the modified CNN for retina disease prediction, incorporating the strengths of SVM classifiers. Finally, the integration of ontology-based speculative sense models and hybrid methods like the SVM-ABC for gene expression data classification illustrates a blend of traditional computational methods with modern deep learning, enhancing accuracy and efficiency. We extend our heartfelt appreciation to the Editor-in-Chief of the journal for granting us the opportunity to organise this special issue. We would also like to express our gratitude to the authors and reviewers for their punctual and valuable contributions.We believe that this special issue will provide an additional valuable contribution to the research community.

The purpose of this paper is to present an effective way to achieve a high-level integration of a clinical decision support system in the general process of heart failure care and to discuss the advantages of such an approach. In particular, the relevant and significant medical knowledge and experts' know-how have been modelled according to an ontological formalism extended with a base of rules for inferential reasoning. These have been also combined with advanced analytical tools for data processing. In particular, methods for the segmentation of echocardiographic image sequences and algorithms for ECG processing have been implemented and integrated into the system.