Attention Performance and Altered Amplitude of Low-frequency Fluctuations in the Attention Network of Patients with MCI: A Resting-state Functional MRI Study by Jia Luo, Yi Zeng, Yuwei Ye, Yiping Xiao, Qiurong Xie, Jijing Zhang, Weilin Cai, Huanyun Xu, Shengxiang Liang, Jia Huang

“…Data preprocessing and analysis were conducted using Data Processing & Analysis of Brain Imaging in MATLAB R2018b. …”
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Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis by Akella S. Narasimha Raju, K. Venkatesh, M. Rajababu, Ranjith Kumar Gatla, Shaik Jakeer Hussain, G. Satya Mohan Chowdary, T. Ganga Bhavani, Mohammed Kareemullah, Sameer Algburi, Ali Majdi, Ahmed M. Abdulhadi, Wahaj Ahmad Khan

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DreamOn: a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers by Luc Lerch, Luc Lerch, Lukas S. Huber, Lukas S. Huber, Amith Kamath, Alexander Pöllinger, Aurélie Pahud de Mortanges, Verena C. Obmann, Florian Dammann, Walter Senn, Walter Senn, Mauricio Reyes, Mauricio Reyes, Mauricio Reyes

“…Additionally, we introduce DreamOn, a novel, biologically inspired data augmentation strategy for medical image analysis. …”
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Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images by Mahdi Azmoodeh-Kalati, Hasti Shabani, Mohammad Sadegh Maghareh, Zeynab Barzegar, Reza Lashgari

“…The advent of whole-slide scanners has revolutionized this process by enabling the use of Computer-Aided Detection (CAD) systems for automated analysis. In this study, we utilize state-of-the-art Convolutional Neural Networks (CNNs), specifically EfficientNetV1 and EfficientNetV2, for the binary classification of the BreakHis dataset—a collection of histopathological images categorized as benign or malignant breast tissues. …”
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Multimodal Morphometric Similarity Network Analysis of Autism Spectrum Disorder by Antonio Del Casale, Darvin Shehu, Maria Camilla Rossi-Espagnet, Clarissa Zocchi, Irene Bilotta, Jan Francesco Arena, Alessandro Alcibiade, Barbara Adriani, Daniela Longo, Carlo Gandolfo, Andrea Romano, Stefano Ferracuti, Alessandro Bozzao, Antonio Napolitano

“…<b>Methods</b>: Data from the Autism Brain Imaging Data Exchange (ABIDE) were analyzed, comprising 597 individuals with ASD and 644 healthy controls. …”
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Rare plants detection using a YOLOv3 neural network by L.A. Gorodetskaya, A.Y. Denisova, L.M. Kavelenova, V.A. Fedoseev

“…The main issue under research is the possibility of training a neural network from peony images collected in an artificial habitat with a subsequent application to images collected in a natural habitat and the possibilities of using multi-temporal data to improve the network training quality. …”
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Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery by Giuliana Bilotta, Luigi Bibbò, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…This project aims to develop an AI classifier utilising high-resolution training data and the resilient architecture of ResNet, in conjunction with the Remote Sensing Image Classification Benchmark (RSI-CB128). …”
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A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks by Alawee Wissam H., Al-Haddad Luttfi A., Basem Ali, Al-Haddad Abdullah A.

“…Prior to analysis, the images were subjected to a sophisticated data augmentation process that leveraged data denoising, contrast enhancement, and the application of a generative adversarial network (GAN). …”
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Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing by Nuphar Avital, Idan Egel, Ido Weinstock, Dror Malka

“…The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. …”
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Seafloor Sediment Classification Using Small-Sample Multi-Beam Data Based on Convolutional Neural Networks by Haibo Ma, Xianghua Lai, Taojun Hu, Xiaoming Fu, Xingwei Zhang, Sheng Song

“…To overcome the scarcity of seafloor sediment acoustic image data, we applied a deep convolutional generative adversarial network (DCGAN) for data augmentation, incorporating a de-normalization and anti-normalization module into the original DCGAN framework. …”
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A Comprehensive Benchmarking Framework for Sentinel-2 Sharpening: Methods, Dataset, and Evaluation Metrics by Matteo Ciotola, Giuseppe Guarino, Antonio Mazza, Giovanni Poggi, Giuseppe Scarpa

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IndoHerb: Indonesia medicinal plants recognition using transfer learning and deep learning by Muhammad Salman Ikrar Musyaffa, Novanto Yudistira, Muhammad Arif Rahman, Ahmad Hoirul Basori, Andi Besse Firdausiah Mansur, Jati Batoro

“…This research addresses the task of classifying Indonesian herbal plants through the implementation of transfer learning of Convolutional Neural Networks (CNN). To support our study, we curated an extensive dataset of herbal plant images from Indonesia with careful manual selection. …”
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CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification [version 1; peer review: 2 approved] by Luca Zammataro

“…Background Medical imaging has seen significant advancements through machine learning, particularly convolutional neural networks (CNNs). …”
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Algorithm development for recognizing human emotions using a convolutional neural network based on audio data by V. V. Semenuk, M. V. Skladchikov

“…This article provides a description and experience of creating the algorithm for recognizing the emotional state of the subject.Methods. Image processing methods are used.Results. The proposed algorithm makes it possible to recognize the emotional states of the subject on the basis of an audio data set. …”
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A Comprehensive Analysis of Data Augmentation Methods for Speech Emotion Recognition by Umut Avci

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Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…These findings suggest that advanced deep learning models employing residual networks, such as VDSR and EDSR, significantly enhance temperature data accuracy over SRCNN. …”
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AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai&#x2013;Tibet Plateau by Fangzhou Hong, Guojin He, Guizhou Wang, Zhaoming Zhang, Yan Peng

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Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images by Jeremiah W. Johnson, Doğacan Su Öztürk, Donald Hampton, Hyunju K. Connor, Matthew Blandin, Amy Keesee

“…In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) data set. …”
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Rapid Discrimination of Aging Year of Chenpi Based on Hyperspectral Images by Cheng LIU, Lulu ZHAO, Songbin ZHOU, Yisen LIU, Tingyou WANG

“…This study proposed a rapid, non-destructive method to discern the aging year of Chenpi by integrating hyperspectral imaging with deep learning. A total of 480 Chenpi samples across four aging years were collected, and their near-infrared hyperspectral data (wavelength range: 935.61~1720.23 nm) were obtained. …”
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Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning by Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri, Peter Nordbeck, Thorsten A. Bley, Bettina Baeßler, Bernhard Petritsch, Julius F. Heidenreich

“…Conclusion: The introduced real-time CMR imaging technique enables high-quality cardiac cine data acquisitions in 1–2 min, eliminating the need for ECG gating and breath-holds. …”
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