A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones by Cássio Danelon de Almeida, Thales Tozatto Filgueiras, Moisés Luiz Lagares, Bruno da Silva Macêdo, Camila Martins Saporetti, Matteo Bodini, Leonardo Goliatt

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Deep learning-driven medical image analysis for computational material science applications by Li Lu, Mingpei Liang

“…IntroductionDeep learning has significantly advanced medical image analysis, enabling precise feature extraction and pattern recognition. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

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Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…The framework is composed of two phases: “deep learning” and “quantitative analysis.” In the “deep learning” phase, a deep convolutional neural network (DCNN)-based color extraction model is designed to automatically learn building color information from street view images; in the “quantitative analysis” phase, building color is quantitatively analyzed at the overall and local levels, and a color clustering model is designed to quantitatively display the color relationship to comprehensively understand the current status of urban building color. …”
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OpenFungi: A Machine Learning Dataset for Fungal Image Recognition Tasks by Anca Cighir, Roland Bolboacă, Teri Lenard

“…The quality of the dataset is demonstrated by solving a classification problem with a simple convolutional neural network. A thorough experimental analysis was conducted, where six performance metrics were measured in three distinct validation scenarios. …”
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Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI by M. Arunachalam, S. Sekar, A. M. Erdmann, V. V. Sajith Variyar, R. Sivanpillai

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Application of a conditional generative adversarial network to denoising solar observations by Md Mushfiqur Rahman, Harry P. Warren

“…Unfortunately, after being in operation since 2006, the EIS detectors have become noisy, which poses a challenge to data analysis. This paper presents a Conditional Generative Adversarial Network (cGAN) tailored to address the unique noise characteristics inherent in EIS data over the mission. …”
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Unleashing the Potential of Residual and Dual-Stream Transformers for the Remote Sensing Image Analysis by Priya Mittal, Vishesh Tanwar, Bhisham Sharma, Dhirendra Prasad Yadav

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Aircraft Recognition Based on CNN Using Satellite Images by Meriç GENC, Yıldıray YALMAN

“…This study examines aircraft recognition using Convolutional Neural Networks (CNN) with satellite-derived image data. The research traces the evolution of deep learning, emphasizing the importance of multi-layer neural networks in addressing the limitations of artificial intelligence. …”
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ChromosomeNet: Deep Learning-Based Automated Chromosome Detection in Metaphase Cell Images by Chih-En Kuo, Jun-Zhou Li, Jenn-Jhy Tseng, Feng-Chu Lo, Ming-Jer Chen, Chien-Hsing Lu

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Benchmarking Deep Learning for Wetland Mapping in Denmark Using Remote Sensing Data by Muhammad Rizwan Asif

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Image Clustering based on Artificial Intelligence Techniques by Baydaa Khaleel

“…We have take the advantage of classification abilities of Artificial Intelligence Techniques (AITs) to classify images data set into a number of clusters. The Gath-Geva (GG) fuzzy clustering algorithm, Artificial Bee Colony algorithm(ABC), Radial Basis Function Network(RBF), and then combined Gath-Geva algorithm with (RBF) algorithm to produce Fuzzy RBF (FRBF) method were applied using images data set to classify this data set into a number of clusters (classes). …”
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Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. …”
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Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias by Kebin Contreras, Julio Gutierrez-Rengifo, Oscar Casanova-Carvajal, Angel Luis Alvarez, Patricia E. Vélez-Varela, Ana Lorena Urbano-Bojorge

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Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer by Taisheng Zeng, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang, Mengde Ling

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Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos by Hajar Keshavarz, Zohreh Ansari, Hossein Abootalebian, Babak Sabet, Mohammadreza Momenzadeh

“…Due to the lack of datasets with nonpolyp images, data collection was carried out. The proposed deep neural network structure was implemented on KVASIR-SEG and CVC-CLINIC datasets as polyp images in addition to the nonpolyp images extracted from the LDPolyp videos dataset. …”
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Pose estimation for health data analysis: advancing AI in neuroscience and psychology by Juan Yu, Daoyu Zhu

“…Traditional methods often struggle with the dynamic and multi-modal nature of health data, which includes electronic health records, wearable sensor data, and imaging modalities. …”
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

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Evaluating the role of training data origin for country-scale cropland mapping in data-scarce regions: A case study of Nigeria by Joaquin Gajardo, Michele Volpi, Daniel Onwude, Thijs Defraeye

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Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management by Nikolaos Ntampakis, Vasileios Argyriou, Konstantinos Diamantaras, Konstantinos Goulianas, Panagiotis Sarigiannidis, Ilias Siniosoglou

“…We employ SPINE (Synthetic Pulmonary Imaging Evaluation) framework, a threefold synthetic images evaluation method including expert domain assessment, statistical data analysis and adversarial evaluation. …”
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